NCI TOBACCO CONTROL MONOGRAPH SERIES

U.S. DEPARTMENT 

OF HEALTH AND 

HUMAN SERVICES

National Institutes 

of Health

National Cancer Institute

Edited by

Gary E. Swan, Ph.D.

Timothy B. Baker, Ph.D.

Laurie Chassin, Ph.D.

David V. Conti, Ph.D.

Caryn Lerman, Ph.D.

Kenneth A. Perkins, Ph.D.

20

Foundations for Genetic Studies of 

Nicotine Use and Dependence

Phenotypes and

Endophenotypes
NCI Tobacco Control Monographs

To cite this monograph in other works, please use the following format: 

National Cancer Institute. Phenotypes and Endophenotypes: Foundations for Genetic 

Studies of Nicotine Use and Dependence. Tobacco Control Monograph No. 20. Bethesda, MD: 

U.S. Department of Health and Human Services, National Institutes of Health, National 

Cancer Institute. NIH Publication No. 09-6366, August 2009.

This monograph and its supplemental materials may be found electronically at 

http://cancercontrol.cancer.gov/tcrb/monographs/20/index.html.

All NCI Tobacco Control Monographs are available from the Web page:

http://cancercontrol.cancer.gov/tcrb/monographs/index.html 

Recently Published Monographs

Risks Associated with Smoking Cigarettes with Low Machine-Measured Yields of Tar 

and Nicotine. Smoking and Tobacco Control Monograph No. 13. NIH Pub. No. 02-5047, 

October 2001.

Changing Adolescent Smoking Prevalence. Smoking and Tobacco Control Monograph 

No. 14. NIH Pub. No. 02-5086, November 2001.

Those Who Continue to Smoke: Is Achieving Abstinence Harder and Do We Need to Change 

Our Interventions? Smoking and Tobacco Control Monograph No. 15. NIH Pub. No. 03-5370, 

September 2003.

ASSIST: Shaping the Future of Tobacco Prevention and Control. Tobacco Control 

Monograph No. 16. NIH Pub. No. 05-5645, May 2005.

Evaluating ASSIST: A Blueprint for Understanding State-level Tobacco Control.

Tobacco Control Monograph No. 17. NIH Pub. No. 06-6058, October 2006.

Greater than the Sum: Systems Thinking in Tobacco Control. Tobacco Control 

Monograph No. 18. NIH Pub. No. 06-6085, May 2007.

The Role of the Media in Promoting and Reducing Tobacco Use. Tobacco Control Monograph 

No. 19. NIH Pub. No 07-6242, August 2008.
iii

Contents

Figures and Tables ...................................................................................................................... vii

Foreword ...................................................................................................................................... xi

Message from the Series Editor ................................................................................................ xiii

Acknowledgments .......................................................................................................................xv

Acronyms and Abbreviations .................................................................................................. xxiii

Part 1—Overview .......................................................................................................1

Chapter 1—Overview and Conclusions ..................................................................................................3

Introduction ................................................................................................................................. 4

About the Monograph .................................................................................................................. 5

Major Accomplishments ............................................................................................................... 7

Major Conclusions ........................................................................................................................ 7

Chapter Summaries and Conclusions ......................................................................................... 8

References ....................................................................................................................................18

Chapter 2—Status of Genetic Studies of Nicotine Dependence .....................................................19

Introduction ............................................................................................................................... 20

Historical Perspective of Genetic Research on Nicotine Dependence ..................................... 22

Nicotine Dependence: A Construct in Need of Refi nement ...................................................... 25

Nicotine-Dependence Phenotypes: A Framework ..................................................................... 27

Genetic Epidemiological Concepts and Their Implications for Studying 

Nicotine Dependence ................................................................................................................31

Summary of Selected Biometric and Measured Genetic Studies of 

Nicotine Dependence ............................................................................................................... 37

Issues in Communication of Genetic Findings ......................................................................... 45

Summary .....................................................................................................................................51

References ................................................................................................................................... 52

Part 2—Theoretical Considerations ....................................................................71

Chapter 3—The Nicotine-Dependence Phenotype: Translating Theoretical 

Perspectives and Extant Data into Recommendations for Genetic Mapping ...............................73

Introduction ................................................................................................................................74

Construct Validation .................................................................................................................. 75

Distal Measures of Dependence ................................................................................................. 78

New Directions for Phenotypic Research: Beyond Distal Measures ........................................105

Summary ...................................................................................................................................118

Conclusions ...............................................................................................................................119

References ................................................................................................................................. 120
iv

Chapter 4—Mouse Models and the Genetics of Nicotine Dependence ......................................133

Introduction ............................................................................................................................. 134

Nicotinic Receptor Functional Diversity ................................................................................. 135

Routes of Nicotine Administration: Interaction of the Drug 

with Physiological Systems ....................................................................................................145

The Mouse Model of Nicotine Dependence ..............................................................................149

Nicotine and Behavioral Changes .............................................................................................151

Nicotine and Reward .................................................................................................................157

Tolerance ....................................................................................................................................162

Additional Directions for Research on the Nicotine-Dependence Phenotype in Mice ...........168

Summary ...................................................................................................................................171

Conclusions ...............................................................................................................................172

References ..................................................................................................................................173

Part 3—Developmental Trajectories of Tobacco Use 

and Their Relation to Tobacco Dependence ....................................................187

Chapter 5—Developmental Trajectories of Cigarette Smoking 

from Adolescence to Adulthood ............................................................................................................189

Introduction ............................................................................................................................. 190

A Developmental Psychopathology Perspective: Studying Multiple 

Trajectories over Time ............................................................................................................191

Empirically Identifi ed Trajectories of Adolescent Smoking ................................................... 202

Statistical Models for Evaluating Alternative Developmental Phenotypes 

of Smoking Behavior ..............................................................................................................214

An Empirical Example: Trajectories in the Indiana University Smoking Survey ......................223

Future Research Directions ..................................................................................................... 233

Summary .................................................................................................................................. 234

Conclusions .............................................................................................................................. 235

References ................................................................................................................................. 236

Chapter 6—Genetic Modeling of Tobacco Use Behavior and Trajectories ................................245

Introduction ............................................................................................................................. 246

Methodological and Conceptual Issues ....................................................................................247

Statistical Framework .............................................................................................................. 248

Review of Genetic Studies of Smoking .................................................................................... 259

Item Response Theory Approach: Application to Virginia Twin Registry Data ..........................269

Limitations ............................................................................................................................... 279

Summary .................................................................................................................................. 280

Conclusions .............................................................................................................................. 280

References ................................................................................................................................. 282

Chapter 7—Trajectories of Tobacco Use from Adolescence to Adulthood: 

Are the Most Informative Phenotypes Tobacco Specifi c? ............................................................. 289

Introduction ............................................................................................................................. 290

Importance of Studying Substance-Use Comorbidity ............................................................ 290

Contents
v

Monograph 20. Phenotypes and Endophenotypes

Common Versus Specifi c Liability to Substance-Use Disorders ............................................ 292

Review of Trajectory Literature................................................................................................ 295

Associations between Substance-Use Trajectories and Other Substance 

Involvement ............................................................................................................................ 296

Modeling Conjoint Trajectories of Substance Use ................................................................... 298

Empirical Example of Modeling Co-Occurring Courses of Substance Use ........................... 307

Method ...................................................................................................................................... 307

Results .......................................................................................................................................310

Summary .................................................................................................................................. 323

Conclusions .............................................................................................................................. 324

References ................................................................................................................................. 326

Part 4—Endophenotypes .....................................................................................337

Chapter 8—Endophenotypes for Nicotine-Dependence Risk at or before 

Initial Nicotine Exposure ....................................................................................................................... 339

Introduction ............................................................................................................................. 340

Endophenotypes ....................................................................................................................... 340

Smoking Initiation and Progression Risk: Examination of Key Candidate 

Psychological Domains .......................................................................................................... 346

Initial Nicotine Exposure Response: Conceptual Framework and Candidate 

Endophenotypes ..................................................................................................................... 363

Discussion of Future Directions ...............................................................................................381

Summary .................................................................................................................................. 384

Conclusions .............................................................................................................................. 385

References ................................................................................................................................. 386

Chapter 9—Nicotine-Dependence Endophenotypes in Chronic Smokers ................................. 403

Introduction ............................................................................................................................. 404

Rationale for Investigating Endophenotypes of Chronic Nicotine Exposure ........................ 406

Motivational Mechanisms .........................................................................................................410

Acute Smoking or Abstinence Effects on Cognitive, Affective, 

and Physiological Function ................................................................................................... 424

Discussion and Recommendations for Future Research ........................................................ 452

Summary ...................................................................................................................................457

Conclusions .............................................................................................................................. 458

References ................................................................................................................................. 459

Part 5—Epidemiological and Methodological Considerations ..................485

Chapter 10—Epidemiological Analysis of Variation in Phenotypic Defi nitions: 

A Proof of Concept Using an Example of a Cessation Phenotype ................................................ 487

Introduction ............................................................................................................................. 488

The Proposed Approach............................................................................................................ 492

Method ...................................................................................................................................... 494

Summary .................................................................................................................................. 503
vi

Conclusions .............................................................................................................................. 504

References ................................................................................................................................. 506

Chapter 11—Incorporating Social Context into Genetic Studies 

of Nicotine Dependence ......................................................................................................................... 509

Introduction ..............................................................................................................................510

Why Incorporate the Social Context?.......................................................................................511

Behavioral Genetic Studies of Smoking That Incorporate Social Context .............................514

Proximal Measures of the Social Context ...............................................................................518

Future Directions ..................................................................................................................... 527

Summary ...................................................................................................................................531

Conclusions .............................................................................................................................. 532

References ................................................................................................................................. 533

Chapter 12—Using Ontologies in Hierarchical Modeling of Genes 

and Exposure in Biological Pathways ................................................................................................ 539

Introduction ............................................................................................................................. 540

Methodological Issues .............................................................................................................. 541

Background on Statistical Approaches.................................................................................... 543

Methods .................................................................................................................................... 550

Example: Nicotine Metabolism ................................................................................................ 562

Discussion ..................................................................................................................................570

Summary .................................................................................................................................. 573

Conclusions ...............................................................................................................................574

Appendix 12A. Estimation for the Hierarchical Model ............................................................575

Appendix 12B. Model Selection Algorithm ..............................................................................577

References ..................................................................................................................................579

Part 6—Future Directions ....................................................................................585

Chapter 13—Future Directions ..............................................................................................................587

Introduction ............................................................................................................................. 588

Genetics and Nicotine Dependence: Implications for Basic and Clinical Research ...................588

Future Directions ......................................................................................................................591

References ................................................................................................................................. 597

Index ......................................................................................................................................... 599

Contents
vii

Figures and Tables

Figures

Figure 2.1 Cigarette Consumption in the United States 

in the Twentieth Century ................................................................................... 20

Figure 2.2 Trajectories as Phenotypic Pathways ................................................................. 23

Figure 2.3 Multipoint Linkage Plot—Chromosome 6 .........................................................41

Figure 2.4 Multipoint Linkage Plot—Chromosome 7 .........................................................41

Figure 2.5 Multipoint Linkage Plot—Chromosome 8 ........................................................ 42

Figure 3.1 Nomological Net: Evaluation Context for a Model of 

Dependence and Its Relation to Genetic Variants ..............................................76

Figure 3.2 Watershed Model of Gene-to-Phenotype Infl uence ........................................... 78

Figure 3.3 Etiologic Path and Locus of Phenotypic Assay .................................................. 83

Figure 3.4 Logistic Regression Curves Predicting Scores on the WISDM 

Subscales from Cigarettes Smoked per Month: Examples 

of an Early-Emergent Motive (Social/Environmental Goads) 

and a Late-Emergent Motive (Tolerance) ............................................................91

Figure 3.5 Latent Class Results from the Combined Data Set ........................................... 92

Figure 3.6 Associating Genes with Phenotypic Stages ......................................................109

Figure 4.1 Nicotinic Receptor Subunit Structure ..............................................................137

Figure 4.2 Three-Dimensional Structure of the Nicotinic Acetylcholine

Receptor ............................................................................................................ 138

Figure 4.3 Infl uence of Subunit Composition on Nicotinic Receptors ............................ 139

Figure 4.4 Nicotinic Receptors of Closely Related Subunit Composition 

Differing in Function through Variation in Subunit 

Stoichiometry ....................................................................................................140

Figure 4.5 Genetic Infl uences on Nicotine Tolerance and 

Self-Administration ...........................................................................................146

Figure 5.1 Five-Class Solution for Waves 1−6 ................................................................... 226

Figure 5.2 Six-Class Solution for Waves 1−8 ..................................................................... 228

Figure 6.1 Basic Path Diagram for the Analysis of Data Collected 

from Pairs of Monozygotic and Dizygotic Twins ............................................. 250

Figure 6.2 Three-Factor Latent Phenotype Model ............................................................ 252

Figure 6.3 Causal Contingent Common Pathway Model .................................................. 253

Figure 6.4 Estimates of the Contributions of Additive Genetic and 

Shared Environmental Factors to Smoking Initiation by 

Sample, Age, and Gender in Published Studies of Adolescent 

Twins ..................................................................................................................261

Figure 6.5 Estimates of the Contributions of Additive Genetic Factors 

in Common with Initiation, Total Additive Genetic, and Shared 

Environmental Factors to Smoking Persistence by Sample, 

Gender, Age, and Measure of Persistence ........................................................ 265
viii

Figures and Tables

Figure 6.6 Estimates of Factor Loadings and Thresholds of NicotineDependence Items in Female Twins from the Virginia 

Twin Registry .................................................................................................... 272

Figure 6.7 Estimates of Factor Loadings and Thresholds of NicotineDependence Items Plotted by Gender and Measurement 

Instrument (FTQ or FTND Scale) .................................................................... 273

Figure 6.8 Estimates of Nicotine-Dependence Item Characteristic Curves

for 20-Year-Old Females ................................................................................... 275

Figure 6.9 Estimates of Factor Loadings, Thresholds, Genetic Variance 

Components Due to the Factor, and Residual Item-Specifi c 

Genetic Variance Components in Virginia Twin Registry Males .................... 277

Figure 7.1 Prevalence of Smoking and Drinking across the Four Study 

Waves ................................................................................................................. 309

Figure 7.2 Underlying General Growth Mixture Model for Characterizing 

Trajectories of Smoking and Trajectories of Drinking and for 

Characterizing Conjoint Trajectories of Smoking and Drinking ....................311

Figure 7.3 Trajectories of Smoking and Drinking .............................................................314

Figure 7.4 Trajectories of Conjoint Drinking and Smoking ..............................................316

Figure 8.1 Hierarchical Structural Model and Hypothesized Physiological 

Concomitants .................................................................................................... 345

Figure 8.2 Example of How Potential Endophenotypes Can Link Genes 

to Nicotine-Dependence Risk at or before Initial Nicotine 

Exposure............................................................................................................ 347

Figure 9.1 Example of How Potential Endophenotypes Can Link Genes 

to Nicotine-Dependence Risk .......................................................................... 409

Figure 10.1 Phenotype Choice Points along the Smoking Trajectory................................ 490

Figure 10.2 Example of Phenotypic Comparison for Drug Response Using 

the Proposed Approach ......................................................................................491

Figure 10.3 Example of Phenotypic Comparison for Progression to Daily 

Smoking (Behavior of Interest) Using the Proposed Approach .......................491

Figure 10.4 Example of Phenotypic Comparison for Sustained Smoking 

Cessation (Behavior of Interest) Using the Proposed Approach ..................... 492

Figure 11.1 Located Cigarette Vendors in Rhode Island ......................................................521

Figure 11.2 Located Cigarette Vendors in Massachusetts .................................................. 522

Figure 11.3 Percentage of Diary Responses Endorsing Cigarette Use 

Stratifi ed by Social Contexts ............................................................................ 530

Figure 11.4 Pattern of Endorsed Smoking Epochs over a Six-Day Period 

for a Concordant Sibling Pair ............................................................................531

Figure 12.1 Evolutionary and Structural Analyses for the CYP2A6*2 Variant ................. 555

Figure 12.2 Examples of Networks ...................................................................................... 556

Figure 12.3 Shrinkage of Test Statistics .............................................................................. 567

Figure 13.1 Some Examples of Hypothetical Gene to Phenotype Pathways ..................... 590
ix

Monograph 20. Phenotypes and Endophenotypes

Tables

Table 2.1 Results from the Genome-wide Association Study 

of Nicotine Dependence ...................................................................................... 44

Table 2.2 Results from the Candidate Gene Study of Nicotine Dependence .................... 44

Table 2.3 Pleiotropic Associations of Genetic Variants Implicated in Smoking .............. 48

Table 3.1 Subscales of the Wisconsin Inventory of Smoking Dependence 

Motives ................................................................................................................ 85

Table 3.2 Dimensions on Which Groups Might Be Constructed to Contrast

Putative High- and Low-Dependence Predispositions ...................................... 96

Table 3.3 Causal Paths from Genetic Variant to Distal Phenotypes ................................102

Table 3.4 Levels of Analysis in Characterizing the Phenotype ........................................109

Table 5.1 Studies of Smoking Trajectories ...................................................................... 203

Table 5.2 Trajectory Group Sizes and Relationship with Gender 

and Educational Attainment ............................................................................ 227

Table 5.3 Stability of Classifi cation across the Waves 1–6 

and Waves 1–8 Models ...................................................................................... 229

Table 5.4 Relationship of Trajectory Group Membership to Family History 

of Smoking .........................................................................................................231

Table 5.5 Relationship of Trajectory Group Membership to Smoking 

Dependence Indices in Wave 8 ..........................................................................231

Table 6.1 Number of Alleles Shared Identical by Descent for a Pair 

of Full Siblings .................................................................................................. 258

Table 6.2 Results from Fitting Measurement Noninvariance and Gender 

Heterogeneity Models to Nicotine Initiation and Dependence 

Data Collected from Twins ................................................................................274

Table 7.1 Overview of the Literature on Conjoint Trajectories 

of Substance Use ............................................................................................... 300

Table 7.2 Correlations across Smoking and Drinking at Each 

of the Four Waves for the Full Sample .............................................................312

Table 7.3 Fit Indices and Likelihood Ratio Tests for Relative Improvement 

in Fit for Smoking, Drinking, and Dual Smoking and Drinking ....................313

Table 7.4 Cross-Tabulations of Frequency and Cell Proportions 

of Group Membership for Smoking and Drinking for the Full Sample ..........315

Table 7.5 Cross-Tabulations of Frequency (Cell Proportion) of Group 

Membership for Twin 1 Versus Twin 2 (across Zygosity) 

for Smoking and for Drinking ...........................................................................318

Table 7.6 Cross-Tabulations of Frequency (Cell Proportion) of Group 

Membership for Twin 1 Smoking by Twin 2 Drinking and 

Twin 2 Smoking by Twin 1 Drinking ................................................................319

Table 8.1 Extant Data on Potential Endophenotypes and Their Measurement ............. 355

Table 9.1 Putative Endophenotypes for Nicotine Dependence: Motivational

Mechanisms and Nicotine or Abstinence Effects ............................................ 453
x

Table 9.2 Putative Endophenotypes for Nicotine Dependence: Acute 

Smoking or Abstinence Effects ........................................................................ 453

Table 10.1 A Comparison of the Data Sets and Variables Used 

in the Analyses Presented in This Chapter ...................................................... 494

Table 10.2 Results from the Analysis of the ATBC Study Data ......................................... 496

Table 10.3 Results from the Analysis of TUS-CPS Data .................................................... 499

Table 10.4 Results from the Analysis of NHANES III Data .............................................. 502

Table 11.1 Sibling Partners Diary Prompts ....................................................................... 528

Table 12.1 Results for Nicotine Clearance ......................................................................... 566

Table 12.2 Results for 3HC/COT Ratios ............................................................................. 569

Figures and Tables
xi

Foreword

The twentieth volume of the Tobacco Control Monograph series of the National Cancer 

Institute reviews the scientifi c foundation for genetic studies of nicotine use and dependence. 

The authors and editors perform an admirable job synthesizing the expanding literature in 

the fi eld and developing a scientifi c blueprint for the integration of genetic approaches into 

transdisciplinary studies of nicotine dependence. This seminal work should be examined 

in the context of global public health action on tobacco prevention and control as well as 

advances in genomics and related technologies.

It is important to ask how genetic studies of nicotine use and dependence can contribute to the 

overall public health effort in tobacco control and prevention. For, despite public health efforts, 

an estimated 45 million people in the United States still smoke. Globally, one billion individuals 

smoke tobacco on a regular basis, and millions of individuals die yearly from illnesses related to 

tobacco. A “one size fi ts all” public health approach has not been fully successful. All available 

tools will be needed to meet the demand for effective and sustainable tobacco control, including 

pharmacogenetic-informed treatments and social policy interventions for smoking cessation.

Clearly, tobacco use in a population is the product of the interaction of agent, genetic, 

and environmental factors. Government policies are important modifi able environmental 

infl uences that can alter how tobacco products are designed and marketed and how 

consumers respond. Understanding individual variation in responses to tobacco can help 

our approach to different programs, policies, and treatments for nicotine dependence. 

Synergy occurs when tobacco control and prevention interventions directed at agent, host, 

and environmental factors are implemented together. However, no studies have adequately 

addressed simultaneously genetic variation, quantitative measures of behavioral, social and 

cultural variation, and the interaction among these sources of variation. This gap refl ects the 

disciplinary silos that were not uncommon in the 20th century scientifi c enterprise.

A few short years after the completion of the Human Genome Project in 2003, we continue 

to witness growing scientifi c discoveries on the genetic contribution to common diseases of 

public health signifi cance, such as cancer, coronary heart disease, and diabetes. The emergence 

of genome-wide association studies over the past two years has contributed to the acceleration 

of genetic discoveries. In addition, the number of genetic and genomic tests used in clinical 

practice continues to grow, including pharmacogenomic applications in clinical practice. 

A new era of personalized health and healthcare seems to be on the horizon. For diseases 

related to tobacco use, we are seeing increasing numbers of important genetic discoveries. 

The need for a wider range of valid phenotypes of nicotine dependence has driven the 

development of this monograph. The scientifi c analysis and synthesis presented in this 

monograph will undoubtedly further the fi eld of behavioral genetics.

Muin J. Khoury, M.D., Ph.D.

Director

National Offi ce of Public Health Genomics

Centers for Disease Control and Prevention

July 2009

xiii

Message from the Series Editor

This volume is the twentieth of the Tobacco Control Monograph series of the National Cancer 

Institute (NCI). The series began in 1991 with a visionary blueprint for public health action 

on tobacco prevention and control. In the years since, it has disseminated important crosscutting research in areas such as the impact of tobacco control policies, the risks associated 

with smoking cigars and low-tar cigarettes, systems approaches to tobacco control, and the 

role of media in promoting and reducing tobacco use.

The subject matter of this monograph began with an informal review and critique of the 

behavioral genetics literature related to smoking. This review showed that genotyping is 

restricted to only a few phenotypes (usually current and former smokers) and that standard 

defi nitions of smoking behavior are not commonly used. For example, in the United States, 

an “ever smoker” has been defi ned generally in the epidemiology and surveillance literature 

as someone who has smoked at least 100 cigarettes in his/her entire life. Similarly, there 

is consensus about the need to separate current smokers into “every day” and “some days” 

smokers. This distinction is rarely made in smoking genetics research. The lack of use of 

standard defi nitions of these and other aspects of tobacco use behavior (for example, smoking 

cessation) not only hinders comparisons among genetics studies, but also the ability to put 

results from these studies into context of knowledge gained from other disciplines. The need 

for standard defi nitions and measures of tobacco use behavior is critical to furthering our 

understanding of the genetic and environmental determinants of tobacco use.

Much of the tobacco literature examines genetic susceptibility to smoking initiation and 

cessation only among very broad groups without an understanding of the complexities or 

variations within these categories in patterns of smoking behavior. Combining very different 

subgroups of smokers into a few common phenotypes (for example, the current smoking 

phenotype often includes both light and intermittent smokers with heavy daily smokers) 

and then using such heterogeneous groups in research studies may be hindering progress 

in our understanding the role of genetics in complex behaviors such as smoking.

Another limitation of genetics research is that it is often based on small, nonrepresentative 

samples of the population, which limits the generalizability and interpretability of the fi ndings. 

The frequency of genetic variants determined from nonprobability-based samples may not 

refl ect the true underlying frequency in the general population. Therefore, epidemiologic and 

etiologic conclusions based on these results may be misleading. Only through the analysis 

of population-based samples will we be able to examine the relative contribution of genetics 

and environment, as well as gene-gene interactions and gene-environment interactions, 

to explaining variations in tobacco use behavior, dependence, and disease risk. Populationlevel genetic analyses will also help us determine if previously identifi ed genetic variants are 

truly associated with smoking and whether confl icting fi ndings in the literature are due to 

population stratifi cation (selection bias).

Finally, while the effect of single genetic variants is likely to be small for complex behaviors 

such as smoking, previous research has not studied the joint effects of different genetic 

variants on tobacco use behavior, dependence, and disease risk. Large, national samples are 

needed to allow us to examine the potential impact of these multiple genetic variants.
xiv

Message from the Series Editor

This monograph was originally intended to demonstrate the need for standard defi nitions 

of tobacco use behavior and to explore the utility of using epidemiologic data from national 

surveys, as well as data from a multitude of other sources (such as smoking topography) and 

disciplines (such as psychology and pharmacology) to identify unique smoking phenotypes 

for genetic analysis. In addition, the original proposal called for exploring the conceptual and 

measurement issues related to describing the entire continuum of smoking behavior, from 

the fi rst few adolescent puffs to “hardcore” dependence. Several steps were outlined to achieve 

these objectives: (1) synthesize the existing literature; (2) conduct original data analyses and 

develop innovative methods to address the research gaps; (3) examine the application and 

usefulness of the potential phenotypes for genetic, epidemiologic, and behavioral research; 

and (4) make recommendations for future conceptualization (theory/model building), 

methods development (new measurement, innovative analytic approaches), and new data 

collection and empirical research.

Researchers within and outside NIH also reached the conclusion around this same time that 

there were a number of questions, including those related to behavioral genetics, which 

could be most effectively addressed only if a clear defi nition for nicotine dependence was 

developed. These researchers, many of whom have subsequently edited or authored this 

monograph, were particularly interested in fi nding ways to defi ne various groups of smokers 

along meaningful dimensions (such as dependence) that could help to advance the fi eld of 

behavioral genetics of smoking. One of the most signifi cant obstacles identifi ed in behavioral 

genetics research of smoking was the lack of valid and useful phenotypes.

Although the content of this monograph has changed somewhat to focus more on nicotine 

dependence phenotypes, it has remained true to the original vision. The publishing of this 

monograph comes at a critical time. The fi eld of behavioral genetics is evolving rapidly. Efforts 

are underway to develop core sets of standardized measures to use in genetic studies. The need 

to identify a broad range of homogeneous phenotypes of nicotine dependence has never been 

clearer. While the literature on the relationship between genetic variation and treatment 

outcomes was not addressed, new and important discoveries presented in this monograph 

concerning the assessment, development, and maintenance of nicotine dependence may help 

clinicians target interventions more effectively—to specifi c components of nicotine dependence 

and to windows of opportunity for more precise timing of intervention delivery. Moreover, 

more refi ned phenotypes may provide more sensitive indicators of the impact of treatment for 

nicotine dependence and, ultimately, lead to a stronger evidence base for pharmacogeneticallyinformed treatments.

Moreover, a better understanding of the role of genetic susceptibility may help the public 

health community enhance already effective public policies for tobacco prevention and 

control. Much progress in reducing tobacco use has already been made and much is already 

known to work; however, even in this context, knowledge gained from genetic studies may play 

an important role in designing new and innovative environmental and policy interventions. 

We hope the science presented in this monograph guides the fi eld for many years to come.

Stephen E. Marcus, Ph.D.

Monograph Series Editor

July 2009
xv

Acknowledgments

This monograph was developed by the National Cancer Institute under the general direction 

of Stephen E. Marcus, Monograph Series Editor, and Senior Scientifi c Editor Gary E. Swan,

along with Scientifi c Editors Timothy B. Baker, Laurie Chassin, David V. Conti, Caryn Lerman,

and Kenneth A. Perkins.

Monograph Series Editor

Stephen E. Marcus, Ph.D.

Epidemiologist 

Tobacco Control Research Branch, 

Behavioral Research Program

Division of Cancer Control and Population 

Sciences

National Cancer Institute

National Institutes of Health

Bethesda, MD

Senior Scientifi c Editor

Gary E. Swan, Ph.D.

Director

Center for Health Sciences

SRI International

Menlo Park, CA

Scientifi c Editors

Timothy B. Baker, Ph.D.

Professor

Center for Tobacco Research & Intervention

Department of Medicine

School of Medicine and Public Health

University of Wisconsin

Madison, WI

Laurie Chassin, Ph.D.

Regents Professor of Psychology

Department of Psychology

Arizona State University

Tempe, AZ

David V. Conti, Ph.D.

Assistant Professor

Zilkha Neurogenetic Institute

Keck School of Medicine

Department of Preventive Medicine

Division of Biostatistics

University of Southern California

Los Angeles, CA

Caryn Lerman, Ph.D.

Mary W. Calkins Professor

Department of Psychiatry and Annenberg 

Public Policy Center 

Deputy Director, Abramson Cancer Center 

University of Pennsylvania

Philadelphia, PA

Kenneth A. Perkins, Ph.D.

Professor of Psychiatry

University of Pittsburgh

Pittsburgh, PA

Authors

Janet Audrain-McGovern, Ph.D.

Associate Professor

Department of Psychiatry

University of Pennsylvania

Philadelphia, PA

Erik Augustson, Ph.D., M.P.H.

Tobacco Control Research Branch

Behavioral Research Program

National Cancer Institute

Rockville, MD
xvi

Acknowledgments

Timothy B. Baker, Ph.D.

Professor

Center for Tobacco Research & Intervention

Department of Medicine

School of Medicine and Public Health

University of Wisconsin

Madison, WI

Andrew W. Bergen, Ph.D.

Director, Molecular Genetics Program

Center for Health Sciences

SRI International 

Menlo Park, CA

Neal L. Benowitz, Ph.D.

Professor of Medicine, Psychiatry and 

Biopharmaceutical Sciences

Chief, Clinical Pharmacology

University of California, San Francisco

San Francisco, CA

Laura Jean Bierut, M.D.

Professor of Psychiatry

Washington University School of Medicine 

St. Louis, MO

Avshalom Caspi, Ph.D.

Professor

Departments of Psychology & Neuroscience 

and Psychiatry & Behavioral Sciences, and 

Institute for Genome Sciences & Policy

Duke University

Durham, NC

and

Social, Genetic, and Developmental 

Psychiatry Research Centre

Institute of Psychiatry

King’s College London

England

Laurie Chassin, Ph.D.

Regents Professor of Psychology

Department of Psychology

Arizona State University

Tempe, AZ

David V. Conti, Ph.D.

Assistant Professor

Zilkha Neurogenetic Institute

Keck School of Medicine

Department of Preventive Medicine

Division of Biostatistics

University of Southern California

Los Angeles, CA

Patrick J. Curran, Ph.D.

Professor

Department of Psychology

The University of North Carolina at 

Chapel Hill

Chapel Hill, NC

Stephen E. Gilman, Sc.D.

Assistant Professor 

Department of Society, Human 

Development, and Health

Department of Epidemiology

Harvard School of Public Health

Boston, MA

Thomas J. Gould, Ph.D.

Associate Professor of Psychology 

Center for Substance Abuse Research

Director of the Brain, Behavior, and 

Cognition Area of Psychology

Temple University

Philadelphia, PA

Kristina M. Jackson, Ph.D.

Associate Professor (Research) 

Department of Community Health

Center for Alcohol and Addiction Studies

Brown University

Providence, RI

Jaakko Kaprio, M.D., Ph.D.

Department of Public Health

Faculty of Medicine

University of Helsinki

and

Department of Mental Health and 

Alcohol Research

National Public Health INsttute

Helsinki, Finland
xvii

Monograph 20. Phenotypes and Endophenotypes

Caryn Lerman, Ph.D.

Professor

Department of Psychiatry

University of Pennsylvania

Tobacco Use Research Centers

Philadelphia, PA

Christina N. Lessov-Schlaggar, Ph.D.

Genetic Epidemiologist

SRI International 

Menlo Park, CA

Juan Pablo Lewinger, Ph.D.

Assistant Professor

Department of Preventive Medicine

University of Southern California

Los Angeles, CA

Hermine H. Maes, Ph.D.

Associate Professor

Department of Human and Molecular 

Genetics and Massey Cancer Center

Virginia Institute for Psychiatric and 

Behavioral Genetics

Virginia Commonwealth University

Richmond, VA

Terrie E. Moffi tt, Ph.D.

Professor

Departments of Psychology & Neuroscience 

and Psychiatry & Behavioral Sciences, and 

Institute for Genome Sciences & Policy

Duke University

Durham, NC

and 

Social, Genetic, and Developmental 

Psychiatry Research Centre

Institute of Psychiatry

King’s College London

England

Michael C. Neale, Ph.D.

Professor

Departments of Psychiatry and 

Human Genetics

Virginia Institute for Psychiatric and 

Behavioral Genetics

Virginia Commonwealth University

Richmond, VA

Joel T. Nigg, Ph.D.

Professor

Department of Psychiatry

Oregon Health & Sciences University

Portland, OR

Kenneth A. Perkins, Ph.D.

Professor of Psychiatry

University of Pittsburgh

Pittsburgh, PA

Clark C. Presson, Ph.D.

Professor

Department of Psychology

Arizona State University

Tempe, AZ

Richard Rende, Ph.D.

Associate Professor of Psychiatry and 

Human Behavior

The Warren Alpert Medical School of 

Brown University

Research Psychologist 

Transdisciplinary Research Group

Butler Hospital

Providence, RI

Scott W. Rogers, Ph.D.

Professor

Neurobiology & Anatomy

University of Utah

Salt Lake City, UT

Richard J. Rose, Ph.D.

Professor Emeritus

Department of Psychological and Brain 

Sciences

Indiana University 

Bloomington, IN

Kenneth J. Sher, Ph.D.

Curators’ Professor of Psychological 

Sciences

University of Missouri-Columbia

Columbia, MO

Steven J. Sherman, Ph.D.

Professor 

Department of Psychological and 

Brain Sciences

Indiana University

Bloomington, IN
xviii

Acknowledgments

Alexandra E. Shields, Ph.D.

Director

Harvard/MGH Center on Genomics, 

Vulnerable Populations & Health 

Disparities

Boston, MA

Cheryl Slomkowski, Ph.D.

Assistant Professor

Psychiatry and Human Behavior

Brown University Medical School

Providence, RI

Gary E. Swan, Ph.D. 

Director

Center for Health Sciences

SRI International

Menlo Park, CA

Paul D. Thomas, Ph.D.

Evolutionary Systems Biology Group

Artifi cial Intelligence Center

SRI International

Menlo Park, CA

Rachel F. Tyndale, M.Sc., Ph.D.

Canada Research Chair in Pharmacogenetics

Section Head Pharmacogenetics

Centre for Addiction and Mental Health

Professor

Department of Pharmacology

University of Toronto

Toronto, Ontario, Canada

Michael Vanyukov, Ph.D.

Associate Professor

Departments of Pharmaceutical Sciences, 

Human Genetics, and Psychiatry

University of Pittsburgh

Pittsburgh, PA

Kay Wanke, Ph.D., M.P.H.

Offi ce of Behavioral and Social Sciences 

Research

Offi ce of the Director

National Institutes of Health

Bethesda, MD

R. J. Wirth, Ph.D.

Statistician

FPG Child Development Institute

The University of North Carolina at 

Chapel Hill

Chapel Hill, NC

Reviewers

Rebecca Ashare

Doctoral Candidate

Department of Psychology

University at Buffao, SUNY

Buffalo, NY

David Balfour, Ph.D.

Professor of Behavioural Pharmacology

Psychiatry Sectrion, Centre for 

Neuroscience

College of Medicine, Dentistry, and Nursing

University of Dundee

Dundee, United Kingdom

Neal L. Benowitz, Ph.D.

Professor of Medicine, Psychiatry and 

Biopharmaceutical Sciences

Chief, Clinical Pharmacology

University of California, San Francisco

San Francisco, CA

Lisa Bero, Ph.D.

Professor

Department of Clinical Pharmacy

School of Pharmacy and Institute for Health 

Policy Studies

School of Medicine

University of California, San Francisco

San Francisco, CA

Daniel M. Bolt, Ph.D.

Professor

School of Education

Department of Educational Psychology

University of Wisconsin

Madison, WI

Dorret Boomsma, Ph.D.

Professor, Department of Biological 

Psychology

Vrije Universiteit Amsterdam

Amsterdam, Netherlands
xix

Monograph 20. Phenotypes and Endophenotypes

Naomi Breslau, Ph.D.

Professor

Department of Epidemiology

Michigan State University

East Lansing, MI

Nilanjan Chatterjee, Ph.D.

Senior Investigator

Division of Cancer Epidemiology and 

Genetics

National Cancer Institute

Rockville, MD

Ralph J. Coates, Ph.D.

Associate Director for Science

National Center for Public Health Genomics

Centers for Disease Control and Prevention

Atlanta, GA

Kevin P. Conway, Ph.D. 

Deputy Director

Division of Epidemiology, Services and 

Prevention Research

National Institute on Drug Abuse

Bethesda, MD

K. Michael Cummings, Ph.D, M.P.H.

Department of Health Behavior

Division of Cancer Prevention and 

Population Sciences

Roswell Park Cancer Institute

Buffalo, NY

Lisa C. Dierker, Ph.D.

Professor of Psychology

Department of Psychology

Wesleyan University

Middletown, CT

Joseph DiFranza, M.D.

Professor

Department of Family Medicine and 

Community Health

University of Massachusetts Medical School

Worcester, MA

Conor Dolan, Ph.D.

Department of Psychology

University of Amsterdam

Amsterdam, Netherlands

David J. Drobes, Ph.D.

Professor, Oncologic Sciences & Psychology

University of South Florida

Senior Member, Tobacco Research and 

Intervention Program

Moffi tt Cancer Center

Tampa, FL

Phyllis Ellickson, Ph.D.

Senior Behavioral Scientist

RAND

Santa Monica, CA

Stanton Glantz, Ph.D.

Professor of Medicine

American Legacy Foundation Distinguished 

Professor in Tobacco Control

Department of Medicine

Division of Cardiology

University of California, San Francisco

San Francisco, CA

George J. Hammons, Ph.D.

Professor and Chair

Department of Chemistry

Philander Smith College

Little Rock, AR

Larry W. Hawk, Ph.D.

Associate Professor 

Department of Psychology

The University of Buffalo, SUNY

Buffalo, NY

Kenneth S. Kendler, M.D.

Director

Virginia Institute for Psychiatric and 

Behavioral Genetics

Rachel Brown Banks Distinguished 

Professor of Psychiatry

Medical College of Virginia

Virginia Commonwealth University

Richmond, VA

Barbara A. Koenig, Ph.D.

Professor of Medicine

Departments of Medicine and Psychiatry

Mayo Clinic College of Medicine

Rochester, MN
xx

Acknowledgments

Pamela Madden, Ph.D.

Associate Professor in Psychiatry

Washington University in St. Louis

Department of Psychiatry

Washington University School of Medicine

St. Louis, MO

Athina Markou, Ph.D.

Professor 

Department of Psychiatry

School of Medicine

University of California, San Diego 

La Jolla, CA

Michael J. Marks, Ph.D.

University of Colorado

Institute for Behavioral Genetics 

Boulder, CO

Colleen M. McBride

Chief & Senior Investigator

National Human Genome Research Institute

National Institutes of Health

Bethesda, MD

Paras Mehta, Ph.D.

Associate Professor

University of Houston

Department of Psychology

Houston, TX

Robin J. Mermelstein, Ph.D.

Director, Institute for Health Research 

and Policy

Professor, Psychology Department

Clinical Professor, Community Health 

Sciences

University of Illinois at Chicago

Chicago, IL

Marcus Munafo, Ph.D.

Reader in Biological Psychology

Department of Experimental Psychology

University of Bristol

Bristol, United Kingdom

Joel T. Nigg, Ph.D.

Professor

Department of Psychiatry

Oregon Health & Sciences University

Portland, OR

Mark Parascandola, Ph.D., M.P.H.

Epidemiologist

Tobacco Control Research Branch

National Cancer Institute

National Institutes of Health

Rockville, MD

Ovide Pomerieau, Ph.D.

Professor

University of Michigan

Ann Arbor, MI

Soo Hyun Rhee, Ph.D.

Assistant Professor

Department of Psychology and Neuroscience

University of Colorado 

Boulder, CO 

Jed E. Rose, Ph.D.

Professor, Department of Psychiatry and 

Behavioral Sciences

Director, Center for Nicotine and Smoking 

Cessation Research

Duke University Medical Center

Durham, NC

Nancy L. Saccone, Ph.D.

Assistant Professor of Genetics

Department of Genetics

Division of Human Genetics

Washington University in St. Louis

St. Louis, MO

John Schulenberg, Ph.D.

Professor of Psychology

Research Professor, Institute for Social 

Research and Center for Human Growth 

and Development

University of Michigan

Ann Arbor, MI

Alexandra E. Shields, Ph.D.

Director

Harvard/MGH Center on Genomics, 

Vulnerable Populations & Health 

Disparities

Boston, MA

Saul Shiffman, Ph.D.

Professor of Psychology, Psychiatry, and 

Pharmacology

University of Pittsburgh

Pittsburgh, PA
xxi

Monograph 20. Phenotypes and Endophenotypes

Wendy Slutske, Ph.D.

Professor

University of Missouri-Columbia

Department of Psychological Sciences

Columbia, MO

Mariana C. Stern

Assistant Professor

Preventive Medicine (Division of 

Epidemiology)

Keck School of Medicine

University of Southern California/Norris 

Comprehensive Cancer Center

Los Angeles, CA

Duncan C. Thomas, Ph.D.

Professor, Co-Director Biostatistics Division 

Preventive Medicine 

Division of Biostatistics

Keck School of Medicine

University of Southern California

Los Angeles, CA

Rachel F. Tyndale, M.Sc., Ph.D.

Canada Research Chair in Pharmacogenetics

Section Head Pharmacogenetics

Centre for Addiction and Mental Health

Professor

Department of Pharmacology

University of Toronto

Toronto, Ontario, Canada

Cornelia Ulrich, Ph.D.

Member

Cancer Prevention Program

Fred Hutchinson Cancer Research Center

Seattle, WA

Jennifer B. Unger, Ph.D.

Professor of Community and Global Health

Claremont Graduate University

Claremont, CA

Michael Vanyukov, Ph.D.

Associate Professor

Departments of Pharmaceutical Sciences, 

Human Genetics, and Psychiatry

University of Pittsburgh

Pittsburgh, PA

Robert West, Ph.D.

Professor of Health Psychology and Director 

of Tobacco Studies

Department of Epidemiology and 

Public Health

Health Behaviour Research Centre

University College London

London, England

Helene R. White, Ph.D.

Professor

Center of Alcohol Studies

Rutgers, The State University of New Jersey

Piscataway, NJ

Michael Windle, Ph.D.

Chair, Rollins Professor

Department of Behavioral Sciences and 

Health Education 

Rollins School of Public Health 

Atlanta, GA

John Witte, Ph.D., M.S. 

Professor and Associate Director

Epidemiology/Biostatistics & Institute for 

Human Genetics

University of California, San Francisco

San Francisco, CA

The editors would like to acknowledge the 

publication support services provided for 

this monograph:

American Institutes for Research

Margot Raphael, Project Director and 

Managing Editor

Allan R. Clyde, Editor

Bethany Meissner, Project Assistant

Matthew Mowczko, Publication Production

Cygnus Corporation

Jennifer Bishop, Publications Manager

Patricia Spellman, Copyeditor

Mary Bedford, Proofreader

Preparation of this monograph was supported 

by the American Institutes for Research 

under Contract No. N02-PC-25041 to the 

National Cancer Institute.

xxiii

Acronyms and Abbreviations

5CSRTT fi ve-choice serial reaction time task

5-HT2A serotonin 2A (receptor)

6-OHDA 6-hydroxydopamine

A2A adenosine 2A

Abeta 25-35 amyloid beta-peptide 25-35

AD Alzheimer’s disease

ADA Americans with Disabilities Act

ADHD attention defi cit hyperactivity disorder

AIC Akaike Information Criterion

ANOVA analysis of variance

ATBC Alpha-Tocopherol, Beta-Carotene (Cancer Prevention Study)

ATR Australian Twin Registry

AUD alcohol-use disorder

BDNF brain-derived neurotrophic factor

BIC Bayesian Information Criterion

CB1 cannabinoid

CHRNA7 a7 nicotinic receptor

CIDI Composite International Diagnostic Interview

cM centimorgan

CNS central nervous system

CO carbon monoxide

COGA Collaborative Studies on Genetics of Alcoholism

COMT catechol-O-methyl-transferase (gene)

COT children of twins

CPD cigarettes per day/cigarettes smoked per day 

CPP conditioned place preference

CPS Current Population Survey

CPT continuous performance task

CPT-IP continuous performance task identical pair

CS conditioned stimulus

CYP cytochrome P-450

CYP2A6 cytochrome P-450 2A6 (gene)

CREB cyclic AMP-response element binding

dB decibel

DHbE dihydro-beta-erythroidine

DHEA dehydroepiandrosterone

DIS Diagnostic Interview Schedule (NIMH)

DRD2 dopamine receptor D2 (gene)

DSM Diagnostic and Statistical Manual of Mental Disorders

DSM-III-R DSM (third edition, revised)

DSM-IV DSM (fourth edition)

EEA equal environments assumption

EEG electroencephalogram
xxiv

Acronyms and Abbreviations

EM Expectation maximization

EMA ecological momentary assessment

ERP event-related potential

FF female-female twin pairs

FHS Framingham Heart Study

fMRI functional magnetic resonance imaging

FMM factor fi xture model

FTND Fagerström Test for Nicotine Dependence

FTQ Fagerström Tolerance Questionnaire

GABA c-aminobutyric acid

GAW Genetic Analysis Workshops

GIS geographic information system

GGMM general growth mixture modeling/models

GMM growth mixture modeling/models

HSI Heaviness of Smoking Index 

HPA hypothalamic-pituitary-adrenocortical

Hz hertz

IBD identical by descent

ICC item characteristic curve

ICD International Statistical Classifi cation of Diseases and Related 

Health Problems

ICD-10 ICD, tenth revision 

ICR mice (some kind of outbred mice)

ICSS intracranial self-stimulation

IP intraperitoneal

IRT item response theory

IV intravenous

kg kilogram

L liter

LCGA latent class growth analysis

LGC latent growth curve

LOD log (or logarithm) of odds

LRT likelihood ratio test

MAO monoamine oxidase

MAOA monoamine oxidase A

mg milligram

mL milliliter

MLA methyllycaconitine citrate

MMMF male-male and male-female twin pairs

MSTF Mid-South Tobacco Family

MTFS Minnesota Twin Family Study

lg microgram

nAChR nicotinic acetycholine receptor

NETSAD Netherlands Twin Study of Anxious Depression

NDSS Nicotine Dependence Syndrome Scale
xxv

Monograph 20. Phenotypes and Endophenotypes

NEAD Nonshared Environment in Adolescent Development (Project)

ng nanogram

NHANES-III Third National Health and Nutrition Examination Survey

NMDA N-methyl-D-aspartic acid

NRT nicotine replacement therapy

PDA personal digital assistant

PET positron emission tomography

PNS peripheral nervous system

PPI prepulse inhibition

PR progressive ratio

PTC phenylthiocarbamide

QSU Questionnaire on Smoking Urges

RNA ribonucleic acid

RSA respiratory sinus arrhythmia

RTU regular tobacco use

RVIP Rapid Visual Information Processing 

S allele short allele

SAS Statistical Analysis Software

SC subcutaneous

SEM structural equation model

SES socioeconomic status

SNP single nucleotide polymorphism

TH tyrosine hydroxylase (gene)

TI tobacco initiation 

TTFC time to fi rst cigarette

TTURC Transdisciplinary Tobacco Use Research Center

TUS-CPS Tobacco Use Special Cessation Supplement to the Current 

Population Survey (U.S. Census Bureau)

VLMR Vuong-Lo-Mendell-Rubin (test)

VNTR variable number tandem repeat

VTA ventral tegmental area

WCST Wisconsin Card Sorting Test

WISDM Wisconsin Inventory of Smoking Dependence Motives

1

Tobacco use is the world’s leading cause of preventable death. This major public 

health threat exists within the context of a complex interplay between genetic and 

environmental causes of nicotine dependence, and understanding this balance may hold 

the key to further reductions in the disease burden and mortality due to chronic tobacco 

use. This monograph explores the role of genetics in the etiology of nicotine dependence. 

It provides a conceptual framework for understanding nicotine dependence and for 

examining the usefulness of a range of potential phenotypes and endophenotypes for 

linking genes to behavior.

This introductory part starts by summarizing the epidemiology of tobacco use, the 

history of genetic studies in tobacco, and the measurement of nicotine dependence. 

It then provides a literature review of selected biometric and genetic studies of nicotine 

dependence and ends with a discussion of some of the most important issues in the 

communication of genetic fi ndings.

Overview

Part

1

3

1

Overview and Conclusions

This chapter introduces a monograph that examines the relationship between genetics 

and nicotine dependence. It summarizes evidence and research accumulated since the 

1950s on the effect of both unmeasured and measured genetic factors, as well as that of 

behavioral and environmental phenotypes on nicotine dependence.

This chapter frames issues addressed in the monograph, and describes its organization 

around topic areas including relating genetic and gene-environment factors to tobacco use, 

linking genetic traits with measures of nicotine dependence, examining the progression 

of tobacco use from adolescence to adulthood and its potential relationship to other 

substance abuse, identifying genetic liability markers for nicotine dependence in chronic 

smokers, and exploring the future of genetic studies for nicotine dependence. In addition 

to noting several “fi rsts” accomplished with the completion of this monograph, the closing 

sections of this chapter present volume and chapter conclusions generated by the work 

presented here.

Experts in psychology, psychiatry, behavioral pharmacology, neurobiology, epidemiology, 

child development, statistical genetics, and bioinformatics were assembled to provide 

data analyses within these pages. It is hoped that this monograph will help defi ne various 

groups of smokers to advance the fi eld of behavioral genetics of nicotine dependence.
4

1. Overview and Conclusions

Introduction

Substantial evidence, accumulated since 

the 1950s, from the study of twins, siblings, 

and nuclear families shows that unmeasured

genetic factors (infl uences estimated 

from analyses of correlations among 

family members for specifi c phenotypes) 

infl uence the likelihood of both initiating 

and maintaining nicotine dependence. 

Beginning with research published in 

1994, studies show that measured genetic 

factors (infl uences estimated from analyses 

of associations between genomic regions 

or specifi c gene variants and specifi c 

phenotypes) and nicotine dependence are 

also related. More than 100 published papers 

have reported associations between tobacco 

use behaviors and variants of candidate 

genes or genomic regions in relevant 

neurobiological and metabolic pathways. 

The combined evidence reveals that genetic 

involvement in nicotine dependence is 

present in adolescents and adults, both 

males and females, and in several cultures.

Concurrent with the work started in the 

early 1990s, new genomic technologies were 

introduced that make previous research 

quickly obsolete. With the rapid decrease 

in costs to genotype individuals for very 

large numbers of variants across the whole 

genome, the whole-genome association 

study has now become possible. Similarly, 

it is now possible to genotype candidate 

genes for many variants (known as single 

nucleotide polymorphisms [SNPs]). 

SNPs account for a large number of 

functional variants in humans, which has 

made them particularly useful in wholegenome research. Along with genomic 

technology, advanced methods from the 

experimental, bioinformatic, statistical, 

and epidemiological fronts now make it 

possible to envision the next generation 

of studies. These advances will support 

the further integration of neurobiological 

mechanisms into public health efforts.

The marked decline in cigarette 

consumption in the United States since 

the 1960s corresponds to increased public 

awareness of the dangers of tobacco use, 

changing social norms about tobacco, 

and increasing governmental actions to 

regulate the use, sale, and advertising of 

tobacco products. The most comprehensive 

environmental changes have been in 

attitudes and rules about smoking in 

enclosed public places. As late as the 

1980s, smoking was present in most 

public places, with smoking allowed 

virtually everywhere (except in areas of 

increased probability of fi res or damage to 

equipment). Over time, the environment 

that had supported smoking indoors 

has transformed. Limiting where people 

can smoke has contributed to the social 

marginalization of smoking as an accepted 

behavior. In addition, tobacco use screening 

and brief intervention by clinicians has 

become a top-ranked clinical preventive 

service on the basis of health impact, 

effectiveness, and cost-effi ciency, further 

reducing cigarette consumption.

Despite enormous progress in the public 

health arena, 45 million individuals 

remain regular users of tobacco, with an 

estimated annual cost to the U.S. economy 

of $167 billion due to premature death 

and disability.1

 Worldwide, approximately 

1 billion people are regular users of tobacco, 

and 3–6 million people die every year from 

illnesses caused by tobacco.2 One reason 

for this is that broad public health and 

community efforts, though found to be 

effective by such groups as the U.S. Task 

Force on Community Preventive Services, 

are not widely implemented and have varied 

substantially across the United States. 

Also, tobacco settlement dollars are spent 

by many states on non-health-related and 

non-tobacco-related activities instead of 

tobacco control and prevention.3

Another reason for continued smoking 

is that the potential of powerful genomic 
5

Monograph 20. Phenotypes and Endophenotypes

tools to answer important questions 

about nicotine dependence has yet to be 

maximized, and increased attention should 

be paid to the nature of the behavioral 

and/or environmental phenotypes included 

in future genomic studies. The majority of 

the published work has relied upon relatively 

broad, nonspecifi c measures of nicotine 

dependence that may, in fact, represent the 

end result of a series of initiating, promoting, 

and maintaining factors, many of which 

interact along the developmental pathway 

to result in full-blown adolescent and adult 

nicotine dependence. The preparation 

of this volume was undertaken to 

provide future genomic investigations 

of nicotine dependence with a review of 

more refi ned phenotypes that derive from 

theory-driven and/or experimental work. 

To accomplish this, a team of experts 

in the areas of psychology, psychiatry, 

behavioral pharmacology, neurobiology, 

epidemiology, child development, statistical 

genetics, epidemiology, and bioinformatics 

was assembled to review the available 

evidence for novel phenotypes that could, 

in turn, meet the requirements of an 

“endophenotype.”

Endophenotypes are presumed to be 

more directly related to the underlying 

characteristics of nicotine dependence 

than are broad inclusive measures. 

To be viewed as an endophenotype, 

a candidate phenotype may be 

neuropsychological, neurophysiological, 

neurobehavioral, biochemical, 

endocrinological, or neuroanatomical 

in nature and must be heritable, stateindependent, cosegregate with nicotine 

dependence in families, and present at a 

higher rate among unaffected relatives 

of those with nicotine dependence than 

in the general population.4,5 One of 

the assumptions of the endophenotype 

concept is that these constituents will 

have simpler genetic underpinnings than 

does nicotine dependence itself. The use 

of endophenotypes in genetic research of 

nicotine dependence has been underutilized. 

Another underutilized approach to the 

study of nicotine dependence involves the 

study of gene-environment interactions in 

which it is assumed that “environmental 

pathogens” cause the expression of a 

disorder such as nicotine dependence only 

in the presence of certain gene variants.6

One of the objectives of the present volume 

is to more fully explore the existing 

approaches and supporting evidence (at both 

the phenotypic and environmental levels) 

available to the next generation of genetic 

studies. These approaches include factors 

and processes that are tobacco specifi c, 

as well as those that are related to broader 

correlated conditions, including other 

forms of substance dependence.

About the Monograph

The overarching goal for the volume was 

for each of the contributing editors and 

authors to review the existing literature, 

and to go beyond it, by identifying new 

concepts, measurements, and strategies to 

more fully enrich the universe of discourse 

and investigation in the area of genetics 

and nicotine dependence. The authors 

were asked to summarize the best available 

evidence and make recommendations 

accordingly. In some cases, when the 

available evidence is thin or nonexistent, 

the authors were asked to conduct original 

analyses or apply innovative methods to 

existing data to move the fi eld forward. 

In addition, the authors were asked to 

provide informed opinions as to where the 

next generation of research should head.

The monograph will be most useful to 

individuals who are or will be designing 

next-generation studies to determine 

causal relationships between genes and 

nicotine dependence. To limit the scope 

of the volume, the emerging literature on 

the relationship between genetic variation 
6

1. Overview and Conclusions

and response to pharmacotherapy for 

smoking cessation is not included. While 

the volume will identify many important 

issues and questions concerning nicotine 

dependence and its measurement, it does 

not seek to resolve the issue of what nicotine 

dependence is nor does it seek to identify 

the “best” measures of nicotine dependence. 

Throughout the volume, the reader will 

clearly see where the evidence is solid in 

support of a phenotype’s potential role as an 

endophenotype and where the evidence is 

weak or simply not yet available.

The chapters of the monograph are 

organized into six parts.

Part 1—Overview, provides background 

context for research in genetic and geneenvironment factors in tobacco use. 

It focuses on phenotypes and endophenotypes 

that may link genes and behavior and be a 

basis for future genetic studies. In addition, 

conceptual, theoretical, and methodological 

considerations in the further study of 

nicotine dependence are examined.

Part 2—Theoretical Considerations,

examines the theoretical basis for constructs 

that may link heritable genetic traits with 

observable measures of nicotine dependence. 

These include phenotypes representing a 

causal path between specifi c genetic actions 

and measures of nicotine dependence, 

as well as endophenotypes measuring 

indirect infl uences, such as those found 

before nicotine exposure. This part examines 

theoretical issues in establishing nicotinedependence phenotypes as well as studies of 

human and animal behavior.

Part 3—Developmental Trajectories 

of Tobacco Use and Their Relation to 

Tobacco Dependence, examines issues in 

the study of trajectories of tobacco use and 

their future potential as a basis for genetic 

studies of nicotine dependence. Chapters 

include a literature review of developmental 

trajectories of cigarette smoking between 

adolescence and adulthood, genetic 

modeling issues in the study of smoking 

trajectories and behavior, and the 

relationship of these with other trajectories 

such as alcohol use or substance abuse.

Part 4—Endophenotypes. Endophenotypes 

serve as intermediary measures that have the 

potential to provide a link between genes, 

smoking behaviors, and nicotine dependence. 

Endophenotypes may help serve as a basis 

for future studies to identify genetic liability 

markers for nicotine dependence. This 

part discusses the evidence base for several 

candidate endophenotypes for nicotine 

dependence at or before initial exposure to 

nicotine as well as for endophenotypes for 

nicotine dependence in chronic smokers.

Part 5—Epidemiological and 

Methodological Considerations, examines 

epidemiological and methodological issues 

related to the future of genetic studies 

of nicotine dependence. These issues 

include the use of epidemiologically-based 

phenotypes for tobacco use; a potential 

etiological architecture for genetic and 

environmental infl uences on smoking 

phenotypes; and the hierarchical modeling 

of gene-gene joint action.

Part 6—Future Directions, comments 

on how continued research in the 

area of genetics may infl uence future 

understanding of the pathways responsible 

for nicotine dependence, the role genetic 

variation plays in its initial acquisition and 

maintenance. It also provides summaries 

and recommendations from each of the 

parts of the monograph and concludes with 

several cross-cutting suggestions for future 

work in this area.

A Note to the Reader

It is not the intention of the authors of 

the volume to suggest that the continued 

substantial prevalence of nicotine 

dependence in the population is solely 
7

Monograph 20. Phenotypes and Endophenotypes

determined by genetic factors. Much of 

the early work in twins indicates that 

environmental infl uences are equally 

important (and may be more so at different 

phases of the development of nicotine 

dependence). An enormous literature, 

evolving separately from that on genetics 

and nicotine dependence, clearly documents 

the effect of specifi c environmental 

infl uences on the likelihood of exposure to 

tobacco, its regular use, its chronic use, and 

the diffi culty some people have in stopping 

its use. Protobacco stimuli are ubiquitous 

in the environment and include advertising 

by the tobacco industry and the portrayal of 

smoking in the movies. Equally important, 

the tobacco industry controls the design 

of cigarettes and, so, the bioavailability of 

nicotine. The form in which the nicotine 

is delivered is an important variable that 

almost certainly interacts with the biological 

factors discussed in the report.

No published study has adequately 

addressed simultaneously genetic variation, 

quantitative measures of social and cultural 

variation, and the interaction between the 

two sources of variation. This gap refl ects the 

fact that scientists from the two traditions 

have not typically worked with each other 

rather than a dismissal of each other’s 

work. Tobacco use as refl ected in population 

trends is the product of the interaction of 

agent, host, and environmental factors. 

Government policies are important 

modifi able environmental infl uences that 

can alter how tobacco products are designed 

and marketed (agent factors) and how 

consumers respond. Individual variation 

in host responses to tobacco is important 

to understand, since this has implications 

for understanding how different people will 

respond to different programs and policies 

(i.e., treatments for nicotine dependence, 

tax increases, mass media campaigns, etc.). 

Synergy occurs when tobacco control 

and prevention interventions directed at 

agent, host, and environmental factors are 

implemented together.

Major 

Accomplishments

In completing the volume, several “fi rsts” 

were accomplished:

■ The fi rst comprehensive review of the 

state-of-the-art in the measurement 

of nicotine dependence and related 

phenotypes within the context of 

genetic studies

■ The fi rst demonstration that heterogeneity 

in tobacco use trajectory from early 

adolescence to early midlife is related to 

both family history of smoking and to 

nicotine dependence in adulthood

■ The fi rst demonstration that conjoint 

trajectories of tobacco and alcohol use 

in adolescents are heritable

■ The fi rst review of biobehavioral 

phenotypes that could be utilized by 

future genomic studies of pre- and 

postnicotine exposure

■ The fi rst demonstration that 

microcontextual effects on nicotine 

dependence can be assessed and are 

informative within the context of a 

genetically informed study

■ The fi rst use of Bayesian analysis as 

informed by a nicotine metabolic 

ontology to determine the relative 

importance of several genes to variation 

in nicotine metabolism

Major Conclusions

Several broad conclusions emerge from the 

volume. These include

1. At every level of analysis (theoretical, 

animal, child, and adult), good candidate 

endophenotypes are available for 

inclusion in future genomic studies of 

nicotine dependence.
8

1. Overview and Conclusions

2. Results from the animal and human 

domains implicate the importance of 

nicotinic acetylcholine receptors in 

nicotine self-administration, reward, 

and dependence.

3. Developmentally, there is evidence from 

latent class growth analysis and growth 

mixture modeling in unrelated and 

related adolescents that familial and/or 

genetic factors play a role in trajectories 

of tobacco use that vary in age of onset, 

level, and chronicity of use, as well as in 

the extent to which tobacco and alcohol 

use co-occur.

4. In children and adults, there are 

neuropsychological, electrophysiological, 

and behavioral laboratory measures 

characterized in other research contexts 

that may shed light on mechanisms 

that promote risk for initiation and 

maintenance of nicotine dependence.

5. Along with more refi ned defi nitions of 

nicotine dependence at the epidemiological 

level and an increased number of options at 

the phenotypic level, several technological 

developments will be important to the 

next generation of studies of nicotine 

dependence, such as whole-genome 

genotyping, epigenetics, proteomics, and 

metabolomics. Complementing these 

technologies are methodological advances 

including Bayesian statistics, behavioral 

ontologies, identifi cation of developmental 

trajectories, and real-time measurement 

of environmental antecedents to nicotine 

dependence.

Chapter Summaries 

and Conclusions

Part 1—Overview

Chapter 1. Overview and Conclusions

Chapter 1 provides an introduction and 

framework for the monograph, describes 

how it is organized, and includes major 

volume conclusions, chapter summaries and 

conclusions, and a look to the future. 

Chapter 2. Genetic Studies of 

Nicotine Dependence: Current Status 

Chapter 2 begins with a brief summary of 

the epidemiology of tobacco use, focusing on 

environmental factors that have been shown 

to promote and reduce smoking behavior. 

Also presented is an integrative model 

of tobacco use and nicotine dependence, 

illustrating the concept of trajectories 

of phenotypic pathways. The chapter 

then provides a history of research in 

the genetic basis of nicotine dependence. 

A full discussion of the limitations in the 

conceptual understanding of the construct 

of nicotine dependence along with a detailed 

framework for moving the fi eld forward is 

then presented. Next follows a summary 

of fi ndings from selected (biometric and 

measured) genetic studies of nicotine 

dependence. The chapter ends with a brief 

discussion of some of the major issues in 

communicating genetic fi ndings.

Part 2—Theoretical 

Considerations

Chapter 3. The Nicotine-Dependence 

Phenotype: Translating Theoretical 

Perspectives and Extant Data 

into Recommendations for Genetic 

Mapping

Chapter 3 examines theoretical issues 

in establishing nicotine-dependence 

phenotypes, including distal measures 

of nicotine dependence focusing on 

mature nicotine dependence, newer 

multidimensional measures of nicotine 

dependence that examine motivational 

factors leading to dependence, 

and endophenotypes and transitional 

phenotypes that may form a causal path 
9

Monograph 20. Phenotypes and Endophenotypes

between specifi c genetic actions and 

measures of nicotine dependence.

Conclusions

1. Most widely used tests of nicotine 

dependence, such as the Fagerström 

Test for Nicotine Dependence and the 

Diagnostic and Statistical Manual of 

Mental Disorders, aggregate data across 

different dimensions of dependence, 

thereby compromising the reliability 

and validity of these measures. Evidence 

suggests, however, that selected items 

from these measures and from newly 

developed dependence scales can be 

relatively coherent, show fairly high 

heritability, and be consistently related 

to core dependence features such as 

relapse likelihood.

2. Although key variance associated 

with the dependence construct will be 

captured by measures of smoking rate, 

latency to smoke in the morning, and 

the likelihood or latency of relapse, 

other complementary measures should 

also be considered such as strength of 

withdrawal symptoms and perceived 

control over smoking. Analytic strategies 

should adjust for environmental 

factors such as home or work smoking 

restrictions, which, in theory, may 

reciprocally affect dependence itself.

3. Nicotine dependence involves both 

environmental and constitutional 

infl uences, and the effects of 

genetic variants associated with 

nicotine dependence require certain 

environmental conditions to infl uence 

the phenotype (at minimum, drug 

access and use). Determining which 

environmental features moderate genetic 

expression and how to incorporate such 

gene-environment interactions into 

genetic mapping remains an area for 

further study.

4. New developments in the assessment 

of the nicotine-dependence phenotype 

include the development of new 

multidimensional measures of nicotine 

dependence, including the Nicotine 

Dependence Syndrome Scale and 

the Wisconsin Inventory of Smoking 

Dependence Motives. These measures 

of mature dependence phenotypes 

provide the opportunity to measure 

relatively discrete dimensions of 

dependence and may permit more 

specifi c gene mapping. 

5. In addition to greater specifi city, it is 

vital to capture important developmental 

processes that may be masked by the 

mature nicotine-dependence phenotype. 

To obtain measures sensitive to 

particular biological mechanisms that 

may have close links to genetic variants, 

researchers may need to develop 

biological, behavioral, and cognitive 

neuroscience assays that complement 

self-report measures. These may 

include measures of endophenotypes, 

or intermediate phenotypes, that 

assess vulnerabilities to dependence 

that preexist nicotine use as well as 

transitional phenotypic measures that 

assess processes that change in response 

to drug exposure and that lead to 

mature dependence.

6. All stages of the genetic mapping of 

nicotine dependence should be guided 

by specifi c theory linking candidate 

genetic variants sequentially with critical 

biological and behavioral processes and, 

ultimately, with phenotypes of clinical 

signifi cance.

Chapter 4. Mouse Models and the 

Genetics of Nicotine Dependence

Chapter 4 examines key issues in using 

mouse models for nicotine dependence, 

including how nicotinic acetylcholine 

receptors contribute to tissue-specifi c 

response within the context of strainspecifi c genetic background, the interaction 

of nicotine with physiological systems and 
10

1. Overview and Conclusions

how experimental results with mice may 

relate to the physiology of human smoking, 

and the way mouse models recapitulate 

many basic features of nicotine dependence 

in humans.

Conclusions

1. Substantial differences exist between 

mouse strains in their response to the 

acute or chronic administration of 

nicotine. These differences implicate 

specifi c neuronal nicotinic acetylcholine 

receptors within a broader genetic 

context, which suggests a central role 

for these genetic variants in nicotine 

dependence in humans.

2. The three most common routes 

of administration (intravenous, 

subcutaneous, and oral) for nicotine in 

rodents vary in the degree to which they 

model key features of human nicotine 

dependence, such as the behavioral 

features of self-administration and the 

acute and chronic physiological effects 

of nicotine. Each administration route 

offers advantages and disadvantages. 

Intravenous self-administration 

permits self-administration but may 

entail receptor-level response artifacts 

due to high dosages. Subcutaneous 

administration allows experimenter 

control of dosage and withdrawal over 

long time periods at a cost of precluding 

self-administration. Oral administration 

via drinking water permits chronic 

nicotine exposure and produces evidence 

of dependence, but is subject to specifi c 

possible side effects, making this issue 

an important variable in research design.

3. While mice generally are less sensitive 

to nicotine than are rats, mouse models 

now have a strong research base for 

nicotine effects. Mice are amenable 

to genetic and pharmacological 

experimental manipulation. They 

exhibit heterogeneity in strain-specifi c 

responses to nicotine, and methods 

of homologous recombination permit 

manipulation of specifi c genes. Data now 

link specifi c mouse strains to genetically 

infl uenced differences in the effects of 

nicotine exposure that can facilitate 

further study of nicotinic acetylcholine 

receptor biology in mice. 

4. Mouse models link nicotine selfadministration to high-affi nity nicotinic 

acetylcholine receptors, genetic 

differences, developmental factors, 

and other potential mechanisms of 

dependence. These models have, in 

addition, linked nicotine reward in the 

form of conditioned place preference 

with genetic strain differences and 

specifi c receptor subtypes and have 

linked acute and chronic nicotine 

tolerance with other genetic and receptor 

differences. The models have also linked 

the a7 and a4b2 receptors with nicotine 

enhancement of working memory, 

learning, and attention and have shown 

strain-specifi c aging effects on nicotinic 

acetylcholine receptor expression.

5. Although substantial differences exist 

in the biology of nicotinic acetylcholine 

receptor expression and function 

between mice, other rodents, and 

humans, nascent research in mouse 

models for nicotine dependence shows 

considerable promise in furthering 

understanding of the biology and 

genetics of nicotine dependence.

Part 3—Developmental 

Trajectories of Tobacco Use 

and Their Relation to Tobacco 

Dependence

Chapter 5. Developmental 

Trajectories of Cigarette Smoking 

from Adolescence to Adulthood

Chapter 5 examines literature concerning 

developmental trajectories of cigarette 

smoking between adolescence and 
11

Monograph 20. Phenotypes and Endophenotypes

adulthood and presents an empirical 

example of these trajectories. This chapter 

also provides a framework for part 3 that 

explores aspects of cigarette smoking 

trajectories and their potential to inform 

further genetic research.

Conclusions

1. Previous studies (and the empirical 

example presented in the chapter) 

have identifi ed multiple developmental 

trajectories of tobacco use from 

adolescence to adulthood. These 

trajectory groups, which vary in age 

of onset, rate of acceleration, and 

persistence of smoking over time also 

vary in their antecedents and correlated 

risk factors. These trajectories may be 

informative as developmental phenotypes 

for genetic studies of tobacco use.

2. Statistical approaches such as latent 

class growth analysis and growth 

mixture modeling can be useful in 

evaluating developmental trajectories 

of smoking behavior. However, challenges 

in using these approaches include the 

handling of within-class random effects, 

the impact of a nonnormal aggregate 

distribution on the classes extracted, 

the need for proper model specifi cation 

and parameterization, the span of 

evaluated data, and the impact of 

abstainers on the model.

3. Analysis of a 25-year cohort-sequential 

study of smoking behavior identifi ed six 

distinct trajectories of smokers across 

eight waves of data collection. These 

trajectory groups were experimenters; 

developmentally limited smokers; earlyonset, persistent smokers; high-schoolonset, persistent smokers; late-onset, 

persistent smokers; and successful 

quitters, with a priori groups of stable 

abstainers, stable quitters, and relapsing/

remitters. Trajectory group membership 

was related to educational attainment, 

family history of smoking, and indicators 

of nicotine dependence.

Chapter 6. Genetic Modeling 

of Tobacco Use Behavior 

and Trajectories

Chapter 6 examines genetic modeling 

issues in the study of smoking trajectories 

and behavior, including methodological 

and conceptual issues, statistical modeling 

considerations, a review of prior genetic 

studies of smoking behavior, and a study 

applying an item response theory approach 

to an analysis of smoking trajectories.

Conclusions

1. Data from twin studies suggest that 

shared environmental factors are 

the predominant source of familial 

resemblance in liability to smoking 

initiation in young adolescents, while 

additive genetic factors appear more 

important in older adolescents.

2. Results from extended twin designs 

show that signifi cant assortative mating 

exists for smoking initiation and that 

the parent-child correlations can 

be almost entirely accounted for by 

genetic factors. This implies a limited 

environmental infl uence of parental 

smoking initiation on smoking initiation 

in their children.

3. In contrast to the signifi cant role of 

shared environmental factors in smoking 

initiation, the liability to smoking 

persistence and nicotine dependence 

appears to be primarily accounted for by 

additive genetic factors. Furthermore, 

the liabilities to initiation and progression 

appear to be substantially correlated. 

Molecular genetic studies may be 

expected to fi nd some genetic variants 

that contribute specifi cally to initiation—

some that are specifi c to dependence and 

some that contribute to both.

4. Future development and applications of 

genetic latent growth curve models and 

genetic latent class models promise to 

improve the understanding of the role 
12

1. Overview and Conclusions

of genes and environment in smoking 

trajectories and transitions from 

nonsmoker to smoking dependence.

5. The search for susceptibility loci for 

smoking-related traits, either through 

linkage or association studies, has not 

identifi ed any convincing replicated 

fi ndings. However, several genomic 

regions and several candidate genes 

have been found to be associated with 

smoking behavior in more than one 

study.

6. Improving the assessment of nicotine 

initiation and dependence by allowing 

for differences in measurement by age 

and gender and taking conditionality 

into account might provide more 

accurate estimates of the contributions 

of genes and environment to different 

stages of smoking.

7. Meta-analyses or mega-analyses of 

studies of smoking phenotypes—both 

genetic epidemiological and molecular 

genetic—should prove useful in 

summarizing the available data and 

results. Possibly, certain data sets may 

produce results that are outliers, and 

controlling for their effects would permit 

fi ner resolution between hypotheses and 

more accurate parameter estimates.

Chapter 7. Trajectories of Tobacco 

Use from Adolescence to Adulthood: 

Are the Most Informative Phenotypes 

Tobacco Specifi c?

Chapter 7 examines the evidence base for 

linkages between substance-use trajectories, 

as well as the results of an original empirical 

study examining smoking and alcohol use 

over time across a cohort group of male 

twins. The areas discussed include common 

versus specifi c liability to substance-use 

disorders, covariate relationships between 

smoking and other substance-abuse 

trajectories, and conjoint trajectories of 

smoking and other substances.

Conclusions

1. Studies examining the developmental 

course of multiple substances have 

shown relatively high concordance 

between identifi ed trajectories despite 

diverse course shapes and different 

course prevalences. 

2. Membership in a given developmental 

trajectory, which can be captured by 

a single categorical latent variable, 

represents age of onset and severity 

as well as change (slope) in use of a 

substance; moreover, membership in a 

trajectory characterized by concurrent 

use of two (or more) substances 

simultaneously provides information 

for multiple substances.

3. Developmental course might serve as a 

valuable phenotype for biometric models, 

and determining the degree to which 

a phenotype of developmental course 

is substance specifi c is valuable for the 

genetic study of addictive behavior. 

4. Evidence using twin data indicates that 

courses of substance use are genetically 

infl uenced, with monozygotic twins 

showing greater concordance for 

smoking and for drinking than do 

dizygotic twins. The genetic contribution 

to the risk of taking different pathways 

in development represents an area for 

further study.

5. Conjoint trajectories of drinking 

and smoking reveal even greater 

concordance than do single-substance 

trajectories, suggesting greater 

heritability for courses extracted from 

several substances. This underscores the 

value of considering substance use across 

multiple domains when constructing 

phenotypes for research and perhaps 

even for clinical use. However, extending 

the concept of the components of 

developmental substance-use phenotypes 

raises new questions such as, Which 

substances? What aspects of substance 

use or its consequences? Which periods 
13

Monograph 20. Phenotypes and Endophenotypes

of development? Thus, the fi ndings 

show the value of extending the concept 

of substance-use phenotypes but not 

necessarily optimal phenotypes that 

“carve nature at its joints.”

6. If resources are limited for genetic 

analyses, focusing on those with the 

most “extreme” phenotypes marked 

by both high initial level and chronic 

continued use may represent an effi cient 

strategy for identifying genes associated 

with more problematic forms of 

substance use.

Part 4—Endophenotypes

Chapter 8. Endophenotypes 

for Nicotine-Dependence Risk 

at or before Initial Nicotine Exposure

Chapter 8 examines the evidence base 

for several candidate endophenotypes 

for nicotine-dependence risk at or before 

smoking and nicotine exposure. Issues 

covered include approach-related smoking 

risk variables, avoidance-related smoking 

risk variables, control-related smoking 

risk based on psychological variables, 

and measures of initial response to 

nicotine exposure.

Conclusions

1. Several higher-order psychological 

constructs can consolidate many 

smoking initiation and progression risk 

variables. These constructs, as well as 

sensitivity to initial nicotine exposure, 

can be related to observable neural, 

physiological, and behavioral measures 

that may, in turn, serve as potential 

candidate endophenotypes for genetic 

research on nicotine dependence.

2. Several laboratory measures exist that 

could be associated with the risk for 

smoking initiation and progression 

and subsequent nicotine dependence, 

but these associations have yet to be 

investigated. Findings are mixed for 

the reliability and heritability of these 

measures, and minimal evidence exists 

for their validity, representing an area 

for further study.

3. Measurement of sensitivity to initial 

nicotine exposure is subject to numerous 

methodological limitations, including 

ethical diffi culties with empirical 

measurement in naive (e.g., previously 

unexposed to nicotine) subjects, a lack 

of consideration of smoking dose and 

context from retrospective self-reports, 

recall bias, and self-selection to early 

smoking experience. At the same 

time, preliminary fi ndings indicate 

that measures of reward and mood 

effects surrounding initial exposure to 

smoking show promise as a potential 

basis for endophenotypes of a genetic 

predisposition to nicotine dependence.

4. The available evidence points to the 

plausibility of endophenotypes that 

link factors at or before initial nicotine 

exposure with the potential for nicotine 

dependence. These endophenotypes 

refl ect approach, avoidance, and 

control-related traits as well as initial 

sensitivity and exposure measures in 

response to nicotine intake. Further 

research is needed to help identify 

endophenotypes that connect risk 

variables for nicotine dependence to 

genetic infl uences.

Chapter 9. Nicotine-Dependence 

Endophenotypes in Chronic Smokers 

Chapter 9 explores the evidence base 

for purported endophenotypes for 

nicotine dependence in chronic smokers. 

Motivational measures, sensory measures, 

measures of cognitive function, measures 

of abstinence-induced and cue-induced 

craving, and affective regulation and 

impulse control are discussed from a 

standpoint of biological plausibility, 

objective measurement criteria and 
14

1. Overview and Conclusions

reliability, genetic infl uences, and 

association with nicotine dependence.

Conclusions

1. Nicotine dependence in chronic smokers 

is characterized by persistent smoking 

behavior despite knowledge of its harm 

(e.g., an inability to sustain a quit 

attempt). Reinforcement measures such 

as nicotine choice have been related to 

nicotine dependence, although further 

research is needed on the relationship 

between dependence and ad libitum drug 

self-administration, behavioral economics, 

and progressive ratio measures. Genetic 

studies in reinforcement measures in 

mice indicate a potential for studying the 

heritability and genetic infl uence for these 

behaviors in humans.

2. Limited evidence exists regarding the 

relation between self-reported measures 

of reward and nicotine dependence in 

humans, while animal studies show a 

potential link between the reward-related 

measure of conditioned place preference 

and nicotine dependence.

3. Evidence of heritability and genetic 

infl uence has been established for 

measures of sensory processing, such as 

resting electroencephalogram activity, 

event-related potentials, and the 

prepulse inhibition of startle response, 

as well as cognitive measures such as 

attention and working memory. Further 

research is indicated to investigate 

the relationship of such measures to 

nicotine dependence in humans.

4. Self-report measures of abstinenceinduced craving have been related 

to the success of cessation efforts 

(i.e., dependence), while neither selfreport nor psychophysiological measures 

of cue-induced craving have been 

reliably shown to relate to nicotine 

dependence. The relationship of these 

measures with genetic factors remains 

an area for further investigation.

5. Self-reported levels of negative affect 

following smoking cessation have been 

strongly related to smoking persistence. 

Persistence has also been associated 

with abstinence-induced changes in 

physiological measures such as cortisol 

and the dehydroepiandrosterone to 

cortisol ratio. Other measures of affect 

have not been shown conclusively 

to relate to measures of nicotine 

dependence. 

6. Impulsivity and cognitive control 

measures such as delay discounting, 

the go/no-go task, and the Stroop 

interference task have not been shown 

conclusively to relate to nicotine 

dependence, while the go/no-go task 

has shown some evidence of heritability 

and relation to genetic factors.

7. Overall, the available evidence supports 

the possibility of endophenotypes 

for nicotine dependence in chronic 

smokers on the basis of motivational 

factors and, to a lesser extent, sensory, 

cognitive, affective, and behavioral 

measures. Further research is indicated 

to help establish a consistent pattern 

of heritability, genetic infl uence, and 

association with nicotine dependence 

for measures in each of these areas.

Part 5—Epidemiological 

and Methodological 

Considerations

Chapter 10. Epidemiological Analysis 

of Variation in Phenotypic Defi nitions: 

A Proof of Concept Using an Example 

of a Cessation Phenotype

Chapter 10 explores the use of an 

epidemiological approach for modeling 

smoking phenotypes that are based on 

transitions along the smoking trajectory 

and prior exposure. It presents three studies 

that examine improved phenotypes based 
15

Monograph 20. Phenotypes and Endophenotypes

on observable transition points in smoking 

cessation with appropriate prior exposure in 

relation to numerous variables for smoking 

behavior and comorbid conditions.

Conclusions

1. More tightly defi ned phenotypes of 

smoking behavior that are based on 

transitions along the smoking trajectory 

and adequate prior exposure have the 

potential to reduce the classifi cation bias 

and lack of specifi city inherent in broader 

existing phenotypes such as current 

smoking status. These improved 

phenotypes, in turn, may lead to closer 

correlations between smoking behavior 

and genetic variables in future studies.

2. Studies involving both longitudinal and 

cross-sectional population data show 

measurable differences among improved 

phenotypes, including sustained 

quitters, relapsers, and never quitters, 

in key markers such as smoking history, 

other indices of nicotine dependence, 

and comorbid conditions such as 

psychological symptoms and alcohol use.

3. Refi ned nicotine-dependence phenotypes 

based on longitudinal characterizations 

of smoking patterns show promise 

for further testing in genetic studies 

in support of potential phenotypegene causal associations for nicotine 

dependence. Research indicates the 

potential need for further refi nement 

of such phenotypes.

Chapter 11. Incorporating Social 

Context into Genetic Studies of 

Nicotine Dependence

Chapter 11 examines the available research 

and future trends related to social context 

factors that could inform subsequent 

genetic studies of smoking. The chapter 

considers macrocontextual factors such 

as culture and socioregional factors, 

microcontextual factors such as smoking 

in specifi c interpersonal relationships, 

and integrated proximal indicators of both 

macro- and microcontext such as ecological 

momentary assessment.

Conclusions

1. Social context infl uences on 

developmental pathways to nicotine 

dependence refl ect gene-environment 

interplay that comprises the elements of 

a traditional epidemiological framework 

including a host (e.g., smokers and 

genetic endowment), environmental 

factors (social network), and an agent 

(e.g., tobacco).

2. Macrocontextual factors such as 

culture, socioregional variables, and 

socioeconomic status can modify or 

even nullify genetic infl uences on 

nicotine dependence. For example, 

a twin study revealed a prevalence rate 

for smoking of less than 1% in Chinese 

women, refl ecting an inhibitory cultural 

infl uence. Family or neighborhood 

socioeconomic status and density of 

tobacco sales outlets are examples of 

specifi c contextual factors that appear 

to infl uence smoking risk among 

adolescents.

3. Microcontextual approaches have 

revealed factors such as exposure to 

parental, sibling, and peer smoking 

that may moderate genetic infl uence 

on behavioral smoking measures. 

The genetically informative Nonshared 

Environment in Adolescent Development 

Project, which comprised twins as well 

as other siblings, indicated that sibling 

interaction patterns may moderate 

the shared environmental effects that 

infl uence adolescent smoking.

4. Studies of smoking behavior using 

ecological momentary assessment, 

designed to measure both macroand microcontextual factors, show 

that smoking behavior varies with 

both location and companions. Such 
16

1. Overview and Conclusions

assessments serve as a possible future 

model for incorporating integrated social 

context issues such as actual clinical and 

public health efforts to reduce tobacco 

use within etiological architectures.

5. Future work incorporating social 

context within gene-environment 

studies of smoking behavior and 

nicotine dependence will benefi t from 

a greater focus on environmental 

factors, including more-fi ne-grained 

and comprehensive assessments of 

potential environmental infl uences.

Chapter 12. Using Ontologies 

in Hierarchical Modeling of Genes 

and Exposure in Biological Pathways

Chapter 12 examines the potential for the 

use of hierarchical modeling techniques 

within the framework of an ontology 

that quantifi es relationships across 

genotypes and phenotypes for nicotine 

dependence. The chapter provides an 

overview of statistical approaches for 

genetic association studies in tobacco use, 

presents the results of a study of nicotine 

metabolism that shows signifi cant genetic 

associations with nicotine clearance 

levels, discusses design and analysis 

considerations in the use of hierarchical 

modeling in conjunction with stochastic 

variable selection, and explores the use of 

ontologies for codifying prior knowledge to 

support effi cient computational analysis of 

hierarchical models.

Conclusions

1. The available knowledge of nicotine 

dependence arises largely from studies 

that model the independent association 

of candidate genes with outcome 

measures. Such studies often fail to 

refl ect the complexity of interacting 

factors and discrete events that can 

infl uence smoking behavior and, 

therefore, may not provide a clear 

picture of biological mechanisms 

affecting nicotine dependence.

2. A promising approach to the study of 

nicotine dependence involves the use 

of prior biological knowledge about 

the relations between genotypic and 

phenotypic variables in a hierarchical 

modeling framework. This allows 

prior knowledge to aid in estimating 

specifi c genotypic effects and to guide 

a stochastic search over all possible 

statistical models. 

3. The use of ontologies is a promising 

new direction for the elucidation of the 

genetic basis of nicotine dependence. 

An ontology is a construct or model that 

represents entities in both genotypic 

and phenotypic domains as well as their 

interrelations. The use of an ontology 

permits the modeling of hierarchical 

relationships by using directed acyclic 

graphs spanning genotypes and 

endophenotypes and phenotypes, while 

taking advantage of prior knowledge to 

quantify these relationships, making 

them amenable to computational 

analysis.

4. A study of nicotine metabolism that 

used data from the Northern California 

Twin Registry to examine the total 

clearance of nicotine and the trans

3-hydroxycotinine to cotinine ratio, 

with the Nicotine Pharmacokinetics 

Ontology as a framework, showed a 

signifi cant association between specifi c 

polymorphisms for CYP2A6 and 

measured nicotine clearance levels as 

well as statistically signifi cant results 

for single nucleotide polymorphism 4 

within UGT1A4.

5. Hierarchical modeling combined 

with the use of an ontology defi ning 

relationships between constructs of 

interest represents a promising area 

for further research in studying a 

possible genetic basis for nicotine 

dependence as well as for understanding 
17

Monograph 20. Phenotypes and Endophenotypes

the interaction between genetics and 

social and environmental infl uences on 

tobacco use and dependence.

Part 6—Future Directions

Chapter 13. Future Directions

Chapter 13 starts with a discussion of 

how examining the genetics of tobacco 

dependence may affect basic and clinical 

research. It then outlines future research 

needs for topics covered in parts 2–5. 

The chapter ends by presenting higher level 

recommendations for future research in 

nicotine dependence that cut across the 

content of this volume. These volume level 

future directions were identifi ed by the 

editors while preparing this monograph 

and after taking into account continuing 

developments in the fi eld.

Crosscutting Issues

■ A comprehensive approach to examining 

and reporting genotype-phenotype 

associations should be adopted; 

single-gene, single-variant association 

studies should be discouraged unless 

accompanied by reports of replication 

and validation.

■ Researchers working in the fi eld of 

genetics and nicotine dependence 

should be mindful of the potential 

for misinterpretation of results by lay 

audiences. Efforts to communicate 

results to the media should include the 

limitations of the work along with the 

extent to which the results are reliable 

and generalizable. Doing so will minimize 

the chances of stigmatizing subgroups in 

the population.

■ An ontology-based approach to nicotine 

dependence, with specifi cation of expected 

relations within and between phenotypic 

domains, will provide an interpretive 

context and more focused hypotheses 

for future research; this will lead to an 

ongoing refi nement of the ontology as 

new information becomes available.

■ A greater use of strategies that combine 

differing levels of analysis is needed. 

The incorporation of measured genetics 

into genetic latent growth curve and/or 

latent class models in extended twin 

designs, for example, will provide 

information on the extent to which 

variation in one or more genes plays a 

role in the overall estimate of genetic 

variation in any particular phenotype. 

In addition, a nicotine reward phenotype 

may be characterized via behavioral 

measures of self-administration, selfreport assays, and imaging measures of 

activity in brain regions associated with 

reward processing. This, in turn, could 

spur the hunt for more genetic variants 

and gene-gene or gene-environment 

interactions to account for more of the 

overall genetic variation estimated in the 

biometric models. Inclusion of quantifi ed 

life events, cultural factors, and extant 

clinical and public health efforts in 

tobacco control and prevention in genetic 

studies is also warranted.

■ Genome-wide association analysis of 

phenotypes considered to be risk factors 

for the adoption or maintenance of 

nicotine dependence would lead to 

further understanding of the pathways 

by which children progress to adult 

nicotine dependence.

■ Given the enormous social, health, 

and economic impacts of nicotine 

dependence, the coordinated effort of 

multiple research teams to address the 

many opportunities for further research 

identifi ed in this volume is warranted.

■ There is a need to examine the association 

between gene variants and phenotypes 

of relevance in both the presence and 

absence of environmental risk factors. 

Emerging evidence from longitudinal 

studies of adolescents suggests that 

genetic associations with indices of 
18

1. Overview and Conclusions

nicotine dependence may be stronger 

and more robust when acting in the 

absence of environmental pressure 

to not use tobacco. Another way in 

which gene-environment interactions 

may infl uence nicotine dependence 

is during and/or following attempts 

to quit the use of nicotine-containing 

products. For example, variation in genes 

responsible for drug metabolism could 

interact with the dosing or duration 

of pharmacotherapy for nicotine 

dependence to reduce drug effi cacy. 

A third possibility for further exploration 

of gene-environment interactions involves 

the period following smoking cessation. 

The relationship between genetic 

variation and the likelihood of relapse 

back to nicotine dependence could well be 

dependent on the presence of conditioned 

cues to smoke or environmental stress.

■ Epigenetic methodologies promise 

to further understanding of the 

impact of the environment on the 

differential expression of gene variants. 

One possible approach, described in 

chapter 2, involves the comparison, 

at the genomic and/or expression level, 

of lymphoblastoid cell lines from identical 

twins discordant for nicotine dependence 

or other characteristics such as nicotine 

metabolism. Informative measures of 

environmental exposures will enhance 

the power of this approach to account for 

monozygotic twin discordance.

■ Much of the tobacco literature examines 

genetic susceptibility to smoking 

initiation and cessation only among very 

broad groups, without an understanding 

of the complexities or variations within 

these categories in patterns of smoking 

behavior. Combining very different 

subgroups of smokers into a few 

common phenotypes and then using 

such heterogeneous groups in research 

studies may be hindering progress in 

understanding the role of genetics in 

complex behaviors such as smoking. 

Moreover, standard defi nitions of 

smoking behavior from epidemiological 

surveys are not commonly used, making 

it diffi cult to compare results among 

genetics studies and to put these results 

into the context of knowledge gained 

from other disciplines. Therefore, 

researchers should be encouraged to 

use existing standardized defi nitions 

and measures of tobacco use behavior 

and to examine the role of genetics and 

environment in a greater number and 

broader range of more homogeneous 

groups of tobacco users.

■ Epidemiologists and surveillance 

researchers should be encouraged 

to contribute more to the 

conceptualization, identifi cation, 

defi nition, and operationalization of 

potential phenotypes of tobacco use 

behavior and then to demonstrate the 

utility, reliability, and validity of these 

potential phenotypes by using data from 

representative national surveys.

References

1. Centers for Disease Control and Prevention. 

2007. Cigarette smoking among adults—

United States, 2006. Morbidity and 

Mortality Weekly Report 56 (44): 1157–61.

2. World Health Organization. 2008. WHO 

report on the global tobacco epidemic 2008: 

The MPOWER package. Geneva: World 

Health Organization.

3. Farrelly, M. C., T. F. Pechacek, K. Y. Thomas, 

and D. Nelson. 2008. The impact of tobacco 

control programs on adult smoking. 

American Journal of Public Health 98 (2): 

304–9.

4. Gottesman, I. I., and J. Shields. 1972. 

Schizophrenia and genetics: A twin study 

vantage point. New York: Academic Press.

5. Gottesman, I. I., and J. Shields. 1973. Genetic 

theorizing and schizophrenia. British Journal 

of Psychiatry 122 (566): 15–30.

6. Caspi, A., and T. E. Moffi tt. 2006. Geneenvironment interactions in psychiatry: 

Joining forces with neuroscience. Nature 

Reviews Neuroscience 7 (7): 583–90.
19

This chapter frames important issues in identifying potential phenotypes of nicotine 

dependence and sets the stage for examining the role of genetics in nicotine-dependence 

research. Key areas discussed include

■ Issues in the defi nition and measurement of nicotine dependence

■ A framework for phenotypes for nicotine dependence that potentially links 

genetics and behavioral traits while showing measurable validity, reliability, 

and heritability

■ The implications of epidemiological concepts in identifying potentially complex 

genetic risk factors for nicotine dependence

■ Measuring environmental infl uences and including them in models of estimates 

of genetic risk and the role epigenetic investigations will play in future 

investigations

■ A review of selected biometric and genetic studies of nicotine dependence

■ The communication and interpretation of fi ndings from genetic studies of 

nicotine dependence, including the need for replication, the potential for 

stigmatization, and value of direct-to-consumer marketing of genetic tests 

based on these fi ndings

This volume examines conceptual, theoretical, and methodological considerations in the 

development of nicotine-dependence phenotypes and endophenotypes. Each of these areas 

shows the potential for future study to help better understand factors in global tobacco use.

2

Status of Genetic Studies 

of Nicotine Dependence

Gary E. Swan, Christina N. Lessov-Schlaggar, Laura Jean Bierut,

Alexandra E. Shields, Andrew W. Bergen, and Michael Vanyukov

The analyses described herein were supported by National Institute of Health grants CA89392, DA005605, DA018019, 

DA018701, DA019157, DA11070, DA21237, and HG003475-02, and University of California Tobacco-Related Disease 

Research Program 7PT-2000. Dr. L.J. Bierut is listed as an inventor on a patent (US 20070258898) held by Perlegen 

Sciences, Inc., covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction. 

Dr. Bierut has acted as a consultant for Pfi zer, Inc. in 2008.
20

2. Status of Genetic Studies of Nicotine Dependence

Introduction

Environmental infl uences on tobacco 

use in the United States have intensifi ed 

dramatically over time and account for the 

sharp reduction in the overall prevalence 

of smoking (fi gure 2.1). An enormous 

body of literature, evolving separately 

from that on genetics and nicotine 

dependence, clearly documents the effect 

of specifi c environmental infl uences on 

the likelihood of exposure to tobacco, 

its regular use, its chronic use, and the 

diffi culty some people have in stopping 

use. Protobacco stimuli are ubiquitous in 

the environment and include advertising 

by the tobacco industry and the portrayal 

of smoking in movies. Equally important, 

the tobacco industry controls the design 

of cigarettes and, so, the bioavailabilty of 

nicotine. The form in which the nicotine 

is delivered is an important variable that 

almost certainly interacts with the biological 

factors discussed in this report. Similarly, 

antitobacco stimuli have become almost 

as widespread in some parts of the world. 

Antismoking media, smoke-free workplaces 

and public places, smoke-free homes, 

concern over secondhand smoke, and the 

pricing of tobacco products are a few of the 

sources of environmental variation.

The marked decline in cigarette 

consumption in the United States since 

the 1960s (most of which has taken place 

since 1981 [640 billion cigarettes consumed 

compared to an estimate of 371 billion 

cigarettes in 2006]) corresponds to 

increased public awareness of the dangers 

of tobacco use, changing social norms 

about tobacco, and increased governmental 

actions to regulate the use, sale, and 

advertising of tobacco products. The most 

comprehensive environmental changes have 

been in attitudes and rules about smoking 

in enclosed public places. As late as 20 years 

ago, smoking was ubiquitous in most places, 

with smoking allowed virtually everywhere 

(unless it posed a danger of fi res or damage 

Figure 2.1 Cigarette Consumption in the United States in the Twentieth Century

0

2,000

1,500

1,000

500

2,500

3,000

3,500

4,000

4,500

5,000

Cigarette consumption per capita

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

Note. From National Center for Health Statistics, Centers for Disease Control and Prevention, 2002. Cigarette consumption: 

U.S. Department of Agriculture, 1900–2000.
21

Monograph 20. Phenotypes and Endophenotypes

to equipment). Over time, the environment 

that had supported smoking indoors 

has transformed. Limiting where people 

can smoke has contributed to the social 

marginalization of smoking as an accepted 

behavior. In addition, another major reason 

for this decline is the associated rise in price 

per pack from about $1.50 in 1980 to more 

than $4.20 in 2007.1

However, despite price increases and 

intensive public health control, an estimated 

45 million people in the United States still 

smoke (17 million attempt to quit annually),2

testifying to the fact that the consistent 

application of already effective methods of 

prevention and intervention is necessary 

to further reduce the prevalence of tobacco 

use in this country.3 The annual cost to the 

U.S. economy is estimated to be $167 billion 

due to premature death and disability.2

It is estimated that approximately 1 billion 

people worldwide are regular users of 

tobacco (96.3% of smokers are outside of 

the United States) and that 3–6 million 

people die every year from tobacco-related 

illnesses.4 This number is expected to 

climb to 9 million by the year 2030.5

The prevalence of smoking outside the 

United States varies widely but is as high 

as 60% among men in some countries. 

The prevalence of smoking among nonAmerican women is generally lower but 

appears to be rising in some countries as 

“westernization” continues.6 These data 

suggest that the effects of culture are 

another important aspect of environmental 

infl uences. For example, in many cultures, 

very few women smoke. The fact that 

more women start to smoke when moving 

from these cultures to the United States 

(or other places where smoking by women 

is accepted), or when exposed to cigarette 

marketing targeted to women, demonstrates 

dramatically the power of the environment 

to infl uence nicotine dependence. On a 

population-wide basis, the great diversity in 

tobacco use behaviors observed both between 

countries and within countries over time 

demonstrates that biology alone cannot fully 

explain variations in tobacco use behaviors. 

These statistics indicate that the demand for 

both prevention and intervention efforts at 

tobacco control will continue to increase and 

will become urgent as the costs to existing 

and emerging economies are realized. 

All available tools will be needed to meet 

the demand for effective and sustainable 

tobacco control, including pharmacogeneticinformed treatments and social policy 

interventions7

 for smoking cessation.

The highly addictive nature of nicotine and 

the more than $13 billion spent annually by 

the tobacco industry8 to market its products 

to the American people contribute much 

to infl uence new and continuing smokers. 

However, the majority of adults and children 

choose not to use tobacco products. 

The answer to the question of intense 

scientifi c interest, “Why do some people 

smoke and others do not?” remains as 

elusive today as it was in 1993 when it was 

articulated by Pomerleau and colleagues.9

Although work in the human domain as 

well as in animal models has contributed to 

knowledge of the processes and pathways 

underlying nicotine dependence specifi cally, 

and addiction more generally, it is fair to 

say that knowledge derived from genetic 

studies of nicotine dependence has yet to 

inform prevention or cessation efforts. This 

has led some to conclude that research on 

nicotine dependence should be given a lower 

priority in the search for genes for complex 

disorders.10,11 However, given the large public 

health burden of tobacco use, the continued 

infl ux of new tobacco users, and the 

demands of sustained smoking cessation, 

it is imperative that the search for answers 

continues unabated.12,13 Environmental 

modifi cation for the prevention and 

management of common conditions has 

been benefi cial, but generic interventions 

should be supplemented by specifi cally 

targeted treatment based on a more precise 
22

2. Status of Genetic Studies of Nicotine Dependence

knowledge of biological mechanisms if 

further progress is to be made.14

Papers such as those by Merikangas and 

Risch10 and Carlsten and Burke11 do not 

address the fact that complex traits such 

as nicotine dependence are multiply 

determined and treated. Previously, 

an integrative model of tobacco use and 

nicotine dependence was described15

(fi gure 2.2) that recognizes the role played 

by individual differences in vulnerability 

factors,16 in tobacco use trajectories,17–20

in environmental exposure,21 and in nicotine 

metabolism and nicotine dependence, 

including motivations to smoke and the 

reinforcement derived from tobacco.22,23

Certain factors such as anxiety, depression, 

use of other substances, and family history 

of tobacco use, along with individual 

differences in nicotine sensitivity or 

metabolism, might themselves have genetic 

components.16 It has been suggested that 

the effects of these variables on subsequent 

likelihood of smoking are mediated by 

personal factors such as lower performance 

on certain tests of cognition, socioeconomic 

status, and the occurrence of events within 

the social environment such as having peers 

who smoke and family discord.16,24

Nicotine, the psychoactive alkaloid found 

in tobacco products, is thought to play a 

major role in nicotine dependence. Most 

smokers tend to ingest similar amounts 

of nicotine from day to day, consistent 

with the idea that they titrate their dose of 

nicotine to achieve desired effects.25 Nicotine 

is extensively metabolized in the body, 

primarily by the liver cytochrome P-450 

enzyme CYP2A6.26,27 Some studies have 

shown that the rate of nicotine metabolism 

may be related to nicotine-dependence 

risk.28,29 Because CYP2A6 activity affects the 

rate at which nicotine is eliminated, genetic 

alterations in the CYP2A6 enzyme may affect 

smoking behavior and nicotine dependence, 

and this deserves additional attention. 

Other genetic factors that may contribute 

to nicotine dependence include variation in 

pathways responsible for nicotine reward 

and pleasure.30–32 An important feature of 

the model for tobacco use over the life span 

is that genetic and environmental factors 

exerting infl uence at different points in the 

development of tobacco use (e.g., initiation, 

maintenance, cessation, and relapse) are 

likely to be different.33

Another feature in fi gure 2.2 that requires 

further investigation by genetic studies of 

nicotine dependence concerns the plethora 

of environmental conditions that are 

recognized to play a role in the acquisition, 

maintenance, cessation, and relapse of 

smoking.34 Simply put, with one exception 

in 2007,35 genetic investigations of smoking 

did not incorporate environmental measures 

into their study designs. It has been shown 

that genetic risk for smoking is lower at 

higher levels of parental monitoring,35

suggesting that parental monitoring may 

help counteract genetic susceptibility to 

smoking behavior.

Historical Perspective 

of Genetic Research on 

Nicotine Dependence

A focused research agenda on the genetic 

basis of nicotine dependence is a relatively 

new development, but there have been 

earlier studies and claims about a potential 

genetic or constitutional basis for smoking 

behavior. Beginning in the 1950s, surveys 

showed that smokers and nonsmokers differ 

on a number of characteristics, including 

personality, occupation, diet, and physical 

characteristics.36,37 In addition, legendary 

British statistician Ronald Aylmer Fisher 

argued that a common cause, likely genetic 

or constitutional, might be responsible 

for a tendency to smoke and increased 

susceptibility to cancer. In two letters to 

Nature, Fisher described two small studies 

of twins suggesting that monozygotic twins, 
23

Monograph 20. Phenotypes and Endophenotypes

Figure 2.2 Trajectories as Phenotypic Pathways

Environmental risk factors

SES, family discord, academic competence, stress, peer smoking, protobacco

advertising, cultural differences, weak antitobacco messages and governmental

policies, increased product availability

Genetic risk factors in relevant biological pathways including

metabolic, dopaminergic, opioidergic, serotonergic, and nicotinic

receptor functioning

Tobacco use

trajectory features

Age at onsetSpeed of transition to regular useVariability of useLevel of useUse of other tobacco, substances

Maintenance

CessationRelapse, lapseAbstinenceCycling

Nicotine

reinforcement

Positive reinforcementRelief from withdrawal

Nicotine

dependence

FTNDNicotine dependenceDSMNicotine metabolism

Motivations

StimulationIndulgentPsychosocialSensorimotorAddictiveAutomatic

Vulnerability factors

Behavioral disorders

AnxietyDepressionAlcohol dependenceOther drug useAggressive behavior

Family history of smokingMaternal smoking during pregnancyEarly smoking experiences

Note. SES = socioeconomic status; FTND = Fagerström Test for Nicotine Dependence; DSM = Diagnostic and Statistical Manual of Mental Disorders. Adapted from Swan, G. E., K. S. Hudmon, L. M. Jack, K. Hemberger, D. Carmelli, T. V. Khroyan, H. Z. Ring, et al. 2003. Environmental and genetic determinants of tobacco use: Methodology for a multidisciplinary, longitudinal family-based investigation. Cancer Epidemiology, Biomarkers & Prevention 12 (10): 994–1005.
24

2. Status of Genetic Studies of Nicotine Dependence

even when separated at birth, tend to have 

similar smoking habits.38 While the evidence 

was limited, Fisher argued that his primary 

purpose was to draw attention to the 

inadequacies of the epidemiological studies 

on smoking and health.39 The landmark 

1964 Surgeon General’s report on 

smoking and health reviewed the evidence 

linking smoking behavior with various 

characteristics and concluded that there 

was “overwhelming” evidence that smoking 

was psychologically and socially determined 

but that there was not yet any consistent 

evidence for constitutional or hereditary 

factors.40(p377) Nevertheless, Fisher’s 

proposal, which came to be known as the 

“constitutional hypothesis,” was one of 

the central arguments used by the tobacco 

industry to question the emerging evidence 

on cigarettes.41

Although it was widely recognized that 

many smokers exhibited characteristics of 

dependence, this phenomenon was initially 

viewed as primarily psychological and social, 

rather than pharmacological.42 The 1964 

Surgeon General’s report specifi cally 

concluded that tobacco dependence should 

be described as “habituation” rather than 

“addiction” to differentiate it from the 

effects of narcotics and other “more potent” 

addicting drugs.40(p350) During the early 

1970s, a few pioneering scientists, notably 

Murray Jarvik and M.A.H. Russell, began 

studying smoking behavior and the role 

of nicotine to understand the dependence 

process.43,44 Yet, it was not until the late 

1970s, that a substantial contingent of 

behavioral scientists who had been studying 

other forms of drug addiction began to 

develop a research agenda around smoking;45

and the 1979 Surgeon General’s report was 

the fi rst to devote substantial attention to 

smoking behavior and dependence.46

Research in humans involving the 

relationship between measured genetic 

factors and smoking was fi rst reported in 

1993.47,48 Since the initial reports, many 

published papers have reported associations 

between smoking behaviors and variants 

in a number of candidate genes. In all cases, 

the effect sizes reported were modest in 

nature, and until 2007, the studies were 

small and relied upon broad categories of 

smoking behaviors. The studies also reported 

single gene associations, some of which 

included variants with no known functional 

consequence. At least four separate metaanalyses of the literature concluded, that 

after interstudy heterogeneity has been taken 

into account, the association between genes 

and smoking behavior is modest indeed.49–52

Approximately 25 linkage studies in 

families have reported cosegregation of 

smoking behaviors with specifi c genomic 

regions. Few to none of these reported 

linkages have been strong enough to be 

called signifi cant by current standards, 

and interestingly, many of the genomic 

regions that have been identifi ed do not 

contain candidate genes of interest. Before 

1978, most of the studies relied upon 

study samples that were constructed for 

reasons other than the study of smoking, 

used broad or imprecise classifi cation of 

smoking behaviors, and relied on relatively 

loosely spaced marker sets with intermarker 

distances of fi ve centimorgans (cM) or more. 

The fi rst attempt to map susceptibility 

loci for nicotine dependence per se used 

the Fagerström Tolerance Questionnaire 

(FTQ)53 (nicotine dependence defi ned as a 

score of seven or more) in a convenience 

sample of 130 families from Christchurch, 

New Zealand;54 the FTQ was the precursor of 

the Fagerström Test for Nicotine Dependence 

(FTND). While initial results by Straub and 

colleagues showed limited evidence for 

linkage with specifi c regions (the strongest 

being a sharp peak at or near D2S1326), 

a subsequent reanalysis of the same data with 

different methods detected the same peak 

with an estimated Z-score of about 2.5.55

Despite the cumulative results from work 

beginning in the early 1990s, there is still 
25

Monograph 20. Phenotypes and Endophenotypes

no example in which these fi ndings have 

made a difference to the early detection, 

prevention, or treatment of nicotine 

dependence. Collectively, the work in 

humans and animals has provided new 

insight into the underlying neurobiology 

by underlining the extreme complexity 

of nicotine dependence. In this regard, 

conclusions from the body of evidence 

from the fi rst generation of measured 

genetic studies of nicotine dependence 

parallels similar conclusions from fi rstgeneration studies of other complex traits 

in general and those from psychiatric 

genetics more specifi cally.

Noting the problems of nonreplication in 

psychiatric genetics research, Caspi and 

Moffi tt in 200656 identifi ed three general 

approaches that have been taken in the 

literature. The fi rst approach assumes 

direct linear relations between gene and 

behaviors, and this would be an accurate 

characterization of the bulk of the work 

on nicotine genetics summarized above. 

The second approach involves the use 

of intermediate phenotypes, also known 

as endophenotypes, that are related to 

an illness, are heritable, and could be 

neuropsychological, neurophysiological, 

biochemical, endocrinological, or 

neuroanatomical in nature. One of the 

assumptions of this approach is that these 

constituents will have simpler genetic 

underpinnings than does the disorder 

itself. The third approach involves the 

study of gene-environment interactions in 

which it is assumed that “environmental 

pathogens” cause a disorder such as 

nicotine dependence only in the presence 

of certain gene variants. The second 

and third approaches have yet to be 

fully exploited in the context of nicotine 

dependence. One of the objectives of the 

present monograph is to more fully explore 

the existing options to inform the next 

generation of genetic studies for all three 

of the research traditions for complex 

genetic traits.

The introduction of powerful, new genomic 

technologies will make previous research 

quickly obsolete. With the decrease in 

costs and the use of platforms to genotype 

individuals for very large numbers of 

variants across the whole genome, the 

genome-wide association study (GWAS) has 

now become possible. Similarly, it is now 

possible to genotype candidate genes not 

just for one variant but for many variants 

(known as single nucleotide polymorphisms 

[SNPs]), many of which are functional in 

nature by virtue of either their location 

or experimental validation. The fi rst 

published example of this approach to the 

study of nicotine dependence and genes is 

summarized later in the section, “Genomewide Association and Candidate Gene 

Studies of FTND.”

Unfortunately, the defi nition and 

measurement of nicotine dependence has 

not kept pace with the increased precision 

in the genomic arena. There is vigorous 

debate over what constitutes the critical 

constituents of nicotine dependence, and 

a defi nition that most or all investigators 

can agree upon remains elusive. One of the 

assumptions of this present volume is that 

until progress is made in understanding and 

resolving the conceptual and measurement 

issues in nicotine dependence, the yield 

from the advances in genomic science to 

better understand nicotine dependence will 

not be fully realized.

Nicotine Dependence: 

A Construct in Need 

of Refi nement

One of the most troubling aspects of the 

state of nicotine-dependence measurement 

is the oft-cited fi nding from Moolchan 

and colleagues57 in which poor agreement 

between the two gold-standard measures 

of nicotine dependence, the FTND and 

the Diagnostic and Statistical Manual of 
26

2. Status of Genetic Studies of Nicotine Dependence

Mental Disorders (DSM), was documented. 

This paper found that the kappa estimate 

of concordance was only .2, not much 

better than that expected by chance alone. 

This initial fi nding was confi rmed in 

adolescents.58 Both papers agree that the 

two measures, although claiming to be 

assessing nicotine dependence, are, in fact, 

assessing two different groups of smokers. 

The DSM-based approach appears to place a 

heavier emphasis on psychiatric symptoms, 

while the FTND appears to place a heavier 

emphasis on physical symptoms.

A consensus has emerged in which nicotine 

dependence is viewed as multidimensional 

and, therefore, should be assessed and 

quantifi ed accordingly.9,23,57,59,60 Although it 

was pointed out earlier59,61 that dependence 

has several dimensions, including physical, 

behavioral, and psychological components, 

the assessment of nicotine dependence 

has relied largely upon the FTQ53 and the 

FTND62 or DSM Fourth Edition (DSM-IV)

criteria, an approach deriving from the 

need to include nicotine dependence in 

psychiatric nomenclature and classifi cation 

and that attempts to adhere to classic 

defi nitions of drug dependence.63,64

Although both paper-and-pencil and 

psychiatric diagnostic approaches have 

provided reliable defi nitions for use in 

many different types of studies, neither of 

the existing assessments relies upon test 

development approaches well grounded in 

psychometric theory. 

Multidimensional scales for assessing 

nicotine dependence have been published. 

However, their incorporation into genetic 

studies (biometric or measured) is only 

just beginning and the question of which

components of nicotine dependence have 

the most or least genetic infl uence has 

only been addressed since 2004. A study by 

Lessov and colleagues,65 described later in 

the section “Heritability of Components of 

Nicotine Dependence in Adults,” was the 

fi rst to document the relative proportion of 

genetic and environmental infl uences on 

diagnostic nicotine-dependence criteria, 

thereby opening the way for future studies 

to investigate dependence at a more 

precise level. Swan and colleagues66 (also 

summarized later in the section “Linkage 

Analysis of FTND and Other Indices of 

Nicotine Dependence”) was the fi rst 

linkage study to recognize the complexity 

of the nicotine-dependence phenotype by 

including multiple phenotypic markers in 

the analyses.

The literature on the test-retest reliability 

of self-reported tobacco use reveals that 

over short and longer intervals, reliability 

is substantial for summary measures 

of nicotine dependence. The FTQ and 

derivatives (mFTQ, FTND) have acceptable 

levels of test-retest reliability that range 

from .72 to .92 over intervals up to 1.8 years 

in length.67–74 Alternative measures of 

nicotine dependence have comparable 2- to 

10-week test-retest reliabilities.22,72,75–84

Only one study has reported test-retest 

reliability over an interval consistent with 

that in typical population surveys (up to 

12 years)85 and found acceptable reliability 

for total FTQ (.62) and FTND (.72) scores.

A number of authors suggest that milestones 

in the development of smoking behavior 

and/or individual items from several of 

the nicotine-dependence measures may be 

good candidates for inclusion in a genetic 

study of nicotine dependence. For example, 

there is signifi cant additive genetic variance 

for age fi rst smoked86,87 and individual 

items from the FTND as well as diagnostic 

nicotine-dependence criteria.65,88,89 However, 

at the level of individual items, it is evident 

that more needs to be known about testretest reliability over intervals consistent 

with those in population-based surveys. 

Reliability estimates are more variable 

(0–.90) for individual items from the 

FTQ,67–69 the FTND,68,85 and the Hooked 

on Nicotine Checklist.80,82 Perhaps not 

surprisingly, reliability of recall for specifi c 
27

Monograph 20. Phenotypes and Endophenotypes

smoking behaviors such as smoking status 

and cigarettes smoked per day (CPD) are 

highly reliable for intervals of 3 years81,90,91

and up to 15 years.85

Initial reactions to the fi rst experience 

with smoking have also been suggested 

as an interesting and potentially 

informative phenotype for further 

genetic investigations.92 Initial sensitivity, 

perhaps related to genetic variation in 

metabolic, neural, and/or airway pathways, 

in combination with the social environment 

may well infl uence which adolescents 

who experiment with tobacco go on to 

become regular smokers. Initial reactions 

and tobacco use milestones could be 

easily assessed in prospective, longitudinal 

studies of adolescents during and after 

experimentation. More commonly, however, 

there is a need in large, population-based 

studies to assess these characteristics 

retrospectively in adults with tobacco use 

history. Initial fi ndings suggest that reported 

age at fi rst cigarette may be easier to recall 

than one’s subjective reaction to the fi rst 

cigarette ever tried.93,94 The extent to which 

the circumstances surrounding tobacco 

use (e.g., stress levels, other smokers, 

and depression) can be recalled reliably 

is of major interest, given the previously 

noted need to test for the presence of 

gene-environment interactions. Chapter 3 

addresses many of the most important 

issues surrounding the measurement of 

nicotine dependence.

Nicotine-Dependence 

Phenotypes: A 

Framework

In most previous behavioral genetic and 

genetic epidemiological studies, “smoking” 

has been assessed as a static phenotype—

that is, as if the behavior is a trait that 

remains constant over time. However, 

a variety of studies from the developmental, 

epidemiological, psychiatric, and smoking 

literature suggests that smoking, in general, 

and the consumption of nicotine on a 

regular basis, specifi cally, is tremendously 

more complex than the simple trait 

perspective.95 Not only do reasons and 

motivations for smoking vary across 

individuals, it is likely that motivations 

(biological, social, and psychological, 

individually and in combination with each 

other) vary within an individual across 

time and situations.34,96,97

The fi eld of psychiatric genetics 

is an area fraught with numerous 

examples of nonreplication.98 However, 

some investigators believe that 

endophenotypes,99,100 relying on actual 

measurements of behavior, physiological 

responses, or biological characteristics, such 

as brain structure from imaging studies, 

will provide more replicable associations 

with genetic variants than have more 

general diagnostic measures.101–104

Endophenotypes are viewed as quantifi able 

components in the genes-to-behavior 

pathway and can be neurophysiological, 

biochemical, endocrinological, 

neuroanatomical, cognitive, or 

neuropsychological in nature. To be 

viewed as a viable candidate for use in 

a genetic study of nicotine dependence, 

an endophenotype must be (1) associated 

with nicotine dependence in the population, 

(2) heritable, (3) state independent, 

(4) cosegregated with nicotine dependence 

in families, and (5) present at a higher rate 

among unaffected relatives of those with 

nicotine dependence than in the general 

population. Waldman105 further suggests that 

candidate endophenotypes should have good 

psychometric properties, such as test-retest 

reliability, and be normally distributed. 

On the basis of work in schizophrenia as 

an example, endophenotypes that meet 

all or most of the criteria listed above 

include prepulse inhibition (a measure of 

sensory motor gating defi cits), eye-tracking 
28

2. Status of Genetic Studies of Nicotine Dependence

dysfunction, and working memory.104,106

Other branches of psychiatry have adopted 

the endophenotypic approach to investigate 

bipolar disorder, depression, Alzheimer’s 

disease, attention defi cit hyperactivity 

disorder (ADHD), autism, alcoholism, and 

personality disorders. Work in the fi eld of 

ADHD, in particular, has benefi ted from 

this approach.107 Some concerns have been 

raised, however, that the endophenotype 

approach may lead researchers to conduct 

smaller, underpowered studies because it is 

assumed that the more proximal measures 

will result in a stronger genetic signal. 

In the fi eld of nicotine dependence, this 

remains an empirical question.108

In the fi eld of tobacco use, numerous 

possibilities exist in which the relationships 

of phenotypes to genetic factors may 

actually be larger should the full range of 

phenotypes be explored. A framework was 

developed to organize phenotype selection 

for genetic investigations of tobacco use 

largely on the extent to which they could 

provide progressively more-fi ne-grained 

markers of nicotine dependence.15

At the least specifi c level, categories of 

smoking status and measures of amount 

smoked (class I) are included. The bulk 

of the work on genetics and smoking has 

relied upon these relatively nonspecifi c 

measures. Chapter 10 presents examples of 

how the defi nition of even broad phenotypes 

can be improved to be more specifi c 

within the context of epidemiological 

research. At the next level (class II), specifi c 

measures of nicotine dependence and 

their constituents are included because, 

while these may be related to quantity 

smoked, they appear to measure additional 

dimensions of nicotine dependence not 

assessed by simple measures of quantity 

consumed. Along with nicotine dependence, 

also included are withdrawal symptoms, 

motivations to smoke, as well as smoking 

topography, and more-fi ne-grained measures 

of how and why people consume tobacco. 

Underlying the class III designation is the 

assumption that how individuals attain

regular tobacco use (e.g., a trajectory) may 

be just as important as the fact that they 

are or have been regular users of tobacco. 

At this level of specifi city, time-based aspects 

of an individual’s history with tobacco 

become important, including the rate, 

level, and variability at which adolescents 

progress to regular tobacco use. The authors 

of chapters 5, 6, and 7 take a deeper look 

at approaches to identify tobacco use 

trajectory subgroups, the feasibility of 

their use as phenotypes in genetic studies, 

and the extent to which conjoint trajectories 

(tobacco and alcohol use) appear to have 

heritable components. At the highest level 

of specifi city—class IV in this scheme—

putative biological or physiological markers 

of nicotine dependence are included, such as 

pharmacokinetics and pharmacodynamics 

of nicotine, changes in neuropsychological 

function in response to nicotine, and 

changes at the receptor level (function, 

density). Chapter 4 addresses the issue 

of neurobiological phenotypes in animal 

models that can be viewed as analogs to 

class IV phenotypes in the human condition.

Available evidence indicates that the 

phenotypic options also vary as to whether 

they can be measured reliably, have validity 

as constituents of nicotine dependence, 

and are heritable—all three characteristics 

being defi ning criteria of endophenotypes. 

The most consistent evidence available is 

for the more general class I phenotypes. 

The crude measures of smoking status and 

quantity smoked can be measured with 

reliability over limited time intervals and 

are consistently correlated with components 

of measures of nicotine dependence. Their 

heritability has been well documented in 

twin and family studies.

Heritability estimates vary depending on 

how a phenotype is defi ned and the types of 

tobacco users (never, occasional, regular) 

included in the phenotypic defi nition. 
29

Monograph 20. Phenotypes and Endophenotypes

Twin studies have addressed the genetic and 

environmental contributions to variation 

in several class I tobacco use phenotypes: 

initiation of tobacco use, measures of 

cigarette-smoking patterns (such as regular 

or current smoking), and measures of 

quantity of use (such as number of CPD). 

Across studies, measures of quantity of 

cigarettes smoked have been shown to be 

signifi cantly heritable with estimates of 

heritability ranging from 45% to 86% for 

number of CPD65,87,109–113 and 46% to 49% 

for heavy smoking.114,115

Lifetime regular smoking (a class I 

phenotype) often has been defi ned as 

having smoked 100 or more cigarettes in 

a lifetime and having exhibited a regular 

pattern of cigarette smoking. Regular 

smoking has been repeatedly shown to 

be moderately to highly heritable with 

an average estimate of approximately 

50%.102,116–119 Smoking persistence (also 

a class I phenotype), defi ned as being a 

current smoker versus a past smoker, also 

has been consistently shown to be heritable, 

with genetic infl uences estimated at 27% 

to 82%.33,117,120–123

Smoking initiation (class I) has generally 

been defi ned as a “yes” response to a 

question assessing whether a respondent 

has ever smoked or has ever tried 

smoking, but some studies have used an 

operationalization of smoking initiation 

(having smoked 100+ cigarettes) that 

could more accurately be described as ever 

smoking.98,117,120,121,123 The smoking initiation 

phenotype thus includes a heterogeneous 

group of smokers, ranging from people who 

may have tried smoking cigarettes only 

once, to heavy, dependent cigarette smokers. 

Not surprisingly, estimates of genetic and 

shared environmental effects on smoking 

initiation vary greatly across studies. Some 

studies have shown greater importance of 

shared environmental effects (44% to 54%) 

compared with genetic effects (11% to 

39%) on smoking initiation;33,113,123–125 other 

studies have shown substantial heritability 

(43% to 85%) and a relatively smaller 

role of shared environment (0% to 68%) 

for smoking initiation.33,98,117,120,121,126–128

Conceivably, studies that report greater 

genetic effects for smoking initiation may 

contain a greater proportion of regular and 

heavier smokers—two smoking dimensions 

with a strong genetic signal, relative to 

occasional or lighter smokers—for whom 

genetic differences from nonsmokers may 

be less important than environmental. 

Also, evidence shows differences in the 

relative contribution of genetic and 

shared environmental effects for smoking 

initiation across gender, age and age cohort, 

and culture.33,120,121,123,126

At the next level of measurement, most 

measures of nicotine dependence, such as 

the FTND and the DSM-based classifi cation, 

have shown acceptable levels of test-retest 

reliability, and some have been reported 

to have high to moderate heritability. 

The validity of many of the measures, 

however, is uncertain because the two 

primary measures of nicotine dependence 

are not correlated with each other, and the 

extent to which nicotine dependence is 

associated with motivations, withdrawal, 

and/or smoking topography is generally 

unknown as well.

For class II nicotine-dependence 

phenotypes, defi ned by the DSM Third 

Edition Revised,129 DSM-IV,63 or Fagerström 

criteria, heritability estimates ranged from 

31% to 60%,65,88,89,128 and for dependence 

as defi ned by the Heaviness of Smoking 

Index (HSI),130 which comprises two of 

the seven FTND items, heritability was 

also high (59% to 71%).65,89 In an analysis 

examining the genetic relationship between 

lifetime regular smoking and nicotinedependence operationalization by using 

items from the FTQ53 and the DSM-IV,63

Kendler and colleagues98 found substantial 

genetic effects for regular smoking (85%), 

substantial overlap in liability for regular 
30

2. Status of Genetic Studies of Nicotine Dependence

smoking and nicotine dependence (60%), 

and moderate residual genetic effects for 

nicotine dependence (22%), suggesting 

overall considerable heritable infl uences 

on both regular smoking and nicotine 

dependence. Individual diagnostic criteria 

also have been shown to be signifi cantly 

heritable (26% to 73%), with little to no 

evidence for a signifi cant contribution 

from shared environmental effects.65,89,131

Individual nicotine withdrawal symptoms 

have been shown to be moderately heritable, 

ranging from 9% to 53%,89 with no evidence 

for a signifi cant contribution from shared 

environmental effects.

Class III phenotypes—or smoking 

trajectories—appear to be reliable across 

a number of studies, although the extent 

to which they can be assessed reliably 

with a retrospective methodology is 

not clear. Their validity as constituents 

(precursors) of adult nicotine dependence 

is unknown as is the extent to which 

membership in a trajectory subgroup 

is infl uenced by genetic factors. Before 

the appearance of the present volume, 

no studies had been published on the 

contribution of genetics to variation in 

longitudinal tobacco use phenotypes 

(class III), such as developmental smoking 

trajectories. One study of adolescent 

twins that involved three data collection 

periods across seven years generated crosssectional smoking groups (never smokers, 

triers, experimenters, current smokers). 

The study examined the cross-sectional 

heritability of a smoking index variable 

that combined frequency and recency of 

cigarette smoking across groups and thus 

captured the smoking experience across 

groups at each time of assessment, but not 

the smoking experience of each group across 

assessments.19 The study found that, at each 

wave of data collection, the smoking index 

variable was signifi cantly infl uenced by 

genetic factors (21.8% at wave 1, 22.8% at 

wave 2, and 35.5% at wave 3) and by shared 

environmental factors (52% at wave 1, 

51.7% at wave 2, and 36.7% at wave 3).19

Of note is the importance of shared 

environmental factors which, while not 

measured in this study, include infl uences 

such as protobacco advertising and product 

availability. The fi rst investigation of twin 

concordance for tobacco- and alcohol-use 

trajectories is presented in chapter 7.

Several studies have examined the relative 

contribution of genetic and environmental 

infl uences on the transitions from smoking 

experimentation to higher levels of 

smoking and nicotine dependence. Using 

correlated liabilities models, these studies 

have shown an overlap in the genetic and 

environmental infl uences on liability to 

smoking initiation with liability to smoking 

persistence,33,117,120 smoking quantity,113

regular use,127 and nicotine dependence.98,127

At least two studies demonstrated that, 

in older age groups (aged 30 years and 

older), genetic and environmental factors 

that determine liability to smoking 

initiation are independent from those 

that determine liability to smoking 

persistence.33,120 Except for one study,127

these studies showed a relatively larger 

infl uence of shared environmental factors 

on smoking initiation and a smaller to no 

signifi cant infl uence of shared environment 

on smoking persistence, quantity smoked, 

or nicotine dependence, consistent with 

much other work, as discussed earlier. 

While not measured in the present study, 

shared environmental infl uences could 

include the well-documented effects of 

smoke-free homes.

Finally, while the measurement properties 

of some of the class IV phenotypes such as 

nicotine pharmacokinetics and dynamics 

are well described, and signifi cant 

heritability has been demonstrated in both 

biometric and measured genetic contexts 

(see the section below, “Heritability of 

Nicotine Metabolism,” for an example), 

these characteristics for many of the 

other potential candidate endophenotypes 
31

Monograph 20. Phenotypes and Endophenotypes

are unknown. Moreover, the validity 

of these measures as constituents of 

nicotine dependence appears thus far to be 

problematic or without documentation.

The majority of adolescent and adult 

twin studies agree that the relative 

contribution of genetic infl uences on 

smoking initiation is smaller than that 

on downstream smoking phenotypes 

such as progression to regular smoking, 

smoking persistence, nicotine dependence, 

and smoking cessation. Conversely, the 

relative contribution of environmental 

factors is larger in smoking initiation than 

in downstream smoking phenotypes. These 

results suggest that environmental factors 

play an important role in experimentation 

with cigarettes, which largely occurs in 

adolescence, and that, beyond a certain 

level of experimentation, genetic liability 

becomes a stronger determinant of 

cigarette smoking. From the perspective 

of intervention and genetic studies of 

smoking, it appears that a genetically 

informative endophenotype for nicotine 

dependence may vary across age, as well 

as across levels of use.

With regard to existing molecular genetic 

literature involving smoking-related 

phenotypes, most reported studies 

examined class I phenotypes measured 

retrospectively.48,132–152 Since 2003, the 

number of papers that use retrospective 

measures of nicotine dependence—that is, 

class II indices—has been increasing.142,153–167

Retrospective case-control designs are 

subject to limitations of recall bias. 

Many of these studies have not received 

independent confi rmation as of the 

writing of this chapter. A notable exception 

to the use of retrospective self-report 

measures of nicotine dependence is the 

paper by Ray and colleagues,168 in which 

an experimental measure of the relative 

reinforcing value of nicotine (a class IV 

phenotype) was found to be associated with 

variation in the gene OPRM1.

Genetic 

Epidemiological 

Concepts and Their 

Implications 

for Studying Nicotine 

Dependence

Characteristics of Complex 

Genetic Traits

Nicotine dependence, a multidimensional 

construct, is a complex genetic trait. 

In this context, the term complexity is 

used as defi ned by the fi eld of genetic 

epidemiology.169–172 A complex trait has 

several defi ning features: (1) it has reduced 

penetrance (i.e., not everyone with a 

susceptibility gene[s] will develop nicotine 

dependence); (2) genetic heterogeneity is 

involved (i.e., a different set of susceptibility 

genes may contribute to the likelihood of 

becoming a smoker in different people); 

(3) pleiotropy is involved (i.e., the same 

genetic risk factors may lead to different 

addictions, such as alcoholism, in addition 

to nicotine dependence in different people); 

(4) epistasis is involved, which refers to 

the situation in which a genetic risk factor 

modifi es the expression of another genetic 

risk factor to produce nicotine dependence; 

and (5) environmental factors can interact 

with genetic risk to alter the likelihood of 

becoming dependent on nicotine.

The available literature involving measured 

genetics and nicotine dependence provides 

ample evidence that it is, indeed, a complex 

genetic trait. Incomplete penetrance is 

demonstrated by the fact that heritability 

of nicotine dependence is roughly 50% 

and that the odds of being a smoker even 

in the presence of a genetic risk factor is, 

on average, higher than in the absence of 

the risk allele.
32

2. Status of Genetic Studies of Nicotine Dependence

Genetic heterogeneity is clearly evident 

when considering the number of genes 

that have been reported to be in association 

with nicotine dependence. These include 

CHRNA4,155,156 CHRNA7,142 CHRNA9,142

CHRNB1,164 CHRNB2,142 CHRNB3,142

OPRM1,168,173 DRD2 (evidence, however, 

suggests that the association with DRD2

may be confounded by close proximity 

to ANKKI, a kinase gene),146,174 DRD4,134

COMT,159 SLC6A3,135 5-HTTLPR,133 5HT2A,147

CYP2A6,29,150,163,175 CYP2E1,161 GABAB2 ,

158

MAOA,148,152,162 TH01,149,153,154 TPH,166

SLC18A2,151 PTEN,157 NTRK2,159 EPAC,160

DDC,165,167 CHRM1,164 CCK,176 and BDNF.177

A close review of these papers indicates 

substantial variation in the nature of the 

phenotype measured, ranging from CPD, 

maximum CPD, nicotine dependence, heavy 

smoking, smoking status, smoking initiation, 

withdrawal, regular smoking, and the 

relative reinforcing value of nicotine. Going 

forward, what is the best way to incorporate 

genetic heterogeneity into studies of nicotine 

dependence? What, if anything, should be 

concluded about the fact that the published 

linkage studies have identifi ed regions 

for the most part that do not contain the 

candidate genes of interest? One answer to 

the problem of genetic heterogeneity is the 

use of an appropriate statistical framework 

capable of incorporating information 

about numerous genetic variants while 

simultaneously taking into account previous 

fi ndings and expert knowledge to make sense 

of the plethora of associations reported. 

The use of Bayesian hierarchical modeling 

as informed by an ontological framework is 

described in chapter 12.

Pleiotropy is apparent because genes such 

as DRD2 have been reported as associated 

with other addictive behaviors and/or 

affective disorders. That any one single gene 

may, in fact, be associated with a number 

of phenotypes is an issue that requires 

much more attention in the literature. 

In addition to being associated with 

smoking-related phenotypes such as ever 

smoking,178 smoking cessation in response 

to acupuncture,179 smoking cessation in 

response to bupropion,180 and smoking cueinduced cigarette craving,181 variation in 

DRD2 has been reported as being associated 

with schizophrenia,182–184 alcoholism,185,186

quantity of alcohol consumed by adolescents 

and young adults,187 obsessive compulsive 

disorder,188 ADHD,189,190 cue-elicited 

craving for heroin,191 comorbid depression, 

anxiety, and social dysfunction associated 

with posttraumatic stress disorder,192

working memory in schizophrenics,193

Tourette’s syndrome,194 anorexia nervosa,195

neuroticism/anxiety in men,196 and opium 

addiction.197

The range of phenotypic correlates suggests, 

at the least, that variation in DRD2 is not 

specifi c to nicotine dependence. The extent 

to which these indices of psychopathology 

are viewed as covariates or confounders 

of the association between nicotine 

dependence and variation in DRD2 is highly 

variable across the published papers on this 

relationship. Another issue lacking clarity 

in the literature is the extent to which 

this plethora of psychiatric phenotypes is 

associated with and/or has any phenotypic 

subcomponent in common with nicotine 

dependence. Similar questions of phenotypic 

covariation can be raised about the 

literature involving variation in OPRM1, 

5HTT, MAOA, and CHRNA4. Evidence is 

addressed in chapter 8 that some of these 

phenotypes may serve as early indicators of 

risk for nicotine dependence before chronic 

exposure to nicotine.

Epistasis, the interaction between genes, was 

fi rst reported by Lerman and colleagues139

for DRD2 and SLC6A3 and then again by 

Lerman and colleagues198 and by Swan and 

others.199 These studies underscore the 

importance of considering the simultaneous 

effect of several genes on behavior in that 

the observed effect of any one gene may 

strictly depend on variation in another gene. 

For example, investigation of the effect of 
33

Monograph 20. Phenotypes and Endophenotypes

genes that are part of a common neural 

pathway that underlies behavior may be an 

effective approach (chapter 12 discusses the 

need for pathway analyses in greater depth). 

No published examples of gene-environment 

interactions in nicotine dependence were 

found in the literature. Emerging work in 

the psychiatric genetic literature provides 

an example that such interactions may 

exist. The work of Caspi and colleagues200

reveals that variation in the 5HTT gene 

may moderate the impact of life stress on 

depression. The subsequent work of Kendler 

and others201 supports Caspi’s original 

fi ndings and extends them by demonstrating 

that the threat level of the life stress may be 

the most critical aspect in interaction with 

5HTT to ultimately produce depression.

The conventional twin model has been 

extended to account more fully for the 

effects of gene-environment interactions 

and/or correlations. Purcell202 provides the 

tools to extend the traditional twin model 

to include a component for the effects of a 

moderator variable which, in the present 

case, could be a measure of the environment. 

Purcell indicates that, while having both 

genes and environment as measured 

variables would provide the most power 

to detect a gene-by-environment (G×E) 

interaction, as in Caspi and colleagues,200

most modern twin studies should be 

able to rely on a latent, unmeasured 

G and a measured E. The most powerful 

approach to the measurement of E will be 

a continuous measure. The application of 

these models202–204 has been demonstrated by 

Button and colleagues,205 who showed that 

the heritability of antisocial scores in young 

twins declines as family dysfunction scores 

increase, and by McCaffrey and others,206

who examined the relationship between 

education level and nicotine dependence in 

twins. The evidence that macrocontextual 

(e.g., cultural and socioregional) factors 

can modify genetic effects is reviewed in 

chapter 11 of this volume. New approaches 

to the assessment of microcontextual 

(e.g., parental and peer smoking) factors 

are also described.

Implications for Selection 

of Nicotine-Dependence 

Phenotypes and Endophenotypes

Are Multiple Nicotine-Dependence 

Phenotypes Associated with Each 

Other?

As discussed in chapters 3 and 4, the issue 

of construct validity is of major importance 

to the pursuit of knowledge in this area. 

The extent to which various nicotinedependence phenotypes are or are not 

associated with each other or with a 

universally accepted gold standard of nicotine 

dependence has not been well studied in the 

literature. For example, while a measure of 

consumption such as CPD may be highly 

correlated with the total FTND score 

(r > 0.60), a measure of nicotine metabolism, 

considered to be a basis for dependence, is 

correlated only modestly with CPD (r = 0.12, 

p < 0.05;207 r = –0.15, p is not statistically 

signifi cant;208 r = 0.33, p < 0.01;209 and not 

at all with the FTND).207,209–211 Similarly, 

while adolescent trajectories of tobacco use 

can be clearly demarcated on the basis of 

number of cigarettes smoked, the extent to 

which adolescent nicotine metabolism is 

associated with trajectory group membership 

is unknown. The fi rst evidence that trajectory 

group membership in adolescence may be 

associated with adult nicotine dependence is 

presented in chapter 5.

Are Multiple Nicotine-Dependence 

Phenotypes Associated with a Single 

Endophenotype?

The relationships that exist between each 

“marker” of dependence within each 

phenotypic domain need to be determined, 

along with the relationships that exist 
34

2. Status of Genetic Studies of Nicotine Dependence

across phenotypic domains, to reach a 

comprehensive understanding of the nature 

of nicotine dependence. For example, a longstanding hypothesis states that the rate of 

nicotine metabolism should be related to 

smoking behaviors, with faster elimination 

of nicotine being associated with increased 

rates of smoking and nicotine dependence.25

While there are few published tests of 

this hypothesis, the papers that have been 

published lend only limited supporting 

evidence, with the rate of nicotine 

metabolism accounting for less than 16% of 

the variation in CPD209,210 and no signifi cant 

amount of variance in the FTND209–211

or in the Horn-Russell Scale.210 Kandel 

and colleagues211 found no signifi cant 

association between the rate of metabolism 

and CPD in a sample of younger, lighter 

smoking, and less dependent smokers. 

A review of the discussion of results from 

these papers offers the following possible 

reasons for the apparent disconnect 

between rate of metabolism and nicotine 

dependence: (1) the questionnaire measures 

of adult nicotine dependence used may not 

be the most sensitive measures of rate of 

metabolism,209,210 (2) the rate of metabolism 

may only be related to nicotine dependence 

during the transition from experimentation 

to “addicted” smoking,209 or (3) the rate of 

metabolism is not an important determinant 

of smoking behavior in younger smokers 

because of a low level of smoking.211

From the standpoint of the present volume, 

the lack of evidence that the rate of nicotine 

metabolism is an important driver of 

nicotine dependence should create some 

urgency as to its construct validity. On one 

hand, the rate of metabolism is associated 

(although weakly) with CPD. CPD, at the 

same time, is substantially correlated with 

most or all existing measures of nicotine 

dependence. While the resolution of the 

apparent logical inconsistencies in the 

literature is beyond the scope of this 

chapter, some suggestions are offered for 

future research. For example, is “time to 

fi rst cigarette after waking up”—one of the 

key components of the FTND—associated 

with nicotine metabolism? One would 

hypothesize that individuals with faster 

clearance of nicotine or more extensive 

conversion of nicotine to cotinine would 

be associated with a shorter time to the fi rst 

cigarette. Again, using nicotine metabolism 

as an endophenotype, is variation in 

nicotine clearance associated with subjective 

reactions to the fi rst cigarette of the day? 

Chapter 3 makes a strong case for developing 

a comprehensive theory of nicotine 

dependence as a way to understand apparent 

logical inconsistencies in research fi ndings.

Are Multiple Nicotine-Dependence 

Endophenotypes Associated 

with a Single Phenotype?

Another set of addressable questions 

emerge when the relationship among 

endophenotypes is considered. For example, 

is variation in nicotine metabolism related 

to performance increases on the measure 

of executive function or to related nicotine 

reward? If metabolism and executive 

function are related, are they associated 

to the same degree with specifi c and 

global measures of nicotine dependence? 

Chapters 8 and 9 suggest that relatively 

little is known about the relationship 

between candidate endophenotypes and 

measures of nicotine dependence.

Why Are Environmental Phenotypes 

Important?

Given the success of policy interventions in 

reducing smoking rates, some have argued 

that resources invested in genetics research 

on smoking would be better spent on those 

intervention strategies. The reasoning is that 

nicotine dependence “appear[s] to be highly 

amenable to environmental modifi cation,” 

and “[r]esources would be far better placed 

in designing effective interventions and 

studying the causes of the gap between 
35

Monograph 20. Phenotypes and Endophenotypes

knowledge and modifi cation of healthrelated behaviors.”10(p601) This argument, 

however, rests on a false dichotomy between 

the roles played by genes and environment 

in the etiology of nicotine dependence.

As argued in rebuttal to the above 

viewpoint,12,212,213 contemporary genetic 

research of complex diseases takes into 

account both genes and environment and 

seeks practical results within the full scope 

of etiologic mechanisms. The environment 

(e.g., aggressive tobacco promotions, 

cigarettes designed to maximize their 

addictive potential), rather than genes, 

is the most likely target of intervention. 

Moreover, the results of some molecular 

genetic studies of behavioral disorders214,215

have shown that genetic information may 

be critical to the discovery of environmental 

effects and vice versa.

Despite the fact that the genetic mechanisms 

of many genetic disorders are already 

known, this has not necessarily translated 

into effi cient interventions exactly for the 

reason that these mechanisms are diffi cult 

to change. Genetic studies of complex 

disorders, in contrast, have barely departed 

from their nascent stage, but the signifi cant 

contribution of environmental factors, 

even in the natural variation in the risk, 

promises a greater payback. It has long been 

understood that, regardless of heritability, 

the individual genotype determines the 

range of possible phenotypes under possible 

environments, the norm of reaction.216

Numerous environmental risk factors for 

acquiring nicotine dependence have been 

identifi ed in the literature.217 However, it 

is not yet clear whether any of these have 

the possibility of interacting with genetic 

risk factors to heighten the likelihood 

that an individual will become dependent. 

A number of these have the potential to 

be an “environmental pathogen”—that is, 

a characteristic of the environment in the 

presence of which a genetic risk factor can 

exert its effect on nicotine dependence.218

One of the challenges in this area is the 

need for optimal measurements of the 

environment so that proximal and distal 

risks can be enumerated, along with 

the documentation of age-specifi c and 

cumulative risk. Moffi tt and colleagues218

identify a strong need for improved 

retrospective measurement of environmental 

pathogens (see chapters 3 and 11 for further 

discussion of environmental pathogens and 

their measurement).

Evidence suggests that a portion of the 

smoking population smokes every day, 

has not previously attempted to quit, 

and has no desire or intention to quit. 

Prevalence estimates for this “hardcore” smoking range from 5% to 16% of 

the smoking population.219–221 Further 

characterization of hard-core smokers 

indicates several characteristics shown to 

have a genetic component (e.g., shorter 

time to fi rst cigarette of the day, heavier 

smoking, concurrent use of other tobacco 

products, use of other abused substances, 

and comorbid depression). Given that 

hard-core smokers tend to be of lower 

socioeconomic status and are more likely to 

be unemployed and living alone, Warner and 

Burns222 have speculated that these types of 

smokers may be living in a more stressful 

environment. Interestingly, stress reactivity 

has been shown to have both genetic223,224

and environmental225 components in its 

variation. This raises some interesting 

questions about the hard-core smoker that 

should be addressed in future research: 

(1) Is the prevalence of certain candidate 

gene variants higher or lower in hardcore smokers? (2) Does the relationship 

between specifi c candidate gene variants 

and hard-core smoking vary as a function 

of exposure to certain environmental risk 

factors? (3) Is the constellation of genetic 

and environmental risk factors different in 

hard-core smokers? (4) Are there subgroups 

within the hard-core smoker population 

that vary in genetic and environmental risk 
36

2. Status of Genetic Studies of Nicotine Dependence

factors? Genetic epidemiology investigations 

of this group of smokers may provide a 

wealth of information to inform future 

tobacco control efforts in hard-to-reach 

segments of the smoking population.

Could Environmental Variation Cause 

Variation in Expression of Genes 

of Relevance to Nicotine Dependence?

The evidence is compelling that 

environmental factors can result in the 

expression of genes in pathways of relevance 

to addiction in general. For example, 

exposure to stress modulates the expression 

of cocaine- and amphetamine-regulated 

transcript (CART) in the hypothalamus and 

amygdala in the rat brain in a region- and 

sex-specifi c manner. CART may, therefore, 

be a mediator peptide in the interaction 

between stress and drug abuse.226 In a 

series of studies, early maternal care was 

shown to have a profound impact on gene 

expression with long-lasting effects on 

the stress response.227,228 Chronic stress 

infl uences gene transcription in the 

hippocampus.229 Differential exposure to 

enriched or impoverished environments 

alters N-methyl-D-aspartate receptor subunit 

expression in the nucleus accumbens core 

and shell.230 Also, exposure to drugs of abuse 

(e.g., opiates) results in a discernible pattern 

of gene expression in the opioidergic and 

other pathways.231–233

Epigenetics refers to heritable variation 

in biochemical modifi cations of both the 

nucleic acid and the protein components 

of chromatin—that is, the methylation 

of cytosine found in cytosine-guanine 

dinucleotides and posttranslational 

modifi cation (methylation, acetylation 

and phosphorylation) of histone proteins, 

generally associated with decreased or 

increased levels of gene expression at the 

corresponding genetic locus.234 Multiple 

approaches to the analysis of epigenetic 

variation and its associations with genetic 

and environmental variation and their 

association with disease are possible. 

One elegant design, however, uses 

monozygotic twins. These twins share 

100% of their genome at the moment 

of twinning and accumulate differences 

thereafter with respect to DNA methylation, 

histone modifi cation, and copy-number 

variation.235,236 Age, diet, gender, and 

environment have been associated with 

global epigenetic modifi cation of the 

genome and both global and locusspecifi c DNA methylation appear to be 

heritable.236–239 While both genetic240 and 

epigenetic234 variation are associated with 

individual differences in gene expression, 

the discordant monozygotic twin design 

may be the most promising design to 

investigate epigenetic regulation of gene 

expression that might underlie differences 

in a phenotype of interest.241

The study of discordant monozygotic twin 

gene expression or epigenetic differences is 

still in the early stages with respect to the 

numbers of studies, numbers of individuals, 

and design characteristics. A review of 

the literature reveals nine investigations 

of gene expression differences between 

discordant monozygotic twins that evaluate 

a panel of genes for gene expression 

differences to identify candidate genes 

potentially associated with the discordant 

phenotype,242–249 with the number of twin 

pairs evaluated in these studies ranging 

from 1 to 11. Five of these studies have 

used lymphoblastoid cell lines as the tissue 

source,242,243,245,249,250 two studies include 

analysis of dizygotic twin pairs or siblings in 

their gene expression analyses,243,248 and fi ve 

studies used the Affymetrix gene expression 

array platform.244–246,249,250 Four studies 

confi rmed specifi c results using individual 

candidate gene expression analysis in 

the discovery sample of RNA from the 

discordant monozygotic twins,245,248–250 and 

one study validated specifi c gene expression 

results in a second RNA sample derived from 

sporadic cases and controls.249
37

Monograph 20. Phenotypes and Endophenotypes

The use of monozygotic twins discordant 

for smoking history or nicotine 

pharmacokinetics for epigenetic studies 

to identify candidate genes infl uencing 

these traits represents a complementary 

approach to candidate gene and genomewide association studies (GWAS). Candidate 

genes identifi ed using these approaches 

can then be evaluated directly for specifi c 

epigenetic differences in genomic DNA 

from the discovery sample of discordant 

monozygotic twins and in genomic DNA 

from other individuals for association 

to the traits of interest. The availability 

of public epigenetic data and additional 

research into the prevalence and correlates 

of epigenetic modifi cations251 will enable 

the data from such twin design epigenetic 

analyses to be placed within the population 

of genetic, environmental, and genomic and 

epigenomic contexts.

Summary of Selected 

Biometric and 

Measured Genetic 

Studies of Nicotine 

Dependence

The following studies are summarized 

below as examples of biometric and 

measured genetic studies of nicotine 

dependence. The reader will see a 

progression from completely biometric 

analyses of components of nicotine 

dependence (class II phenotypes),65 biometric 

and measured genetic analyses combined 

(class IV phenotypes),252 to measured 

genetics with a range of class I and II 

phenotypes,66 and, fi nally, many measured 

genetic variants in relation to one class II 

phenotype.13,253 The studies are included as 

representative of the state of the science 

involving genetics and nicotine dependence. 

The reader will see, however, that none of 

the examples address issues raised in the 

present volume concerning theoretical and 

measurement issues of nicotine dependence 

and, therefore, set a baseline from which 

future studies should progress.

Heritability of Components 

of Nicotine Dependence in Adults

A study in 2004 identifi ed genetically 

informative nicotine-dependence criteria in 

a large community sample of adult (aged 24–

36 years) Australian male and female twins.65

The phenotypes under investigation were the 

seven DSM-IV nicotine-dependence criteria63

and two FTND items (CPD and time to fi rst 

cigarette in the morning) that together make 

up the HSI.130 In the fi rst step of the analysis, 

the phenotypic factor structure of the nine 

nicotine-dependence criteria in ever smokers 

resulted in two highly correlated factors for 

both women and men, with items related 

to smoking quantity loading on the fi rst 

factor (DSM-IV nicotine tolerance and both 

HSI items), and DSM-IV items related to 

withdrawal and diffi culty in quitting smoking 

loading on the second factor (withdrawal, 

smoking more than intended, diffi culty 

in quitting smoking cigarettes, giving up 

important activities to smoke, and smoking 

despite physical or psychological problems 

caused by or exacerbated by smoking). Chain 

smoking, corresponding to the DSM-IV

criterion of a great deal of time spent using 

the substance loads equally strongly on both 

factors and, in exploratory analysis, loaded 

highly on a third factor for both women 

and men, suggesting that this item does not 

correlate with endorsement of the remaining 

items. Internal consistency was high for both 

factors in women and men (Cronbach’s alpha 

ranged from 0.78 to 0.79).

Factor analysis suggested similarity in 

the pattern of endorsement of nicotinedependence criteria in women and men. 

Further, the weak factor loading of time to 

fi rst cigarette in the morning on the factor 

for which withdrawal had a strong loading 

suggested that latency to fi rst morning 

cigarette does not index nicotine withdrawal. 
38

2. Status of Genetic Studies of Nicotine Dependence

Finally, the DSM-IV criterion of giving up 

important social and occupational activities 

to smoke had the weakest correlation 

with the total factor score. Factor internal 

consistency improved without this item in 

both women and men. This result, together 

with this item being the least commonly 

endorsed, suggests that giving up activities 

to smoke may not be an important indicator 

of nicotine dependence in adults.

Genetic factor analysis of the same nine 

nicotine-dependence criteria resulted in two 

genetic factors and one shared environmental 

factor for both women and men. High item 

loadings were observed for all items on the 

fi rst genetic factor with weaker loadings on 

the second genetic factor, suggesting that 

similar genetic factors contribute to interitem 

correlation. Factor loadings on the second 

genetic factor were opposite in sign in the 

women compared to the men, implying the 

infl uence of different genetic factors. Factor 

loadings on the shared environmental factor 

were low in women and moderate in men, 

and were opposite in sign between women 

and men, suggesting gender differences 

in the shared environmental factors that 

contribute to interitem correlation.

A study by Lessov and colleagues65 also 

examined the relative contribution of genetic 

and environmental infl uences on variance to 

individual nicotine-dependence criteria and a 

nicotine-dependence diagnosis as defi ned by 

the DSM-IV and the HSI. The results showed 

substantial heritability for DSM-IV nicotine 

tolerance (73%), withdrawal (53%), smoking 

more than intended (62%), and both HSI 

items—time to fi rst cigarette in the morning 

(68%) and number of CPD (70%). Relatively 

moderate heritability was observed for 

DSM-IV items: ever chain smoked (45%), 

smoking despite physical or psychological 

problems (39%), and giving up important 

activities to smoke (26%). There was no 

evidence for a signifi cant contribution 

by shared environmental effects for any 

of these items and no gender differences. 

One exception was the DSM-IV criterion 

of diffi culty in quitting smoking, which 

was strongly heritable in women (68%), 

with no signifi cant contribution for shared 

environment, and relatively more weakly 

heritable in men (54%), with signifi cant 

contribution of shared environmental 

effects (26%). Nicotine dependence 

defi ned by DSM-IV criteria (i.e., endorsing 

three or more of seven items in the same 

12-month period lifetime) was moderately 

heritable (56%); higher heritability was 

observed for HSI-defi ned dependence 

(71%). For both dependence defi nitions, 

there was no evidence for signifi cant shared 

environmental effects or gender differences.

Taken together, the results from the Lessov 

and colleagues study65 suggest that the 

DSM-IV criteria of giving up important 

activities to smoke and chain smoking 

(i.e., spending a lot of time using nicotine) 

may not be useful indicators of nicotine 

dependence for the purpose of genetic 

research. However, the DSM-IV criteria 

of tolerance, withdrawal, and diffi culty in 

quitting smoking, and the two HSI items—

time to fi rst cigarette in the morning and 

CPD—may be the most salient genetic 

indicators of nicotine dependence in adults. 

The results also suggest that factor analytic 

approaches may identify highly genetically 

informative dependence phenotypes. 

Future work will examine the phenotypic 

and genetic factor structure of nicotine 

dependence in adolescents, which could be 

expected to be different from that of adults, 

considering differences in the importance of 

social and cultural pressures in relation to 

cigarette smoking.

Heritability of Nicotine 

Metabolism

The twin design has been used previously to 

investigate the genetic and environmental 

variance of the metabolism of a variety 

of substances including ethanol, lithium, 

and halothane (an anesthetic), but its 
39

Monograph 20. Phenotypes and Endophenotypes

application to the study of nicotine 

metabolism did not begin until 2004. 

Although the body of evidence from the 

early twin studies shows substantial genetic 

involvement in drug metabolism, the extant 

literature has (1) examined relatively 

few pharmacokinetic indices of drug 

metabolism, (2) relied on very small samples 

of twins, (3) not used state-of-the-art 

techniques for quantifi cation of the relative 

contribution of genetic and environmental 

infl uences, and (4) been unable to examine 

the impact of measured P450 genotype on 

estimates of broad heritability.

In a series of papers, the adaptability of the 

twin design to a variety of purposes was 

demonstrated.252,254–259 When combined with 

methodologies from molecular genetics, 

the design becomes highly informative 

with regard to the impact of measured 

genotype on estimates of heritability when 

the family nature of the data is used, as well 

as the impact of genotype on the metabolic 

phenotypes when the data are treated as 

coming from unrelated individuals.

Although certain genes for enzymes, such as 

CYP2A6, are clearly implicated in relevant 

pathways for nicotine metabolism,26,27,260

development of a complete understanding of 

all relevant candidate genes (e.g., CYP2B6 261

and CYP2D6 262,263) and their interactions 

in the pathways is still under way. For the 

purposes of the present example, the focus 

is only on that portion of the metabolic 

pathway that involves principally the action 

of CYP2A6 in the conversion of nicotine 

to cotinine.252 One hundred and thirtynine twin pairs—110 monozygotic and 

29 dizygotic—underwent a 30-minute 

infusion of stable-isotope-labeled nicotine 

and its major metabolite, cotinine, followed 

by an 8-hour in-hospital stay. Blood and 

urine samples were taken at regular intervals 

for analysis of nicotine, cotinine, and 

metabolites by gas chromatography–mass 

spectrometry or liquid chromatography–

mass spectrometry and subsequent 

characterization of pharmacokinetic and 

metabolism phenotypes. DNA was genotyped 

for zygosity and for variation in the gene for 

the primary enzyme involved in nicotine 

metabolism, CYP2A6 (alleles tested: *1, *1/2, 

*2, *4, *7, *9, and *12 ).

Standard pharmacokinetic parameters were 

estimated from blood concentration data 

by using model-independent methods as 

described elsewhere.264,265 Univariate genetic 

analyses were used to quantify the relative 

contribution of genetic and environmental 

infl uences. All analyses were adjusted for 

age, current smoking, and oral contraceptive 

use in women.

Approximately 60% of the variability 

in clearance of nicotine, and clearance 

of nicotine via the cotinine pathway, 

was attributable to additive genetic effects. 

The estimate of additive genetic variation 

in the clearance of cotinine was smaller 

(33.3%). All three clearance parameters were 

signifi cantly faster in the CYP2A6 wild-type 

homozygous participants compared with 

those with at least one reduced metabolizing 

gene variant.

It was hypothesized that the estimate of 

additive genetic infl uence on measures of 

clearance would be reduced after adjusting 

for the effects of the CYP2A6 genotype. 

The effect of measured CYP2A6 genotype 

was tested by (1) including genotypic status 

(wild-type homozygotes or the presence of 

at least one reduced metabolizing variant) 

as a covariate in the genetic models and 

estimating the relative contribution of 

genetic and environmental effects to the 

residual phenotypic variance and (2) fi tting 

models to metabolism data after excluding all 

individuals with at least one CYP2A6 variant.

The inclusion of the CYP2A6 genotype as a 

covariate in the biometric models did not 

signifi cantly alter the estimate for additive 

genetic effects on the three measures of 

clearance. As hypothesized, point estimates 
40

2. Status of Genetic Studies of Nicotine Dependence

for the additive genetic effects decreased, 

but only to a small degree (e.g., decreases 

of 7.7%, 9.0%, and 7.4% for clearance 

of nicotine, clearance of cotinine, and 

clearance of nicotine, via the cotinine 

pathway, respectively). Similarly, exclusion of 

individuals with reduced metabolizing allele 

variants of CYP2A6 resulted in best-fi tting 

models with decreased but still signifi cant 

heritability for the residual phenotypic 

variation in the clearance measures 

(e.g., decreases from point estimates for the 

sample as a whole of 8.8%, 26.4%, and 14.8% 

for clearance of nicotine, clearance of 

cotinine, and clearance of nicotine, via the 

cotinine pathway, respectively). These results 

suggest that, to the degree that twin variation 

in these nicotine clearance parameters is 

attributable to variation in CYP2A6 allele 

status, the effect is small and that genetic 

infl uences in addition to variation in CYP2A6

contribute to the heritability of nicotine and 

cotinine clearance (see chapter 12 for an 

analysis of nicotine metabolism involving 

multiple genes). The small association 

between the CYP2A6 genotype and clearance 

measures, both at the phenotypic and 

genotypic levels, may partly explain why the 

relationship between the CYP2A6 genotype 

and smoking behavior is inconsistent.51,52

In addition, this study by Swan and 

colleagues252 tested relatively few CYP2A6

variants (i.e., those known or very likely to 

have an impact on the structure of the gene 

and resulting protein). New variants are 

appearing at a very rapid rate, and many are 

not yet characterized or numbered.266 The 

majority of these variants are in the 5′ and 

3′ noncoding regions, which some studies 

have found to alter levels of transcription.

Linkage Analysis of FTND 

and Other Indices of Nicotine 

Dependence

The family study by Swan and others66

sought to identify loci that segregate 

with nicotine dependence as determined 

by the FTND. Additional measures were 

included to capture the complexity of the 

nicotine-dependence phenotype.66 These 

included: (1) elements from the DSM-IV

dependence criteria,63 (2) smoking frequency 

and quantity, and (3) quitting history. 

Individuals who never tried even a puff of a 

cigarette were excluded from all defi nitions. 

Individuals who tried smoking, and those 

who smoked 100 or more cigarettes in their 

lifetime but were never daily smokers, are 

included in the zero category of DSM-IV

measures. All other measures included 

lifetime daily smokers only.

For the FTND summary score, a maximum 

logarithm of odds (LOD) score of 2.7 

was seen at 178 cM on chromosome 6 

(fi gure 2.3). The marker closest to the peak 

was D6S446. The support interval (defi ned as 

the region in which LOD scores are within 

the value of one less than the maximum 

LOD score) included 156–191 cM (D6S1581-

D6S281). In subsequent analyses, additional 

tobacco use phenotypes were examined 

for evidence of linkage. To minimize 

the reporting of results due to chance, 

individual LOD scores of 2.7 or greater 

only were reported. The support interval 

for withdrawal severity overlapped the 

FTND support interval on chromosome 6 

(fi gure 2.3) with a peak LOD score of 2.7. 

Also shown in fi gure 2.3 is a quitting-history 

phenotype, short-term quit, that had a 

peak LOD score of 1.9 in the same region. 

The largest LOD score for any nicotinedependence phenotype (LOD score = 3.0) 

was observed for DSM-IV-like nicotinedependence severity near D7S636 (164 cM; 

support interval 159–167 cM; fi gure 2.4). 

For the dichotomous DSM-IV-like nicotinedependence measure, a maximum LOD 

score of 2.7 was observed on chromosome 8 

at 31 cM and 35 cM (near marker D8S258; 

fi gure 2.5).

Previous work has identifi ed linkage peaks 

at or near the support interval reported 
41

Monograph 20. Phenotypes and Endophenotypes

Figure 2.3 Multipoint Linkage Plot—Chromosome 6 LOD score

–1

3.5

3

2.5

2

1.5

1

0.5

0

4

– 0.5

Chromosome 6 position (cM)

0 50 100 150 200 250

D6S1581 D6S281

OPRM1 LPAAT- MAP3K4

delta

FTND

Withdrawal symptoms (severity)

Morning smoking

Note. FTND = Fagerström Test for Nicotine Dependence; LOD = Logarithm of odds; cM = centimorgans; Bergen and colleagues267

(1999; ever smoke) and Sullivan and colleagues55 (2004; Fagerström Tolerance Questionnaire) have peaks at beginning of support 

interval. From Swan, G. E., H. Hops, K. C. Wilhelmsen, C. N. Lessov-Schlaggar, L. S. Cheng, K. S. Hudmon, C. I. Amos, et al. 2006. 

A genome-wide screen for nicotine dependence susceptibility loci. American Journal of Medical Genetics Part B, Neuropsychiatric 

Genetics 141 (4): 354–60.

Figure 2.4 Multipoint Linkage Plot—Chromosome 7 LOD score

–1

3.5

3

2.5

2

1.5

1

0.5

0

4

– 0.5

Chromosome 7 position (cM)

0 20 40 60 80 100 120 140 160 180 200

D7S661 D7S483

HTR5A

DSM-IV nicotine dependence (severity)

Note. DSM-IV = Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; LOD = logarithm of odds; cM = centimorgans. 

Sullivan and colleagues55 (2004; Fagerström Tolerance Questionnaire) have a peak near the start of the support interval. From 

Swan, G. E., H. Hops, K. C. Wilhelmsen, C. N. Lessov-Schlaggar, L. S. Cheng, K. S. Hudmon, C. I. Amos, et al. 2006. A genome-wide 

screen for nicotine dependence susceptibility loci. American Journal of Medical Genetics Part B, Neuropsychiatric Genetics 141 (4): 

354–60.
42

2. Status of Genetic Studies of Nicotine Dependence

here for FTND.55,267 Moreover, the support 

interval is very close to the OPRM1

gene and contains MAP3K4 and LPAATdelta, both candidate genes for nicotine 

dependence.55 It is encouraging that 

several loci reported here have been 

detected in other linkage studies as well. 

The support interval on chromosome 7 

observed here for DSM-IV-like nicotinedependence severity is near the linkage 

peak, D7S1804, reported previously for 

the FTQ,55 and is near the candidate gene 

HTR5A. The support interval seen in the 

present study on chromosome 8 for DSM-IVlike nicotine dependence is near previously 

reported linkage peaks for the ever-smoking 

phenotype267 and is close to the candidate 

genes CHRNA2 and ADRA1A. Whether 

the heterogeneity across chromosomes 

for indices of nicotine dependence derives 

from genetic or measurement sources 

cannot be determined from the present 

study and needs to be addressed in future 

research.

Genome-wide Association 

and Candidate Gene Studies 

of FTND

Smoking initiation occurs with the 

experimentation and use of cigarettes, 

often in adolescence. After smoking 

100 or more cigarettes, a person passes 

the threshold to become a “smoker,” the 

term used in most health and populationbased surveys. Various behaviors are seen 

among smokers, ranging from low-level 

cigarette use by “chippers” to heavy 

smoking by nicotine-dependent individuals 

who have diffi culty quitting. Different 

factors contribute to the transition from 

one smoking level to the next, including 

genetic and environmental factors. Some 

of the risk and protective factors that 

play a role in smoking transitions include 

underlying biological predispositions 

(pharmacogenetic response to nicotine 

and nicotine metabolism), comorbid 

Figure 2.5 Multipoint Linkage Plot—Chromosome 8 LOD score

–1

3.5

3

2.5

2

1.5

1

0.5

0

4

– 0.5

Chromosome 8 position (cM)

0 20 40 60 80 100 120 140 160 180

D8S552 D8S258

ADRA1A CHRNA2

DSM-IV nicotine dependence (dichotomous)

Smoked more than intended

Time spent smoking cigarettes

Note. DSM-IV = Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; LOD = logarithm of odds; cM = centimorgans. 

Bergen et al.267 (1999; ever smoke) reported three signifi cant peaks within support interval. From Swan, G. E., H. Hops, 

K. C. Wilhelmsen, C. N. Lessov-Schlaggar, L. S. Cheng, K. S. Hudmon, C. I. Amos, et al. 2006. A genome-wide screen for nicotine

dependence susceptibility loci. American Journal of Medical Genetics Part B, Neuropsychiatric Genetics 141 (4): 354–60.
43

Monograph 20. Phenotypes and Endophenotypes

disorders (alcohol dependence, major 

depressive disorder, and anxiety disorders), 

and environmental exposures (cigarette 

taxation, peer smoking, cigarette 

advertising, antitobacco programs, and 

parental smoking).

A GWAS, which involves scanning genetic 

variants across the genomes of many 

individuals, is among the newest and most 

powerful methods to uncover unique genes 

and pathways that contribute to a disorder. 

These large-scale genetic studies are now 

possible because of the rapid technological 

advancements in genetic research. To focus 

on the examination of genetic factors in the 

transition from smoking to the development 

of nicotine dependence, low-level smokers 

(defi ned as a lifetime FTND of zero) were 

compared to nicotine-dependent smokers 

(defi ned as having an FTND score of four or 

more) in a GWAS.13

Several novel genes were identifi ed as 

potential contributors to the development 

of nicotine dependence in this GWAS, 

such as neurexin 1 (NRXN1), TRPC7,

and others. The neurexin genes are 

expressed in neurons and are hypothesized 

to infl uence the balance of excitatory 

glutamatergic and inhibitory GABAergic 

synapses.268 Because substance dependence 

is modeled as a relative imbalance of 

excitatory and inhibitory neurotransmission, 

the neurexin genes are plausible new 

candidates that contribute to the 

neurobiology of dependence. An additional 

piece of evidence on the importance of 

the neurexin gene family comes from a 

pooled GWAS by Uhl and colleagues for 

polysubstance addiction, which identifi ed 

NRXN3.269 A second gene of interest 

is TRPC7, which encodes a subunit of 

a multimeric calcium channel. In an 

animal model using C. elegans, genes 

in this family functionally regulated 

nicotine-induced neuronal activity.270 This 

animal model provides insight into the 

role this gene may play when nicotine is 

ingested. Although these results require 

validation in independent samples, they 

represent some of the new leads that a 

GWAS can uncover.

In parallel with the GWAS, a second aim 

of this genetic project was to examine a 

comprehensive set of candidate genes to 

detect variants associated with nicotine 

dependence. Over 350 genes were 

genetically queried by using approximately 

4,000 SNPs for genotyping. The genes for 

study included the nicotinic receptors as 

well as genes known to be involved in the 

neurobiological pathways that contribute 

to the development of dependence, such 

as dopamine and c-aminobutyric acid 

(GABA) receptors. Genes were nominated 

by a skilled committee of investigators 

from the National Institute on Drug Abuse 

Genetics Consortium271 with expertise in 

the study of nicotine and other substance 

dependence.

Genetic variants in the nicotinic receptors 

dominated the association results for 

nicotine dependence. Genetic association 

with the CHRNB3-CHRNA6 nicotinic 

receptor locus on chromosome 8 was the 

most signifi cant fi nding in the candidate 

gene study, and this cluster was also 

identifi ed in the GWAS.13,253 Compelling 

fi ndings were also seen in the group of 

SNPs in the CHRNA5-CHRNA3-CHRNB4

cluster of nicotinic receptor genes on 

chromosome 15. Evidence shows at least 

two independent signals in this gene 

cluster. The fi rst is a genetic variant that 

codes for a nonsynonymous coding SNP 

in the a5 nicotinic receptor subunit gene 

(*RS16969968). There is evidence of at 

least one other independent signal in this 

gene cluster marked by *RS578776. These 

results highlight the importance of the 

pharmacogenetic response to nicotine 

as a contributor to the development of 

nicotine dependence. Tables 2.1 and 2.2 

summarize the results from each of the 

studies. The chromosome 15 fi ndings from 
44

2. Status of Genetic Studies of Nicotine Dependence

this study subsequently received support 

from analysis of independent data sets.272,273

Three studies have further implicated 

the same gene cluster in predisposition 

to lung cancer.274–276 Whether this effect 

is independent of an effect on smoking is 

controversial.277

In summary, these large-scale studies are 

a step in the process to identify genetic 

contributions to nicotine dependence. 

They can, it is hoped, provide insights to 

understand the genetic contribution to 

nicotine dependence so that new approaches 

can be developed to reduce tobacco use, 

especially cigarette smoking. Although a 

substantial majority of smokers report that 

they want to quit (70%), and an estimated 

41% try to quit in a given year, most 

smokers are not successful (although many 

are successful over time), and nicotine 

dependence is a strong predictor of failed 

Table 2.2 Results from the Candidate Gene Study of Nicotine Dependence

SNP Gene Chr Pos(bp) Risk Allele

Primary 

p-value

Male

odds ratio 

(95% CI)

Female

odds ratio 

(95% CI)

*RS6474413 CHRNB3 8 42,670,221 *T (0.81/0.76) 9.36E-05 1.2 (0.9–1.5) 1.5 (1.3–1.9)

*RS578776 CHRNA3 15 76,675,455 *G (0.78/0.72) 3.08E-04 1.5 (1.2–1.9) 1.3 (1.1–1.6)

*RS6517442 KCNJ6 21 38,211,816 *C (0.34/0.28) 5.62E-04 1.4 (1.1–1.7) 1.3 (1.1–1.5)

*RS16969968 CHRNA5 15 76,669,980 *A (0.38/0.32) 6.42E-04 1.3 (1.1–1.7) 1.3 (1.1–1.5)

*RS3762611 GABRA4 4 46,838,216 *G (0.93/0.91) 9.22E-04 2.1 (1.4–3.2) 1.3 (0.9–1.8)

Note. SNP = single nucleotide polymorphism; Chr = chromosome; Pos(bp) = chromosomal position, base pairs; CI = confi dence interval. 

Results for all SNPs are posted at http://zork.wustl.edu/nida. Adapted from Saccone, S. F., A. L. Hinrichs, N. L. Saccone, G. A. Chase, 

K. Konvicka, P. A. Madden, N. Breslau, et al. 2007. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association 

study targeting 348 candidate genes with 3713 SNPs. Human Molecular Genetics 16 (1): 36–49.

Table 2.1 Results from the Genome-wide Association Study of Nicotine Dependence

SNP Gene Chr Pos(bp) Risk Allele

Primary 

p-value

Male

odds ratio 

(95% CI)

Female

odds ratio 

(95% CI)

*RS4142041 CTNNA3 10 68,310,957 *G (0.41/0.34) 5.64E-06 1.7 (1.4–2.2)* 1.1 (1.0–1.4)a

*RS999 b GPSM3, 

AGPAT1

6 32,261,864 *C (0.96/0.94) 1.42E-05 1.9 (1.1–3.5) 2.5 (1.6–4.0)

*RS12623467 NRXN1 2 51,136,740 *C (0.96/0.92) 1.48E-05 2.4 (1.5–3.9) 1.6 (1.1–2.3)

*RS12380218 VPS13A 9 77,165,214 *G (0.24/0.19) 2.09E-05 1.2 (0.9–1.6) 1.6 (1.3–1.9)

*RS2673931 TRPC7 5 135,717,335 *T (0.66/0.61) 3.89E-05 1.7 (1.3–2.1)a 1.0 (0.9–1.2)a

*RS2791480 CLCA1 1 86,680,605 *G (0.78/0.72) 4.38E-05 1.5 (1.2–2.0) 1.3 (1.1–1.6)

*RS10490162 NRXN1 2 51,159,308 *T (0.91/0.86) 5.66E-05 1.9 (1.3–2.8) 1.4 (1.1–1.8)

*RS13277254 CHRNB3 8 42,669,139 *A (0.81/0.76) 6.54E-05 1.2 (0.9–1.6) 1.6 (1.3–1.9)

*RS10793832 FBXL17 5 107,348,129 *C (0.32/0.26) 8.13E-05 1.1 (0.9–1.4) 1.5 (1.2–1.8)

*RS2302673 FTO 16 52,625,622 *T (0.87/0.84) 8.85E-05 1.0 (0.8–1.4)a 1.8 (1.3–2.2)a

Note. SNP = single nucleotide polymorphism; Chr = chromosome; Pos(bp) = chromosomal position, base pairs; CI = confi dence interval. 

Results for all SNPs are posted at http://zork.wustl.edu/nida. Adapted from Bierut, L. J., P. A. Madden, N. Breslau, E. O. Johnson, 

D. Hatsukami, O. F. Pomerleau, G. E. Swan, et al. 2007. Novel genes identifi ed in a high-density genome wide association study for 

nicotine dependence. Human Molecular Genetics 16 (1): 24–35.

a

Signifi cantly different odds ratio for men and women.

b

The allele frequency for *RS999 is quite different in these data than reported in the SNP database; this may represent a failure to 

accurately genotype this SNP in this study.
45

Monograph 20. Phenotypes and Endophenotypes

smoking cessation. This systematic 

survey of the genome nominates novel 

genes that increase an individual’s risk of 

transitioning from smoking to nicotine 

dependence, and the candidate-gene 

study has provided persuasive evidence 

of the role of the nicotinic receptors in 

the transition from smoking to nicotine 

dependence. The continued genetic and 

biological characterization of these genes 

will help in understanding the underlying 

causality of nicotine dependence and may 

provide novel drug development targets 

for smoking cessation.

Issues 

in Communication 

of Genetic Findings

A full discussion of the ethical, legal, 

and social implications of this research 

is beyond the scope of this monograph. 

For a full discussion of these issues, see 

Caron and colleagues278 and Shields and 

colleagues.279 However, there are several 

important issues in the interpretation 

and communication of genetic fi ndings 

that will be addressed here because: 

(1) unreplicated fi ndings of gene–nicotine 

dependence associations could lead to 

erroneous conclusions based on falsepositive results; (2) discrimination or 

stigma could accrue to individuals or 

groups identifi ed as being at greater risk 

for nicotine dependence, especially if the 

prevalence of genetic risk factors varies 

as a function of ethnicity or of psychiatric 

comorbidity; and (3) available genetic 

tests for nicotine-dependence liability or 

treatment responsiveness are of questionable 

value at the individual level. While this 

portion of the chapter is not intended to be 

a comprehensive review of all the relevant 

issues, its purpose is to draw attention 

to the importance of understanding the 

broader implications of new research 

fi ndings and the need for further research 

and discussion in this area.

The Need to Replicate 

Gene–Nicotine Dependence 

Associations

As indicated earlier in this chapter, the 

bulk of the fi ndings reporting associations 

between genetic variation and nicotinedependence phenotypes has been derived 

from studies of single genes in relatively 

small samples. The effect sizes tend to be 

small, and the results have been notoriously 

diffi cult to replicate because of crossstudy differences in sample ascertainment, 

population stratifi cation, lack of consistency 

in SNP genotyping and phenotype 

defi nition, and failure to understand the role 

of linkage disequilibrium. This conclusion 

applies not only specifi cally to studies of 

nicotine dependence but more generally to 

studies of complex traits.

The advent of GWAS has the potential to 

alter the course of scientifi c progress in the 

fi eld of nicotine dependence, as well as that 

of many other complex traits.280 Because it 

is now possible to study many SNPs (up to 

1 million, using the Illumina platform) 

in multiple genes in relevant pathways, 

technological advances, if applied carefully, 

promise to encourage comprehensive 

investigation of genetic variation in relation 

to nicotine dependence and to do so in a 

much more rapid fashion. Concurrent with 

the application of this new technology are 

methodological developments centered on 

the best use of the GWAS approach. Best 

practices for SNP selection, phenotypic 

defi nition, incorporation of prior biological 

knowledge, multistage genotyping, proper 

handling of the multiple-comparison 

problem within the GWAS context, and the 

critical importance of replication are being 

proposed and incorporated into requirements 

for grant funding and publishing in top-tier 
46

2. Status of Genetic Studies of Nicotine Dependence

journals. Independent confi rmation of the 

association between variation in the a3 

and a5 gene cluster on chromosome 15, 

fi rst reported by Saccone and colleagues,253

has now been reported by Bierut and 

colleagues273 and Berrettini and colleagues.272

As of April 2009, the GWAS approach has 

been successfully applied to two complex 

traits: age-related macular degeneration 

and type II diabetes,281,282 with many more 

applications of varying maturity being 

reported in the literature (including 

atherosclerosis and cancer, both reviewed 

in Kronenberg;283 brain aging and 

cognition;284 longevity;285 sleep and circadian 

phenotypes;286 and general cognitive 

ability287). It is clear that the GWAS approach 

will enjoy much popularity in the foreseeable 

future for the study of complex traits.

However, just as in the previous generation 

of single-gene, single-variant studies of 

nicotine dependence, investigators will 

need to apply with determination and 

vigilance the fundamental principles of good 

science and of interpreting results to the 

lay press and the public. Whereas previous 

headlines announced, “Gene for smoking 

identifi ed,” the press, now with GWAS 

results in hand, might broadcast, “Multiple 

genes for smoking identifi ed.” Unless the 

investigators involved are careful to point 

out the limitations of their fi ndings and the 

associated effect sizes (which are destined 

to be modest with odds ratios of 1.5 or less), 

it is entirely likely that even more confusion 

will reign in the public mind as to the 

meaning of these results.

Discrimination or Stigma May 

Accrue to Individuals or Groups 

Identifi ed as Being at Greater 

Risk for Nicotine Dependence

Several concerns have been raised regarding 

the potential for information about genetic 

risk for nicotine dependence or response 

to smoking cessation treatment based on 

genotype to be used against individuals or 

groups in harmful ways.288 These concerns 

pose a barrier to physicians’ willingness to 

offer a new genetic test to tailor smoking 

treatment to their patients279,289,290 and to 

smokers’ willingness to undergo genetic 

testing to be matched to optimal treatment. 

Some of the primary critiques offered with 

respect to labeling individuals (especially 

youth) and groups are addressed below.

Might knowledge of one’s genetic status 

with respect to nicotine dependence be 

useful in deterring potential smokers from 

initiating smoking? Some might argue that 

informing adolescents that their genetic 

profi le places them at greater risk of nicotine 

dependence may give them an incentive not 

to initiate smoking. However, evidence from 

other cases indicates that being identifi ed 

as “at risk” is apt to have little effect on 

behavior. In the case of phenylketonuria 

(PKU), for example, dietary management 

is critical to maintaining phenylalanine 

levels to avoid developmental problems. 

In a U.K. study of PKU management, 

compliance with dietary restrictions to 

maintain phenylalanine levels among 

informed children decreased from 70% 

among children aged 10 to approximately 

20% among children aged 15,291 illustrating 

the limited ability of personalized feedback 

about risk to infl uence adolescents to 

change their behavior to maintain healthy 

habits. Thus, it is unclear what, if any, 

benefi t such information might have for 

smoking prevention in practice.

Several additional risks associated with 

“labeling” adolescents as being susceptible 

to nicotine dependence have been 

identifi ed.292 Such labeling may result in 

a sense of fatalism among adolescents, 

leading to a perceived lack of ability to 

control their future, a higher willingness 

to smoke, and a resistance to considering 

public health messages about the risks 

of smoking.293–295 Youths identifi ed as 
47

Monograph 20. Phenotypes and Endophenotypes

at higher risk for nicotine dependence 

who already smoke may interpret this 

information as meaning that it is futile for 

them to try quitting. On the other hand, 

adolescents without a given genetic variant 

associated with increased risk of nicotine 

dependence may erroneously believe they 

can smoke and not become addicted. Thus, 

the provision of genetic information to 

adolescents could have signifi cant positive 

or negative impact and psychological effects. 

More research is needed to understand 

adolescents’ comprehension of the meaning 

of genetic risk for nicotine dependence 

and how this comprehension is likely to 

affect smoking behavior. The results of this 

research could then inform proactive public 

health messages that emphasize the health 

consequences from tobacco smoke that 

accrue regardless of genetic background 

and/or specify tailored methods to reduce 

chances for nicotine dependence.

Another area of intense debate concerns 

the framing of genetic information about 

risk of addiction or response to treatment 

in racial terms. Despite heated debates and 

numerous appeals for more careful use of 

racial categories in genetics research,296–300

many genetic studies continue to use selfidentifi ed racial variables in statistical 

analyses, resulting in research fi ndings 

framed in “racial” terms. While it is essential 

to consider and control for population 

structure in genetic studies, using selfdefi ned racial or ethnic categories as proxies 

for human genetic heterogeneity is less 

scientifi cally precise (more robust measures 

are available for assessing geographical 

ancestry) and fraught with potential for 

social harm.299 When research results are 

framed in racial terms, great harm can 

accrue to subpopulations identifi ed as 

more likely to carry certain risk alleles, 

such as those that confer increased risk 

of addiction.

A well-documented example of the kind 

of stigma that can accrue to a particular 

population is found in the early screening 

efforts for sickle cell hemoglobin among 

African Americans, which immediately 

resulted in considerable racial discrimination 

in both health insurance and employment 

contexts. This occurred despite the 

reality that a similarly high prevalence 

of the sickle cell trait was found in other 

subpopulations.294,301 At the same time, nonAfrican-Americans, who were not socially 

viewed as being associated with sickle cell, 

often went undiagnosed until screening was 

implemented for all newborn infants.

Similarly, research results reporting that 

genotypes linked to nicotine dependence, 

cocaine, and other substances occur at a 

higher rate in African Americans than in 

European Americans holds the potential for 

exacerbating existing racial discrimination. 

Such research results are not received in 

a vacuum but are read in the context of 

social history and can lead to racism and 

marginalization of an entire portion of 

society, given the contentious history of 

racial stereotypes in the United States.301

Studies have shown, for instance, that 

physicians already prescribe pain medication 

in smaller doses to African American 

patients than to European American patients 

with similar symptoms, refl ecting a possible 

assumption that African Americans are more 

likely to become addicted to opiates.302,303

Because of the well-documented racial 

disparities in access to and quality of health 

care,304–309 investigators must seriously 

consider the unintended consequences of 

incorporating genetic information into risk 

assessment related to nicotine dependence.

The Association between Gene 

Variants, Nicotine Dependence, 

and Psychiatric Conditions May 

Also Result in Increased Risk 

for Stigmatization

As has been argued by Shields and 

colleagues, social sensitivity related to 
48

2. Status of Genetic Studies of Nicotine Dependence

the pleiotropic associations of genetic 

variants implicated in nicotine dependence 

or response to treatment are intensifi ed 

when they intersect with data on racial 

differences in the frequency of such risk 

alleles.299 One feature of the genetics of 

complex traits, such as smoking, that 

raises a host of social and ethical concerns 

is the pleiotropic associations of key 

genetic variants with many other traits. 

An early example of a pleiotropic genetic 

test is the test for apolipoprotein E, which 

simultaneously provides information on risk 

for cardiac disease and risk of developing 

late-onset Alzheimer’s disease.310–312 Genes 

hypothesized to play a key role in increased 

risk of nicotine dependence also have 

been associated with increased risk of 

addiction to cocaine, alcohol,313,314 sexual 

activity,315 compulsive gambling,313 novelty 

seeking,316,317 and to other neuropsychiatric 

conditions. (Table 2.3 by Shields and 

colleagues,299 Billett and colleagues,318

Comings and colleagues,319,320 Muglia and 

colleagues,321 Nielsen and colleagues,322

and Rowe and colleagues.323) Many of these 

conditions and behaviors are very socially 

sensitive.324–329 Persons identifi ed as having 

these genotypes may be stigmatized or 

discriminated against. One might assume 

that persons fi nding out they had a genetic 

profi le of increased risk for nicotine 

dependence if they experimented with 

cigarettes might be deterred from initiating 

smoking. However, this profi le could not be 

obtained without simultaneously generating 

information with other, more onerous 

implications. Similarly, it might be useful 

to tailor smoking cessation treatment to 

genotype to match patients to the treatment 

likely to work best for them (see below for 

a discussion of the evidence for the use 

of such tests), but such genetic testing 

would simultaneously generate additional 

information about a person’s genetic risk for 

other addictions and psychiatric conditions. 

For these reasons, Shields and colleagues 

have argued that, in weighing the pros and 

cons associated with decisions regarding 

genetic testing to tailor smoking prevention 

Table 2.3 Pleiotropic Associations of Genetic Variants Implicated in Smoking

Genetic Variants

Complex Traits

Tobacco 

Use

Addictive 

Behaviors

Psychiatric 

Conditions

Behavior 

Patterns

Dopamine Pathway

DRD1 (dopamine D1 receptor) Smoking Cocaine, Alcohol Tourette’s Syndrome Gambling

DRD2 (dopamine D2 receptor) Smoking Alcohol, Cocaine ADHD,a PTSDb Sexual Activity

DRD4 (dopamine D4 receptor) Smoking Alcohol ADHD,a OCDc Novelty seeking

SLC6A3 (dopamine transporter, DAT) Smoking Alcohol Anxiety, Tourette’s 

Syndrome

DBH (dopamine beta-hydroxylase) Smoking Paranoia

Serotonin Pathway

5HTTLPR (serotonin transporter) Smoking Alcohol Depression, Anxiety

TPH (tryptophan hydroxylase) Smoking Alcohol Suicide, Depression Aggression

Note. Copyright © 2005 by the American Psychological Association. Adapted with permission. The offi cial citation that should be 

used in referencing this material is, Shields, A. E., M. Fortun, E. M. Hammonds, P. A. King, C. Lerman, R. Rapp, and P. F. Sullivan. 

2005. The use of race variables in genetic studies of complex traits and the goal of reducing health disparities: A transdisciplinary 

perspective. American Psychologist 60 (1): 77–103. The use of APA information does not imply endorsement by APA.

a

ADHD = attention defi cit hyperactivity disorder

b

PTSD = post traumatic stress disorder

c

OCD = obsessive compulsive disorder
49

Monograph 20. Phenotypes and Endophenotypes

and treatment strategies, decisions should 

be made based on the most potentially 

harmful uses of information generated by 

such testing.288

In what ways might these pleiotropic 

associations exacerbate concerns about 

identifying individuals at increased risk 

for nicotine dependence or raise new 

concerns? There have been cases of insurers 

increasing premiums or denying coverage 

to benefi ciaries on the basis of genetic 

susceptibility tests for breast and ovarian 

cancer and for Alzheimer’s disease.330

Smokers have long been charged higher 

health insurance premiums and identifi ed as 

a socially stigmatized group.331 In addition, 

the well-established adverse impact of 

smoking on employers’ health care costs 

and worker productivity has led to instances 

in which employers have discriminated 

against smokers in hiring practice.332 It is 

therefore not impossible to imagine that 

some employers might consider genetic 

testing as a screening tool in considering 

prospective employees. Such discrimination 

would likely be exacerbated when this 

genetic status is linked to an increased 

risk of alcohol or drug addiction, since 

this is a source of high health care costs.333

Such discrimination might be more likely 

to take place within self-insured fi rms, in 

particular, since they more directly manage 

and bear the costs of their employees’ 

health care.334

The issue of harm to individuals from 

disclosure of genetic information is not 

new, and this issue has been addressed in 

many contexts.335–338 While some progress 

was made in protecting individuals against 

discrimination with the 1990 Americans 

with Disabilities Act (ADA)339 and Executive 

Order 13145, which prohibits discrimination 

against federal employees on the basis of 

genetic information,340 no comprehensive 

federal law bans genetic discrimination 

for the general population. State laws 

remain the primary source for protection 

of genetic information. As of 2007, only 

41 states banned genetic discrimination 

by health insurance companies, and 

only 32 states had passed laws that ban 

the misuse of genetic information by 

employers.341 Greater federal protections 

are provided by the passage of the Genetic 

Information Nondiscrimination Act (GINA) 

of 2008.342 On April 24, 2008, the Senate 

amended and passed GINA as H.R. 493. 

The House reconciled and agreed to the 

Senate bill on May 1, 2008.

Although GINA became law under 

President George W. Bush and addresses 

many concerns about discrimination 

and privacy, gaps remain in protection, 

including important omissions in consumer 

protections against employers discriminating 

against potential employees on the basis of 

genetic status.343,344 As Rothstein points out, 

GINA makes it unlawful for an employer 

to request, require, or purchase genetic 

information about an employee or applicant, 

yet section 102(d)(3) of the ADA still allows 

employers to require a signed authorization 

to release all of an individual’s health record 

(including genetic information) after a 

conditional offer of employment.344 Moving 

forward, it will be essential to identify and 

close persisting gaps in protections to 

reassure patients who may benefi t from 

genetic testing that information from such 

tests will not be used to discriminate against 

them in health insurance or employment. 

Failure to address these gaps will seriously 

undermine any future efforts to use genetic 

information to guide smoking prevention or 

treatment strategies.

The research involving genetics and 

nicotine dependence (and associated 

concerns) is occurring within the context 

of a much broader series of developments 

at the federal level, as described in the 

document, “Personalized Health Care: 

Opportunities, Pathways, Resources.”345

The report identifi es several future 

outcomes of personalized health care: 
50

2. Status of Genetic Studies of Nicotine Dependence

(1) prediction of individual susceptibility 

to disease, (2) provision of more useful and 

person-specifi c tools for preventing disease, 

(3) detection of the onset of disease at the 

earliest possible moment, (4) preemption of 

the progression of disease, and (5) targeting 

of medicines and dosages more precisely 

and safely to individual patients (p. 1). 

In addition, the report identifi es the 

need to (1) make the individual patient’s 

health information available on demand, 

(2) provide necessary support to clinicians 

when needed to use information concerning 

genetic and molecular factors, (3) bring 

large data sets together from real-world 

medical practices through secure networks 

to accelerate identifi cation of best and 

safest practices, and (4) use data from 

data networks to understand differences 

in patients’ responses to drugs and other 

therapies (p. 2).

The Value of Genetic Tests 

to Assess for NicotineDependence Liability 

or Treatment Responsiveness 

Is Questionable 

Despite the best efforts and intentions of 

the scientists involved in the work discussed 

in this monograph, vigilance and proactive 

planning are needed to minimize the risk 

of misunderstanding, misinterpreting, 

misusing, or otherwise abusing the results 

demonstrating associations between 

genetic factors and nicotine dependence.278

Documented examples exist of at least some 

instances of unintended consequences 

of this work. For example, a commercial 

company has been created to promote the 

sales of a genetic test (in this case, DRD2) 

that purports to predict the likelihood for 

success (smoking cessation) in response to 

certain pharmacological agents. Not only 

is there an inadequate knowledge base to 

support the widespread clinical use of this 

test, but also the cost-effectiveness of such 

a test has been called into question.346,347

More generally, the rapidly developing 

fi eld of direct-to-consumer marketing of 

genetic tests with little or no supporting 

evidence of their value at the individual 

level has generated a great deal of concern 

in the literature348 and calls for a regulatory 

framework to protect consumers from 

misleading claims made by commercial 

interests promoting these tests.349 Scientists 

in the fi eld of genetics and nicotine 

dependence will need to stay informed 

regarding developments in this area of 

personalized medicine so that their work 

can be placed in the broader context of this 

emerging fi eld.

The majority of scientists involved in the 

work described in this monograph are 

most interested in the implications of their 

work for understanding basic processes 

underlying nicotine dependence and, more 

generally, addiction. They are far less, if at 

all, interested in turning this work into 

for-profi t, commercially available tests or 

products. Nevertheless, the ethical scientifi c 

community must be vigilant to quickly 

identify and challenge claims made about a 

test’s predictive value for assigning smoking 

cessation treatments at the individual 

level. Similar concerns arise for claims that 

genetic variation can be used to predict 

whether a young child will become addicted 

to tobacco despite the fact that scientifi c 

work in this area has only just begun to 

explore this question.

Simply put, the work described in this 

monograph and in the fi eld of genetics and 

nicotine dependence is in an early stage, 

and the body of available evidence is not 

suffi cient to support any kind of predictive 

testing at the individual level. This may 

not be the case in other fi elds, such as 

the genetics of cancer, in which many 

decades have been spent by thousands of 

scientists to identify the genetic basis of 

cancer. By comparison, the fi eld of genetics 

and nicotine dependence represents a 

tiny fraction of the total effort in the 
51

Monograph 20. Phenotypes and Endophenotypes

fi eld of cancer genetics, even though 

tobacco use remains an undisputed 

major risk factor for cancer. Evidence of 

a potential overlap between gene variants 

in the a3–a5 nicotinic receptor cluster on 

chromosome 15, which are associated with 

nicotine dependence13,253,272 and with lung 

cancer,274–276 however, may cause the two 

fi elds to converge.

Summary

This chapter provides a framework for 

understanding nicotine-dependence 

phenotypes and an overview of major 

concepts, along with a summary of selected 

fi ndings from the tobacco genetic literature. 

This chapter also raises important issues as 

to how genetic research is communicated 

and understood by the media and the public.
52

2. Status of Genetic Studies of Nicotine Dependence

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B. Davidoff-Feldman. 1998. Physician’s 

duty to warn third parties about the risk of 

genetic diseases. Pediatrics 102 (1 Pt. 1): 

140–42.

338. U.S. Congress, Offi ce of Technology 

Assessment. 1992. Cystic fi brosis and DNA 

tests: Implications of carrier screening, 

OTA-BA-532. Washington, DC: U.S. 

Government Printing Offi ce. http://govinfo

.library.unt.edu/ota/Ota_1/DATA/1992/

9208.PDF.

339. Americans with Disabilities Act of 1990, 

42 U.S.C. § 12101 et seq. (1990).

340. Federal Register. 2000. Presidential 

Documents: Executive Order 13145—

To Prohibit Discrimination in Federal 

Employment Based on Genetic Information. 

Federal Register 65 (28): 6877–80. 

341. National Human Genome Research 

Institute. 2008. Genetic Information 

Nondiscrimination Act: 2008. http://

www.genome.gov/Pages/PolicyEthics/

GeneticDiscrimination/GINAInfoDoc.pdf.

342. Genetic Information Nondiscrimination 

Act of 2008, Pub. L. 110-233, 122 Stat. 881 

(2008). http://frwebgate.access.gpo.gov/

cgi-bin/getdoc.cgi?dbname=110_cong

_bills&docid=f:h493enr.txt.pdf.

343. Hustead, J. L., and J. Goldman. 2002. 

Genetics and privacy. American Journal of 

Law and Medicine 28 (2–3): 285–307.

344. Rothstein, M. 2007. Genetic Information 

Nondiscrimination Act is a fi rst step; 

won’t solve the problem. The Metropolitan 

Corporate Counsel, October: 42. http://

www.metrocorpcounsel.com/pdf/2007/

October/42.pdf.

345. U.S. Department of Health and Human 

Services. 2007. Personalized health care: 

Opportunities, pathways, resources.

U.S. Department of Health and Human 

Services. http://www.hhs.gov/myhealthcare/

news/phc-report.pdf.

346. Heitjan, D. F., D. A. Asch, R. Ray, M. Rukstalis, 

F. Patterson, and C. Lerman. 2008. Costeffectiveness of pharmacogenetic testing 

to tailor smoking-cessation treatment. 

Pharmacogenomics Journal 8 (6): 391–99.

347. Welton, N. J., E. C. Johnstone, S. P. David, 

and M. R. Munafó. 2008. A cost-effectiveness 

analysis of genetic testing of the DRD2 

Taq1A polymorphism to aid treatment 

choice for smoking cessation. Nicotine & 

Tobacco Research 10 (1): 231–40.

348. Janssens, A. C., M. Gwinn, L. A. Bradley, 

B. A. Oostra, C. M. van Duijn, and 

M. J. Khoury. 2008. A critical appraisal of 

the scientifi c basis of commercial genomic 

profi les used to assess health risks and 

personalize health interventions. American 

Journal of Human Genetics 82 (3): 593–99.

349. Katsanis, S. H., G. Javitt, and K. Hudson. 

2008. Public health. A case study of 

personalized medicine. Science 320 (5872): 

53–54. 

71

The use of appropriate measures of nicotine dependence remains a key area for future 

research on the effects of genes and gene-environment interaction on tobacco use. This 

part examines the theoretical basis for constructs that may link heritable genetic traits 

with observable measures of nicotine dependence, including phenotypes representing a 

causal path between specifi c genetic actions and measures of nicotine dependence, as well 

as endophenotypes measuring indirect infl uences such as those found prior to nicotine 

exposure.

The fi rst chapter of this part examines theoretical issues in establishing nicotinedependence phenotypes in humans, including new and existing measures of nicotine 

dependence, as well as traits that may link specifi c genetic actions and measures of 

nicotine dependence. A subsequent chapter explores key issues in using mouse models 

of nicotine dependence. These issues include the use of nicotinic acetylcholine receptors 

to examine tissue-specifi c responses to nicotine within specifi c genetic strains, relating 

routes of administration in mice to the physiology of human smoking, and correlating 

mouse models of nicotine-response behavior with nicotine dependence in humans.

Theoretical Considerations

Part

2

73

3

The Nicotine-Dependence Phenotype: 

Translating Theoretical Perspectives 

and Extant Data into Recommendations 

for Genetic Mapping

Timothy B. Baker, David V. Conti, Terrie E. Moffi t, and Avshalom Caspi

The search for a possible genetic basis for nicotine dependence requires constructs that 

serve as a link between genes and behavior. Common existing measures of nicotine 

dependence are highly heritable and have high predictive validity for smoking outcomes 

yet lack specifi city relative to the underlying biological mechanisms that could inform 

future genetic research. This chapter examines theoretical issues in establishing nicotinedependence phenotypes, including

■ Distal measures of nicotine dependence, such as the Fagerström tests, the 

Diagnostic and Statistical Manual of Mental Disorders, and others, focusing on 

mature nicotine dependence

■ Newer multidimensional measures of nicotine dependence, such as the 

Nicotine Dependence Syndrome Scale and the Wisconsin Inventory of Smoking 

Dependence Motives, examining motivational factors leading to dependence

■ Endophenotypes and transitional phenotypes, measuring quantities before and 

after nicotine exposure, respectively, that may potentially form a causal path 

between specifi c genetic actions and measures of nicotine dependence, including 

cognitive, affective, and craving factors

Further study is needed to establish the validity of such endophenotypes and transitional 

phenotypes for upstream measures of nicotine dependence and their relationship with 

genetic and gene-environment infl uences, which, in turn, may support further research 

on the impact of such infl uences on smoking outcomes and behavior.

The analyses described herein were supported by the National Institute of Health grant CA/DA19706. 
74

3. The Nicotine-Dependence Phenotype

Introduction

This chapter examines a theoretical basis for 

the assessment of phenotypes for nicotine 

dependence, focusing on strategies that 

investigators might use to assess nicotine 

dependence with the goal of uncovering 

its genetic bases. First, an epistemological 

system (i.e., construct validation) is 

discussed for studying such complex 

constructs as dependence. This system 

provides a vocabulary, a set of principles, and 

an inferential basis for evaluating evidence 

for the assessment of nicotine dependence. 

In addition, a conceptual model is presented 

that shows how dependence assessments 

may be characterized by their specifi city 

and their proximity to genetic variants 

and the biological mechanisms that the 

variants directly express. This model reveals 

that genetic variants may be related to a 

developmental progression of phenotypes: 

those preceding nicotine exposure 

(intermediate or endophenotypes), those 

that arise out of initial nicotine exposure 

but precede frank dependence (transitional 

phenotypes), and those regarded as mature 

clinical phenotypes. 

Next, the chapter reviews evidence on 

existing measures of dependence, including 

both traditional diagnostic measures and 

newer multidimensional measures. This 

evidence is used to draw inferences about 

the nature and structure of dependence, 

especially as it manifests in long-term, heavy 

smokers. Then, existing data are used to 

address general questions about strategies 

for genetic mapping. These questions include 

whether different types of assessments 

need to be used for different smoker 

subpopulations, which particular measures 

need to be used to assess the core and 

breadth of the phenotype, and how to model 

environmental infl uences in such mapping. 

Then, the chapter addresses future directions 

for phenotypic assessment, especially the 

need to develop assessments that refl ect the 

different stages in progression to dependence 

(intermediate and transitional phenotypes). 

Finally, issues regarding the integration of 

phenotypic measures with research design 

and analytic strategies are addressed. 

Appropriate and accurate assessment of the 

nicotine-dependence phenotype construct is 

needed to understand better the molecular 

genetic basis of tobacco use and nicotinedependence. The nicotine-dependence 

phenotype comprises the measurable 

manifestations of heritable information 

that result in nicotine use that produces 

persistent socially, clinically, or medically 

signifi cant distress or dysfunction. Although 

the use of any sort of nicotine delivery system 

might satisfy this criterion, this review will 

concentrate on research and theory relevant 

to the smoking of tobacco cigarettes.

Most measures of nicotine dependence 

(e.g., diagnostic items) assess general 

features of dependence that are causally 

distal to underlying genetic infl uences. 

Common distal measures such as the 

Fagerström tests and the criteria of 

the American Psychiatric Association’s 

Diagnostic and Statistical Manual of 

Mental Disorders, fourth edition (DSM-IV)1

tend to assess mature states of nicotine 

dependence. Such measures could mask 

upstream causal factors that may, in fact, 

be closer to a genetic basis for nicotine 

dependence. Nevertheless, these distal 

measures assess constructs or dimensions 

that are highly heritable. Examining newer 

multidimensional criteria for nicotine 

dependence, as well as other approaches that 

take more of a causal and developmental 

perspective, can serve as an important key 

to developing phenotypes, endophenotypes, 

and intermediate phenotypes that, in turn, 

may correlate more closely with gene action.

The need to assess the phenotype accurately 

is patent. Genetic variants, by themselves, 

are relatively uninformative. They attain 

greater information value to the extent 
75

Monograph 20. Phenotypes and Endophenotypes

that they are associated with biological, 

behavioral, cognitive, or clinical outcomes 

of interest. Thus, the phenotype confers 

clinical, societal, and/or theoretical meaning 

on such genetic variants as alleles. For this 

reason, phenotypic assessment is central to 

uncovering the genetic substrata relevant 

to the maintenance of chronic tobacco use 

and dependence. However, the attempt to 

measure this phenotype well is a daunting 

task that demands that investigators have 

a clear idea of what they want to measure 

and why. 

One basic question that investigators must 

address is whether they are interested 

in “nicotine dependence” as opposed to 

“tobacco dependence.” This decision could 

have implications for the selection of 

genetic variants and phenotypic measures. 

For example, if one is interested in tobacco 

dependence, one might assess orosensory 

perceptual processes that could infl uence 

a person’s gustatory reaction to tobacco. 

In addition, an investigator must decide 

whether to focus on researching “chronic 

tobacco use” or “dependence.” The two 

terms refer to constructs that are related 

to one another, but are nevertheless 

distinct, and have important implications 

for dependence assessment. The focus in 

this chapter is largely on the assessment of 

nicotine dependence per se.

Construct Validation

The term dependence denotes hypothetical 

variables or constructs. A construct has 

been defi ned as “some postulated attribute 

of people, assumed to be refl ected in test 

performance.”2(p283) Test performance is 

broadly defi ned and may comprise any 

characteristic or behavior of the person 

that can be measured and that is thought to 

refl ect the construct. Thus, the hypothesized 

features of nicotine dependence should be 

designed to explain a set of observations 

that are thought to constitute important 

outcomes or manifestations of dependence. 

These outcomes might include the 

diffi culty that smokers have in quitting, the 

escalation of smoking over time, craving 

and withdrawal, and so on. The process of 

attempting to both uncover the nature of 

a construct and accurately measure it is 

known as construct validation. This chapter 

uses the construct validation approach as 

an interpretive structure or metatheory to 

guide a discussion of the available research 

and theory regarding nicotine dependence. 

The construct validation approach 

typically starts with a set of behaviors or 

outcomes that an investigator wishes to 

explain. The behaviors or outcomes are 

usually of social or clinical importance and 

serve as criteria in construct validation 

research (fi gure 3.1). The investigator 

then hypothesizes a set of features and 

processes that seem to account for the 

outcomes; these are construct properties.

Finally, the investigator must select or 

develop two sorts of assessments: those that 

measure the construct properties sensitively 

(i.e., the assessment instrument) and those 

that tap the outcomes of the construct. 

Thus, the construct validation approach 

involves identifying (1) a set of behaviors 

or outcomes to be explained (the criteria), 

(2) hypothesized features or processes 

that are thought to produce the outcomes 

(these features or processes are actually 

the mechanisms of the targeted construct, 

i.e., dependence), and (3) measurement 

strategies that accurately assess the 

construct (processes) and its manifestations 

(i.e., the criteria; fi gure 3.1). The construct 

validation approach should have two 

important payoffs: it should inform people as 

to the nature of the construct, and it should 

simultaneously allow them to measure the 

construct accurately.

A construct validation approach is most 

needed when there is no single adequate 

measure of an entity;3

 in such cases, the 

investigator must use multiple measures as 
76

3. The Nicotine-Dependence Phenotype

Figure 3.1 Nomological Net: Evaluation Context for a Model of Dependence and Its Relation 

to Genetic Variants

Heavy,

Pervasive

Use

Expectancies of

Drug Effects

Preoccupation

with Drug Use

Impulsivity Use Despite

Physical Harm

Use Despite

Social Sanctions Neuroticism

Tendency

to Relapse

= Core

 criteria

= Secondary

 criteria

= Related

 constructs 

= Items to tap

 dependence criteria

Moderation

by age of

regular

tobacco

exposure

Genetic Variant 

Tolerance to

Aversive Effects

Withdrawal

Genetic Variant Genetic Variant 

Items Addressed:

1. Latency to smoke in

the morning

2. Peak smoking rate

(cigarettes/day)

Items Addressed:

1. Response to severity

of withdrawal

symptoms upon

reducing or quitting

smoking

Dependence

Items Addressed:

1. History of long-term 

successful cessation

in a small number of

attempts

2. Reported cessation

difficulty
77

Monograph 20. Phenotypes and Endophenotypes

a means of estimating a person’s standing 

on the targeted construct. The notion 

is that agreement or consensus across a 

group of related but imperfect indicators 

will yield better construct estimates than 

would the use of any single measure. Thus, 

a construct is a latent variable inferred 

from variables that can be directly observed 

(manifest variables—that is, the construct 

assessment or test). There would be no 

need to assess nicotine dependence as a 

latent variable if one could directly measure 

the pathological processes that cause it. 

For example, a defi nitive diagnosis of 

hypertension can be made in response to 

elevated blood pressure. In the absence of 

direct assessment of disease processes or 

pathognomonic signs, multiple converging 

measures can enhance diagnostic inferences. 

The construct validation approach requires 

several distinct but interrelated questions 

to be addressed. For example, what are 

the criteria or outcomes that a nicotinedependence measure should be able to 

predict? Just as the construct of gravity is 

invoked to explain the behavior of falling 

bodies, how can nicotine dependence be 

designed to explain certain clinical and 

societal phenomena? Figure 3.1 provides 

examples of core and secondary criteria 

that a model of nicotine dependence 

might comprise. Core criteria are those 

that are societally and clinically essential 

for the construct measures to explain 

(account for); secondary criteria are those 

that may provide useful information 

about the construct but are of somewhat 

lesser importance. The model depicted 

in fi gure 3.1, as an example, posits that, 

although nicotine dependence should be 

refl ected in positive expectations about 

nicotine effects, the model’s ability to 

predict relapse is more important.

A construct validation approach is a 

theory-based approach to epistemology. 

An investigator should select a dependence 

measure (items on a test) that accords with 

the investigator’s theory of dependence: 

it would measure those variables that 

refl ect, in the investigator’s opinion, the 

presence and magnitude of the critical 

underlying features or mechanisms of 

nicotine dependence. The theory also should 

explain why those hypothesized features 

or processes affect both the dependence 

measure as well as the criteria. In sum, in 

the construct validation approach, a test is 

a measure of a mechanism or cause, such as 

a measure of blood pressure, that predicts 

and explains a person’s status on a set of 

socially or clinically important criteria 

(e.g., risk of stroke, heart disease, need 

for treatment). And, if the measure of the 

mechanism does indeed predict the criteria, 

the researcher not only validates the test but 

also simultaneously supports the theoretical 

model of the studied disorder. Finally, the 

researcher would like to see that the test 

has discriminative validity; that is, it is 

most sensitive to the particular construct 

that is targeted (nicotine dependence) and 

less sensitive to related, but not central, 

constructs (e.g., regular smoking, problems 

caused by tobacco use).

Note that the construct validation approach 

is somewhat different from alternative 

approaches that view addiction as a social 

construction that cannot be verifi ed or 

evaluated on the basis of relations with 

objective criteria.3

 In the customary 

treatment, addiction and dependence are 

viewed as equivalent to one another (but not 

equivalent to physical dependence, which 

is inferred from a withdrawal syndrome). 

Moreover, this treatment recognizes that 

dependence is a construction based upon 

social and theoretical perspectives, as 

does the approach advocated by West.3

The construct validation approach, however, 

illustrates how to evaluate the validity of 

one’s strategy for measuring dependence 

on the basis of the empirical relations 

stipulated by a guiding theoretical model. 

Therefore, it permits different investigators 

to hold very different views of dependence, 
78

3. The Nicotine-Dependence Phenotype

but at the same time, it provides a logical 

system for the simultaneous evaluation 

of both the theoretical and measurement 

models of dependence.

Most attempts to measure nicotine 

dependence have not used a formal 

construct validation approach. The fi eld has, 

in general, attempted to measure nicotinedependence criteria directly rather than 

attempting to measure the mechanisms 

or processes of nicotine dependence. 

For example, this is the approach modeled by 

the DSM and other diagnostic assessments. 

Such syndromal assessments are atheoretical 

and tend to focus on observable outcomes 

of a disorder. One reason for this approach 

is, no doubt, that investigators had only 

inchoate notions of dependence mechanisms 

or features and thus were unprepared 

to assess dependence mechanisms more 

directly. In addition, the purpose of such 

clinical instruments typically is to detect 

suffering and compromised function, not 

to assess causal infl uences. Regardless of 

the reasons, investigators have usually tried 

to measure general outcomes or criteria 

of severe nicotine dependence rather than 

mechanisms.

Distal Measures 

of Dependence

Figure 3.2 depicts a “watershed” model of 

disorder. This model assumes that different 

etiologic paths may lead to clinical levels 

of symptomatology. Approaches may focus 

on end points that vary in terms of their 

proximity to specifi c genetic variants in the 

causal chain of the disorder. This model 

will be discussed in greater detail later. 

Figure 3.2 Watershed Model of Gene-to-Phenotype Infl uence

Genetic influences

Clinical symptoms and societal impacts

“Proximal” phenotypes

reflecting specific

endophenotypes

“Distal,” downstream phenotypes

shared by all affected persons 

Dimensions

Distal Proximal

Proximity to

Genetic Influence

Quantitative

Differences

Qualitative

Differences

Specificity vs.

Universality
79

Monograph 20. Phenotypes and Endophenotypes

At this point it is worth noting that most 

nicotine-dependence assessments, thus far, 

have focused on assessing the “mature” or 

distal nicotine-dependence phenotype; that 

is, they have measured phenomena that are, 

no doubt, distant from, and nonspecifi c to, 

many of the underlying causal processes 

(including genetic variants) that contribute 

to the disorder. This has important 

implications for how nicotine dependence 

and genetic infl uences on it are viewed. 

As noted, most research on nicotine 

dependence has used self-report measures 

that tap distal end products of dependence 

processes (e.g., smoking a great deal, being 

unable to quit) that have social and clinical 

import. These measures include scales 

contained in nosologies such as the DSM,

as well as brief questionnaires such as the 

Fagerström Test for Nicotine Dependence 

(FTND).4

 In addition, researchers have 

used subsets of items or individual items 

from such scales (e.g., the Heaviness of 

Smoking Index [HSI]).5 Such measures 

may have poor signal-to-noise ratios 

relative to biological mechanisms that 

underlie nicotine dependence and that 

may sensitively refl ect status on particular 

genetic variants; that is, they may refl ect 

numerous infl uences and are most likely 

causally remote from basic biological 

mechanisms of nicotine dependence 

(see fi gure 3.2). Despite these limitations, 

such distal measures have yielded clues as 

to the nature of nicotine dependence and 

its genetic infl uences.

The distal measures considered initially in 

this section (e.g., the FTND and psychiatric 

diagnostic criteria such as those in the 

DSM-IV1* elicit information regarding the 

general consequences or characteristics of 

nicotine dependence. Thus, these measures 

elicit information about how much people 

smoke, whether they experience withdrawal 

symptoms or craving, whether they tend 

to return to tobacco use once they stop, 

whether they have trouble controlling 

tobacco use, and so on. These measures 

were designed to capture major clinical 

manifestations of addiction,3

 not to assess 

features of dependence with strong genetic 

association.

Fagerström Measures

The Fagerström Tolerance Questionnaire 

(FTQ),6 and measures derived from it, 

were intended to be unifactorial measures 

of nicotine dependence. These measures 

make up the FTQ itself, as well as the 

six-item FTND,4 and the two-item HSI.7

These measures are based on the construct 

of physical dependence, which was 

hypothesized to include facets such as the 

need to smoke early in the morning to 

alleviate overnight withdrawal, the need to 

smoke numerous cigarettes per day, and the 

invariance of smoking behavior—that is, 

smoking even when one is ill.6

Two questions on the FTND (i.e., questions 

1 and 4) and the two questions of the HSI 

assume a pattern of daily smoking. It is 

very likely that scores on these items will 

have reduced validity if used with nondaily 

smokers.

Compared with the FTQ, the FTND has 

demonstrated better psychometric properties 

such as internal consistency.8–10 However, 

these improved reliability coeffi cients are 

still low8,11 and are below traditionally 

accepted standards for clinical use (a = .80).12

Some studies show that the FTND has a 

two-factor structure, suggesting that it does 

*

The Cigarette Dependence Scale is also designed to assess a single factor of dependence. (See Etter, J. F., 

J. Le Houezec, and T. V. Perneger. 2003. A self-administered questionnaire to measure dependence on 

cigarettes: The cigarette dependence scale. Neuropsychopharmacology 28 (2): 359–70.) At present, 

there is too little evidence on this scale to permit its evaluation with regard to genetic mapping.
80

3. The Nicotine-Dependence Phenotype

not measure a unitary construct of physical 

dependence.8,9,13–16 Factor analytic research 

tends to show that even if more than one 

factor is obtained, the two factors are 

highly correlated.13,17 Interitem correlations 

also reveal that not all items are highly 

related (r = .06–.39).18 The various factor 

analytic studies differ in terms of factoritem linkages.8,13,16 However, the weight of 

the evidence suggests the existence of two 

factors, with one of the factors suggesting 

a pattern of compulsive smoking, and 

the other factor refl ecting what is termed 

“morning smoking” (e.g., whether one 

smokes more in the morning than at other 

times). The items that typically load on the 

compulsive smoking factor are those that 

assess the number of cigarettes smoked per 

day, time to fi rst cigarette, and diffi culty 

refraining from smoking when ill. There is 

some variability in which specifi c items load 

on this compulsive smoking factor, no doubt 

because the factors are intercorrelated and 

some items are highly correlated with both 

factors.13,16 What is clear is that, in general, 

the fi rst principal component or main factor 

is the compulsive smoking factor and that 

it accounts for the lion’s share of predictive 

validity of the FTND.8 Latent class analyses 

suggest that the FTND ranks smokers in a 

manner that corresponds fairly well to an 

empirically derived method.19

The HSI comprises only two items, which 

limits the relevance of internal consistency 

estimates. However, zero-order correlations 

between the two items in the measure 

indicate moderate levels of association 

(e.g., r’s ≈ .30).18 Both of these items tend 

to load statistically on a factor typically 

labeled “compulsive smoking.” 

The FTND and HSI predict both behavioral 

and biochemical indices of smoking 

(e.g., carbon monoxide [CO], cotinine, 

lifetime amount smoked).4,5,7,14,20,21 This 

should not be surprising, given that the 

FTND and HSI directly assess smoking 

heaviness. However, it is encouraging to 

note that smokers are able to estimate 

their amount of smoking as indexed by 

biochemical tests in response to single 

items (e.g., “How many cigarettes/day do 

you smoke?”). The FTND has demonstrated 

an ability to predict cessation outcomes in 

smoking cessation studies.18,22–25 However, 

the HSI appears to account for much of 

the predictive validity of the FTND.5,18,26

Population-based studies conducted in 

Australia, Canada, the United Kingdom, 

and the United States found that the two 

HSI items (number of cigarettes smoked per 

day and time to fi rst cigarette [TTFC] in the 

morning) were the strongest predictors of 

quitting.27,28 Furthermore, later research 

has shown that a single item on both the 

FTND and HSI, the TTFC, predicts relapse 

vulnerability as well as, or better than, much 

longer multidimensional instruments.18

Additional population-based research shows 

that a single item on the HSI (TTFC) is 

highly effective in predicting the likelihood 

of future cessation.18 Finally, latent class 

analyses suggest that the TTFC is highly 

informative for discriminating empirically 

derived classes.19

The DSM and the International 

Statistical Classifi cation 

of Diseases and Related 

Health Problems

Two different diagnostic systems commonly 

are used to diagnose tobacco dependence, 

and both typically are considered to be 

unidimensional measures of tobacco 

dependence. One is the DSM-IV,1 which is 

based on an empirically driven, syndromal 

medical model, rather than on a theoretical 

model of dependence. The second is the 

International Statistical Classifi cation of 

Diseases and Related Health Problems, 

Tenth Revision (ICD-10),29 an international 

diagnostic classifi cation system that came 

into use in World Health Organization 

member states in 1994. Most of the extant 

research has utilized DSM criteria and 
81

Monograph 20. Phenotypes and Endophenotypes

will be the focus of this chapter’s review 

of diagnostic classifi cations of tobacco 

dependence.

Structured clinical interviews based on 

the DSM and the ICD, such as the World 

Mental Health Survey Initiative version of 

the Composite International Diagnostic 

Interview (CIDI)30 or the National Institute 

of Mental Health Diagnostic Interview 

Schedule (DIS), comprise a series of 

branching questions that are aimed at 

eliciting information about features relevant 

to nicotine dependence; they have been 

translated into various languages and used 

in multiple population-based studies.31–34

Data on the reliability and structure of 

diagnostic interview measures of nicotine 

dependence arise from studies using 

face-to-face administration strategies. 

Therefore, the following conclusions cannot 

necessarily be generalized to a different 

administration format. There is evidence 

that the various structured diagnostic 

measures yield reliable diagnoses as assessed 

by test-retest reliability (j = .63),35 j = .88,33

and j = .73.36 One factor analysis indicated 

that responses to the CIDI had a strong 

single-factor structure,37 although other 

factor analyses of the structured diagnostic 

items found that a two-factor structure was 

a better fi t.38–40

Evidence suggests that the small set of 

dichotomous DSM items can distinguish 

between light versus heavy smoking.37

An epidemiological study found that the 

DSM (third edition revised [DSM-III-R] ), 

as assessed by the DIS, was a signifi cant, 

though weak, predictor of cigarette 

abstinence over one year, but that the FTND 

was a better predictor, and that number 

of cigarettes smoked per day was the best 

predictor.26 Another study showed that 

DSM-IV diagnoses of nicotine dependence 

predicted heaviness of use and cessation 

outcome in a population-based study of 

college students.41 Several studies have 

shown that DSM-IV nicotine-dependence 

diagnosis is associated with greater risk 

of psychiatric comorbidities in adults and 

youth.35,42,43 In sum, there is substantial 

evidence that DSM and ICD diagnoses are 

meaningfully related to smoking heaviness 

and psychiatric status.

Multidimensional Measures 

of Nicotine Dependence

Multidimensional measures offer some 

promise in elucidating the nature of 

dependence and in helping to refi ne the 

phenotype so as to foster more informative 

genetic mapping. Figure 3.2 shows a 

watershed model of how genetic infl uences 

may affect a complex phenotype across 

ontogeny.44 This model conveys the notion 

that a fi nal disease phenotype may be the 

product of diverse types of infl uences, and 

that some infl uences may be operative for 

some people, while other infl uences are 

operative for other people. However, these 

DSM-IV Criteria

1. Tolerance

2. Withdrawal

3. Use in larger amounts/over longer 

period than intended

4. Persistent desire/unsuccessful efforts 

to cut down or quit

5. Great deal of time using/recovering

6. Important activities given up

7. Continued use despite emotional/

physical problems

HSI Questionsa

1. “At present, how long after waking 

do you wait before having your fi rst 

cigarette (in mins)?”

2. “How many cigarettes do you smoke 

per day at present?”

a

HSI questions from Heatherton and 

colleagues.5(p793)
82

3. The Nicotine-Dependence Phenotype

diverse “feeder stream” infl uences are 

somewhat compensatory and interchangeable 

with respect to contributing to “downstream” 

processes that produce mature features 

of nicotine dependence. It is conceivable 

that these infl uences may exert additive or 

interactive effects and that they might be 

differentially sensitive to environmental 

events. Such infl uences could be viewed 

as refl ecting the myriad infl uences that 

constitute quantitative trait loci.

The assumption is, however, that there 

is a “fi nal common pathway” (ultimate 

downstream) set of processes and symptoms 

that is manifest once a disorder achieves 

some level of severity. Thus, at clinical levels 

of a disorder, sufferers appear similar to 

one another, but this similarity may mask 

diverse etiologic paths. Diagnostic measures 

of dependence such as the DSM, FTND, and 

HSI are intended to index the “fi nal common 

pathway” of nicotine-dependence processes, 

rather than the “feeder streams” (relatively 

discrete pathways) that may individually 

and collectively infl uence the disorder and 

that (in theory) share stronger relations 

with particular genetically infl uenced 

biological processes. These measures can 

be labeled “distal” in that they are relatively 

remote from the genetic variants that the 

phenotypic measures are intended to refl ect.

The FTND, HSI, and the DSM-type diagnostic 

measures were intended to measure a 

unitary, synthetic clinical manifestation of 

dependence (albeit, the measures may not 

in fact be unidimensional). The two distal 

measures reviewed below are intended to 

be multifactorial. They were developed in 

response to emerging data that nicotine 

dependence itself appears to comprise 

multiple dimensions.45 The relevance of 

such measures to genetic mapping is that 

they contain heterogeneous items, which 

may help elucidate the sorts of items that 

are, and are not, sensitive to genetic variants 

(permit distillation of the phenotype). 

Further, if nicotine dependence involves 

multiple components, assessment of each 

dimension may permit the detection of 

subgroups of smokers who show unique 

or qualitatively different manifestations 

of dependence. Subgroups that differ 

on the basis of relevance or intensity of 

dependence dimensions are termed mature 

subphenotypes in this chapter. The concept 

of the “mature subphenotype” is based on 

the notion that some groups of smokers 

may differ qualitatively in dependence such 

that different measures of dependence 

are more sensitive to dependence in one 

subgroup versus another (fi gure 3.3). 

This would occur if the processes that 

contribute to the fi nal common pathway 

of dependence (e.g., tolerance, tendency to 

relapse back to tobacco use, activation of 

incentive structures in response to nicotine 

anticipation) do not completely mask 

diversity in etiology.

Two relatively new multifactorial scales have 

been developed that are designed to identify 

somewhat distinct dimensions of nicotine 

dependence. These are reviewed here with 

an eye toward evaluating their basic features 

and their potential utility in genetics 

research. It is important to bear in mind 

that neither of the reviewed instruments 

was designed specifi cally to assess nicotinedependence dimensions for the purpose 

of molecular genetics research; other 

goals were operative, such as isolating the 

relatively distinct motivational dimensions 

of dependence.

Nicotine Dependence Syndrome Scale

The Nicotine Dependence Syndrome 

Scale (NDSS) is a 19-item self-report 

measure developed to assess nicotine 

dependence on the basis of Edwards’s 

(1976) theory of the alcohol dependence 

syndrome.46 Edwards’s theory identifi ed 

the core elements of alcohol dependence as 

(1) narrowing of the repertoire of drinking 

behaviors, (2) increased salience of drinkseeking behaviors, (3) increased tolerance, 
83

Monograph 20. Phenotypes and Endophenotypes

(4) occurrence of withdrawal symptoms, 

(5) use of alcohol to avoid or relieve 

withdrawal, (6) subjective awareness of a 

compulsion to drink, and (7) a tendency 

to resume alcohol use after abstinence.47

The NDSS comprises fi ve different 

subscales: Drive—craving, withdrawal and 

smoking compulsions; Priority—preference 

for smoking over other reinforcers; 

Tolerance—reduced sensitivity to the 

effects of smoking; Continuity—regularity 

of smoking rate across place and time; and 

Stereotypy—the invariance of smoking. 

The NDSS has the advantages of a clear 

theoretical basis and evidence46 that 

shows that either the whole scale, or some 

individual subscales, are signifi cantly related 

to nicotine-dependence indices such as 

smoking heaviness, withdrawal measures, 

and other dependence measures such as the 

FTND. In addition, the NDSS can distinguish 

between chippers (chronically light smokers) 

Figure 3.3 Etiologic Path and Locus of Phenotypic Assay

Etiologic periods Assessment construct 

Preceding

exposure period 

Peridrug exposure,

“etiologic assays”

Mature

subphenotypes

Temperament

Endophenotypic measures

Response to

nonpharmacological

rewards 

Trajectories of use

Transitional phenotypes 

Rate of tolerance

development 

Subtypes

Subclasses

Time/Distance, developmental trajectory 

Positive

response

to bupropion

Positive

response

to nicotine

replacement

Strong

incentive

sensitization

Severe

physical

dependence

Note. Phenotypes may be dynamic (e.g., responses to environmental challenges such as drug dose, withdrawal). Some processes 

may exert effects across etiologic periods.
84

3. The Nicotine-Dependence Phenotype

and heavy smokers.48 Thus, it would be 

possible to use these subscales to select 

subgroups or types of smokers or to relate 

genetic variants to a continuous dependence 

subdimension represented by these scales.

Although the NDSS has promise as a 

measure of nicotine-dependence subtypes, 

the scale could be improved further for 

genetics research for the following reasons: 

(1) Some individual subscales have modest 

internal consistencies (or reliabilities), 

which undercut their use in measuring 

discrete dependence subtypes or elements.49

(2) Some subscales comprise quite disparate 

types of items. For example, the Drive 

subscale mostly comprises items that 

measure withdrawal, but not exclusively. 

The somewhat heterogeneous item sets 

were apparently needed because of the 

breadth of the constructs targeted. This 

item heterogeneity also accounts, no doubt, 

for the modest internal consistencies 

of some of the subscales. Differential 

weighting of items via factor scores does 

not apparently mitigate this effect.49,50

(3) Like the other distal measures previously 

reviewed, the NDSS subscales do not appear 

to tap constructs that are tightly linked to 

relatively discrete, fundamental biological 

processes that should, themselves, refl ect 

variation in the genetic variants of interest. 

Although the NDSS does not provide specifi c 

indices of particular biological processes, 

its subscales assess relatively discrete 

dimensions of nicotine dependence; these 

may shed light on the core features of 

dependence, which might, in turn, promote 

more effective assessment strategies.

Wisconsin Inventory of Smoking 

Dependence Motives

The Wisconsin Inventory of Smoking 

Dependence Motives (WISDM) is a 68-

item scale that comprises 13 subscales.50

The primary goal in developing the WISDM 

was to create a theory-based research 

instrument to identify fundamental 

motivational processes that ultimately 

infl uence dependence criteria (e.g., relapse, 

withdrawal severity). In other words, the 

scale is designed to measure motivational 

infl uences that lead to dependence features 

or criteria.

The WISDM comprises the subscales 

listed in table 3.1 (table 3.1 also provides a 

rationale for each subscale). The WISDM 

has some advantages for genetics research. 

One is that the overall scale score and many 

of the subscales predict classic dependence 

criteria such as self-administration rate, 

withdrawal magnitude, and relapse.50

Moreover, each subscale has acceptable 

reliability. This means it may be used 

profi tably as an independent assay of a 

particular smoking motive. 

The subscales were designed to refl ect 

discrete motives that drive tobacco use in 

addicted individuals. Some of these motives 

may be associated with particular biological 

response systems and structures that may 

suggest genes that deserve investigation. 

As an example of this, the Taste/Sensory 

Processes subscale was developed because 

research showed the importance of 

gustatory and sensory cues in motivating 

smoking.51,52 Taste sensitivity, especially 

the ability to taste bitter fl avors, is related 

to phenylthiocarbamide (PTC) haplotype 

status.53 Subsequent research has shown 

that smokers who achieve higher scores 

on the Taste/Sensory Processes subscale 

tend to possess PTC haplotypes associated 

with an inability to taste bitter tastes.54

In other words, those smokers who can 

taste bitter fl avors are less likely to smoke 

for taste reasons. The importance of 

specifi city in the assessment of dependence 

dimensions is suggested by the fi nding 

that the Taste/Sensory Processes subscale 

became more highly associated with PTC 

status once nontaste items were removed 

from the subscale. Thus, the relation 

depended on taste per se, rather than other 

orosensory factors.
85

Monograph 20. Phenotypes and Endophenotypes

Other subscales, such as Cue Exposure/

Associative Processes and Positive 

Reinforcement, were designed to refl ect 

activity in dopaminergic structures, such 

as the nucleus accumbens, that impart or 

mediate the processing of the incentive 

value of drug cues as well as drug induced 

pleasure or reward.63,71–73 Such responses 

may account for the potent impact of drugpaired cues on nicotine motivation.74

Although the WISDM subscales hold some 

promise for refl ecting relatively discrete 

dimensions of nicotine dependence, 

like the NDSS subscales, they also have 

signifi cant limitations. For example, 

psychometric analysis has shown that 

some of the subscales are highly correlated 

with one another and load onto a common 

factor. In other words, these subscales 

may measure a fi nal common pathway 

(fi gure 3.2) more than a discrete dependence 

motive. In addition, although an attempt 

was made to link the targeted discrete 

motives with underlying biology, for many of 

the subscales the self-report dimensions are, 

Table 3.1 Subscales of the Wisconsin Inventory of Smoking Dependence Motives

Subscale Construct rationale: evidence base

Affiliative Attachment Use of addictive drugs, including nicotine, is motivated by the impact of the drug on 

social affection systems and is manifest as emotional attachment to the drug.55,56

Automaticity Drug self-administration and supportive information processing becomes 

automated.57,58

Behavioral Choice/Melioration Drug use is inversely proportional to constraints on access to drug and to other 

reinforcers.59

Cognitive Enhancement Nicotine enhances cognitive processing or via suppression of withdrawal.60 This 

may be especially important to certain populations.61

Craving Craving reflects not only magnitude of physical dependence50 but also error 

signals indicative of conflict over drug-use decisions in such structures as the 

anterior cingular cortex.62

Cue Exposure/Associative 

Processes

Conditioned responses to drug cues activate drug motivational processing and 

encourage self-administration and may reflect activity in dopaminergic incentive 

systems.63

Loss of Control Strong dependence motivation is related to the perception of loss of volition.

Negative Reinforcement Drug use is motivated by strong negative affect occurring via either withdrawal 

or stressors; source of negative affect may be linked with relevant processing 

substrata such as the amygdala or extended amygdala.57

Positive Reinforcement Drug use is motivated by desire to experience mood enhancement (rush, high) 

even in the absence of distress; may be linked to mesotelecephalic structures 

such as the nucleus accumbens.64,65

Social/Environmental Goads Social cues associated with drug use can increase drug motivational processing 

or self-administration.55,66

Taste/Sensory Processes Taste and orosensory processes play a strong motivational role in smoking; may 

be linked to the phenylthiocarbamide haplotype and associated gustatory sensory 

systems.51,52,67

Tolerance Rate of tobacco clearance and tolerance to nicotine actions may permit high levels 

of self-administration; may be linked to nicotine metabolism or distributional 

tolerance in the brain.68,69

Weight Control Nicotine appears to lower body weight set-point, and this may motivate nicotine 

self-administration,70 especially among those seeking weight loss; may be related 

to sensitivity to nicotine’s effects on hypothalamic weight regulatory centers or to 

systems that affect taste hedonics.
86

3. The Nicotine-Dependence Phenotype

no doubt, only remotely related to activity 

in any particular biological system. Finally, 

it seems clear that even with 13 subscales, 

there are potentially discriminable 

dimensions that should be assessed but 

are not. For example, one could easily 

argue that one useful subphenotype might 

be the anticipatory excitement or arousal 

that precedes drug use in a motivated, 

deprived smoker.75 Another target might 

be the anhedonia of withdrawal—that is, 

the inability to experience pleasure during 

withdrawal, which may be related to elevated 

reward threshold in mesotelencephalic 

dopaminergic systems.76 Finally, it is unclear 

that some of the individual subscales of 

either the NDSS or the WISDM share 

strong relations with classic dependence 

criteria (e.g., relapse).46,50 Thus, the 

construct validity of each subscale must 

be demonstrated before strong inferences 

regarding nicotine dependence can be 

made (fi gure 3.1).

In summary, both multifactorial measures 

of nicotine dependence (i.e., the NDSS 

and the WISDM) have some promise for 

measuring relatively discrete dimensions 

of nicotine dependence, and these measures 

will, no doubt, prove useful as predictors 

of relapse, withdrawal, and other nicotinedependence criteria. In addition, some 

particular subscales may have potential 

utility in molecular genetics research. 

However, some of the subscales are not 

ideal for this purpose. The constructs 

they target cannot be tightly related to 

an underlying biology, and some of the 

subscales appear to refl ect broad, rather 

than specifi c, dimensions of nicotine 

dependence. 

Smoking, Initiation of Smoking, 

and Distal Measures 

of Dependence

Distal measures have shown that nicotine 

dependence is under considerable genetic 

control. Heritability of DSM-III-R nicotine 

dependence was estimated to be 60% in 

a sample of Vietnam veteran male twins77

and 44% in Minnesota adolescents.78

Moreover, biometric modeling suggests 

an overlap (60%) in the genetic substrata 

for smoking versus the development 

of nicotine dependence79 but also a 

moderate residual genetic effect for 

nicotine dependence (22%). In general, 

such modeling shows proportionally 

larger genetic contributions to nicotine 

dependence than to smoking or smoking 

initiation and smaller environmental 

infl uences.80–86 Thus, evidence shows 

overlap in the genetic infl uences for 

initiation of smoking and the development 

of nicotine dependence, and genetic 

infl uence that is unique to dependence.87,88

Accordingly, distal measures have the 

potential to identify nicotine-dependence 

phenotypes that do, and do not, have 

associations with causal genetic variants.

Epidemiological research using distal 

measures also has revealed that heavy 

smoking and nicotine dependence can 

be extremely common, at times almost 

modal, across large populations. Thus, 

it is possible, or even likely, that large 

portions of the population possess 

genes that promote or permit such 

phenotypes. Under such a circumstance, 

it may be a more viable strategy to 

search for genetic infl uences that 

discourage or prevent regular tobacco 

use, rather than to identify the potentially 

ubiquitous variants that permit nicotine 

dependence; that is, variants that 

discourage nicotine dependence might 

have greater discriminative effi ciency. 

There is precedent for this in the alcohol 

literature in which polymorphisms of the 

ALDH2*2 and ADH1*2 alleles appear to 

affect alcohol metabolism. A proposed 

mechanism of infl uence for these alleles 

is that they code for increased levels 

of the metabolite acetaldehyde, which 

may impart unpleasant peripheral effects 
87

Monograph 20. Phenotypes and Endophenotypes

that discourage high levels of alcohol 

intake.89,90*

Distal measures have also revealed another 

important general feature of nicotine 

dependence: it is not equivalent to regular 

smoking per se. Indeed, epidemiological 

data show that a signifi cant proportion of 

daily smokers, perhaps one-half, do not 

warrant nicotine-dependence diagnoses.32,91

Thus, research shows that many individuals 

may engage in heavy amounts of smoking 

and yet never report having experienced 

strong withdrawal, that their smoking 

is out of control, or that they have given 

up important activities because of their 

smoking (i.e., with dependence indexed 

by DSM or ICD-10 type of criteria). These 

observations are consistent with the 

notion that there appear to be degrees 

of severity in nicotine dependence even 

among inveterate smokers, at least to the 

extent that commonly used distal measures 

have some validity as measures of nicotine 

dependence. The researcher’s task is to 

determine how to measure dependence in 

a manner that refl ects its biological and 

genetic infl uences.

In summary, distal measures have revealed 

that (1) nicotine dependence is under 

considerable genetic control, (2) it is 

equivalent to neither regular smoking 

nor smoking initiation, and (3) its genetic 

origins are somewhat distinct from those 

that support or permit the development 

of regular smoking. These observations 

suggest that while regular smoking may 

be a component of nicotine dependence, 

nicotine dependence assays must go 

beyond assessments of smoking features 

per se to capture important dimensions of 

the construct.

A Core Dimension of Nicotine 

Dependence

One of the anomalies in dependence 

assessment is that although dependence 

measures often show poor internal 

consistency and poor relations with one 

another,33,49 factor analyses show that such 

measures often load highly onto common 

factors; even when the factor analyses 

suggest multiple factors, the factors are 

highly intercorrelated.46,50,92 In addition, 

zero-order correlations often show strong 

interrelations among particular dependence 

measures.18,50

When items derived from the FTND, HSI, 

or diagnostic criteria are factor analyzed, 

they show that measures that tap into 

heaviness of smoking, and pervasiveness of 

smoking across time or occasion, tend to 

load most highly on principal component 

or initial factors. For instance, Muthén 

and Asparouhov39 factor analyzed items 

tapping DSM-IV1 symptoms of dependence 

in a general population sample. This 

research showed that symptoms were 

best accounted for by a multidimensional 

model. The pattern of covariation among 

the symptoms yielded a fi rst factor with 

relatively high loadings for items assessing 

“tolerance,” “larger amounts,” and “time 

spent using.” “Tolerance” refl ects taking 

increased amounts of nicotine/tobacco to 

achieve desired effects. “Larger amounts” 

refl ects self-administering nicotine in larger 

amounts, or over longer periods of time 

than intended. “Time spent using” refl ects 

the amount of time the individual expends 

in actual smoking, procuring cigarettes, 

and so on. Thus, the fi rst factor seems to be 

highly related to the amount smoked and the 

amount of time spent smoking. The second 

*

Of course, protection versus vulnerability is a relative thing; one haplotype could be viewed as a 

vulnerability factor, or its complement could be considered a protective factor. However, it may be 

possible to show both protective and vulnerability effects versus a “neutral” haplotype. More to the 

point, the search for protective factors (versus vulnerabilities) might suggest different phenotypes, 

different genetic variants, different measurement cut-scores, and different experimental designs.
88

3. The Nicotine-Dependence Phenotype

factor was somewhat more related to 

“persistent desired/unsuccessful efforts 

to cut down or quit,” and “continued use 

despite emotional/physical problems.”39(p1052)

Confi dence in this solution is bolstered by 

the fact that it was obtained in three separate, 

relatively large groups of individuals 

(Ns = 8,552–26,946). Thus, the three types of 

items that loaded onto the fi rst factor refl ect 

heaviness or consistency of use across time.

Other factor analytic studies have generated 

complementary patterns of fi ndings. Lessov 

and colleagues92 constructed biometric 

models with a sample comprising male 

and female dizygotic and monozygotic 

twins (as well as different-gender dizygotic 

twins) who all said that they had either 

experimented with, or tried, smoking 

(N = 6,249). As part of this research, the 

authors factor analyzed individual nicotinedependence items obtained from the DSM-IV

dependence criteria as well as the two items 

that constitute the HSI derived from the 

FTND.5

 The authors reported a two-factor 

solution. The fi rst factor consisted of the two 

HSI items (TTFC and cigarettes smoked per 

day [CPD]) and the DSM-IV Tolerance item 

(largest number of cigarettes smoked in a 

single day). The second factor consisted of 

DSM items concerning withdrawal, smoking 

more than intended, experiencing diffi culty 

Inferences about Dependence Derived from Distal Measures

Many distal measures of nicotine dependence have modest psychometric properties (i.e., reliability 

and validity). For example, scales such as the Fagerström Tolerance Questionnaire (FTND)a

 and 

scales comprising Diagnostic and Statistical Manual of Mental Disorders (DSM) items tend to have 

modest internal consistencies (scores on the items are not highly correlated with one another).b

There is also copious evidence that some dependence scales (e.g., the FTND and the DSM criteria) 

are not highly correlated with each other.b,c,d A number of factors could account for this lack of 

agreement—for instance, measures or items that are poorly worded, a lack of variance in terms of 

the assessed construct in the sampled populations, error that differentially affects the measures, 

and the fact that the different assessments are measuring somewhat different constructs. Curiously, 

the lack of agreement of dependence measures is actually quite useful. It allows one to determine 

which types of measures are highly related to each other, and to dependence criteria, and which 

are not. This provides some insight into core features of dependence and permits the distillation of 

essential features. Data are considered in this chapter that link particular measures of dependence 

to principal dependence criteria (e.g., as depicted in fi gure 3.1).

It appears that the evidence of agreement or commonality among dependence measures is 

much greater than the evidence of disagreement or inconsistency. Moreover, the evidence of 

disagreement can be accounted for by logical distinctions among dependence constructs that 

are targeted by measures and by the fact that different measures or items are susceptible or 

vulnerable to different sources of error.

a

Heatherton, T. F., L. T. Kozlowski, R. C. Frecker, W. Rickert, and J. Robinson. 1989. Measuring the heaviness 

of smoking: Using self-reported time to the fi rst cigarette of the day and number of cigarettes smoked per day. 

British Journal of Addiction 84 (7): 791–99.

b

Piper, M. E., D. E. McCarthy, and T. B. Baker. 2006. Assessing tobacco dependence: A guide to measure 

evaluation and selection. Nicotine & Tobacco Research 8 (3): 339–51.

c

Breslau, N., and E. O. Johnson. 2000. Predicting smoking cessation and major depression in nicotinedependent smokers. American Journal of Public Health 90 (7): 1122–27.

d

Moolchan, E. T., A. Radzius, D. H. Epstein, G. Uhl, D. A. Gorelick, J. L. Cadet, and J. E. Henningfi eld. 2002. 

The Fagerström Test for Nicotine Dependence and the Diagnostic Interview Schedule: Do they diagnose the 

same smokers? Addictive Behaviors 27 (1): 101–13.
89

Monograph 20. Phenotypes and Endophenotypes

quitting, and smoking despite physical or 

psychological problems. Again, the fi rst 

factor extracted refl ects the heaviness and 

pervasiveness of smoking. It is notable 

that the TTFC item loaded more highly 

on the fi rst factor than on the second 

factor, upon which the withdrawal item 

loaded. This suggests that the TTFC item 

refl ects a pattern of heavy smoking rather 

than severe withdrawal after overnight 

abstinence. These results are consistent 

with results obtained when FTND items are 

factor analyzed (results discussed earlier). 

That is, the principal component of such 

items is consistent with a pattern of heavy, 

pervasive smoking, and the TTFC item tends 

to load on this fi rst factor.8,13 Research using 

multidimensional scales sheds additional 

light on these fi ndings (discussed below).

Other research using distal instruments has 

yielded similar fi ndings, with items refl ecting 

heavy use constituting an initial factor 

explaining the majority of variance in the set 

of items or instruments,38 while secondary 

factors refl ect variance related to withdrawal 

severity, instrumental reasons for smoking 

(e.g., to suppress withdrawal), or an inability 

to quit. Although there is some variability in 

the results of such factor analyses (e.g., the 

principal axis and maximum likelihood 

factor analytic solutions in Breteler and 

colleagues13), the bulk of research suggests 

that most of the variance in diagnostic 

criteria and the FTND is captured by items 

that refl ect a pattern of heavy smoking.

There are various reasons that such a 

pattern of results might not be interesting 

or important. For instance, it may be that 

items refl ecting smoking heaviness have 

the greatest representation on the factoranalyzed93 instruments, and this accounts 

for their high loadings on the initial factors. 

Or, it may be that items asking about 

smoking heaviness or pervasiveness are 

simply easier for smokers to answer than 

are other items, and therefore, they can be 

answered with relatively little error. This 

might occur because such items have some 

fairly discrete referents (e.g., number of 

cigarettes smoked per day, time of day of 

initial smoking). However, while relative 

saturation of true score variance may account 

for relatively high levels of covariance among 

such items, such an effect, by itself, could 

not account for the substantial evidence 

that items that tap smoking pervasiveness 

and heaviness have impressively strong 

and consistent relations with some critical 

dependence criteria and also appear to refl ect 

dispositions that are highly heritable.

The two items making up the HSI, 

in particular, have shown impressive 

relations with a host of behavioral and 

biochemical measures that refl ect smoking 

heaviness.4,5,7,26,93 Interestingly, these items 

have also been more consistently predictive 

of the ability to quit smoking than perhaps 

any other set of dependence measures.18

For instance, in a Transdisciplinary Tobacco 

Use Research Center (TTURC) paper,18

the TTFC item in the FTND was shown to 

be superior to multiple alternative measures 

in predicting the likelihood of successful 

cessation. In fact, this item showed greater 

predictive validity than any instrument with 

which it was compared (e.g., the NDSS and 

its subscales, the WISDM and its subscales, 

and the FTND total score and any other 

single item from that scale). Interestingly, 

the TTFC item predicted relapse vulnerability 

better than did biochemical measures of 

smoke exposure such as CO, suggesting that 

it may refl ect behavioral and motivational 

components of self-administration not 

entirely captured by drug dose delivered 

per se. These fi ndings on the TTFC are 

impressive in that they were demonstrated 

in multiple clinical samples. In addition, 

this item was shown to predict cessation 

likelihood in population samples gathered 

in four different countries.18 While there is 

substantial evidence that the TTFC item is 

consistently predictive of quitting likelihood, 

there is also substantial evidence that both

items of the HSI have impressive predictive 
90

3. The Nicotine-Dependence Phenotype

validities, relative to other instruments, 

for both cessation likelihood and indices of 

tobacco consumption.3,7,8,26

There is also substantial evidence that 

measures of smoking heaviness are highly 

heritable. For instance, in a study by Lessov 

and colleagues,92 items that loaded most 

highly on the fi rst factor (DSM-IV Tolerance, 

TTFC, and CPD) had somewhat higher 

heritability estimates (additive genetic 

effects: variance components = .68–.73 as 

estimated via the univariate model) than did 

the other items. In addition, a later paper 

by Haberstick and colleagues estimated 

heritability coeffi cients for the FTND, the 

HSI, and individual items on those scales.94

The sample comprised 1,154 young adults 

between 18 and 25 years of age who were 

from full-sibling, half-sibling, and twin 

pairs. Multivariate modeling revealed a 

highly heritable factor (76%), the strongest 

salient of which was time to fi rst cigarette 

in the morning (i.e., TTFC). The HSI also 

generated a substantial heritability estimate 

(61%). These estimates agree with those 

generated by other studies.88 Haberstick and 

colleagues94 conclude that the TTFC item 

assesses an “urgency” to smoke throughout 

the day and is the “single best measure 

in the FTND for examining the genetic 

contributions to nicotine dependence.”94(p663)

Therefore, the evidence suggests that items 

that tap heavy and pervasive smoking—for 

instance, smoking that begins perforce as 

soon as the individual wakens—assess a core 

feature of nicotine dependence, one that is 

highly heritable. This conclusion is buttressed 

by additional research, reviewed below, that 

uses the newly developed multifactorial 

measures of nicotine dependence. Thus, these 

measures appear to be serving one of the 

functions for which they were designed—that 

is, elucidating the nature of dependence.

As noted previously, the TTURC paper18

showed that the TTFC item was signifi cantly 

related to both cigarettes smoked per day 

and to relapse likelihood. In addition, this 

research showed that this item was highly 

correlated with a small number of subscales 

from the WISDM and NDSS nicotine 

dependence questionnaires. In particular, 

the TTFC was related to the Tolerance and 

Automaticity subscales from the WISDM 

and the Stereotypy subscale from the NDSS. 

These subscales appear to assess a pattern 

of smoking that is heavy and pervasive 

(fairly continuous across time and context) 

and that has become highly ingrained or 

automatic (i.e., does not involve conscious 

cognitive control).

It is interesting that the scales so highly 

correlated with the TTFC item are those 

that measure characteristic “late-emergent” 

dependence motives50; that is, light smokers 

are relatively less likely to endorse these 

motives, relative to other sorts of motives 

(e.g., smoking for taste, smoking in response 

to environmental cues) than are heavy 

smokers. Figure 3.4 depicts the different 

logit curves refl ecting scores on two 

WISDM subscales, Tolerance and Social/

Environmental Goads, relative to cigarettes 

smoked per month. (The term late emergent

refers to appearance across the continuum 

of smoking heaviness; these data may not 

refl ect the order of emergence across time, 

because the data are cross-sectional and 

do not permit strong inferences about 

developmental patterns.) It is clear that the 

Tolerance subscale is relatively insensitive 

to light amounts of smoking but that scores 

increase exponentially at high smoking 

rates. (The Automaticity subscale showed a 

similar ogive pattern.50) This suggests that 

items that tap a pervasive smoking pattern 

or tendency are particularly sensitive to 

high levels of smoking.

Latent profi le modeling with the WISDM 

suggests that smoking that is heavy, 

pervasive, and automatic may be both 

necessary and suffi cient for signifi cant 

nicotine dependence. Across four 

separate samples of smokers, the WISDM 
91

Monograph 20. Phenotypes and Endophenotypes

Automaticity, Tolerance, Craving, and Loss 

of Control subscales (table 3.1) characterized 

a unique smoker profi le.95 Some smokers 

had high scores on only these four subscales 

(fi gure 3.5). All other smokers showed 

subscale elevations that were of relatively 

equal magnitude across the subscale types. 

The results show that no group of smokers 

was signifi cantly dependent without 

having elevations on these four subscales. 

Piper and colleagues,95 therefore, labeled 

these subscales as “primary smoking 

motives scales.”

The latent profi le analysis discussed above 

constitutes a person-centered analysis that 

highlights subscales that may be necessary 

for signifi cant dependence development. 

However, these results, by themselves, 

do not fl esh out the construct validity of the 

amalgam of these subscales as a synthetic 

assay of nicotine dependence. To do this, 

Piper and colleagues95 conducted variablecentered analyses in which status on the 

four primary motives scales was related to 

meaningful indices of nicotine dependence 

(see Muthén96 for a discussion of the blending 

of person- and variable-centered approaches). 

The ability of these scales to predict these 

dependence criteria was compared to the 

predictive validities of the other WISDM 

subscales (labeled the “secondary motives” 

scales). Specifi cally, the relative predictive 

validities of each set of subscales were 

established.95 It was then determined 

whether the secondary dependence motives 

Figure 3.4 Logistic Regression Curves Predicting Scores on the WISDM Subscales 

from Cigarettes Smoked per Month: Examples of an Early-Emergent Motive 

(Social/Environmental Goads) and a Late-Emergent Motive (Tolerance)

Log (cigarettes/month + 1)

Mean item score

0 1 2 3

7

6

5

4

3

2

1

Early-Emergent Motive

Late-Emergent Motive

Note. This fi gure illustrates the different curves of the early-emergent motives and late-emergent motives, using the Social/

Environmental Goads and Tolerance scales as prototypes of each motive, respectively. The early emergent motive has a higher 

intercept at low rates of smoking than does the late-emergent motive and has consistent linear growth as smoking rates increase. 

The late-emergent motive is not endorsed by light smokers but as smoking rates increase, there is an exponential increase in the 

rate of endorsement. WISDM = Wisconsin Inventory of Smoking Dependence Motives.
92

3. The Nicotine-Dependence Phenotype

could account for signifi cant variance in 

dependence criteria once the primary motives 

scales were entered into regression models.

These variable-centered analyses indicate 

that a limited subset of WISDM subscales, 

the primary motives scales, carry the 

lion’s share of predictive validity regarding 

nicotine dependence. The primary 

dependence motives scales (Automaticity, 

Craving, Loss of Control, and Tolerance), by 

themselves, were highly predictive of such 

important dependence criteria as ability 

to maintain abstinence, scores on other 

dependence measures (i.e., the FTND), 

smoking heaviness (i.e., cigarettes smoked 

per day, baseline CO), smoking history 

(i.e., age of initiation, age of daily smoking, 

number of previous quit attempts), and 

the magnitude of the increase in craving 

that occurred immediately postquit.95

The relative validity of these scales versus 

the secondary scales can be gauged from 

analyses in which the mean score for the 

primary dependence motives was entered 

into prediction models along with the 

mean score for secondary motives. Such 

analyses revealed that the predictive validity 

of the primary dependence motives scales 

was little affected by the addition of the 

secondary scale composite in the models. 

To an impressive degree, the primary scales 

remained consistently predictive of the 

dependence criteria in the multivariate 

Figure 3.5 Latent Class Results from the Combined Data Set

0

1

2

3

4

5

6

7

Automaticity

Loss of control

Craving

Tolerance

Affiliative attachment

Behavioral choice/Melioration

Cognitive enhancement

Cue exposure/Associative process

Negative reinforcement

Positive reinforcement

Social or environemental goads

Taste/Sensory processes

Weight control

Very low Low Unique Medium High

Motive endorsement magnitude (Likert scale)

Note. The profi les generated via latent profi le analyses of some 2,256 smokers from different data sets that contained both 

treatment seekers and general smoker populations. The same basic profi le patterns were present in all four samples of smokers 

when they were analyzed separately.
93

Monograph 20. Phenotypes and Endophenotypes

models, while the predictive relations of the 

secondary scales became weak or anomalous 

(negatively related to criteria). Finally, 

it is important to note that these four 

primary motives scales are highly coherent, 

with an average intercorrelation of about 

r = 0.77. Ironically, the multidimensional 

instruments might have contributed to the 

overall understanding of dependence by 

illuminating the “fi nal common pathway” 

of dependence, rather than by assessing 

subphenotypes.

The notion that heavy, automatic smoking is 

indicative of dependence fi ts with other data 

in the fi eld and is in accord with the view 

that, as dependence becomes entrenched, 

control over smoking is shifted from 

cognitive-control systems to automatic 

motor-control systems that execute selfadministration without such control and, 

perhaps, without awareness.18,58,62 Thus, 

as smoking becomes ubiquitous and 

automatic, smokers may believe that it has 

become noncontingent with instrumental 

uses.13,97,98 Considerable basic behavioral and 

neuropharmacological research supports 

the notion that dependence involves a shift 

from instrumental, goal-driven behavior to 

automatized, habitual response patterns. 

As Everitt and Robbins99 note in an 

infl uential review in 2005: 

In theoretical terms, it seems reasonable 

to characterize such compulsive behavior 

as a maladaptive stimulus-response habit 

in which the ultimate goal of the behavior 

has been devalued so that the behavior 

is not directly under the control of the 

goal.... Rather, responding is governed by a 

succession of discriminative stimuli, which 

also function—when they are presented as 

a consequence of instrumental responses—

as conditioned reinforcers. Hypothetically, 

such stimulus-response associative 

(‘habit’) learning occurs in parallel with 

instrumental action-outcome learning 

but, with extended training, eventually 

dominates behavioral output.99(p1485)

Thus, smokers with this unique profi le may 

represent highly dependent individuals in 

whom this process is more advanced or who 

are simply more aware of its occurrence 

(and therefore, rate secondary motives 

relatively low).

The Craving subscale of the WISDM was 

identifi ed as one of the primary dependence 

motives. This is compatible with the 

notion that as addictive behavior becomes 

automatic, urges are caused by blockade of 

the automatized drug self-administration 

sequence.58,62 That is, the co-occurrence 

of strong craving and high levels of 

automaticity is supported by theory that 

links the two constructs mechanistically.

Another source of evidence further 

buttresses the notion that a pervasive 

pattern of heavy smoking indexes 

dependence. A study by Goedeker and 

Tiffany100 used taxometric procedures to 

determine whether nicotine dependence 

constitutes a taxon (best conceptualized as a 

category of individuals qualitatively different 

from other individuals) or a continuum 

in which individuals lie on a relatively 

continuous range. The authors used data 

from the 2003 National Survey on Drug 

Use and Health (N = 11,441) and employed 

multiple criteria to assess the structure 

of dependence. The results supported the 

notion that nicotine dependence can be 

viewed as a taxon—a qualitatively discrete 

category. Approximately 48% of those 

smoking in the last 30 days belonged to 

this taxon, and members of this taxon 

were characterized by high scores on the 

FTND TTFC question (smoking relatively 

soon after awakening), by smoking a large 

number of cigarettes per day, and by high 

scores on three of the NDSS subscales: 

Drive, Continuity, and Tolerance. Drive 

taps craving intensity; Continuity taps 

smoking patterns that are consistent over 

time—that is, patterns that show little 

variation due to situational or temporal 

factors; and Tolerance assesses the tendency 
94

3. The Nicotine-Dependence Phenotype

or ability to smoke heavily without adverse 

impact. Thus, the taxon appears to be 

distinguished by measures that tap the 

same sorts of constructs that characterize 

the necessary and suffi cient features of 

dependence as revealed in the latent profi le 

research (i.e., the primary dependence 

motives)95 and that are effective at predicting 

relapse.18,95 Thus, the hypothesized taxon 

is characterized by smoking that is heavy 

and pervasive throughout the day and by 

strong urges.

Additional evidence also supports the 

fundamental relation between a pervasive 

drug-use pattern and dependence. 

For example, Shiffman and Paty101 found 

that chippers (those who have established 

a stable pattern of infrequent smoking) 

and heavy smokers are distinguished by 

the fact that the former evidence smoking 

that is contextually discriminated, whereas 

the latter show patterns that are relatively 

heavy and invariant across time and place. 

In addition, as noted earlier, examination 

of posterior probabilities generated by 

latent class analyses show that items that 

assess pervasive, heavy smoking tend to 

distinguish classes that are highest in 

dependence.34,39,102

The reviewed research suggests that 

the highly dependent person is not best 

distinguished by endorsements of smoking 

as a means of controlling affect, reducing 

withdrawal, experiencing a “high,” or 

controlling weight.18,95 This perspective 

meshes nicely with a great deal of behavioral 

animal research that shows that early in 

the course of drug self-administration the 

organism’s behavior is highly affected by 

the potency of the reinforcer. However, 

with extensive drug self-administration 

experience, the animal’s behavior seems 

more stimulus driven and noncontingent 

with the reinforcer.103,104 The data from 

the latent profi le study95 suggest that 

some smokers may become aware of this 

noncontingency and can report on it.

The data from the TTFC paper,18 the 

taxometric paper,100 and the latent profi le 

studies95 all suggest that dependence 

is characterized by smoking that is not 

highly discriminated on contextual and 

temporal cues. One possibility is that truly 

dependent smoking takes on a life of its 

own and proceeds without cueing. This, 

however, would fl y in the face of a great 

deal of evidence that shows that cues can 

powerfully affect self-administration and 

other indices of drug motivation,57 and it 

would contradict the notion that addictive 

behavior refl ects strong stimulus-response 

mapping.104 Instead, it seems much more 

likely that smoking is highly cue dependent, 

but that the cueing is often relatively 

inaccessible to awareness. This might 

occur because the cues are interoceptive 

(e.g., refl ective of falling levels of drug in 

the body), or are exteroceptive, but the 

cue–self-administration response sequence 

has become proceduralized and unfolds 

with little awareness.57,58,62

One of the roles of dependence assessments 

is to provide insight into the nature of 

dependence processes—insights that 

extend beyond those afforded by the direct 

assessment of dependence criteria per se49

(fi gure 3.1). The review of the evidence 

presented above suggests that the use 

of multidimensional dependence scales 

may be achieving this goal by casting in 

greater relief those behaviors and motives 

that are most tightly linked to dependence 

criteria. It is clear that the distal measures 

implicated in dependence in the above 

research (e.g., the Automaticity and 

Tolerance subscales) do not truly assess 

underlying dependence processes per se, 

but they may serve as manifestations of 

such processes—for instance, implicating 

mechanisms such as the strengthening of 

stimulus-response mapping.

The above analysis suggests that existing 

distal measures that are especially sensitive 

to pervasive, automatic, and heavy 
95

Monograph 20. Phenotypes and Endophenotypes

smoking probably tap processes of greater 

biological signifi cance and relevance to 

dependence than do measures that tap social 

or functional consequences of smoking 

or an awareness of such consequences. 

Therefore, measures such as the FTND 

that more uniformly tap pervasive, heavy 

smoking (and presumably related scales 

from multidimensional instruments)97,98

should yield stronger and more informative 

genetic relations than does the collection of 

DSM criteria. Evidence from behavioral and 

molecular genetics studies is beginning to 

support this hypothesis.92,94,105–107

Covariation Among Measures 

of Dependence

If it is indeed the case that nicotine 

dependence can be assessed reliably by 

a relatively coherent set of items, this 

does not explain the lack of consistent 

covariation among dependence measures 

that was noted above.26,33,49,98 This lack of 

covariation is likely to be caused by several 

factors. First, the various dependence 

indices were developed with guidance 

from very different conceptual models of 

dependence (see West3), and as the construct 

validation model makes clear (fi gure 3.1), 

different conceptual models will generate 

very different types of items. For instance, 

the model that guided the development of 

the DSM measures defi ned “dependence” 

as a socially defi ned phenomenon indexed 

by a collection of indicants that, together, 

refl ect severity. It is an implicit assumption 

of this model that the features should not 

necessarily be highly coherent in that they 

are intended to convey additive, and not 

necessarily redundant, information that 

indexes extent of behavioral, functional, 

and social disruption. Thus, such measures 

were designed, in part, to refl ect awareness 

of diverse types of social and functional 

disruption, which could be viewed as 

criteria, or socially important outcomes, 

of dependence, rather than dependence 

processes per se (fi gure 3.1). One reason 

that criteria may have modest relations 

with measures of dependence mechanisms 

(e.g., selected subscales of the NDSS or 

WISDM) is that measures of social or 

functional disruption are highly dependent 

upon the social and life context of individuals 

and the functional demands placed upon 

them. And, interestingly, as West3

 points 

out, the diagnostic items used in the DSM

and other major diagnostic inventories were 

designed originally to diagnose other types of 

addictive disorders in which the drug leads 

to greater social and functional impairment. 

Thus, these items may be of limited use 

in assessing mechanisms of tobacco 

dependence.3 This means that very different 

sources of error and extraneous infl uences 

likely affect criterion measures than affect 

core dependence measures—for example, 

patterns, types, and intensities of smoking.

The same principle applies to other criteria 

such as relapse. It should be of no surprise 

that there are inconsistent or modest 

relations between dependence measures and 

relapse likelihood in that relapse likelihood 

is strongly related to such variables as 

whether smoking is permitted in the home, 

the educational and income status of the 

individual, and the density of smoking 

cues in the person’s environment.18,108–111

Similarly, dependence measures often 

show modest relations with withdrawal 

severity.112–114 Withdrawal severity has 

been shown to refl ect such environmental 

features as the presence of smoking 

cues.115 Many criteria measures are highly 

sensitive to contextual infl uences but are 

also necessary to make inferences about 

the construct validity of any dependence 

measure (fi gure 3.1). This raises questions 

about how to distill variance within 

criteria so they are maximally sensitive to 

biological or genetic infl uences and how 

to model dependence in the face of modest 

intercorrelations among the criteria.

There are other reasons that dependence 

measures may show modest relations 
96

3. The Nicotine-Dependence Phenotype

with one another. For instance, some 

measures may be more sensitive to 

dependence at different periods or 

intensities in its development.49,116,117

This is consistent with evidence that 

dependence items show different patterns 

of endorsement across different latent 

classes that are organized along an intensity 

dimension;102,118–120 that is, some types of 

items are more sensitive to low versus 

high levels of dependence, and other items 

are more sensitive to severe dependence. 

Thus, disagreement might be attributed 

to differences in the “diffi culty level” of 

an item.12

Integrating Phenotypic Measures 

with Analytic Strategies

Selecting good measures of the phenotype 

is just one step in examining the relation 

between the phenotype and genetic variants 

(single nucleotide polymorphisms, alleles). 

One must decide how to use such measures 

so they sensitively capture differences 

among individuals in terms of nicotine 

dependence. This demands an integration 

of both theoretical and psychometric 

considerations.12

One strategy often used in genetics 

research is the construction or selection 

of groups that are intended to be maximally 

dissimilar in possession of targeted genetic 

variants. Ideally, one attempts to construct 

groups on the basis of phenotypic features, 

so one group has all the genetic infl uences 

that promote a disorder, while the other 

group has none (an “extreme” groups 

approach).

The information reviewed above suggests 

strategies that might be used to construct 

such groups. For example, it suggests that 

a group possessing the genetic complement 

for nicotine dependence should show 

high scores on the scales and items that 

refl ect heavy, pervasive, automatic smoking 

(table 3.2). A critical question is whether 

the investigator needs to use additional 

criteria to determine membership in this 

group. For instance, the investigator must 

decide whether to make membership or 

nonmembership contingent upon factors 

such as additional dependence dimensions 

(in addition to the primary dependence 

features discussed above), the presence 

of person factors associated with type 

or severity of dependence (psychiatric 

comorbidity, gender), and factors that 

Table 3.2 Dimensions on Which Groups Might Be Constructed to Contrast Putative Highand Low-Dependence Predispositions

High genetic proneness Low genetic proneness

■ Smokes within 30 minutes of awakening

■ Lifetime peak smoking >20 CPD

■ Severe withdrawal upon reducing or quitting smoking

■ Reports great difficulty in quitting/failure to quit in 

multiple attempts

■ Daily smoker for at least 20 years

■ High scores on WISDM subscales: Loss of Control, 

Automaticity, Tolerance, Craving

■ High scores on NDSS subscales: Tolerance, 

Continuity, Drive

■ Smokes after 30 minutes of awakening

■ Lifetime peak smoking <15 CPD

■ Mild or no withdrawal upon reducing or quitting 

smoking

■ Reports ease of quitting in few attempts/successful 

long-term abstinence

■ Initiated smoking before the age of 16 years

■ Smoked daily for at least 1 year 

■ Currently a nonsmoker for at least 2 years

■ Low scores on WISDM subscales: Loss of Control, 

Automaticity, Tolerance, Craving

■ Low scores on NDSS subscales: Tolerance, 

Continuity, Drive

Note. CPD = cigarettes smoked per day; WISDM = Wisconsin Inventory of Smoking Dependence Motives; NDSS = Nicotine 

Dependence Syndrome Scale.
97

Monograph 20. Phenotypes and Endophenotypes

might moderate the relation of genetic 

variants with phenotypes.

Assessment of Complementary 

Dimensions of Dependence

Considerable evidence attests to the 

centrality of the primary smoking factors 

that have been highlighted (pervasive, 

automatic, and heavy smoking). In keeping 

with this, it may be benefi cial to supplement 

the measures listed in table 3.2 with some 

additional measures that tap the same 

construct to achieve a more reliable index of 

this central construct. Thus, one might use 

biochemical measures of self-administration 

(serum cotinine levels), metabolic tolerance 

or clearance,121 and perhaps, laboratory 

measures of the automaticity of information 

processing related to self-administration 

and related constructs.58

In addition, there may be some value in 

including other dependence characteristics 

to supplement the assays of primary 

factors. Ideally, one would wish to select 

assessments that are associated with fairly 

severe dependence and that are fairly highly 

heritable. Further, it seems best to select 

assays that are not highly infused with error. 

The considerations listed above suggest at 

least two types of measures that might be 

combined with measures of the primary or 

core factors to yield groups extreme across 

the breadth of the nicotine-dependence 

measurement domain—namely, measures 

that tap diffi culty or inability to cut down 

or quit (control) smoking and severity 

of withdrawal symptoms (e.g., related 

DSM-type items). Including assessments 

of withdrawal and ability to cut down 

or stop among phenotypic measures is 

supported by three considerations: (1) Items 

tapping these factors are conceptually and 

psychometrically distinct from measures 

of smoking heaviness and pervasiveness 

(core features). For instance, they tend 

to load on different factors than do items 

measuring the primary factors.39,92 In fact, 

the evidence is compelling that while 

measures of smoking heaviness and 

pervasiveness do account for variance in 

withdrawal severity and quitting ability, 

much of the variance in these criteria is 

orthogonal to heaviness indices.46,49 If these 

dependence criteria are critical to the 

construct, they should be refl ected in group 

composition. (2) Items tapping ability to quit 

or cut down and tapping withdrawal severity 

tend to show high levels of endorsement 

by the most dependent smokers as revealed 

by latent class analyses.39,92,102 (3) At least 

with regard to global ratings of withdrawal 

intensity, there is evidence of only partial 

overlap with the heritability of items 

that measure the primary dependence 

factors.34,122,123 Therefore, the inclusion of 

such criteria for extreme group membership 

(i.e., ratings of ability to cut down or 

quit and withdrawal measures) might 

permit a more comprehensive gleaning 

of dependence-relevant genetic variants. 

The addition of measures of withdrawal 

and ability to cut down or stop smoking 

means that the criteria for extreme group 

membership would assess the fi ve nicotinedependence symptoms that Lessov and 

colleagues92 found had “high phenotypic 

and genetic factor loadings as well as high 

heritability: tolerance, time to fi rst cigarette 

in the morning, number of cigarettes 

smoked per day, withdrawal, and diffi culty 

quitting.”92(p875) Moreover, items tapping 

these dimensions would correspond to the 

dimensions that Furberg and colleagues124

identifi ed as distinguishing latent classes 

of regular cigarette smokers: smoking 

heaviness and latency to smoke upon 

awakening, diffi culty or inability to cut 

down or quit smoking, and severity of 

withdrawal symptoms.

Consideration of Person Factors

It is clear that smokers are a heterogeneous 

group. Moreover, some individual differences 

or person factors (stable traits), other than 

nicotine dependence per se, might refl ect 
98

3. The Nicotine-Dependence Phenotype

a different type or severity of nicotine 

dependence. Do such differences have 

implications for genetic mapping? Might 

such factors be relevant to assessing the 

nicotine-dependence phenotype or suggest 

particular genetic targets for association 

with phenotypes? For example, phenotype 

measures could be modifi ed to target the 

assessment of such person factors. Or, a 

researcher could take such person factors 

into account when constructing extreme 

groups. Thus, questions relating to person 

factors really speak to the notion of whether 

such factors organize types of nicotine 

dependence.

It is tempting to imagine that psychiatric 

comorbidity may refl ect affective or 

motivational processes that create protean 

complexity in the nature or structure of 

nicotine dependence. There is certainly 

evidence that comorbidity affects the 

manifestation of nicotine dependence 

(hence its infl uence on nicotine dependence 

as depicted in fi gure 3.1). For example, 

nicotine dependence is highly comorbid with 

other psychiatric disorders. Rates of current 

alcohol abuse or dependence, mood disorder, 

anxiety disorder, or personality disorder are 

two to three times more prevalent among 

smokers than among nonsmokers.35,43

Not only are psychiatric comorbidities 

more common among smokers, but also 

smoking and nicotine dependence are also 

especially prevalent among those with such 

comorbidities.35,43,125,126 Thus, a person with 

a psychiatric disorder is much more likely 

to have nicotine dependence, and vice versa. 

In addition, some data suggest that the 

presence of comorbidity not only indexes an 

increased likelihood of nicotine dependence 

but also a more severe form. For example, 

data show that smokers with psychiatric 

comorbidities smoke a disproportionately 

large number of cigarettes given their 

prevalence in the population,35 suggesting 

heavier smoking among those with 

comorbidities. Further, analytic strategies 

such as latent class analysis show that 

the presence of comorbidities helps 

defi ne the classes that generate the most 

extreme scores on nicotine-dependence 

assays.21,35,43,102,127 Finally, there is evidence 

of substantial shared genetic infl uence on 

the regular use of tobacco and alcohol and 

on dependence on both substances.77,128,129

Despite all the evidence linking 

externalizing disorders with nicotine 

dependence, there is little evidence that 

nicotine-dependence assessments, or 

construction of extreme groups, should 

be modifi ed on the basis of comorbidity. 

Moreover, there are reasons for assuming 

that while comorbidity is associated with 

dependence severity, it does not index a 

qualitatively distinct subtype of dependence. 

In other words, even if such comorbidity 

affects the severity of dependence, this effect 

is captured by standard dependence assays. 

This is suggested by the studies showing 

that standard dependence measures are 

sensitive to comorbidity-linked increases in 

dependence.35,102 In addition, as noted earlier, 

some evidence suggests that personality 

dimensions associated with comorbidity 

are more tightly linked with smoking 

initiation than with severity of nicotine 

dependence.60,82,130,131 Thus, personality 

could affect exposure to environmental 

factors related to initiation (e.g., peers).132–137

Finally, individuals with no comorbidity 

become nicotine dependent. The goal of 

phenotypic refi nement in the pursuit of 

genetic correlates dictates that investigators 

focus on phenotypic features that are 

necessary or suffi cient, and psychiatric 

comorbidity is neither. 

If psychiatric comorbidities are not 

intrinsic to dependence and do not organize 

meaningful nicotine-dependence taxa, what 

might explain the associations between 

comorbidity and nicotine-dependence 

measures? The available evidence makes 

various causal relations possible. It is 

possible that a personality dimension 
99

Monograph 20. Phenotypes and Endophenotypes

such as behavioral undercontrol, which 

is associated with externalizing disorders 

and traits of risk-taking and impulsivity, 

increases the likelihood of initiating the 

use of diverse substances. Such traits may 

also increase exposure to environmental 

factors such as availability, modeling, 

and other peer group infl uences. Such 

environmental infl uences could account for 

why comorbidities seem to be associated 

with a particularly severe form of nicotine 

dependence.21,35,43,102,127 That is, the genetic 

diathesis for personality and/or comorbidity 

would infl uence initiation of nicotine use 

and, in addition, might yield environmental 

exposures that foster greater nicotine use 

over the lifetime (e.g., poor educational 

achievement, socializing with smoking or 

substance-using peers); hence, an active 

gene-environment correlation may be at 

work. It is, of course, possible that while 

psychopathology and associated personality 

dimensions do not produce different types 

of nicotine dependence, other factors do. 

For instance, there is evidence that gender 

interacts with nicotine-use motivation such 

that men tend to smoke more for nicotine 

receipt per se but women smoke more for 

nonpharmacological factors.138 It may be 

that taste and correlated environmental 

factors have been rendered more reinforcing 

in women because of nicotine’s ability to 

modulate incentive value.139 Thus, it is 

possible that, among men, dependence 

phenotypes are more highly related to 

genes that infl uence direct pharmacological 

reinforcement. Among women, dependence 

may be more highly related to genes that 

infl uence incentive sensitization and 

related associative processes. At present, 

there is insuffi cient evidence to determine 

the relevance of such evidence for genetic 

mapping.92 Similarly, there is insuffi cient 

evidence, at present, to support tailored 

assessment on the basis of race.140,141 In sum, 

as opposed to the case of a disorder such 

as schizophrenia, for which there is some 

consensus that the diagnostic category 

comprises multiple distinct disorders, each 

perhaps with unique genetic infl uences, 

it does not appear necessary at this point 

to try to target specifi c subtypes of severe 

nicotine dependence.

Preserving a Role for the Environment

Earlier, this chapter alluded to the 

important role of the environment in 

creating error in measures of nicotine 

dependence. For example, living in a 

home that has a smoking ban may cause 

one to smoke later in the day than would 

otherwise occur. This may bias TTFC items 

as measures of nicotine dependence.18 It is 

possible that such home smoking bans 

might ultimately reduce dependence, 

but this is merely a hypothesis, one that 

might require some time to unfold. 

Another example is smoking restrictions 

at work, which might reduce the number 

of cigarettes per day that individuals can 

smoke. Indeed, increasing environmental 

restrictions on smoking may affect the 

validity of inferences based upon all of the 

measures of heavy, pervasive, uniform, 

and automatic smoking that undergird 

the central core of available measures 

of nicotine dependence. In addition, the 

presence of smoking cues and cigarette 

availability might also lead to a greater 

likelihood of relapse to smoking in any given 

quit attempt. Researchers may take possible 

environmental infl uences into account in 

several ways. For instance, the investigator 

might use environmental features when 

constituting extreme groups, ensuring that 

members of both low- and high-dependence 

groups have few environmental restrictions 

on their smoking. Or, the researcher could 

use statistical procedures to perform partial 

variance in dependence assays. These 

options are discussed further below.

Environmental infl uences may play an 

additional role. Increasing attention is 

being directed at the notion that genetic 

variants may interact with environmental 

events or characteristics.142 Indeed, there 
100

3. The Nicotine-Dependence Phenotype

is evidence that such interactions may be 

relatively common. Moffi tt and colleagues143

list multiple examples in which the 

relation of genotype with the phenotype 

varied signifi cantly as a function of some 

environmental factor.

In considering whether to pursue 

investigation of potential gene-environment 

interactions, it is important to consider 

whether there are good candidate 

environmental factors that signifi cantly 

affect the disorder under study. Moffi tt 

and colleagues143 suggest that good 

candidates for environmental moderators, 

or risk factors (i.e., “environmental 

pathogens”), are variables that exert 

signifi cant main effects on the disease 

severity or occurrence. This principle 

suggests numerous environmental factors 

that might moderate gene-environment 

interactions—for example, early exposure 

to smoking peers, chronic environmental 

stress/poverty, intrauterine exposure to 

nicotine, traumatic stress such as childhood 

or adolescent sexual assault/abuse, and 

alcohol intake.60,142,144 Thus, intrauterine, 

developmental, and adult-onset events, 

both episodic and chronic, could serve 

as pathogens.

One candidate moderator of particular 

interest is age of onset of signifi cant 

nicotine exposure. There is substantial 

evidence for a sensitive developmental 

period after which tobacco exposure 

is relatively unlikely to yield nicotine 

dependence. Human research shows that 

an early onset of smoking is associated 

with greater consumption of cigarettes in 

adulthood,145–148 a relative inability to quit 

smoking,28,145,149,150 and a more severe form 

of nicotine dependence.34,102,151–153 Animal 

research shows that nicotine exposure 

during adolescence induces long-lasting 

biochemical, anatomical, physiological, and 

behavioral changes that differ markedly 

from those seen with adult exposure.154–157

In addition, adolescence is both a period 

of heightened sensitivity to nicotine’s 

rewarding actions and a period of decreased 

sensitivity to nicotine’s aversive actions.158–162

Evidence suggests that adolescent exposure 

in rats results in increased nicotine 

self-administration that persists into 

adulthood.163 Therefore, it is possible that 

genetic variants for nicotine dependence will 

become much more strongly associated with 

nicotine-dependence phenotypes among 

those individuals with early, rather than late, 

exposure (perhaps smoking before versus 

after 16 years of age).34 In fact, later research 

shows a signifi cant interaction between 

haplotypes of the CHRNA5-A3-B4 subunit 

cluster and age of smoking onset, such 

that there are strong associations between 

haplotypes and a rather comprehensive 

set of dependence measures, including the 

FTND, WISDM, and relapse latency.164,165

It is possible that a variable might not 

produce a signifi cant main effect on 

nicotine-dependence measures but could 

still produce signifi cant moderation 

(e.g., if the pathogen were active in the 

presence of a relatively rare allele/genotype). 

Therefore, one should also assess and test 

pathogens that are substantively important. 

For example, some theories emphasize 

the importance of stress as an important 

modulator of reinforcement via psychomotor 

stimulants,166,167 and other theories 

emphasize the role of peers, especially in 

terms of initiation.60 In short, selection 

of candidate environmental variables in 

moderation models requires an examination 

of empirical evidence as well as theory.

Of course, moderated analyses include genepathogen relations as well as pathogenphenotype relations. That is, one must 

decide not only which pathogen(s) to 

explore, but also which genetic variants 

would, in theory, affect behavioral or 

biological processes that are differentially 

affected by the pathogen. Therefore, one 

should have a model of the gene-pathogen 

relation that makes biological sense. Thus, 
101

Monograph 20. Phenotypes and Endophenotypes

one advantage of testing moderated relations 

is that it forces one to think deeply about 

the potential causal links between genes, 

pathogens, and the phenotype. 

Moffi tt and colleagues143 suggest a set of 

considerations to guide the selection of 

genetic variants (e.g., haplotypes, alleles) 

that might serve as good, independent 

variables in moderational models. 

For example, they note that candidate 

polymorphic variants should occur relatively 

frequently in the population. The notion 

is that if a gene exerted a powerful main

effect on a signifi cant disease process, its 

frequency would be suppressed because of 

decreased fi tness (although it is unclear 

if risk for smoking would signifi cantly 

decrease fi tness). In addition, selection 

would be aided by evidence that the 

polymorphism has effects on brain systems 

relevant to a disorder and that it affects 

reactivity to the environmental pathogen/

event under study. Thus, in the case of 

nicotine-dependence research, investigators 

using early exposure to tobacco as the 

environmental pathogen might study 

polymorphisms that are related to nicotine 

self-administration in animal research 

(chapter 4). Finally, the specifi city of a 

particular gene-pathogen-disorder relation 

can be tested by systematic substitution of 

different polymorphisms and environmental 

pathogens into the analyses.

The study of moderation is compatible 

with the study of relatively specifi c mature 

subphenotypes. For example, moderated 

relations may pertain to only a subset of 

those with nicotine dependence; that is, 

only a subpopulation should be affected 

by the targeted gene as a function of the 

environmental pathogen*

 (see table 3.3).

Thus far, researchers have not detected 

stable gene-environment interactions in 

molecular genetics investigations of nicotine 

dependence. There have been reports of an 

interaction between status on the serotonin 

transporter gene (SLC6A4) and neuroticism 

in the prediction of likelihood of being 

a smoker.168,169 However, as Lerman and 

colleagues169 suggest, the interaction may 

be attributed to distinguishing between 

more and less highly heritable forms of 

neuroticism, or it may refl ect epistatic 

effects. (See Kremer and colleagues170 and 

Gerra and colleagues171 for further research 

on 5-HTTLPR status and smoking.) 

Findings of interactions would pose 

interpretive challenges.143 For example, 

environmental exposures may appear to 

“cause” a phenotypic response, but the 

environmental exposure may occur because 

it is correlated with genes and therefore 

indexes only a third (genetic) variable effect. 

For example, reports of severe stress may 

refl ect the presence of polymorphisms 

related to neuroticism. This possibility could 

be appraised by careful assessment of the 

phenotype before and after the occurrence 

of the stress or stressor.

There are other challenges to the evaluation 

of gene-environment interactions; 

principal among them is the appropriate 

measurement and modeling of the pathogen. 

Recall measures of environmental pathogens 

may be biased by a host of memory/recall 

*

Here, the reader may justifi ably ask what is meant by a smoker “subpopulation” or mature 

subphenotype. This does not necessarily imply the existence of multiple taxa, that is, fundamentally 

different types of smokers in terms of dependence. Rather, the researcher might fi nd that some smokers 

may differ qualitatively or quantitatively in the extent to which certain processes contribute or relate to 

dependence. However, these differences do not mean that core features or manifestations of dependence 

differ. For instance, smokers may differ in the extent to which they smoke for taste factors. However, 

despite this motivational difference, dependence per se could still be registered by standard dependence 

measures. Thus, smokers may differ in taste as a motive, and this may have a distinct genetic basis,54 but 

the dependence of such smokers might still be captured well by the major distal phenotypic measures. 
102

3. The Nicotine-Dependence Phenotype Table 3.3 Causal Paths from Genetic Variant to Distal Phenotypes Nicotine reward Metabolic capacity Taste/gustatory sensitivity Incentive salience/ sensitization Cognitive control/ impulsivity Withdrawal Affective control Genetic variant candidates CHRNB2 CHRNA7 CHRNA5 CYP2D6 CYP2A6 CYP1A1 PTC DRD2 SLC6A3 DRD4 MAOA/a4 subunit

a7 subunit/GABAA

SLC6A4

Endophenotypic index candidates

Proteomic/expression patterns

Proteomic/expression patterns

Inability to taste bitter tastes

Enhanced nucleus accumbens activity to nondrug incentives

a. Behavioral 

Undercontrol

b. P300 amplitudec. Poor Stroop test 

performance

d. Attentional focus 

on dominant response

e. Poor integration of 

caudal cingulate with amygdala (via fMRI)

Information processing performance after removal of an appetitive stimulus

a. Poor fear/anger 

extinction

b. Internalizing 

symptoms upon stress exposure

c. Poor subgenual 

cingulateamygdala integration (via fMRI)

d. Stronger urges 

in response to stress

Transitional/mature subphenotypes

a. Report of nicotine 

reward (“buzz,” “rush”)

b. Rapid escalation 

of smoking upon initiation 

c. High selfadministration 

rates

a. Rapid 

development of tolerance

b. High selfadministration 

rates

a. Taste motive for 

smoking

a. Strong nucleus 

accumbens response to nicotine anticipation

a. Rapid escalation 

of smoking upon initiation

b. Externalizing 

comorbidity

Severe withdrawal symptoms upon drug abstinence

a. Relapse in 

response to stressors

b. Smoking for 

stress relief

Distal phenotypes a. Heavy, 

regular selfadministration

b. Withdrawal 

symptoms upon abstinence

c. Tendency to 

relapse

Note. fMRI = functional magnetic resonance imaging.
103

Monograph 20. Phenotypes and Endophenotypes

biases, and convenient self-report measures 

may not capture important temporal 

dynamics of such variables over time. 

In addition, one must decide at what age or 

developmental period the pathogen is most 

active. Furthermore, one must consider 

the latency between pathogen occurrence 

and its impact on the phenotype—addictive 

behavior tends to be relatively refractory 

over many years. This must be considered 

when trying to model the time course via 

which a pathogen might affect nicotinedependence markers and at what etiologic 

stages such effects would be manifest. 

Although relapse latency might be reactive 

to a phasic environmental event, other 

markers of nicotine dependence might be 

relatively refractory. Moreover, Moffi tt and 

colleagues143 note that some pathogens 

exert cumulative effects172,173—for example, 

living with a smoker. In sum, modeling the 

potential effects of a pathogen on nicotine 

dependence requires consideration of 

developmental period, etiologic period, 

the dose of pathogen needed to exert effects, 

the latency between pathogen exposure and 

disease end points, and which particular 

phenotypic features will be affected by the 

pathogen.

Improving Distal Measures

Earlier, this chapter reviewed evidence 

suggesting that existing distal measures 

have considerable potential in genetic 

mapping research. However, it is important 

that investigators be aware of the limitations 

of these measures. Such limitations not 

only should foster caution in drawing 

strong inferences regarding underlying 

mechanisms but should also serve as prods 

for the development of new phenotypic 

assessment strategies.

Although the reviewed data suggest some 

phenotype measures to use in future 

genetics research, restricting phenotypic 

measures to DSM and FTND items could 

signifi cantly handicap researchers. 

For example, use of a single item to 

measure withdrawal severity captures an 

impoverished domain of targeted constructs 

(e.g., DSM-type withdrawal items do 

not assess different types of withdrawal 

symptoms, such as urges and hunger). 

Also, different withdrawal symptoms show 

very different profi les or trajectories over 

time.174 It is unlikely that global, temporally 

remote, single-item, self-report measures of 

withdrawal can capture the distinct dynamic 

patterns of withdrawal over time—patterns 

that account for differential likelihood of 

relapse.115 Further, at present, all of the 

commonly used distal measures rely upon 

self-report, and this assessment strategy 

may be infl uenced by broad attitudes and 

verbal resemblances that have little to do 

with the biological or genetic underpinnings 

of dependence. Moreover, diagnostic 

criteria, and similar sorts of categorical 

items, were not designed to possess optimal 

scale properties or to covary highly to 

achieve internal consistency. In addition, 

if used as a categorical diagnostic index, 

the DSM generates outcomes that do not 

agree with empirically based methods of 

nicotine-dependence classifi cation such as 

latent class analysis. For example, a factor 

mixture analysis39 did not correspond well 

with the scoring rules for dependence as 

specifi ed in the DSM-IV (i.e., three of seven 

dependence symptoms must be endorsed). 

Many individuals placed in the highest 

class of dependence via factor mixture 

analysis did not satisfy the DSM threshold 

for dependence. (See Storr and colleagues19

with respect to correspondence between 

latent class analysis and FTND classifi cations 

of smokers.)

Thus, commonly used nicotine-dependence 

measures have clear limitations, which, 

in theory, should limit their effective use 

in genetic mapping. There are clearly 

opportunities for new approaches to 

dependence assessment. Obviously, one 

approach would be to develop superior 

measures of the same constructs.
104

3. The Nicotine-Dependence Phenotype

For instance, it is by no means clear that the 

wording and structure of existing items are 

ideal for genetics research. Future research 

should attempt to improve upon the existing 

dependence items by examining issues such 

as whether their response options are ideal 

and whether the questions are posed at the 

proper “diffi culty level.” It is also possible 

that some of the newer multidimensional 

dependence instruments might be 

psychometrically superior to the critical 

DSM and FTND items and assess the same 

or similar constructs.

Another strategy would be to make greater 

use of behavioral measures. For instance, 

one strategy would be to relate genetic 

variants to withdrawal data recorded 

via ecological momentary assessment 

methods or to laboratory measures such 

as compensatory behavior in response 

to nicotine restriction. These strategies 

seem to offer clear advantages. Behavioral 

measures tend to be face valid (their 

signifi cance requires little inference), can 

be measured with precision, and should be 

relatively free of certain self-report biases 

(e.g., response styles).

Although the direct assessment of behavior 

or criteria has potential, it is not yet known 

whether this approach will be superior 

or inferior to the collection of general, 

synthetic, and impressionistic measures of 

withdrawal, or ability to quit smoking, and 

so on. For instance, measures of a single 

episode of withdrawal might overweight 

the idiosyncratic events affecting that 

quit attempt episode. Thus, the more 

impressionistic, global measure might 

provide a better synthesis of withdrawal 

across time and a better index of the 

attendant subjective distress. In addition, 

some evidence suggests that some 

temporally remote assessments of smoking 

and dependence can be surprisingly 

accurate and reliable.175,176 Thus, while 

complex behavioral or laboratory measures 

hold promise, they remain undelivered 

promissory notes. Perhaps the optimal 

behavioral assays of nicotine dependence 

would require collection of data over 

long periods of time, permitting the 

statistical synthesis of data gathered across 

repeated episodes of cessation attempts 

and withdrawal. This would also permit 

extensive behavioral assessment of the 

heaviness or situational pervasiveness of 

smoking (e.g., time blocks per day in which 

no smoking occurs, average intercigarette 

interval, extent to which smoking is 

cue contingent), perhaps via ecological 

momentary assessment.177

Concerns about the sensitivity of phenotypic 

measures to underlying biology anticipate 

issues to be addressed in later sections of 

this chapter that deal with the assessments 

of intermediate phenotypes—that is, more 

focal and specifi c measures of nicotine 

dependence.

A Summary of Inferences from 

Distal Measures Research

The picture of nicotine dependence 

emerging from the reviewed research is 

refl ected in a fairly coherent set of core 

features: smoking is heavy and pervasive 

across time and place, occurs without 

signifi cant cognitive control, and is related 

to strong urges. Although such features 

appear to constitute core, relatively 

coherent elements, dependence also 

seems to be refl ected in somewhat distinct 

complementary factors. In particular, 

withdrawal severity and an inability to 

quit or cut down on nicotine or tobacco 

use seem to be important in this regard. 

Certainly, other constructs are related to 

dependence and predict dependence criteria. 

For instance, smoking for taste reasons 

(e.g., as assessed by the WISDM Taste/

Sensory Processes subscale) is signifi cantly 

related to dependence measures and to the 

likelihood of smoking.54 Yet, research does 

not suggest that such factors are necessary 

or suffi cient for severe dependence.54,95
105

Monograph 20. Phenotypes and Endophenotypes

Such assessments may, in fact, refl ect 

“upstream” vulnerabilities to initiate or 

escalate smoking (fi gure 3.2), but they 

do not strongly determine variance in 

“downstream” levels of severity.

New Directions 

for Phenotypic 

Research: Beyond 

Distal Measures

The measures discussed above are all 

“distal” measures—that is, measures 

refl ecting dependence indices that are 

likely causally remote from the biological 

processes activated by relevant genetic 

variants (fi gure 3.2). As fi gure 3.2 makes 

clear, mature dependence phenotypes may, 

as implied by the developmental concept of 

equifi nality, constitute one or more rather 

homogeneous outcomes of diverse etiologic 

paths. The use of distal measures may 

limit and distort the appraisal of nicotine 

dependence for a variety of reasons.

It is possible that many of the conclusions 

adopted in the earlier discussion are largely 

a product of the measures available to us. 

For instance, the evidence that nicotine 

dependence can be well modeled by a 

dimension of heavy, pervasive smoking may 

be due, in part, to the global and categorical 

nature of distal measures. If measures are 

employed that refl ect specifi c candidate 

biological pathways to nicotine dependence, 

it is possible that our view of nicotine 

dependence might change. For example, 

as fi gure 3.2 depicts, there may be diverse 

genetically infl uenced factors that promote 

and permit tobacco use, and these ultimately 

summate to yield nicotine dependence. 

It is conceivable that different specifi c 

(ontogenetic) pathways are more meaningful 

for some individuals than for others. For 

example, one person’s heavy smoking might 

be driven by rapid nicotine metabolism, 

whereas another person’s might be driven 

by strong dopaminergic response in brain 

incentive structures. “Downstream,” 

or distal, measures might not be sensitive 

to differences in such discrete pathways. 

An often used analogy is the case of a car 

(or clock) that will not run. General distal 

measures (e.g., lack of motion, lack of 

exhaust) will provide superfi cial evidence 

that something is wrong. However, more 

focused measures that are sensitive to 

particular mechanical pathways are needed 

to detect specifi c causal mechanisms. 

Thus, it may be that phenotypic measures 

targeted at specifi c biological pathways 

are needed to produce a group of 

smokers who share (are homogeneous 

for) a particular genetically mediated 

vulnerability to nicotine dependence. 

In theory, multidimensional dependence 

assessments such as the NDSS or WISDM 

might serve this need. However, such 

measures may inadequately target particular 

biological systems. Indeed, an exclusive 

reliance on self-report may preclude 

precise targeting. Finally, it may be the 

case that comprehensive characterization 

of the phenotype requires a developmental 

research strategy; that is, a richer 

portrayal of the phenotype may emerge 

from measures gathered across ontogeny 

(fi gures 3.2 and 3.3).

The above reasons, and others, encourage 

the use of phenotypic measures that 

are sensitive to specifi c causal pathways 

thought to lead to nicotine dependence—

pathways that are, in theory, more 

proximal to the biological effects of the 

polymorphisms under study and that are 

used in a developmentally informed manner. 

Dissatisfaction with global distal measures, 

and the desire to assess genetic risk across 

the development of disorders, have led to 

the assessment of intermediate phenotypes, 

or endophenotypes (see below).

A basic assumption of the endophenotypic 

approach is that genetic infl uences 
106

3. The Nicotine-Dependence Phenotype

will be more straightforward, and less 

complex, when the phenotype is relatively 

circumscribed (e.g., involving relatively 

few biological systems or processes), 

which should clarify the contributing 

genetic architecture. In addition, the 

endophenotype should be manifest relatively 

early in the causal chain leading to the 

syndrome of interest (it should be causally 

“upstream”; fi gure 3.2). The latter feature 

should enhance penetrance and, therefore, 

result in a stronger genetic signal. At the 

end of this section, some potential risks 

of the endophenotypic approach are 

reviewed. It is conceivable that mapping 

distal phenotypes could, under some 

circumstances, constitute a more effi cient 

research strategy.

It is clear that genes do not encode for 

psychopathological syndromes or symptoms 

per se; however, they do encode for less 

complex biological and behavioral processes. 

Diseases or syndromes that affect multiple, 

diverse organ systems seem more likely to 

be affected by numerous causal infl uences, 

including genetic infl uences. Moreover, 

it also seems likely that as the number of 

genetic infl uences increases, so does the 

possibility of heterogeneity across persons

in such infl uences. Measuring a distal 

outcome such as number of cigarettes 

smoked per day might obscure individual 

variation in phenotypic differences and 

genetic infl uences.

In short, using proximal phenotypes should 

enhance genetic mapping by tapping 

genetic signals with greater penetrance 

and by reducing multiple phenocopies.178

An example of the advantage of relatively 

discrete phenotypic measures, and ones 

that are more tightly linked to underlying 

biology, can be found in the area of gene 

mapping in hypertension: stronger gene 

mapping was found for angiotensinconverting enzyme than for blood pressure 

or hypertension diagnosis.179 Figure 3.244

conveys the notion that a fi nal disease 

phenotype may be the product of diverse 

types of infl uences, and that some 

infl uences may be operative for some 

people, while other infl uences are operative 

for other people. However, these diverse 

“feeder stream” infl uences are somewhat 

compensatory and interchangeable with 

respect to contributing to “downstream” 

processes that produce mature features 

of nicotine dependence. It is conceivable 

that these infl uences may exert additive 

or interactive effects and be differentially 

sensitive to environmental events. 

The assumption is, however, that there 

is a fi nal common pathway (ultimate 

downstream) set of processes and symptoms 

that is manifest once a disorder achieves 

some level of severity. Thus, at clinical 

levels of a disorder, sufferers appear similar 

to one another, but this similarity may 

mask diverse etiologic paths.

Endophenotypes, or “intermediate” 

phenotypes, link disease-promoting or 

disease-permitting sequence variations in 

genes to lower-level biological processes 

and link lower-level biological processes 

to the downstream observable syndromes 

that constitute diagnostic categories of 

disorders.180,181 A genetic variant that 

affects an upstream process affects all the 

downstream processes that depend on it 

(fi gure 3.2). Thus, the genetic variants 

associated with a particular endophenotype 

should be associated with multiple 

(downstream) phenotypes that are causally 

dependent on that endophenotype. There 

may be considerable individual variation 

in the sets of possible upstream infl uences 

that contribute to nicotine dependence 

in one individual compared with another; 

that is, there is a fi nal common pathway 

of processes, but highly heterogeneous 

infl uences may lead to that pathway. 

The next section discusses the concepts 

of “endophenotypes” and “transitional 

phenotypes” that differ from distal 

phenotypes in their specifi city and causal 

priority.
107

Monograph 20. Phenotypes and Endophenotypes

Phenotypes along 

the Causal Chain of Dependence 

Development

In this chapter, the term endophenotypes

is defi ned in a manner that is consistent 

with earlier defi nitions by Gottesman and 

others:44,181,182

■ Endophenotypes should be heritable. 

The endophenotype cannot transmit 

information about genetic differences 

if it is not sensitive to such differences. 

Although endophenotypes must 

be heritable, they may also be 

highly responsive to environmental 

manipulations.

■ Endophenotypes should be associated 

with the causes, rather than the effects, 

of disorders. Ideally, endophenotypes 

should be located in the causal path 

to the disorder, not be a consequence 

of the disorder or its treatment. 

Endophenotypes may also be useful to 

the extent that they are markers of a 

disorder (i.e., they correlate meaningfully 

with phenotypes that are on the causal 

path). A causal role for an endophenotype 

is suggested by the endophenotype 

preceding the disorder ontogenetically or 

developmentally in affected individuals 

(fi gure 3.3). Moreover, it should not 

appear to be merely a prodromal or less 

intense manifestation of the disorder. 

■ Assuming the endophenotype is 

heritable, the presence or magnitude 

of the endophenotype should refl ect 

the genetic relatedness to an individual 

diagnosed or affected by the disorder. 

Thus, if appetitive motivational response 

to an anticipated reward is a heritable 

endophenotype for nicotine dependence, 

then two individuals of biologically 

similar relatedness to a smoker should 

show the same level of this phenotype, 

even if these individuals are discordant for 

smoking. However, this situation may not 

hold even with useful endophenotypes: 

the endophenotype may be heritable, 

and may precede the appearance of a 

disorder, but may also be affected by 

environmental factors and by the disease 

process per se. For example, some 

schizophrenics show abnormalities of the 

prefrontal cortex and working memory, 

and these defi cits predate schizophrenia 

onset, consistent with an endophenotype. 

However, the evidence suggests that 

these defi cits are also exacerbated by the 

illness,44 showing apparent reciprocal 

relations between the endophenotype 

and disorder (or perhaps its treatment 

or other consequences of the disorder). 

A similar complex relation has been found 

for hippocampal volume differences, with 

such volume differences being related 

to genetic load for schizophrenia in a 

stepwise manner, and yet, the genetic 

load for schizophrenia appears to render 

a person more susceptible to the effects 

of fetal hypoxia on hippocampal volume. 

In other words, genetic load appears 

to moderate the effects of hypoxia on 

hippocampal volume. Reverse causation 

of this nature183 may certainly occur in 

nicotine dependence; that is, ingestion 

of nicotine may affect phenotypic 

assays. This calls for careful separation 

of endophenotypes and transitional 

phenotypes (discussed below).

■ The endophenotype should not be 

redundant with disorder status; it 

should account for only a portion of 

the variance in disorder severity or 

course. The endophenotype is intended 

to refl ect more discrete features or risk 

factors for a disorder than are refl ected 

in the disorder per se. Because of this, 

multiple endophenotypes would need to 

be identifi ed to account for signifi cant 

variance in a disorder phenotype and to 

identify genetic variants that confer risk.

■ The endophenotype is a mediator. 

The molecular genetic variant should 

account for signifi cant variance in 
108

3. The Nicotine-Dependence Phenotype

the endophenotypic measure, and the 

endophenotypic measure should account 

for signifi cant variance in the phenotype. 

Ultimately, one would need to use 

multimediator models to explain large 

proportions of variance in the phenotype. 

This means that one needs two theoretical 

models: one of the gene-endophenotype 

relation and one of an endophenotypephenotype (disorder) relation. Successful 

use of the endophenotypic approach 

requires that investigators test causal 

models comprising (1) specifi c 

biologically relevant processes 

thought to contribute to clinically 

meaningful nicotine dependence 

(the endophenotype-phenotype model); 

(2) specifi c genetic variants thought 

to infl uence those biological processes 

(the gene-endophenotype model); and 

(3) an assessment plan that is sensitive 

to the specifi c biological process(es) 

and to the ultimate phenotype.

■ Endophenotypes should be present 

before, or in the absence of, a disorder 

(fi gure 3.3). Thus, as noted earlier, 

the endophenotype truly conveys a 

risk factor and not a prodromal feature 

or disease manifestation. Thus, to 

permit the strongest causal inferences, 

endophenotypes should be assessed before 

the development of nicotine dependence 

and be present in nondependent 

individuals as a function of consanguinity. 

Thus, endophenotypes are both more 

specifi c than the clinical phenotype and 

possess temporal and causal priority. 

However, it may be useful to distinguish 

a host of factors signifi cantly more 

specifi c than the clinical phenotype but 

that manifest only when disease causal 

processes have been induced. These 

might be termed transitional phenotypes

(see fi gure 3.3). Unlike endophenotypes, 

these do not occur before exposure to 

a pathogen (e.g., high stress, nicotine). 

Tolerance development to nicotine would 

be an example of such a transitional 

phenotype. Tolerance development 

could not be assessed directly outside 

of exposure to nicotine. Many of 

the important theories of nicotine 

dependence development really refer to 

phenomena that could be captured as 

transitional phenotypes. For example, 

the development of tolerance, withdrawal 

symptoms, sensitization to nicotine’s 

incentive effects, and the development 

of conditioned reinforcement would all 

depend on exposure to the direct effects 

of nicotine. The distinction between 

endophenotypes and transitional 

phenotypes is an important one because 

very different causal claims are being 

made in the two cases. The distinction 

also has clear implications for 

experimental design: in the case of 

endophenotypes, one would assay, ideally, 

individuals with little or no nicotine 

exposure; in the case of transitional 

phenotypes, one would assay those who 

have initiated tobacco use. 

■ Endophenotypes should manifest causal 

effects across different levels of analysis 

or different points of the causal chain 

(table 3.4). Figure 3.6 shows a model in 

which an attention defi cit endophenotype 

affects the likelihood of downstream 

transitional and mature phenotypes. 

In theory, the same polymorphisms 

might be related to phenotypic variance 

at all three stages of the causal path, 

to the extent that the endophenotypes 

were genetically infl uenced and served 

as setting events or instigators for the 

subsequent transitional and mature 

phenotypes. Thus, pleiotropy would be 

evident in these relations to the extent 

that an endophenotypic or transitional 

effect determined downstream 

manifestations of nicotine dependence. 

Of course, it is always possible that 

a phenotype that is associated with a 

developmentally early stage of smoking 

will not account for signifi cant variance 

in measures of clinical, or mature, levels 
109

Monograph 20. Phenotypes and Endophenotypes

of dependence. Such factors might be 

endophenotypes for initiation rather 

than dependence.

■ Figure 3.3 makes another factor 

clear: it is likely that different genetic 

variants are associated with risk at the 

different levels of etiologic development. 

For example, some genetic variants 

may contribute to nicotine-dependence 

development only after considerable 

nicotine exposure (e.g., variants that 

foster tolerance or permit heavy nicotine 

use). Thus, as fi gure 3.3 illustrates, 

a complete theoretical model of 

nicotine dependence should address 

which biological processes, associated 

with which genetic variants, infl uence 

nicotine-dependence development 

at which points in the causal path. 

The ability to track a postulated causal 

path across levels of analysis and across 

points in the causal pathway adds greatly 

to the strength of inferences linking all 

elements in the phenotypic causal chain.

■ Endophenotypes for one disorder 

should predict occurrence of genetically 

related disorders. Some disorders are 

Figure 3.6 Associating Genes with Phenotypic Stages

Endophenotype

(attention deficit)

Transitional phenotype

(improved attentional

function with nicotine)

Mature phenotype

(severe attentional

symptoms upon druguse discontinuation) 

Specific subclass

(e.g., cognitively 

dependent smokers)

Genetic variant 

Table 3.4 Levels of Analysis 

in Characterizing the Phenotype

Level Example

Transcripts/gene 

expression

Polypeptide synthesis

Postreceptor processing Second-messenger 

response

Neurotransmitter system 

status

Response to nicotine 

challenge

Brain morphology Density of receptor cell 

types

Physiological morphology 

and function

Liver clearance rates

Brain function fMRI

Discrete cognitive 

processing

Anterior attentional 

function

Discrete behavioral 

domains

Smoking rate

Emergent cognitive 

function

IQ, memory

Emergent behavior/

psychological traits

Personality, attitudes

Note. fMRI = functional magnetic resonance imaging.
110

3. The Nicotine-Dependence Phenotype

substantially correlated with one another, 

and their intercorrelation is due to 

shared genetic infl uences.

Caveats to Assessing 

Endophenotypes

Although there are clear potential 

advantages to a strategy that focuses 

on specifi c endophenotypes, potential 

hazards exist as well. That is, it is possible 

that greater progress might be made, 

and made more quickly, by concentrating 

on mapping distal, or clinical, phenotypes 

rather than endophenotypes. This might 

occur because the endophenotype may itself 

be a nonspecifi c or promiscuous causal 

infl uence, giving rise to myriad outcomes 

(e.g., not only nicotine dependence but 

also a variety of externalizing and attention 

defi cit disorders). Thus, the probability of a 

criterion given a particular response is not 

equivalent to the probability of a response 

given the criterion (P [C | R] ≠ P [R | C]). 

In this case, one might detect strong 

associations between an endophenotype and 

genetic variants, but the relation might have 

little to do with nicotine dependence per se. 

In addition, it is unavoidable that individuals 

with nicotine dependence will possess 

numerous candidate endophenotypes. It is 

clear that the distal phenotype (at least with 

a large representative sample) comprises all 

of the possible endophenotypes and their 

genetic substrata. It is only in this context 

that the relative and incremental value of 

the relevant genes can be determined. That 

is, the endophenotype per se will likely not 

provide a context that allows one to identify 

those genetic variants that are relatively

infl uential. Thus, the value of a particular 

genetic variant becomes known only after 

its relation with the distal phenotype is 

known. It may be most effi cient to try to 

ascertain this relation directly without 

recourse to the endophenotype.

Another concern is that some 

endophenotypes may be very diffi cult to 

assess in a manner that is reliable and 

appropriate to the purpose of molecular 

genetics. For example, some potential 

endophenotypes (e.g., impulsivity) are 

very diffi cult to measure for any purpose 

and, in fact, may be more diffi cult to 

measure than is the distal nicotinedependence phenotype. Related to this 

point, it is likely that only a portion of 

variance in an endophenotypic measure 

is, in fact, related to progression to a 

disorder end point. The task of refi ning 

endophenotypic measures appropriately 

could be tremendously diffi cult and timeconsuming. Finally, it is important to 

remember that nicotine dependence, like 

other psychiatric disorders, is contextually 

and socially defi ned. A distal phenotype 

tells which individuals have developed a 

disorder despite, or because of, a host of 

environmental or developmental events. 

Different phenotypes (i.e., endophenotypes 

versus distal phenotypes) will refl ect the 

infl uences of an entirely different set of 

developmental and environmental events. 

Thus, distal clinical measures may be most 

likely to refl ect those genetic infl uences 

that create signal against a context of 

relevant environmental and life events. 

Of course, one could eventually discover and 

address these threats via endophenotypic 

research, but this might not be the most 

effi cient strategy. 

Finally, as noted earlier, there may be great 

value in studying specifi c subtypes among 

those in whom nicotine dependence is clearly 

present (mature subphenotypes; fi gure 3.3). 

These would, in theory, share the same virtue 

of specifi city, as would endophenotypes and 

transitional phenotypes; that is, their greater 

specifi city might permit stronger relations 

in genetic mapping than would occur with 

broader, more encompassing phenotypes. 

An example of a mature subphenotype 

might be smokers who are fast nicotine 

metabolizers or who smoke for taste 

reasons.54,68 The use of mature subphenotypes 

need not depend on the assumption that 
111

Monograph 20. Phenotypes and Endophenotypes

nicotine dependence comprises multiple 

unique taxa. The approach might be 

profi table even if one assumes that the 

disorder is affected by distinct, continuously 

distributed dimensions (neuroticism, 

behavioral undercontrol, reward reactivity, 

taste sensitivity, and so on). The strategy 

might reveal populations of smokers for 

whom certain dependence processes are 

relatively important.

Causal Paths Comprising 

Endophenotypic, Transitional, 

and Distal Phenotypic Measures

Although questionnaires hold promise for 

elucidating the genetic basis of nicotine 

dependence, a better understanding of the 

molecular genetics of nicotine dependence 

may require the use of additional assessment 

strategies (e.g., imaging strategies;184 see 

chapters 7 and 8).

A tremendous variety of endophenotypic 

and transitional phenotypic assessments 

are potentially available. However, many are 

possibly quite costly and labor-intensive. 

In such circumstances, the investigator 

wishing to assess and test relatively specifi c 

phenotypes should be guided by an explicit 

model of nicotine dependence. Figure 3.1 

depicts a model based on distal measures 

of nicotine dependence, but if the models 

are needed to support an assessment of 

endophenotypic and transitional phenotypes, 

then causal paths should be articulated. 

These paths arise out of the available theories 

and data linking genetic variants with a 

developmental trajectory comprising (1) the 

endophenotypes that place a person at risk 

for experimentation and risk for initiation 

and progression to nicotine dependence, 

(2) the transitional phenotypes that become 

relevant once tobacco use commences, and 

(3) mechanisms via which endophenotypes 

and transitional phenotypes are related 

to clinical/distal measures of nicotine 

dependence and to mature subphenotypes.

Table 3.3 depicts a working model of how 

particular genetic variants might manifest 

different endophenotypes, transitional 

phenotypes, mature subphenotypes, 

and ultimately, distal phenotypes. These 

causal pathways are offered as illustrative 

exemplars and are not meant to constitute 

a complete model of nicotine dependence. 

However, there is some evidence that 

supports each causal pathway and its 

phenotypic markers. For example, all of 

the genetic variants have been linked to 

either smoking or nicotine dependence, 

or to constructs strongly implicated in 

risk for smoking or nicotine dependence, 

such as for nicotinic receptors/nicotine 

reward (e.g., CHRNB2),82 for taste sensitivity 

(PTC),44 for the dopaminergic mechanisms/

incentive salience and sensitization 

(e.g., DRD2, DRD4, SLC6A3),185–187

and metabolic capacity (e.g., CYP2D6, 

CYP2A6).188–190 Other relevant constructs 

might be impulsivity linked to decreased 

cognitive control (e.g., MAOA),181,191

withdrawal (e.g., GABAA or a7 nAChRs),192–195

and affective control/stress recovery 

(SLC6A4).196–199 For purposes of illustration, 

a possible genetic infl uence will be traced 

across several selected causal paths. 

Incentive Salience and Sensitization

Considerable evidence indicates that 

craving, especially cue-induced craving, 

appears to be related to activity in 

dopaminergic systems.63,200 This is 

indicated, in part, by responsiveness 

of dopaminergic, mesotelencephalic 

structures, such as the nucleus accumbens, 

to drug stimuli and drug anticipation in 

addicted populations.71,201,202 Dopaminergic 

responsivity might be refl ected by 

both endophenotypic and transitional 

phenotype markers. The former would 

be assessed by functional magnetic 

resonance imaging (fMRI) assessment of 

activity in mesotelencephalic dopamine 

structures in response to anticipation of 

nonpharmacological reward (before any 
112

3. The Nicotine-Dependence Phenotype

lifetime nicotine exposure); transitional or 

mature subphenotypes could be assessed 

on the basis of activity in the same brain 

regions, but in response to anticipation 

of nicotine delivery to smokers.202 These 

phenotypic variants, as well as more distal 

disease phenotypes, could be tested for their 

association with genetic variants that code 

for structural and functional properties of 

the dopamine system (e.g., DRD2, SLC6A3,

and DRD4 genes).185,186 Such variants have 

been associated with smoking,203 supporting 

their potential involvement in at least a 

subset of habitual smokers.

Cognitive Control and Impulsivity

With respect to the cognitive control/

impulsivity causal path, a pattern of 

violence and impulsivity has been associated 

with a variable number tandem repeats 

polymorphism in MAOA, which encodes a 

key enzyme for the catabolism of serotonin 

and other neurotransmitters. The lowexpression variant interacts with stressful 

experiences that occur early in life, with 

the combination predicting violent offenses 

in males.191 Imaging studies link this lowexpression variant with poor integration 

among the amygdala, the subgenual and 

caudal portions of the cingulate gyrus, 

and with the orbitofrontal cortex.181,204

A great deal of evidence implicates the 

cingulate and prefrontal regions with an 

integrated cognitive-control system.205,206

A mounting body of evidence also implicates 

cognitive-control functioning, and 

activity in these brain regions, with drug/

nicotine motivational processing.62,201,207

In addition, there is considerable evidence 

that impulsivity and related constructs, 

including externalizing psychopathologies, 

are related to initiation of smoking and 

intensity of nicotine dependence.60 Certain 

laboratory tasks are sensitive to externalizing 

personality infl uences and the tendency 

to develop specifi c externalizing disorders, 

(e.g., P300 in the oddball task)208 and some 

tasks are sensitive to extreme dominant 

response focus.209 Such tasks require no prior 

exposure to nicotine and, therefore, might 

serve as sensitive endophenotypic measures.

Genetic risk for reduced cognitive control 

might also be associated with transitional 

patterns such as the tendency to show 

rapid escalation once smoking begins.210

Whether the candidate genetic variant and 

associated externalizing tendencies are 

causally determinant of initiation per se, 

or both initiation and nicotine-dependence 

development, must be elucidated by future 

longitudinal molecular genetics research. 

Thus, it is possible that some genetic 

variants might infl uence some upstream 

nodes of the causal chain (initiation) but 

not later causal processes directly leading 

to severe nicotine dependence.

Affective Coping

The affective coping causal path might 

be linked to SLC6A4, which contains a 

variable number tandem repeats variant in 

the 5′ promoter region (5-HTTLPR), with 

reduced transcription with the short (S) 

allele relative to the long allele. Research 

shows that individuals with the S allele tend 

to report more internalizing symptoms or 

traits, and this predicts greater likelihood 

of depression in response to environmental 

adversity. In addition, evidence suggests that 

S allele carriers show unusually high levels 

of amygdala activity in response to stressors 

consistent with an inability to modulate 

affective reaction.197,198 Interestingly, the 

subgenual cingulate provides inhibitory 

feedback to the amygdala to regulate 

processing of environmental threats or 

stressors. In addition, studies have shown 

that the S allele carriers show reduced 

coupling of the subgenual cingulate and the 

amygdala, which was, in turn, associated 

with degree of internalizing symptoms.211

In sum, results suggest that possession of 

the S allele leads to weakened integration 

and inhibitory feedback from the subgenual 

cingulate and the amygdala, and this is 
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Monograph 20. Phenotypes and Endophenotypes

associated with reduced affective control 

(e.g., fear extinction).

In theory, an inability to control affective 

response may render a person more 

dependent on a drug for affective control. 

Affective control is a primary motive 

offered by dependent smokers,50 and this is 

especially true of smokers with internalizing 

symptoms. A dependence on smoking for 

affective control may be one reason that 

individuals with internalizing disorders 

smoke more cigarettes than do other 

smokers and are more likely to be diagnosed 

with nicotine dependence.35 In addition, both 

affect and cognitive-control mechanisms 

appear to account for urge occurrence,62

suggesting that smokers with impaired 

cognitive control, and a resultant inability 

to cope with negative affect via endogenous 

control mechanisms, may be especially 

likely to experience high levels of negative 

affect and urges and to relapse in response 

to stressors.

Thus, the available evidence suggests 

that the relevant effects of the S allele 

variant of SLC6A4 may be detected with 

endophenotypic measures, such as the 

emotional Stroop paradigm,212 and fMRI 

or startle-probe measures of affective 

responsivity in response to stressors. 

Moreover, S allele carriers may report 

higher scores on the WISDM Negative 

Reinforcement subscale and show higher 

levels of relapse in response to stressors.

In sum, table 3.3 contains examples of 

strategies that might be used for genetic 

mapping before nicotine exposure 

(endophenotypic markers) and during the 

periexposure and postexposure periods 

(transitional and mature subphenotypic 

markers). The table collapses across 

transitional and mature subphenotypes for 

ease of exposition. The table makes clear 

that all genetic variants and the processes 

that they encode may lead to elevated 

levels on the distal phenotype measures 

(but this is not to say that there will not be 

signifi cant variation on such measures as 

a function of differences across the various 

causal paths). Thus, it is important to note 

that although two individuals may arrive at 

the same severity of nicotine dependence 

according to status on distal measures, the 

two individuals very likely are not equivalent 

at earlier stages of the causal chain 

(including genetic variants). This is why 

mature subphenotypic measures may detect 

differences among heavy smokers.

Finally, table 3.3 does not depict how 

the various causal paths might merge 

or interact. The table and the associated 

discussion make clear that causal paths 

refl ect the synthesis of evidence related to 

functional signifi cance of genetic variants, 

the constructs and processes related to 

smoking and nicotine dependence, and 

knowledge regarding measures that 

refl ect nicotine dependence as well as 

endophenotype-related processes and other 

causal mechanisms.

Analytic Strategies

The scope of this chapter is such that 

approaches to data analysis cannot be 

discussed in depth. But certain conceptual 

issues should be mentioned so investigators 

might bear in mind how such considerations 

are relevant to their research goals. Data 

analysis in association studies is escalating 

dramatically in complexity because of 

advances in genotyping and the use of 

more complex phenotypes. For instance, 

phenotypes are being developed that involve 

complex developmental-change functions 

and stage-specifi c dependence measures. 

The complex causal models depicted in 

table 3.3 would also entail complex causal 

modeling. The discussion of analytic 

strategies that follows does not address 

how such complex phenotypes may be most 

effectively modeled. Chapters 5–9 address 

issues relevant to such complex phenotypic 

modeling. However, the discussion below 
114

3. The Nicotine-Dependence Phenotype

focuses on how distal phenotypes may be 

most effectively used in mapping analyses. 

Constructing Extreme Groups

As noted earlier, one common strategy 

for associating genetic variants with 

phenotypes is via designing extreme 

groups.105 In constructing extreme 

groups, the goal is to achieve a contrast 

of two groups, one of which comprises 

a full complement of genetic variants 

that promote vulnerability for nicotine 

dependence and has no variants that protect 

against dependence. The second group 

comprises variants that protect against 

nicotine dependence but has no variants 

that foster dependence. This is, obviously, 

an ideal that can only be approximated. 

One assumption in building extreme 

groups is that genetic vulnerabilities and 

protective factors cannot be expressed unless 

individuals have had some level of exposure 

to nicotine; that is, the assumption is that 

the genetic variants that affect smoking 

initiation differ from those that affect the 

development of dependence.79,81,82,213

The review of the nicotine dependence 

measures and infl uences presented above 

suggests one strategy for formulating 

the two extreme groups. The dependent 

group members should show high scores 

on all of the measures listed in table 3.2 

for high genetic proneness: they should 

smoke heavily, pervasively, automatically, 

and uniformly; smoke early in the day; 

report severe withdrawal upon attempts to 

quit; and report an inability to cut down or 

control their smoking. The nondependent 

group should, of course, have none of 

these features.

Both groups should have had some exposure 

to nicotine, although how extensive 

that exposure must be is unclear. If the 

investigator believes that there is a true 

nicotine-dependence taxon, then the 

genetic variants particular to that taxon 

might be best captured by a contrast 

of two groups with extensive smoking 

histories—for instance, two groups 

comprising those who have smoked daily 

for at least one year. This would ensure 

that the two groups differ on variants that 

are relatively specifi c to dependence per se. 

To the extent that the groups differ greatly 

on exposure to tobacco, the more likely 

it is that differences in genetic variants 

may be related to factors that promote or 

retard smoking initiation per se versus 

dependence. Thus, it is incumbent upon 

the investigator to decide how specifi cally 

to target severe dependence per se in the 

search for genetic variants.

In forming extreme groups, investigators 

may wish to consider the timing of 

nicotine exposure in addition to the sheer 

amount of exposure. In keeping with the 

review of gene-environment interactions, 

investigators may wish to incorporate 

the notion that all individuals contrasted 

(in both of the extreme groups) have 

exposure to tobacco relatively early in life. 

This would be based upon the notion that 

early exposure is needed for the expression 

of genetic infl uences.21,34,162,214 It may be that 

late-onset experimentation is unlikely to 

lead to severe nicotine dependence even if an 

individual possesses genetic vulnerabilities. 

Obviously, the investigator would wish to 

conduct additional analyses of results yielded 

by an extreme groups design. For instance, 

the investigator would wish to ensure that 

the effects of the contrast are not specifi c to 

gender and that the effects of early smoking 

are not produced by that variable’s relations 

with syndromes of disinhibition (which 

would encourage early drug exposure). 

Such follow-up analyses would address the 

generality of the effects obtained and the 

underlying causes of such effects.

The extreme groups strategy is not without 

its drawbacks. Requiring subjects to be 

maximally divergent on a host of indicators 
115

Monograph 20. Phenotypes and Endophenotypes

of dependence, as indicated in table 3.2, 

may lead to several problems. For example, 

even if the measures in table 3.2 agree with 

one another substantially, selecting on the 

basis of high scores on all of such measures 

might entail screening of tremendous 

numbers of individuals to identify subjects 

who are uniformly high, or low, across the 

diverse criteria. Not only might this be 

impractical and expensive, but it also raises 

concerns about the representativeness of the 

samples generated. For example, some forms 

of psychopathology are associated with very 

severe nicotine dependence;21,35,118,215 thus, 

it is possible that selecting extreme subjects 

might result in the unintentional selection 

of those with syndromal or subsyndromal 

comorbidities. This means that one might 

inadvertently select for genetic variants 

associated with conditions or dispositions 

associated with correlates of extreme 

nicotine dependence but that are not central 

to the construct. Of course, all of these 

concerns are related to where one sets cutscores on the various measures. 

An additional concern with constructing 

extreme groups is that it severely limits the 

sort of analytic strategies that one can use to 

explore the nature of the relations of genetic 

variants with the phenotypic measures.216

For instance, as Preacher and colleagues216

observe, use of the extreme groups design 

precludes characterization of genetic variant 

and phenotypic relations across the full 

range of these variables and may produce 

model misspecifi cation. In addition, even 

with the use of taxometric procedures or 

signal detection methods, it is diffi cult to 

know where to place cut-scores to ensure 

that the dependent group exclusively 

comprises dependent individuals but does 

not entail excessive screening. Also, such 

designs entail the possibility of classifi cation 

error beyond the usual measurement error 

assumed with classical test theory. Thus, 

the procedure may reduce reliability in ways 

that would be diffi cult to detect and correct. 

In particular, the use of extreme scores may 

yield classifi cation error due to regression 

to the mean as such scores are likely to be 

unstable across time. Of course, the use of 

multiple classifi cation criteria would protect 

against this threat somewhat. Finally, it is 

true that extreme groups designs confer 

greater power and, therefore, are more likely 

to lead to higher likelihood of statistical 

signifi cance relative to continuous designs 

with similar Ns. However, these designs 

may produce signifi cant effects even 

though effect sizes are trivial. Therefore, 

it seems advisable to use extreme groups 

designs in exploratory research—when a 

research area is in its infancy, costs are high, 

and a premium is placed upon detection of 

possible effects rather than an estimation 

of their magnitude. Of course, one should 

use an extreme groups design if it is clear 

that a nicotine-dependence taxon really 

exists,100 because the nature of the design 

would match the true distribution of 

nicotine dependence. However, a good deal 

of research suggests that differences among 

smokers may be explained on the basis of 

different intensities of a single dimension, 

rather than refl ecting distinct, qualitatively 

different types.102,118–120 (See also Muthén 

and Asparouhov 2006.39)

One last observation about the structure or 

nature of nicotine dependence is relevant 

here. Questions about the structure of 

psychiatric disorders—whether it is taxonic 

or continuous—are very diffi cult to resolve. 

For example, it is possible that a continuous 

dimension may appear categorical or 

taxonic, while a categorical dimension 

may appear to be continuous, depending 

on scaling and measurement properties 

of the variables involved.217 This means 

that even though an item generates scores 

that suggest the presence of two distinct 

groups of individuals, such a pattern may be 

caused by peaked indicators of a continuous 

dimension. Moreover, it is also the case that 

the categorical/continuous distinction is a 

false dichotomy. As Haslam and Kim note, 

“matters of kind and matters of degree, 
116

3. The Nicotine-Dependence Phenotype

itself [might] be a matter of degree.”218(p311)

Thus, nicotine dependence might be 

caused by a certain all-or-none genetic 

infl uence coupled with other graded genetic 

and environmental infl uences. In short, 

distinguishing the underlying structure 

of a disorder is a complex and diffi cult 

undertaking; thus, no defi nitive conclusions 

are possible at this time. The bottom line is 

that whenever investigators use an extreme 

groups design, they should be aware of the 

limitations and assumptions entailed.

Alternatives to Extreme Groups 

Classifi cation

One strategy takes advantage of a 

theoretically guided selection of criterion 

measures; it refl ects the multifactorial 

nature of nicotine dependence but does 

not require that individuals have uniformly 

extreme scores on all measures. This 

strategy involves combining measures 

so they refl ect a linear dimension of 

nicotine dependence. One challenge with 

a combinatorial strategy is how to achieve 

proper or appropriate weighting of the 

predictors. Since the true relation between 

each nicotine-dependence indicator 

(criterion) and polymorphisms will 

likely be unknown, the investigator must 

devise a system for weighting the various 

predictors or criteria used to select subjects. 

Numerous strategies for this are possible. 

One strategy is to construct an improper 

linear model219 that comprises the 

principal nicotine-dependence indicators 

that the researcher believes will tap the 

major nicotine-dependence facets. In this 

approach, the researcher can use unit 

weighting, or weights based on substantive 

considerations, to create a composite—that 

is, adding subjects’ scores across the set of 

variables to create a somewhat continuous 

index of risk. Weights might refl ect 

correlations among the nicotine-dependence 

criteria, heritability estimates yielded by 

biometric research, and the importance of 

the criteria in the investigator’s theory of 

nicotine dependence. This composite would 

allow for compensatory contributions of 

variables such that high scores on some 

elements would compensate for low scores 

on other measures, and thus, in theory, all 

subjects could be included in analyses.

Further, the investigator might examine 

associations of selected polymorphisms 

with extremes on the composite dimension 

or on a quasi-continuous dimension 

constituted of quintiles or deciles of risk. 

If promising relations ultimately are found 

in such analyses, these relations could 

be unpackaged by examining relations of 

polymorphisms with individual elements 

of the composite. This strategy has several 

advantages: (1) inclusion of multiple indices 

of nicotine dependence; (2) potential to 

weight the nicotine-dependence indicators 

(variables) in a manner consistent with 

their theoretical importance or empirical 

support; (3) potential retention of most or 

all subjects; (4) ability to examine relations 

across a quasi-continuous distribution; 

(5) ability to unpackage relations with 

composite elements; and (6) a composite 

that, no doubt, is more reliable than any 

single composite element—a desirable 

feature when dealing with potentially 

unreliable categorical variables.

The model just presented is compatible 

with the view that (1) nicotine dependence 

is fostered or permitted by multiple 

polymorphisms; (2) persons with nicotine 

dependence are heterogeneous with respect 

to which polymorphisms are present (few or 

none are necessary and suffi cient) due, 

in part, to the fact that some phenotypic 

measures are more relevant to some 

nicotine-dependence cases than to others; 

(3) different nicotine-dependence measures 

or criteria are modestly intercorrelated, 

refl ecting somewhat distinct causal 

infl uences, including genetic infl uences; 

and, therefore, (4) the greatest likelihood 

of capturing a full complement of variants 
117

Monograph 20. Phenotypes and Endophenotypes

promoting nicotine dependence is to use a 

composite comprising multiple nicotinedependence criteria that index the various 

relevant polymorphism. If this approach is 

used, it should capture relevant genes even 

in the presence of epistasis and pleiotropy 

or genetic heterogeneity. Such potential 

causal infl uences could be examined when 

the composite measure is unpacked and its 

elements related to individual genotypes/

haplotypes.

The analytic strategies outlined thus 

far have all been highly theory driven. 

Of course, an alternative to a theorybased approach is to use empirical search 

strategies or data-driven approaches220 to 

uncover gene, intermediate, and phenotype 

associations. It is possible that a lack of 

guiding theory, and a virtually limitless 

number of potential associations, might 

yield fortuitous associations and lack of 

replication.221,222 Moreover, a strongly 

empirical strategy merely forestalls the 

need to hypothesize biological mechanisms 

and achieve theoretical integration.

Other new approaches to data analysis 

combine strengths of a priori theoretical and 

empirical approaches. The approaches have 

been fostered by challenges and complexities 

posed by advances in genotyping, as well 

as increased recognition of the diffi culties 

in modeling complex phenotypes. 

These complexities become even more 

pronounced with the “omicization” of 

observational studies and the availability 

of new genome-wide technologies for 

genomics, transcriptomics, metabolomics, 

proteomics, and so forth.223 Although 

much of this chapter has been focused 

on refi ning the phenotype and exploring 

endophenotype defi nitions, one must also 

pay close attention to how one measures 

the biologically relevant factors. Mimicking 

the move from the evaluation of a single 

polymorphism to evaluating haplotypes 

within a single candidate gene, the fi eld 

is rapidly moving from the evaluation of a 

single candidate gene to the investigation 

of numerous gene regions either within a 

suspected etiologic pathway or even on the 

genome-wide scale. Combine this wealth 

of genetic information with potential 

intermediate measures, biomarkers, 

environmental factors, endophenotypes, 

and distal phenotypes, and the “curse of 

dimensionality,” and multiple comparison 

issues quickly dampen any hope of 

a statistically signifi cant result with 

conventional tests of association. However, 

in a similar fashion to how the watershed 

model (fi gure 3.2) provides a structure 

for the nicotine-dependence phenotype, 

structure for this statistical analysis can 

be gained via knowledge of the biology. 

Although standard multivariate techniques, 

such as linear regression, are one way of 

providing structure by limiting the analysis 

to main effects, two-way interactions, and so 

forth, these techniques quickly reach their 

limit with sparse data bias and unstable 

estimation when the number of terms 

approaches the number of individuals.224,225

This diffi culty often forces the investigator 

into choosing a reduced or “best” model via 

a stepwise selection criterion—a procedure 

well known to lead the analyst astray of 

the true model and that, furthermore, 

does not include the uncertainty in model 

determination in fi nal inference.226,227

As an alternative, one may use hierarchical 

modeling and Bayesian model averaging 

to inform fi nal inference via statistical 

modeling.228,229 Hierarchical modeling 

treats the coeffi cients from a regression 

model as random effects and incorporates 

known information about the relations 

among factors.230–233 This specifi es a 

joint distribution that both stabilizes the 

fi nal effect estimates and incorporates 

dependencies across multiple tests of 

association. In contrast, Bayes model 

averaging seeks to use prior knowledge to 

guide a stochastic model selection approach 

to models that include more biologically 

relevant terms.234,235
118

3. The Nicotine-Dependence Phenotype

Thus, instead of being faced with an 

impossible number of interacting terms and 

possible models, the process is reduced by 

using biological information to inform and 

guide the search procedure. In the process 

of the stochastic search, the data will serve 

to update the prior probability and disclose 

the impact of each factor via the posterior 

probability of the models selected. If the 

knowledge specifying the structure of the 

relations is very well defi ned, one may use 

structural equation modeling or, in the case 

of metabolic pathways, pharmacokinetic 

models to specify the topology between 

factors.236 Of course, such approaches 

are extremely model dependent and may 

have serious identifi ability problems for 

intermediate latent variables if the study 

is limited to genes and distal phenotype 

measures only. However, one may enhance 

estimation within this framework by 

gathering intermediate or endophenotype 

measures on a small subsample of 

individuals and then performing a combined 

analysis (main study and substudy) to 

inform the latent structure of the topology. 

Finally, in an extreme example of using 

the known biology and sampling schemes 

to inform an analysis, a novel approach 

is to adopt a Mendelian randomization 

approach in which genetic variants are 

used to make inferences about critical 

intermediates.183,237,238 The basic idea is that 

if a causal pathway is correctly specifi ed, 

then the effect of an intermediate factor 

on an outcome can be estimated through 

the ratio of coeffi cients of the regression 

of the outcome on the gene and of the 

intermediate on the gene. Thus, the gene 

acts as a randomized control for the 

intermediate’s effect on the outcome, 

helping to protect against reverse causation 

and unknown confounding effects. 

An example of this might be if one or more 

genetic variants were found that resulted 

in high levels of cotinine to accrue in 

response to nicotine use. If this were found 

to relate strongly to the nicotine-dependence 

phenotype, one might infer that a high level 

of cotinine is an important pathogen (at least 

for some people, if it is a moderator). 

Thus, in a gene-environment interaction 

approach, this would suggest a gene-gene 

interaction that would allow one to make 

inferences about a gene-environment 

interaction. Although the use of biology 

guiding statistical analysis is not new, 

the direct incorporation of that knowledge 

into statistical inference is still in its infancy. 

Questions still remain as to what kind of 

information is most relevant and how much 

of the fi nal inference is dependent on prior 

structuring of the relations and topology.

Summary

One’s model of nicotine dependence guides 

decisions about the measurement of the 

nicotine-dependence phenotype—whether 

such guidance is explicit or implicit. It is 

best if the investigator makes such guidance 

explicit by clearly articulating hypotheses 

about the nature of dependence processes, 

the biological origins of such processes, and 

how they manifest phenotypically. Research 

suggests that while nicotine dependence 

is multidimensional, measures that assess 

heavy, pervasive, and automatic smoking 

appear to capture core variance related to 

this construct. However, the investigator 

should consider how to incorporate the 

assessment of complementary dimensions 

of dependence, how to control for error in 

dependence assessments, and whether to 

incorporate gene-environment interactions 

into attempts at genetic mapping. More 

thorough assessment of the dependence 

construct may require the development of 

new dependence assays that are focused on 

relatively discrete biological mechanisms. 

In addition, a comprehensive portrayal 

of nicotine dependence may require the 

development of measures of intermediate 

and transitional phenotypes that capture 

processes in dependence across its 
119

Monograph 20. Phenotypes and Endophenotypes

development. The investigator must also 

decide on how to integrate a model and 

assessment of dependence with particular 

experimental and analytic strategies. This 

endeavor should also be guided by the 

investigator’s explicit hypotheses about 

the nature of dependence and how it 

should manifest across persons and across 

the developmental process. Finally, it is 

important to note that numerous theories 

of nicotine dependence are possible, and the 

investigator should systematically examine 

competing and distinct models. 

Conclusions

1. Most widely used tests of nicotine 

dependence, such as the Fagerström 

Test for Nicotine Dependence and the 

Diagnostic and Statistical Manual of 

Mental Disorders, aggregate data across 

different dimensions of dependence, 

thereby compromising the reliability 

and validity of these measures. Evidence 

suggests, however, that selected items 

from these measures and from newly 

developed dependence scales can be 

relatively coherent, show fairly high 

heritability, and be consistently related 

to core dependence features such as 

relapse likelihood.

2. Although key variance associated 

with the dependence construct will be 

captured by measures of smoking rate, 

latency to smoke in the morning, and 

the likelihood or latency of relapse, 

other complementary measures should 

also be considered such as strength of 

withdrawal symptoms and perceived 

control over smoking. Analytic strategies 

should adjust for environmental 

factors such as home or work smoking 

restrictions, which, in theory, may 

reciprocally affect dependence itself.

3. Nicotine dependence involves both 

environmental and constitutional 

infl uences, and the effects of 

genetic variants associated with 

nicotine dependence require certain 

environmental conditions to infl uence 

the phenotype (at minimum, drug 

access and use). Determining which 

environmental features moderate genetic 

expression and how to incorporate such 

gene-environment interactions into 

genetic mapping remains an area for 

further study.

4. New developments in the assessment 

of the nicotine-dependence phenotype 

include the development of new 

multidimensional measures of nicotine 

dependence, including the Nicotine 

Dependence Syndrome Scale and 

the Wisconsin Inventory of Smoking 

Dependence Motives. These measures of 

mature dependence phenotypes provide 

the opportunity to measure relatively 

discrete dimensions of dependence and 

may permit more specifi c gene mapping. 

5. In addition to greater specifi city, it is 

vital to capture important developmental 

processes that may be masked by the 

mature nicotine-dependence phenotype. 

To obtain measures sensitive to particular 

biological mechanisms that may have 

close links to genetic variants, researchers 

may need to develop biological, 

behavioral, and cognitive neuroscience 

assays that complement self-report 

measures. These may include measures 

of endophenotypes, or intermediate 

phenotypes, that assess vulnerabilities to 

dependence that preexist nicotine use as 

well as transitional phenotypic measures 

that assess processes that change in 

response to drug exposure and that lead 

to mature dependence.

6. All stages of the genetic mapping of 

nicotine dependence should be guided 

by specifi c theory linking candidate 

genetic variants sequentially with critical 

biological and behavioral processes and, 

ultimately, with phenotypes of clinical 

signifi cance.
120

3. The Nicotine-Dependence Phenotype

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206. Fan, J., J. Fossella, T. Sommer, Y. Wu, and 

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207. Volkow, N. D., J. S. Fowler, and G. J. Wang. 

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2006. P300 amplitude as an indicator 

of externalizing in adolescent males. 

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209. Vitale, J. E., J. P. Newman, J. E. Bates, 

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211. Pezawas, L., A. Meyer-Lindenberg, 

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A. R. Hariri, and D. R. Weinberger. 2005. 

5-HTTLPR polymorphism impacts human 

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213. Vink, J. M., G. Willemsen, and 

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133

Detailed studies of inbred mouse strains have provided remarkable insights into how 

genetics shape complex processes, ranging from cancer susceptibility to immunity. 

The mouse models of response to addictive substances such as nicotine are now showing 

similar promise for revealing the underlying complex genetics and physiological 

mechanisms contributing to dependence. This chapter examines key issues in using 

mouse models for nicotine dependence, including

■ The molecular biology of the nicotinic acetylcholine receptors and how these 

receptors contribute to tissue-specifi c responses within the context of strainspecifi c genetic background

■ The interaction of nicotine with physiological systems through oral, intravenous, 

and subcutaneous administration and how experimental results from these 

routes of administration in mice may relate to the physiology of human smoking

■ The way mouse models recapitulate many basic features of nicotine dependence 

in humans, including behavioral reinforcement, self-administration, development 

of tolerance, and altered reward-related behavior

On the basis of available evidence, and given its receptiveness to genetic manipulation, 

the mouse model appears to hold promise as a powerful tool for understanding how 

genetics and behavioral measures combine to individualize the response to nicotine.

4

Mouse Models and the Genetics 

of Nicotine Dependence

Scott W. Rogers, Thomas J. Gould, and Timothy B. Baker

The analyses described herein were supported by National Institute of Health grants AA015515, AG17517, CA/DA84718, 

CA/DA19706, DA01749, and HL72903. The authors acknowledge the Val A. Browning Charitable Foundation of Utah.
134

4. Mouse Models and the Genetics of Nicotine Dependence

Introduction

This chapter provides an evidence-based 

review of issues in using mouse models for 

genetic research in nicotine dependence, 

including the biology of neuronal nicotinic 

acetylcholine receptors (nAChRs), issues 

in the administration and metabolism of 

nicotine, experimental design, and strain 

selection considerations, and aspects 

of behavioral responses to nicotine in 

mice. These factors all contribute to a 

knowledge base for the design of effective 

mouse model research that, in turn, may 

contribute to further understanding of 

the genetic basis of nicotine dependency 

in humans.

Experimentally defi ning the genetics 

that shape the brain—and ultimately 

the behaviors it controls, such as those 

leading to the complex outcomes of 

dependence—is a challenging but 

promising endeavor. Humans and mice 

share a close genetic and physiological 

relationship; comparisons of the human 

and mouse genomes indicate 85% identity. 

These genomes compare favorably in 

their susceptibility to many simple and 

complex genetic diseases including those 

related to addictive drugs such as nicotine. 

Intensive inbreeding has provided many 

hundreds of genetically isogenic strains 

with phenotypically distinct features that 

have been very successfully exploited to 

identify and often defi ne the genetics of 

well over 100 models of human disease.1

Some mouse strains also display responses 

that closely parallel responses and 

behaviors seen in humans. These strainspecifi c genetic characteristics are stable 

over decades of inbreeding,2 providing 

considerable stability in gene-phenotype 

relations. Mice have additional features 

conducive to long-term developmental 

research—for example, relatively small 

size, economical maintenance, and rapid 

development.

Factors such as these, together with 

the species’ amenability to genetic 

manipulation, allow for the study of 

complex genetic contributions to behaviors 

that occur in a nexus of physical maturation 

and environmental exposure. Consistent 

with these virtues, mouse strains were 

recognized more than four decades ago3

 as 

a resource for examining the distinct and 

often highly varied responses to nicotine 

on behavior. Subsequent studies have 

extended these early observations to provide 

considerable insight into how the genetics 

of this animal model can be exploited to 

examine a broad range of mechanisms 

through which nicotine imparts its effects, 

including possible physiological substrata 

of nicotine dependence.4–8

At the same time, caution is needed when 

embarking upon experiments using the 

mouse model system. Among the most 

important is the consideration that 

behavioral-genetic relations are fi ne-tuned 

over the natural history of this species. 

Thus, mice may—or may not—be physically 

able or behaviorally motivated to perform 

tasks that would be appropriate for closely 

related species such as rats.2 Therefore, 

each experiment and fi nding must be 

evaluated as to species-specifi c response 

to stressors (e.g., noise, time of day, 

handling); appropriateness of experimental 

manipulations, equipment, and assessment 

strategy (e.g., platform height, visual 

lines); and strain and species limitations 

in behavioral and adaptive repertoires 

(e.g., congenital retinal degeneration in 

C3H mice).

Animal models of dependence not only 

involve inferences and generalizations across 

species (e.g., mouse to human), but they 

also involve inferences and generalizations 

across behavioral and physiological 

phenomena. Investigations are predicated 

on the assumption that the behaviors 

(e.g., conditioned place preferences) and 

physiological responses (e.g., receptor 
135

Monograph 20. Phenotypes and Endophenotypes

upregulation) observed have relevance to 

human dependence. Thus, experimental 

procedures must be appropriate for 

both the organism and for transspecifi c 

inferences regarding dependence processes. 

Investigators must not only consider species 

and strain differences, and the validity of 

their dependence assays, but they must also 

consider other issues, such as developmental 

processes and how these may affect the 

biological and behavioral processes relevant 

to dependence, as well as render behavioral 

assays that are more or less appropriate.

Of course, dependence phenomena are, 

no doubt, affected by multiple genephenotype relations. This means that the 

considerations and caveats listed above may 

be conditional upon the particular genetic 

variants targeted. Different variants will 

exert different infl uences on biological 

and behavioral processes, and these will 

show different patterns as a function of 

development, strain and species, and 

dependence assay. Thus, a signifi cant goal 

of genetic mapping of nicotine dependence 

in the mouse is the strategic selection 

of experimental strategies that (1) are 

appropriate for the behavioral repertoire 

of the organism, (2) target behavioral 

and biological processes of relevance to 

clinical dependence phenomena, (3) are 

developmentally appropriate both in terms 

of the animal’s repertoire and in terms 

of targeted dependence processes, and, 

perhaps most important, (4) cosegregate 

with the targeted genetic variants.

Within this context, an overview is provided 

of what is known of how gene function, 

within the context of mouse-strain-specifi c 

anatomical architecture and physiology, 

can shape the varied behavioral responses to 

nicotine. This overview is intended to permit 

a more meaningful interpretation of past 

research and foster improved experimental 

strategies to model homologous processes 

that contribute to nicotine dependence 

between mice and humans.

Nicotinic Receptor 

Functional Diversity

The family of neuronal nAChRs are excellent 

and obvious “candidate genes” for examining 

the genetics contributing to the physiological 

process of nicotine dependence because they 

are a defi ned target of this agent’s action. 

However, these receptors do not act alone; 

their function in the broader genetic context 

of multiple genes and biological cascades 

must also be considered. This complexity 

is refl ected in dramatic differences among 

mouse strains in response to acute and/or 

chronic administration of nicotine. In the 

brain, the sustained presence of nicotine 

alters neurotransmission at the level of the 

synapse because, unlike the endogenous 

neurotransmitter acetylcholine, it is neither 

rapidly degraded, nor is it actively removed 

from the synapse. This sustained presence 

can lead to both persistent activation of some 

nAChRs as well as induction of a desensitized 

or “nonactive” state that reverses slowly, 

or possibly in some cases, not at all.9–12

Chronic nicotine exposure may also induce 

the curious phenomenon of “upregulation”; 

that is, the number of high-affi nity 

nicotine-binding sites in the brain actually 

increases.13,14 However, not all nAChRs are 

of high affi nity, nor do they all upregulate. 

Further, other mouse strain differences 

that have not traditionally been assumed 

to be directly infl uenced by nicotine, such 

as strain differences in pro-infl ammatory 

status or metabolic rates, may partly account 

for strain differences in nicotine sensitivity 

and behavioral response.8,15,16 Therefore, 

the considerable genetic variability across 

mouse strains is likely to summate across 

all of these processes. It is important to 

consider the complex interplay of regionally 

specifi c nAChR expression, the nature of 

the specifi c behavioral tests employed, 

and physiological responses to identify 

genetic contributions to the effects of 

nicotine, especially those contributing to 

the development of dependence.
136

4. Mouse Models and the Genetics of Nicotine Dependence

Molecular Biology of the nAChR 

Gene Family

Acetylcholine receptors, like other ligandactivated neurotransmitter receptors, consist 

of two major subtypes: the metabotropic 

muscarinic receptors and fast-ionotropic 

nicotinic receptors.9,12,17 Both share 

the property of being activated by the 

endogenous neurotransmitter acetylcholine, 

and they are both expressed in neuronal 

and nonneuronal cells throughout the 

body. The metabotropic receptors are 

second-messenger, G-coupled, seventransmembrane proteins classically defi ned 

as activated by muscarine and inhibited by 

atropine. The other subtype of acetylcholine 

receptors comprises the microsecond-fast 

ionotropic cationic channel acetylcholine 

receptors that are distinguished by their 

sensitivity to nicotine (fi gures 4.1 and 4.2).

Although all receptor channels are 

permeable to sodium ions, which are 

the major agent of depolarization, there 

is also variable permeability to calcium. 

Because calcium is an important mediator 

of second-messenger and posttranslational 

processes such as gene expression and 

proteolysis, the regulation of local calcium 

concentrations imparted by various 

nAChRs is an important element in how 

these receptors contribute to establishing 

physiological microdomains and impact 

on overall metabolic tone. All subunits 

(fi gure 4.1) also share a conserved structure 

of a large extracellular N-terminal domain 

and four transmembrane domains, as well 

as a cytoplasmic domain of variable size 

and sequence that resides between the 

third and fourth domain (also referred to 

as the 3+1 confi guration). Each subunit 

also harbors a cysteine (Cys) loop in the 

extracellular domain that is defi ned by two 

cysteines that, in the mammalian subunits, 

are separated by 13 intervening amino 

acids (fi gure 4.1). The 3+1 transmembrane 

domain arrangement in combination with 

the Cys-loop defi nes an extended family 

of ligand-activated ion channels that, 

in addition to nAChRs, includes GABAA, 

glycine, and 5HT3 (serotonin) receptors.

All mammals examined so far share a 

similar nAChR genetic composition of 

17 homologous subunits.17,20 These are 

classifi ed into alpha and nonalpha subunits 

on the basis of the presence of a Cys-Cys 

pair in the major extracellular domain near 

the entrance of the fi rst transmembrane 

crossing (fi gure 4.1). A Cys-Cys pair is 

required (but not necessarily suffi cient) 

for agonist binding to form the ligandbinding site for receptor activation, and it 

imparts the “alpha” designation. Subunits 

without this primary structural feature 

receive the nonalpha designation.20 This 

leads to the subdivision of nAChRs into 

the muscle or neuronal nAChR subtypes. 

The muscle receptors consist of fi ve 

subunits (a1 and nonalpha subunits 

named b1, delta, gamma, and epsilon). 

The neuronal nAChR subunits consist of 

the alpha-like subunits termed a2, a3, a4, 

a5, a6, a7, a9, a10 (a8 is an avian-specifi c 

subunit) and three nonalpha subunits 

termed b2, b3, and b4, respectively. 

The term neuronal was applied to these 

subunits on the basis of their cloning from 

the neuronal-like PC-12 pheochromocytoma 

cell line and brain-derived complementary 

DNA (cDNA) libraries.21 In general, the 

number assigned refl ects the order of 

discovery. Although the present review 

focuses on nicotine and its effects on 

functional states of the central nervous 

system (CNS), ample evidence indicates that 

most “neuronal” nAChR subunits are also 

expressed by neuronal and nonneuronal 

cell types throughout the body, where 

they infl uence multiple physiological and 

metabolic processes.22–24

Assembly and Functional 

Diversity of nAChRs

The mature nAChR is a pentamer assembled 

from varied combinations of the starting 
137

Monograph 20. Phenotypes and Endophenotypes

subunit pool (fi gure 4.2). In the muscle, 

this is a developmentally regulated process 

in which receptors develop such that they 

comprise two a1 subunits separated by an 

intervening subunit that is either a gamma 

(immature muscle), or epsilon (mature 

muscle) along with two additional subunits 

including one b1 and delta (fi gure 4.2). 

Figure 4.1 Nicotinic Receptor Subunit Structure

Note. Panel A. A linear presentation is shown of the basic structure shared by all neuronal nicotinic acetylcholine receptors 

(nAChRs). This includes an extracellular domain, four transmembrane domains (TMs), and a cytoplasmic domain that is located 

between TM3 and TM4 and varies considerably in size and amino acid sequence between subunits. TM2 lines the ion channel. 

Short connecting sequences between TM1 and TM2 (cytoplasmic) and TM2 and TM3 (extracellular) are shown in brown and 

contribute to channel gating and receptor fl exibility. The highly conserved Cys-loop structural motif (extracellular domain) 

places nAChRs in the superfamily of ligand-gated ion channels (see text). All alpha subunits by defi nition contain a Cys-pair 

that is important for binding ligand, which is absent in nonalpha subunits. The extracellular domain is initially translated with 

a leader sequence that is prototypically removed. The extracellular domain also includes glycosylation sites (blue “Y”), and 

amino acids that in addition to the Cys-pair are important to ligand binding (in purple; tyrosines (Y) and a tryptophan (W)). Panel 

B. The 3-dimensional folded structure of the nAChR subunit in panel A is depicted, as reported by Unwin.18 Molecular graphics 

images were produced using the Chimera package from the Resource for Biocomputing, Visualization, and Informatics at the 

University of California, San Francisco19 (NIH P41 RR-01081). The color coding is matched between panels, although glycosylation 

is omitted. Also not shown in this depiction, but returned to in fi gure 4.2, are the tyrosine and tryptophan amino acids that, with 

the loop harboring the Cys-pair, form the ligand-binding site (red circle). Sequence differences among nAChR subunits in these 

domains contribute to the unique ligand selectivity and functional properties of the assembled receptors (see text).
138

4. Mouse Models and the Genetics of Nicotine Dependence

The rules of neuronal receptor assembly are 

less well defi ned (fi gure 4.3). Some nAChRs 

are homomeric, including those assembled 

exclusively from a7 subunits25 or possibly 

a9 subunits,26 respectively. Others are 

heteromeric and are, in general, formed 

from at least two alpha subunits (including 

a2, a3, a4, a6, a7, and a9) and structural 

subunits including a5, a10, b2, b3, and b4. 

In this case, it is notable that the alpha 

designation applies to a5 and a10 because of 

the presence of the Cys-Cys pair, but neither 

can form a ligand-binding site or functional 

receptor without coassembly with other 

alpha subunits.

Examples of how various subunits 

alter receptor function and subcellular 

localization are abundant. Receptors 

constructed from various combinations 

Figure 4.2 Three-Dimensional Structure of the Nicotinic Acetylcholine Receptor (nAChR)

Note. Panel A. A side view is shown of the Torpedo (musclelike) completely assembled nicotinic receptor as resolved by Unwin.18

Five subunits coassemble to form a tubelike structure through the membrane. The alpha subunits (a1) are paired with either a 

delta or gamma subunit. The beta subunit (b1) fi lls the fi fth position and does not directly participate in ligand binding, although 

it does infl uence receptor pharmacology and function. In this receptor, the cytoplasmic domains are depicted as single alpha 

helices that form a loosely associated structure within the cell. Note how the Cys-loop approaches the extracellular membrane 

surface. Panel B. The image in panel A is rotated 90° to look down on the receptor from the extracellular face. In this image, 

the organization of subunits around a central pore is apparent. When two agonist molecules (e.g., acetylcholine or nicotine) bind in 

the ligand-binding pocket between the a1 subunits and their respective adjacent subunits (red arrows), there is a conformational 

change to increase the pore size (gate the channel) and permit ion passage, as shown by an asterisk). Upon removal of the agonist, 

the receptor closes. The receptor can, however, close if the agonist remains associated with the ligand-binding site, which is 

termed desensitization. Panel C. A closer view of the ligand-binding site (red arrow) between the a1 and adjacent c subunit shows 

how the Cys-pair (yellow), tyrosines, and the tryptophan (depicted by purple and shown in fi gure 4.1, panel A) converge in the 

3-dimensional structure to form a “pocket” within the structure of the receptor. Also contributing to this pocket are amino acids 

from the adjacent subunit (pink). When a ligand occupies the pocket, as shown in the lower panel for nicotine (red), the receptor 

closes around it to induce a conformational change that gates the channel. Through varying subunit assembly, the contributions by 

unique amino acid sequences to this pocket and the activation mechanisms for pore opening customize the function of the various

nAChRs to their physiological function.
139

Monograph 20. Phenotypes and Endophenotypes

of alpha and structural subunits exhibit 

dramatic differences in ligand affi nity, 

agonist and antagonist effi cacy, rates of 

desensitization, and response to modulators 

(fi gure 4.3). Although alpha subunits control 

much of the determinants of selectivity for 

ligand binding, the nonalpha subunits have 

a signifi cant impact on function. One of the 

earliest examples27,28 of this was the fi nding 

that when nAChRs composed of a3b4 

subunits were exposed to an agonist, bursts 

of activity followed that were often clustered 

and of relatively long duration. In contrast 

the a3b2 receptors exhibit frequent and 

rapid bursting.

Customizing these properties is consistent 

with the need to adjust their function within 

the context of local physiological demands 

or neurotransmission specifi cations 

(fi gure 4.3). Hence, receptors harboring 

the b4 subunit tend to be expressed during 

development in autonomic ganglia where 

they provide longer and more sustained 

bursts to enhance their functional 

impact. Receptors with the b2 subunit 

are more often involved in modulating 

neurotransmission in which rapid and 

precise bursting is favored.12,29–31 Subsequent 

investigations have shown that b4 also 

imparts novel pharmacological properties 

involving altered agonist and antagonist 

effi cacy32–34 and altered sensitivity to other 

compounds including sensitivity to zinc,35

mercury,36 and cocaine.37,38

Another example of how receptor 

heterogeneity can be generated from 

a limited array of subunits is through 

altering either the stoichiometry of a4b2-

containing receptors (fi gure 4.4), or whether 

a subunit such as a5 is included in the 

structural fi fth position to close the receptor 

(fi gure 4.4). Of note is that receptors with 

considerably different pharmacological, 

physical, and ion permeability can be 

generated from these receptors of varied, 

but similar, subunit composition.39,40 In a 

similar context, the homomeric a7 nAChR 

provides another example of how local 

regulation of the expression of a relatively 

few subunits can dramatically infl uence 

the diversity of how the overall system 

response will be affected by nAChRs. This 

receptor desensitizes rapidly, but while 

the channel is open, it is highly calcium 

permeable.12 This receptor also tends to 

localize away from the synaptic junction 

and has been reported to aggregate in lipid 

rafts,41,42 indicating that local increases 

Figure 4.3 Infl uence of Subunit Composition on Nicotinic Receptors

Note. Examples are given of the local expression of nicotinic receptors of different subunit composition where they contribute to 

both tissue-specifi c physiological and disease processes (see text). Major receptor subtypes (viewed from the top) are depicted in 

proposed subunit stoichiometries, which, except for the muscle receptor, are not known and may vary (see text). Also absent are

receptors harboring a 2 or other possible combinations (e.g., a4b 4) whose physiological functions are not well defi ned. The use 

of “peripheral cells” refers to both neuronal and nonneuronal cells located outside of the central nervous system. CNS = central 

nervous system; Symp. = sympathetic; Neurotransm. = neurotransmission; Parasymp. = parasympathetic; Intracel = intracellular; 

GABA = c-aminobutyric acid; AD = Alzheimer’s disease.
140

4. Mouse Models and the Genetics of Nicotine Dependence

in calcium can impart signaling though 

calcium-activated second-messenger 

systems. This further distinguishes the a7 

nAChR from other nAChRs and even other 

ligand-activated ion channels. However, 

calcium permeability and sensitivity to 

agonists and antagonists can be altered 

by the coexpression of additional nAChR 

subunits. Finally, the structural subunits 

such as a5 nAChR also exert an effect on 

function and subcellular localization.43

When this subunit is coassembled with 

a7 nAChR in heterologous expression 

systems, the receptors have similar but 

distinct properties, including altered rates 

of desensitization relative to the homomeric 

channel. Similarly, if a5 incorporates 

into receptors containing a3b2 nAChR 

subunits,44,45 the resulting receptor exhibits 

distinguishing functional characteristics, 

but the differences tend to be small. 

In contrast, if this subunit incorporates 

into receptors with a3b4 nAChRs, as in 

the peripheral nervous system,45 the burst 

duration of the resulting receptor channel 

is increased almost threefold.

Another practical concern is how 

subunit diversity affects interpretation of 

experiments that use ligand binding or 

limited pharmacological methods to infer 

identity or changes in nAChRs during a 

treatment regime. For example, muscarinic 

versus nicotinic receptor contribution 

may be blurred in some instances because 

nAChRs comprising either homomeric 

a9 or heteromeric a9a10 subunits are 

sensitive to blockade by the traditionally 

Figure 4.4 Nicotinic Receptors of Closely Related Subunit Composition Differing in Function 

through Variation in Subunit Stoichiometry

Note. Measuring or predicting the contribution of different subunits to neuronal nicotinic receptors is complicated by the 

possibility that receptors of similar (if not identical) subunit composition, but different relative stoichiometries, can be assembled 

in different cells or brain regions. This is depicted for a4b2 receptors where the fi fth position of the receptor can be fi lled by an 

additional a4, b2, or a5 subunit. Depending upon which receptor is assembled, there are notable differences in their expression, 

affi nity for ligand,46 and function, including susceptibility to magnitude and the rate at which desensitization occurs, as well as 

degree of calcium permeability.39,40 The possibility that infl ammatory cytokines can infl uence this process47 further emphasizes 

how genetic strain background can infl uence nicotinic receptor expression and function and reveals that much remains to be 

determined about how receptor assembly infl uences the effects of nicotine in mouse strains and different pathologies.
141

Monograph 20. Phenotypes and Endophenotypes

muscarinic antagonist atropine.48,49

Similarly, the function of a3-containing 

receptors50 and a4b4 nAChRs51 can be 

modulated in a dose-dependent manner 

by relatively high concentrations of this 

“muscarinic“ receptor antagonist, although 

such concentrations are commonly used 

in buffers by electrophysiologists to 

ensure that only nAChRs are recorded in 

response to acetylcholine administration. 

Consequently, investigations may yield 

confusing or possibly misleading results 

when only a single assay of nAChR function 

is used. Certain other nAChRs of diverse 

subunit composition may also have 

overlapping pharmacology, or they are 

simply not detected by available methods. 

This could be true of a9 and a9a10 

nAChRs, which might be mistaken for a7 

subtype receptors because they also exhibit 

exquisite sensitivity to a-bungarotoxin.26

Although overlap of these respective 

receptor subtypes appears to be very small 

in central systems, there is substantial 

a9-subtype expression in peripheral 

systems,52–55 and consequently, the identity 

of the nAChR subtype being measured must 

be carefully assigned.

Finally, traditional ligand-binding 

determination methods (e.g., high-affi nity 

nicotine or a-bungarotoxin binding) 

may be inadequate to infer the fi ner 

aspects of nAChR involvement in local 

circuitry, especially in regions such as the 

hippocampus. It is now well established 

that the nAChR systems in this limbic 

region affect both inhibitory and excitatory 

tone through modulating inhibitory 

interneuron activity.56–58 In particular, 

differing combinations of a7, a4b2, 

and a3b4 nAChRs, respectively, have been 

implicated in collectively establishing thetawave synchronization59–61 and mechanisms 

of long-term potentiation.11,58,59,62–64

Therefore, mixed combinations of 

receptors on restricted numbers of 

inhibitory interneurons whose location is 

strategically placed within the circuit will 

contribute signifi cantly to establishing the 

hippocampal activity imparting a behavior. 

However, methods to distinguish among 

the expression of these various receptors 

can be very challenging technically. In some 

cases, they are missed entirely when 

high-affi nity nicotine or a-bungarotoxin 

binding methods are used, or if their overall 

abundance is too low to be detectable 

over the background from the entire 

cellular milieu. However, the addition of 

new high-affi nity ligands (e.g., the frog 

toxin epibatidine) or some with varied, 

but defi ned, nAChR subtype selectivity 

(e.g., the a-conotoxin MII) are proving 

to be of exceptional value for identifying 

the coexpression of these receptors.65

Nevertheless, the limitations of such assays 

should be borne in mind when designing 

new studies or when evaluating extant data. 

The lack of sensitivity and specifi city of 

such assays may be responsible for some 

of the inconsistent results yielded by early 

studies in this area.

Customizing Local nAChR 

Function through Limiting 

Subunit Expression

Among the fi rst discoveries following the 

discovery of the cDNA family of nAChR 

subunits was that their expression in the 

brain was restricted to subunit-specifi c 

patterns that overlapped in various 

brain regions.12,66–68 This manifests in 

considerable overlap: a4 and b2 are widely 

and coincidently expressed.65,69 Together, 

they form the majority of high-affi nity 

nicotine-binding sites in the CNS and 

are the primary receptor to undergo 

upregulation in response to nicotine.70

Another receptor, a7, is also expressed 

throughout the CNS, but not in all regions. 

Its relatively high permeability to calcium 

and rapid desensitization to nicotine 

make it a particularly important subunit 

for regulating second-messenger and 

transcriptional mechanisms (see below 
142

4. Mouse Models and the Genetics of Nicotine Dependence

and fi gure 4.3). However, there is incomplete 

understanding of the contribution 

of each subunit to proposed regional 

specialization of nAChR structure and 

function (fi gures 4.3 and 4.4). This is due, 

in part, to measurements that rely solely 

upon RNA analysis. Such analyses can vary 

in sensitivity, and do not provide spatial 

resolution of the fi nal receptor product, 

which can be located very distantly from 

the site of synthesis because of processes 

such as axonal transport. One example of 

this is the expression of the b4 subunit 

in the adult CNS. The expression of this 

subunit was originally reported to be highly 

restricted to only a very few brain regions, 

most notably the medial habenula,71,72 or 

in the peripheral nervous system with 

a3.73 When other studies were conducted 

that used assays of increased sensitivity 

and resolution,74 including single-cell 

polymerase chain reaction (PCR),75 b4 was 

found to be more widely expressed in the 

CNS.76,77 Similarly, immunohistochemical 

measurement of b4 reveals that this subunit 

may be expressed at sites very distal to the 

cell body, as in axon terminal fi elds of the 

barrel cortex whose cell bodies originate 

in the ventral thalamus, or in terminal 

fi elds of the lateral lemniscus within the 

inferior colliculus.78 Therefore, the site 

of the nAChR contribution to regulating 

local circuitry may be distal from its site 

of synthesis.

In the many brain regions, 

electrophysiological recordings and 

immunolocalization reveal a more 

complicated story.11,64,79–81 Nicotinic 

receptors can be located presynaptically, 

postsynaptically, and nonsynaptically 

(e.g., aggregates of a7 nAChR) on both 

pyramidal and nonpyramidal interneurons. 

Differential subcellular localization can, 

in turn, lead to at least three different, 

and often complementary, outcomes on 

cell response.9,12,64 First, when located 

presynaptically, depolarization through 

these nAChRs can add to or sustain 

the activation of voltage-gated calcium 

channels to enhance neurotransmitter 

(either excitatory or inhibitory) release. 

Second, when nAChRs are localized to 

the postsynaptic face of the synapse, they 

can participate directly in promoting fastexcitatory neurotransmission. Finally, as 

when located in lipid rafts, the collective 

activation of these receptors can directly 

affect the intensity of local intracellular ion 

concentrations to infl uence downstream 

pathways, leading to changes in gene 

expression, metabolic and physiological 

stasis, and even proteolytic mechanisms.

One example of when different nAChRs 

are coexpressed to combine to modulate 

the overall tone in a local circuit involves 

the GABAergic interneurons of the 

hippocampus. In fact, no fewer than three 

subclasses of these inhibitory interneurons 

can be distinguished on the basis of the 

unique expression of different nAChR 

subtypes64,79 such as a7, a4b2, and a3b4, 

respectively, although some interneuron 

subtypes also express combinations of these 

receptor types and possibly others.75 These 

interneurons collectively play an important 

role in the magnitude of GABAergic 

inhibition exerted by nAChR activation in 

the CA1 region. For example, activation of 

interneurons harboring the a7 nAChR in the 

stratum lacunosum moleculare is strongly 

inhibited to produce a selective disinhibition 

of the dendritic segments of pyramidal 

neurons innervated by axon terminals of 

the perforant path. In contrast, activation 

of a4b2 nAChRs inhibit interneurons in the 

stratum radiatum and stratum lacunosum 

molecular to produce disinhibition of 

dendritic areas innervated by both neuron 

types. Moreover, nAChR immunoreactivity 

has been localized to astrocytes in this 

brain region.69,81 Given that these astrocytes 

release agents that interact with glutamate 

receptors to maintain excitatory “tone,”82

it is not surprising that the nAChRs have 

been implicated in neurological diseases 

ranging from schizophrenia to Tourette 
143

Monograph 20. Phenotypes and Endophenotypes

syndrome to neurodegeneration, as seen in 

Alzheimer’s disease (fi gure 4.3). Therefore, 

when assessing the impact of nAChRs with 

respect to nAChR subunit composition, 

the magnitude of expression, the site 

of fi nal receptor localization, and the 

anatomical context in which nAChRs are 

expressed are important considerations 

in unraveling the strain-specifi c effects of 

nicotine responses.

Nicotine’s Function as Agonist 

and Antagonist

Ligand-activated ion channels, by 

necessity, are transmembrane proteins that 

when fully assembled create a hydrated 

receptor channel permeable to selected 

ions that are regulated by conditions 

of the extracellular, intracellular, and 

transmembrane environment (fi gure 4.2). 

Notably, ion-channel receptors reside in 

a constant equilibrium between open and 

closed states, and their tendency to open is 

carefully regulated through the presence 

of neurotransmitters or other agents 

that broadly fall under the functionally 

defi ned categories of agonist (activator) 

or antagonist (inhibitor). Nicotine is most 

often viewed as an agonist of nAChRs. 

However, this compound is not rapidly 

degraded or transported away from the 

receptor (as are normal endogenous 

transmitters), and in the absence of 

compensating mechanisms, the sustained 

presence of an agonist would lead to 

increased receptor opening and, in turn, 

cell death (fi gure 4.2). The compensatory 

mechanism in this case is the process of 

receptor desensitization, which permits the 

receptor to close in the presence of sustained 

agonist exposure. When acetylcholine is the 

neurotransmitter, desensitization is brief, 

because this endogenous transmitter is 

rapidly degraded by acetylcholine esterases. 

In contrast, nicotine may accumulate in 

the receptor vicinity; this has the effect 

of actually favoring the desensitized 

or the nonfunctional state. Notably, 

persistent, elevated concentrations of an 

agonist can even result in a state of deep 

desensitization that can lead to complete 

receptor inactivation or degradation.12

In practical terms, this produces cases in 

which nicotine becomes a potent inhibitor 

of receptor function that can actually exceed 

the antagonism accomplished by many 

pharmacological agents designed for this 

purpose. Consequently, the effect of nicotine 

on a system may, in some cases, be more 

accurately ascribed to sustained receptor 

inactivation rather than to activation.

Whether activation or desensitization 

dominates the effect of nicotine is, in part, 

determined by the receptor subtype(s) 

expressed. For example, some receptors 

are activated by very low concentrations 

of nicotine but become desensitized as the 

concentration increases (e.g., a7 subtypes), 

while others (a3b4 subtypes) may be fully 

activated only at concentrations at the 

high end of physiological relevancy.12,64

In some cases, both conditions may occur, 

as in the nucleus accumbens, where most 

nAChRs desensitize (or even inactivate) 

rapidly to nicotine,83 yet dopamine overfl ow 

related to nAChR activation persists well 

after exposure.84–86 Other mechanisms 

associated with nicotine’s actions may 

actually be imparted through indirect or 

conditional mechanisms. These include 

the production of nicotine metabolites or 

mediators of stress responses such as salt 

imbalance (especially if nicotine tartrate is 

used) or local pH (also note that nicotine 

is most stable when acidic).87 Further, 

the differential expression of nAChRs by 

multiple cell types can collectively infl uence 

the activation of additional signaling 

pathways such as those that are downstream 

of cyclic adenosine monophosphate–

response element binding (CREB) activation 

(see below and Brunzell and colleagues88) 

or the enhancement of nitric oxide 

release.89,90 So, while the focus of this review 

is on the impact of this drug on immediate 

nicotinic cholinergic mechanisms and 
144

4. Mouse Models and the Genetics of Nicotine Dependence

responses, a full causal account could be 

highly complex because other signaling and 

neurotransmission systems are, no doubt, 

also involved. While this has produced 

some confusion, it emphasizes the need 

to view the nAChR system as a modulator 

of physiological “tone.” This also includes 

the infl uence of the rate of nicotine 

administration, its absolute dose, and its 

local persistence. This suggests that the 

route of administration is a vital element of 

experimental design and is discussed later 

in this chapter.

Nicotinic Receptor Upregulation

When a tissue receives sustained exposure 

to nAChR ligands such as nicotine, the 

curious phenomenon of upregulation 

occurs. This was fi rst recognized when 

quantitation of high-affi nity nicotinebinding sites from brain tissue taken from 

rats13 or DBA/2 mice exposed to chronic 

nicotine,14 revealed that such sites were 

increased by as much as fourfold over 

nonnicotine-treated controls. Further, 

this was similar to the increased number 

of high-affi nity nicotine-binding sites 

measured in brain tissues from smokers.91,92

Subsequently, upregulation has been 

measured directly using brain imaging 

methods such as quantitative dynamic 

single-photon-emission computed 

tomography of the living baboon brain93 and 

in human smokers.94 Immunoprecipitation 

studies of the high-affi nity nicotinebinding sites in rats fi rst demonstrated 

that the a4b2 subtype of nAChR was 

essential to both high-affi nity binding 

and coincident upregulation in response 

to nicotine.70 This property is intrinsic to 

this receptor-subunit composition because 

upregulation of this receptor occurs in all 

animals so far examined and in receptors 

expressed in heterologous expression 

systems including transfected human 

embryonic kidney cells.95 The importance 

of subunit composition is critical to the 

relative degree of upregulation, and in 

fact, not all nAChRs exhibit this property 

(e.g., all receptors harboring a4b2 appear 

to upregulate, while receptors composed of 

a4b4 show reduced upregulation, and those 

harboring a3 do not upregulate95). Mice 

defi cient for b2 through subunit knockout 

exhibit essentially no high-affi nity binding 

sites and do not upregulate receptors.96,97

Therefore, the identity of the alpha and 

beta subunits contributes qualitatively 

and quantitatively to upregulation. This 

phenomenon, which has been related to the 

development of reinforcement (see Picciotto 

and colleagues98 and below), produces a 

situation in which there are more receptors 

but the overall function is reduced.12,64 Also, 

because upregulation infl uences receptor 

subtypes preferentially, the importance of 

this process to optimizing the performance 

of local circuitry is likely to be equally 

specialized (fi gure 4.3), as indicated by the 

dominance of a4b2 in central systems such 

as the basal ganglia and hippocampus versus 

the autonomic nervous system, where 

the majority of receptors are composed of 

a3b4 subunits.17,73,99

The cellular mechanisms underlying 

this important cellular phenomenon 

are not yet resolved, and the published 

explanations can differ, often from the 

same laboratory.100–102 What these studies 

and others seem to suggest is that multiple 

mechanisms underlie this effect, including 

increased assembly effi ciency,103 altered 

receptor stoichiometry,46,102 increased 

export from the endoplasmic reticulum 

and increased surface traffi cking,101,104

altered affi nity for ligand,105 and decreased 

degradation.100,106 It is likely that 

upregulation refl ects the summation of 

several processes whose regulation and 

relative contribution depend upon the 

cell type, the subunits expressed by a 

cell, and conditions in the surrounding 

environment (fi gure 4.4).

Finally, some reports suggest that some 

receptors may downregulate in response 
145

Monograph 20. Phenotypes and Endophenotypes

to nicotine. For example, chronic exposure 

to nicotine downregulates the expression 

of a6-containing receptors in mice and 

rats;107,108 however, those composed of 

a6b3 are unregulated by chronic nicotine 

in transfected cells.106 Therefore, while 

the understanding of upregulation, and 

possibly downregulation, remains cloudy, 

the importance of this process to the 

outcome of nicotine’s effects on behavior 

are certain and are revisited below. Thus, 

the issue of receptor down- or upregulation 

is just one more consideration that 

investigators must ponder when attempting 

to link genetic variants and physiological 

substrata with dependence-related 

phenotypes.

Routes of Nicotine 

Administration: 

Interaction of the Drug 

with Physiological 

Systems

Just as age, gender, and general health 

affect nicotine metabolism in people,15,16,109

the same is true of mice (fi gure 4.5). 

Also, how nicotine enters the body can 

affect nicotine effects in ways that must 

be carefully considered when attempting 

to draw inferences about nicotine actions 

and their genetic bases. Although plasma 

levels of nicotine are easily determined, 

such levels may not accurately refl ect 

functional exposure of critical, or targeted, 

tissue. In fact, tissue levels of nicotine 

can vary dramatically from plasma 

levels. Researchers have characterized 

the effects of 24 hours of constant 

intravenous (IV) infusion of nicotine on 

tissue distribution and concentration of 

nicotine in rabbits.8,15,16,109 In such studies, 

the brain, heart, liver, and gastrointestinal 

tract contained three- to fourfold more 

nicotine than did the plasma, whereas 

the increment in muscle and lung was 

approximately twofold. The major site of 

excretion, the kidneys, can exceed a 21-fold 

increment. One curiosity is that adipose 

tissue exhibits relatively poor nicotine 

retention (approximately one-half the 

concentration of plasma). Nicotine also 

crosses the placenta16 and is concentrated 

in breast milk in which concentrations 

can reach threefold that of plasma.8

Nicotine also concentrates in the brain or 

lungs following direct infusion and may 

achieve concentrations tenfold greater 

than those of the plasma. Finally, nicotine 

delivered directly to the rat lung becomes 

concentrated in that tissue and is slow to 

enter into circulation.110 These data show 

highly variable concentrations of nicotine 

in different tissues or body compartments 

and that specifi c concentrations refl ect 

route of administration.

An often overlooked concern is that 

some methods of administration may 

introduce undesirable contaminants, such 

as lipopolysaccharides (LPSs), or induce 

local infl ammatory events. Using saline 

as the control in these experiments is 

inadequate in that the contaminants in 

the saline are generally nonpyrogenic 

(LPS-free), whereas nicotine from the 

shelf or other commercial sources is 

likely to have been contaminated at some 

time with small amounts of bacteria. 

Several researchers have discussed this 

issue.111–113 It is possible that conditions 

related to chronic infl ammation such as 

fatigue and cachexia could complicate, 

and even be confused with, the drug 

effect. Consequently, the duration of 

exposure, route of administration, and the 

tissue being examined are important 

variables when assessing the effects of 

nicotine. Therefore, since no single route 

of administration models all aspects of 

the behavioral components of nicotine 

dependence, it is important to carefully 

defi ne the behavior or motivational 

phenomenon of interest and to employ 

a route of administration that is both 
146

4. Mouse Models and the Genetics of Nicotine Dependence

theoretically relevant and does not create 

artifacts that mask or distort target effects. 

A review by Matta and colleagues8 provides 

an outstanding and comprehensive resource 

for questions concerning routes of nicotine 

administration for the mouse.

Intravenous Nicotine 

Administration

IV administration is a commonly used 

delivery system to study the effects of 

nicotine. The tendency for a behavior 

to become routine or automatic may 

contribute to high levels of drug use.114,115

Similarly, cues associated with nicotine 

self-administration may also elicit strong 

dopaminergic neurotransmission and 

instigate increased self-dosing.83 If these 

elements are not represented in the 

phenotype, important genetic bases of 

nicotine dependence and vulnerability 

may go undetected. One way this has 

been dealt with, especially in rats, is 

through establishing regimes that allow 

for nicotine self-administration through 

control of IV injection of nicotine.116–120

This introduces a rapid rise in plasma 

nicotine during active cycles that 

resembles the pulselike use of nicotine 

seen in humans. One disadvantage is that 

active cycles of infusion may produce 

nicotine concentrations that exceed 

plasma concentrations achieved by 

normal physiological routes by possibly 

as much as 10-fold.121 This exceedingly 

high concentration of nicotine can 

produce unanticipated and possibly 

nonphysiological effects, including rapid 

and generalized receptor desensitization 

(or even receptor inactivation). Also, 

the drug can readily exceed 100 microns 

in the vicinity of the injection to produce 

undesirable side effects, such as neuronal 

death by excitotoxicity.122–124 Therefore, 

the control of nonspecifi c effects (possibly 

via a receptor knockout mouse as described 

below) is important if an experimental goal 

is to distinguish the behavioral outcome 

as being related to a rapid increase in 

receptor activation or possibly reduced 

receptor function.

Figure 4.5 Genetic Infl uences on Nicotine Tolerance and Self-Administration

Note. A comparison showing the often dramatic difference for key responses to nicotine administration by different mouse strains 

are shown for C3H/HeN and C57BL/6 mice. These data are taken directly from Table 6 of a study by Crawley and colleagues7 in 

which similar values for additional effects of nicotine that include as many as 16 additional strains can be found. As reported

there, the threshold tolerance dose is reported in milligrams per kilogram (mg/kg) of nicotine infused per hour and refl ects the 

minimal infusion dose that increased the effective dose for nicotine to reliably produce tolerance on activity and temperature 

thresholds. The maximal dose (IC50) for nicotine consumption (mg/kg/day) and the nicotine concentration (microgram per milliliter) 

that decreases preference ratios to 50 percent are shown.
147

Monograph 20. Phenotypes and Endophenotypes

Subcutaneous Nicotine 

Administration

The osmotic minipump,125–127 an effi cient 

subcutaneous method for administration 

of nicotine into the periphery or brain, 

has been used with success for many years 

(for a review, see Marks and colleagues14). 

It permits relatively prolonged periods of 

exposure (perhaps as long as six weeks) 

and affords control over the rate and 

timing of infusion. In addition, removal 

of the minipumps allows investigators to 

match the specifi c duration of withdrawal 

with changes in cellular events. Thus, 

for experiments in which withdrawalinduced changes in cell signaling are 

assessed, use of the minipump may be 

more advantageous than methods that do 

not allow for precise timing of cessation 

of nicotine administration, such as oral 

self-administration.

As with every method of chronic nicotine 

administration, some disadvantages exist, 

but they are often specifi c to experimental 

design and focus. For instance, implantation 

and removal of the pump requires minor 

surgery; although the surgery lasts fewer 

than fi ve minutes, the use of anesthesia 

or introduction of other confounding 

effects such as animal handling may be 

problematic for some studies. Also, a 

potential problem is that weight gain could 

affect dosage;8

 however, this depends on the 

length of the study and may be a greater 

concern for studies in rats, which may show 

greater weight gain than do mice. Finally, 

any discussion of methods of chronic 

nicotine administration must consider how 

the model relates to features of human 

nicotine dependence that may be important 

to elucidating its genetic substrata. 

For instance, the minipump system does 

not involve a self-administration ritual. 

Studies have shown that self-administration 

ritual and response to environmental cues 

may infl uence behaviors related to nicotine 

dependence.128–130 In addition, the minipump 

continuously administers nicotine in 

comparison to episodic administration. 

There is evidence that rate of rise time of 

nicotine receipt in critical brain regions 

determines certain hedonic reactions to 

the drug such as elation and euphoria, 

“buzz,” “rush,” and “high.”131–133 However, 

smokers may exhibit different patterns of 

smoking, with some smokers seeking boosts 

in plasma nicotine levels and other smokers 

attempting to maintain steady-state plasma 

nicotine levels.50,134

As a result, it is not clear how best to model 

human nicotine consumption, and it is not 

clear if different genetics are involved in the 

different consumption patterns. The use 

of different methods of chronic nicotine 

administration in mice would allow for 

this genetic question to be investigated. 

Finally, no matter what method of delivery 

is chosen, it must be remembered that 

the half-life of nicotine in the mouse is 

approximately 7–10 minutes,135 compared 

to approximately 60 minutes in rats136 and 

approximately 120 minutes in humans.137

Thus, intermittent administration of 

nicotine that will produce similar plasma 

steady-state levels of nicotine as seen in 

smokers may be diffi cult to achieve because 

of the short half-life in mice.

Oral Administration

Oral administration of nicotine (e.g., via 

drinking water) has become increasingly 

popular as a method to achieve chronic or 

long-term nicotine exposure in primates 

and rodents.8 This route of administration 

also has some physiological and sensory 

relevance to humans who self-administer 

tobacco or nicotine orally. For instance, 

smokeless tobacco and nicotine aerosols 

involve oral use, and much of the available 

nicotine ultimately is swallowed.138

The main advantage of using drinking 

water as a vehicle is that it is relatively 

easy, inexpensive, and reduces considerably 

the handling and manipulation of the 
148

4. Mouse Models and the Genetics of Nicotine Dependence

animal. In addition, it yields plasma 

nicotine concentrations that are similar 

to those observed in smokers, and because 

most drinking occurs in the evening 

hours, this method reproduces the cyclic 

(episodic) increase and decrease of nicotine 

administration that occurs in smokers.8

For example, drinking-water administration 

yields plasma nicotine levels that range 

from 10–20 nanograms per milliliter 

(ng/mL);8,88,139,140 these levels are similar to 

the lower ranges observed in smokers. 

Further, oral nicotine produces a broad 

range of effects associated with chronic 

nicotine exposure and dependence, effects 

that were originally obtained in animals 

with IV-injection methods—for example, 

receptor upregulation, tolerance, 

neuroprotection,77,141–145 and mousestrain-specifi c responses refl ective of 

physical dependence.146 However, nicotine 

in drinking water, like injection, does 

not replicate the prolonged exposure of 

the oral mucosa to nicotine, which is 

an important determinant of nicotine 

absorption with some methods of human 

self-administration.8

Other problems encountered with this 

method of administration include the 

bitter taste of nicotine (which can limit 

consumption in a strain-specifi c manner8) 

and the failure of some mice to tolerate 

the amount of nicotine consumed, possibly 

because of toxicity and occasionally 

irritation of the gastrointestinal tract. 

The bitter taste is usually overcome by 

supplementing the drinking water with 

1%–2% saccharin. Animals receiving only 

saccharin water are routinely used in 

studies to ensure control for nonspecifi c 

effects.8,88,139,140

For oral administration, it is important 

to use nicotine in the free-base form to 

avoid the complications related to tartrate 

salt. The authors of this chapter found 

that C57BL/6, CBA/HeN, and C3H strains 

can all be administered oral nicotine for 

time periods ranging from several weeks 

to years.81,139,147 Finally, while taste may 

constrain dependence development in some 

mouse strains, this may not be a problem 

to the extent that taste sensitivity plays a 

signifi cant role in affecting dependence 

vulnerability in humans.148

In terms of how route of administration 

affects intake and dose, reports examining 

the biological activity of cotinine—the major 

metabolite of nicotine—raise interesting 

questions about the impact of nicotine 

and the suitability of different dosing 

paradigms. Administration of cotinine to 

rhesus monkeys and rats can recapitulate 

many of nicotine’s effects, including 

protective effects to differentiated PC-12 

cell survival, but it fails to induce receptor 

upregulation.149 However, cotinine is 

generally much less effi cacious than is 

nicotine,143 although it occurs at greater 

concentrations in the plasma. This must 

be kept in mind when evaluating nicotine 

effects yielded by systems such as direct 

nicotine infusion into the brain. This 

strategy provides little opportunity for 

nicotine metabolism and might obviate 

the effects of cotinine in a manner 

inconsistent with human nicotine use 

and dependence. At the very least, such 

pathways must also be considered when 

assessing the genetic contribution to 

the addictive process, especially when 

extrapolating to humans.

Finally, the route of administration may 

have additional unintended consequences 

on the outcome of experiments and possibly 

mask important aspects of nicotine biology. 

For example, different routes of nicotine 

administration (e.g., injection versus 

oral) may enhance or abrogate nicotine’s 

anti-infl ammatory effects in ways not yet 

entirely defi ned.24,150 Curiously, to the 

extent that nicotine administration is 

anti-infl ammatory, it would allow some 

mouse strains to tolerate experimental 
149

Monograph 20. Phenotypes and Endophenotypes

conditions that would otherwise be 

intolerable because of infl ammatory 

complications. In effect, alternative routes 

of nicotine administration can affect proinfl ammatory systems differently; this 

may compromise both reproducibility and 

possibly the translatability of results to 

other systems. Of course, this remains to 

be clearly demonstrated experimentally, 

but the possibility that the route and rate of 

administration can infl uence experiments 

and even modulate strain-specifi c responses 

to this drug remains an important open 

question.

The Mouse 

Model of Nicotine 

Dependence

Basics of Experimental Design

Researchers using animal models have often 

observed that “mice are not little rats.” 

Similarly, a mouse is not just a mouse; 

different strains can vary dramatically 

in characteristics2–4,7 that may directly 

or indirectly affect phenotypic measures 

The Metabolic Fate of Nicotine

The extent to which the features of nicotine use and dependence are related to metabolite levels 

and actions requires further investigation. What is clear is that catabolism of nicotine promotes 

differential responses to nicotine and that both route of administration and strain-specifi c 

genetics contribute to this effect. There is evidence that metabolism is an important infl uence 

on nicotine self-administration and magnitude of drug effect. For example, studies of rats, 

mice, and humansa

 show signifi cant intraspecifi c variability in nicotine metabolism, including 

differences in plasma levels of several major catabolites such as cotinine.b,c,d Among humans, 

disparities in nicotine intake have been directly related to differences among individuals in their 

respective rates of nicotine catabolism.e

 Further, altered oxidation of nicotine,c,f and conversion to 

cotinine in some individuals, corresponds with an allelic form of the principal enzyme of nicotine 

metabolism, CYP2A6.a,c In mice, the CYP2A6 homologue is Cyp2a5; this, too, appears to contribute 

to differential nicotine consumption behaviors between strains, as witnessed in male F2 mice that 

exhibit increased Cyp2a5 expressiong

 and corresponding changes in metabolic rates and increased 

nicotine self-administration. Basically, slow metabolism may produce an accumulation of nicotine 

and toxicity (e.g., activation of muscle receptors or autonomic neurons), and thereby contribute to 

limited intake. Rapid clearance, however, decreases the effective pharmacological dose.

a

Swan, G. E., N. L. Benowitz, C. N. Lessov, P. Jacob 3rd, R. F. Tyndale, and K. Wilhelmsen. 2005. Nicotine 

metabolism: The impact of CYP2A6 on estimates of additive genetic infl uence. Pharmacogenetics and 

Genomics 15 (2): 115–25.

b

Svensson, C. K. 1987. Clinical pharmacokinetics of nicotine. Clinical Pharmacokinetics 12 (1): 30–40.

c

Messina, E. S., R. F. Tyndale, and E. M. Sellers. 1997. A major role for CYP2A6 in nicotine C-oxidation by 

human liver microsomes. Journal of Pharmacology and Experimental Therapeutics 282 (3): 1609–14.

d

Terry Jr., A. V., C. M. Hernandez, E. J. Hohnadel, K. P. Bouchard, and J. J. Buccafusco. 2005. Cotinine, a 

neuroactive metabolite of nicotine: Potential for treating disorders of impaired cognition. CNS Drug Reviews

11 (3): 229–52.

e

Perez-Stable, E. J., B. Herrera, P. Jacob 3rd, and N. L. Benowitz. 1998. Nicotine metabolism and intake in 

black and white smokers. JAMA: The Journal of the American Medical Association 280 (2): 152–56.

f

Siu, E. C., and R. F. Tyndale. 2007. Non-nicotine therapies for smoking cessation. Annual Review of 

Pharmacology and Toxicology 47:541–64.

g

Nakajima, Y., A. M. DelliPizzi, C. Mallouh, and N. R. Ferreri. 1996. TNF-mediated cytotoxicity and resistance 

in human prostate cancer cell lines. Prostate 29 (5): 296–302.
150

4. Mouse Models and the Genetics of Nicotine Dependence

(fi gure 4.5). Although the laboratory rat has 

been used extensively for examining the 

behavioral effects of nicotine, the mouse as 

a model system is relatively new. The basic 

premise that the mouse model can be 

successfully exploited to reveal human-related 

traits is supported by more than three decades 

of successful translational research conducted 

by immunologists and cancer biologists. 

Although the application of the mouse to 

complex behavioral traits is relatively new, 

the popularity of books such as What’s Wrong 

with My Mouse?151 and similarly oriented 

introductory “how-to” Web sites is indicative 

of the growing interest in this model system.

Numerous mouse-rat differences make it 

hazardous to extrapolate across these species. 

For instance, the mouse is, in general, 

much less sensitive to nicotine than is the 

rat.8 Nevertheless, the mouse has several 

advantages. First, the mouse model has a long 

and detailed record for being used successfully 

to measure the effects of nicotine on behavior, 

physiology, biochemistry, and a variety of 

diseases. Second, the mouse is particularly 

amenable to well-defi ned genetic and 

pharmacological experimental manipulation, 

and this model has been used successfully to 

reveal key nAChRs important in mediating 

the effects of nicotine. Third, heterogeneity 

in the response of different mouse strains 

to nicotine provides a valuable opportunity 

to identify strain-linked genetic differences 

that affect the magnitude and persistence of 

nicotine’s effects. Fourth, genes can be readily 

manipulated through methods of homologous 

recombination. Finally, mice are much 

cheaper to acquire and maintain in large 

numbers. Although the many mouse varieties 

provide a remarkable array of experimental 

opportunities, the selection of the strain 

appropriate to the experimental paradigm is 

crucial, and this topic is discussed below.

Selecting a Mouse Strain

Ultimately, the goal for using mice in the 

study of nAChR biology is to understand 

how nicotine use leads to dependence. 

Chronic nicotine use and the phenotypes 

of dependence are closely associated, 

in both humans and other animals, with 

concurrent physiological changes in nAChR 

function and expression. The measurement 

of acute and chronic effects of nicotine 

administration in at least 19 mouse strains 

has yielded a remarkable database. This 

database quantitatively describes multiple 

genetically infl uenced physiological and 

behavioral differences in the effects of 

nicotine exposure.7

Studies using genetic manipulation of 

mouse nAChR subunits in combination with 

pharmacological and functional measures 

are beginning to add experimental details 

that contribute to the understanding of 

strain-related results and to the design 

of future genetic analyses of nicotine 

dependence. Excellent resource information 

is available regarding these selections in an 

extensive and growing database including 

hundreds of strain- and gender-specifi c 

behavioral and physiological traits of mice 

that can be accessed on the Web from the 

Mouse Genome Informatics database.152,153

Upon selecting the best strain, it is 

important to ensure it has the desired 

phenotype and genotype. For example, 

certain inbred colonies can undergo 

subtle genetic drift relative to a colony at 

another institution, making it important 

that the strain of mouse selected actually 

exhibits the reported traits. One example 

of the infl uence of strain-related genetic 

drift is that very young DBA/2 mice 

exhibit sensitivity to auditory seizure 

if purchased from Jackson Laboratory 

but not if obtained from Charles River 

Laboratories.154 The problem of unknown 

genotype can be particularly serious in 

studies that routinely mix multiple strain 

backgrounds during the production of 

knockout or knockin mice. This can lead to 

unexpected phenotypes. Problems caused 

by strain mixture may arise when a newly 
151

Monograph 20. Phenotypes and Endophenotypes

made transgenic mouse is crossed with a 

parent from a different strain. For instance, 

transgenic mice may be crossed to CBA 

mice to increase hybrid vigor and enhance 

the possibility of obtaining offspring. 

However, CBA mice experience hereditary 

retinal degeneration that, despite their 

dark black eyes, renders them severely 

visually impaired, if not blind. When these 

animals are used in experiments that require 

the use of visual cues for the behavioral 

endpoint, they can produce spurious results 

of limited value (e.g., see discussion by 

Crawley and colleagues7).

Similar problems of unexpected phenotypes 

can occur in homologous recombination 

experiments that are most easily 

accomplished in stem cells from two mouse 

strain backgrounds (129 and FVB/N) and then 

commonly backcrossed into the C57BL/6 

mouse. Notable, and often substantial, 

differences in the basic neuroanatomy 

between the 129 and B6 strains are of 

suffi cient relevance to warrant one mouse 

brain atlas to show these differences side 

by side (e.g., corpus callosum agenesis in 

strain 129155). As a consequence, it is not 

surprising that 129/Sv mice are impaired on 

many learning tasks.7 Crossing them with 

other mouse strains produces a complex 

background in which the respective parental 

gene interactions and related but ill-defi ned 

environmental interactions156 can impart a 

signifi cant range of interactive effects not 

necessarily controlled for by litter mates.

When using backcrossed mice, an 

important issue is how many backcrosses 

ensure genetic background homogeneity. 

One common approach is to use mice 

following 6 to 10 backcrosses into the desired 

parental strain. However, this method is 

highly subjective. Detailed analyses suggest 

that even after 10 backcrosses, which can 

take three years to complete, there is little 

guarantee that strain purity will exceed 

the optimal target of approximately 99%. 

The purity of the parental background can 

vary considerably among offspring; by some 

estimates, as much as ~5% of the genetic 

variation may remain even after as many as 

50 random backcrosses.156 Ideally, markerassisted, accelerated, backcrossing strategies, 

also referred to as speed congenics, should 

be used to optimize backcrossing effi ciency, 

minimize the time required to accomplish 

the optimal genetic background, and ensure 

optimal genetic uniformity among the 

animals being compared. In this method, 

strain-specifi c PCR-based procedures that are 

commercially available (e.g., Charles River 

Laboratories, Harlan Sprague, Dawley, Inc., 

or Jackson Laboratory) permit assessment 

of strain-specifi c DNA marker density of at 

least 15 centimeters on all chromosomes for 

no fewer than 15 commonly used strains. 

This quantifi es background contamination 

and permits selection of strains with more 

than 95% of the desired parental strain 

background, often with fewer than fi ve 

backcrosses.

Nicotine 

and Behavioral 

Changes

Addictive substances share common 

features, including an ability to produce 

behavioral reinforcement, promote selfadministration, and alter reward-related 

behavior. The mouse model recapitulates 

each of these basic features of dependence 

and has facilitated the identifi cation of 

genetic, neural, and behavioral substrata 

promoting these changes. This section will 

review mouse models of reinforcement, 

self-administration, reward, and tolerance, 

with emphasis on the genetic and neural 

systems that are implicated by these models.

Reinforcement

The reinforcing properties of nicotine are 

often assessed by studies that measure 

the ability of nicotine to maintain 
152

4. Mouse Models and the Genetics of Nicotine Dependence

self-administration. In mice, both IV and 

oral nicotine self-administration models 

have demonstrated nicotine reinforcement. 

Both will be discussed below. As a note, 

the reporting of nicotine doses varies across 

studies. Some studies report nicotine doses 

as base weight, and some studies using 

nicotine tartrate salt report nicotine doses as 

salt weight. To facilitate comparisons across 

studies, doses are standardized to refl ect base 

weight. In addition, one of the diffi culties 

in comparing genetic infl uences across 

studies is that methodologies often vary. 

Factors that can vary across studies include 

strains used, doses used and effective doses, 

routes of administration, and treatment of 

the mice. This chapter attempts to provide 

information on doses tested, strains used, 

and methodological variables for the selfadministration studies and the studies 

examining reward and tolerance. Factors 

that infl uence reinforcement include nAChR 

properties, genetics, developmental changes, 

and nonnicotinic neural mechanisms. 

The following sections provide an overview 

of these factors.

Intravenous Nicotine SelfAdministration

Inferences Regarding Critical nAChRs

Self-administration has helped both to 

reveal the behavioral properties of nicotine 

reinforcement and to elucidate the neural 

substrates of reinforcement from the level 

of neural area, to receptor subtypes, to 

underlying cell-signaling cascades. Multiple 

early studies suggest that high-affi nity 

nAChRs are involved in the reinforcing effects 

of nicotine. First, IV self-administration 

of nicotine and other high-affi nity nAChR 

agonists was examined in NMRI mice.157

Each self-administration session began with 

administration of a priming infusion of the 

test compound for that session. A range of 

nicotine doses was tested (0.01, 0.03, 0.04, 

and 0.06 milligrams per kilogram [mg/kg]/

infusion); the 0.01 and 0.03 mg/kg/infusion 

doses of nicotine were both associated with 

increased nose pokes (the drug-contingent 

response), but the rate of nose pokes for 

higher doses (0.04 and 0.06 mg/kg/infusion) 

was no different from the rate in yoked 

controls. Self-administration was also seen 

for the high-affi nity nAChR agonists cytisine 

(at doses of 0.025, 0.05, and 0.075 mg/kg/

infusion) and lobeline (at doses of 0.25, 0.5, 

and 0.75 mg/kg/infusion); however, selfadministration of the high-affi nity nAChR 

agonists ABT-418 and epibatidine was not 

seen at the doses tested.

Studies in nAChR subunit knockout 

and knockin mice have produced direct 

evidence in support of the high-affi nity 

nAChRs, especially b2-containing nAChRs, 

in promoting the reinforcing effects of 

nicotine. Mice defi cient in b2 nAChRs 

failed to self-administer nicotine and did 

not develop behaviors consistent with 

reinforcement.96,158,159 In these studies, 

b2 knockout mice were trained fi rst to 

execute nose pokes for cocaine (0.8 mg/kg/

infusion) by responding to an active versus 

inactive port in an operant chamber. After 

achieving an asymptotic level of response, 

wild-type mice were switched to saline or 

0.03 mg/kg/infusion of nicotine and b2; 

knockout mice were switched to 0.03 mg/

kg/infusion of nicotine. The rates of nose 

pokes signifi cantly decreased for wild-type 

mice receiving saline and b2 knockout mice 

receiving nicotine, but not for wild-type 

mice receiving nicotine.

Direct drug infusion has been a powerful 

approach for identifying nAChRs subtypes 

involved in the reinforcing effects 

and also for identifying the associated 

neural substrata. Direct infusion of 

pharmacological agents into the ventral 

tegmental area (VTA), an area shown to 

be involved in drug-seeking behavior, 

has established VTA nAChR involvement 

in self-administration.160 In rats, direct 

infusion of the a4b2 nAChR-favoring 
153

Monograph 20. Phenotypes and Endophenotypes

antagonist dihydro-b-erythroidine (DHbE) 

into the VTA disrupted IV nicotine selfadministration, suggesting that the 

reinforcement effect of nicotine emanated 

from high-affi nity nAChRs in the VTA, such 

as the a4b2 nAChR.161

Studies in mice have similarly demonstrated 

the involvement of VTA high-affi nity nAChRs 

in the reinforcing effects of nicotine. Besson 

and colleagues162 examined if wild-type and 

b2 knockout mice would self-administer 

either the vehicle, 100 ng of nicotine, 

or 200 ng of nicotine directly into the VTA. 

This study used a hybrid self-administration 

paradigm that required the mice to navigate 

a Y-maze. One arm of the Y-maze was 

associated with direct infusion of nicotine 

into the VTA. Wild-type mice showed a 

high level of self-administration for both 

doses of nicotine, and self-administration 

decreased when nicotine was replaced with 

the vehicle. In contrast, b2 knockout mice 

had equal levels of the vehicle and nicotine 

self-administration. However, when nicotine 

was switched to morphine, the level of drug 

self-administration in b2 knockout mice 

increased. This suggests that b2-containing 

nAChRs in the VTA are not involved in 

generalized reinforcing effects of drugs of 

abuse but are specifi cally involved in the 

reinforcing effects of nicotine. This was 

further substantiated163 by showing that viralmediated reexpression of the b2 subunit in 

the VTA of b2 mice would restore IV nicotine 

self-administration. This study also used the 

hybrid Y-maze self-administration paradigm 

with one arm associated with direct infusion 

of 36 ng of nicotine into the VTA. Infusion 

of nicotine into the VTA of wild-type mice, 

but not b2 knockout mice, was reinforcing. 

However, the reexpression of the b2 subunit 

in the b2 knockout mice was associated 

with increased intra-VTA nicotine selfadministration.

Because b2 is widely expressed in the 

nervous system and is a promiscuous 

subunit, subsequent studies explored 

the roles that different alpha subunits 

play in processes that may mediate selfadministration. Such studies have revealed 

that a6 is particularly important to neurons 

of the VTA and basal ganglia,107,164–167 where 

it forms receptors composed of a4a6b2b3 

subunits.68 In fact, receptors harboring a6 

may be disproportionately upregulated,102

or possibly downregulated,107,108 in response 

to chronic nicotine. In either case, 

this reinforces the hypothesis that this 

subunit plays a special role in the effects 

of nicotine related to self-administration 

and reinforcement.

In summary, studies demonstrate an 

important role of b2-containing nAChRs in 

the reinforcing effects of nicotine. Factors 

that infl uence b2-containing nAChR 

function, such as the inclusion of other 

subunits (e.g., a6) or other less defi ned 

genetic considerations, should alter nicotine 

self-administration. However, genetic 

infl uences on b2-containing nAChR function 

may not be the only factors that contribute 

variation in nicotine self-administration.

Behavioral Genetics of SelfAdministration

Behavioral genetics studies that have 

compared oral nicotine self-administration 

across inbred strains of mice have helped 

to advance understanding of the genetics 

of nicotine dependence. For instance, in a 

study that compared oral self-administration 

of nicotine, ethanol, amphetamine, and 

aspartame between C57BL/6 inbred mice 

and DBA/2 inbred mice, clear differences 

were observed.168 Because the focus of this 

monograph is on nicotine, only the treatment 

procedure for nicotine will be presented.

After eight days of habituation to the twobottle-choice cages, one bottle was replaced 

with a 0.38-microgram per milliliter 

(lg/mL) nicotine bottle. After two days, 

the nicotine concentration was increased 

to 0.61 lg/mL for two days, followed by 
154

4. Mouse Models and the Genetics of Nicotine Dependence

0.96 lg/mL for two days. The 0.96-lg/mL 

concentration was followed by four days 

at 1.54 lg/mL, six days at 2.42 lg/mL, and 

then eight days at each of the following 

concentrations: 3.84, 6.14, 9.60, 15.36, 

24.19, and 38.39 lg/mL of nicotine. C57BL/6 

mice displayed a greater preference for 

nicotine than did the DBA/2 mice. This was 

also true for ethanol and amphetamine, 

but not for aspartame, which DBA/2 mice 

preferred. Because C57BL/6 and DBA/2 mice 

are inbred strains, variance in behavior 

between the strains refl ects the infl uence of 

genetics on the behavior. Thus, the results 

from this study demonstrate the existence 

of genetic infl uences on nicotine, ethanol, 

amphetamine, and aspartame oral selfadministration. The study also suggests that 

the genetic infl uences on aspartame selfadministration differ from those affecting 

nicotine, ethanol, and amphetamine selfadministration.

A similar procedure was used in an expanded 

inbred mouse-strain survey of oral nicotine 

self-administration.6

 C57BL/6, C3H, DBA/2, 

BUB, A, and ST/b mice were presented with 

a two-bottle choice: nicotine versus vehicle. 

The vehicle was either water or 2% saccharin. 

The concentration of nicotine changed 

from 10 to 20 to 35 to 50 to 65 to 80 to 

100 to 125 to 160 to 200 lg/mL every four 

days. Increased nicotine concentration was 

inversely related to nicotine consumption, 

and this relationship was infl uenced by 

genetics. The concentration of nicotine that 

produced a 50% decrease in consumption 

relative to the 10 lg/mL concentration was 

compared across strains. This concentration 

was highest in C57BL/6 mice, followed by 

DBA mice, then BUB, A, C3H, and ST/b mice, 

respectively. Saccharin infl uenced nicotine 

intake only in C57BL/6 mice at low nicotine 

concentrations and in ST/b mice across all 

nicotine concentrations.

Comparable results were found in 

another strain survey of oral nicotine 

self-administration.169 A two-bottle-choice 

paradigm was used to compare oral selfadministration of water and escalating 

doses of nicotine (1.75, 3.51, 8.77, 17.54, 

26.31, and 35.08 lg/mL; fi ve days of 

administration per level) across C57BL/6, 

C3H/J, DBA/2, ST/b inbred mice, NMRI 

outbred mice, and an A/J×NMRI cross. 

As reported before, strain differences existed 

in amount of nicotine consumed. C57BL/6 

consumed the most nicotine, followed by 

C3H/J mice, the A/J×NMRI cross, DBA/2 

mice, NMRI mice, and ST/b mice. Clearly, 

genetics contributes to differences in oral 

nicotine self-administration; however, in 

all three studies nicotine consumption 

was lower than vehicle consumption. 

Therefore, in these studies, it is unclear 

if genetics is infl uencing preference for 

nicotine or sensitivity to aversive effects 

of nicotine. One strategy for addressing 

this question would be to compare oral 

nicotine self-administration and IV nicotine 

self-administration across strains of inbred 

mice to determine if genetics of oral and 

IV nicotine self-administration are similar. 

This type of study remains to be done; most 

mouse behavioral genetic studies of nicotine 

self-administration have used oral nicotine 

self-administration.

One study directly tested if preference for 

oral self-administration of nicotine over 

water could be established.170 Outbred 

CD-1 mice were maintained on a waterrestriction schedule with water access 

limited to two hours. Mice were then 

presented with a two-bottle choice of water 

versus 10 mg/L nicotine for a two-hour 

period. In a follow-up experiment, the 

concentration of nicotine was reduced to 

the following levels every two days: 7, 5, 

3.5, and 2.5 mg/liter (L). Mice preferred 

nicotine-containing solutions to water, and 

as the concentration of nicotine decreased, 

the fl uid intake for nicotine, but not for 

water, increased. Individual differences in 

nicotine preference were then assessed. Mice 

were given another choice of a vehicle or 

10 mg/L of nicotine; however, the vehicle 
155

Monograph 20. Phenotypes and Endophenotypes

used a 10% sucrose solution to mask the 

bitter taste of the drug. After six days of 

training, the vehicle was switched to water 

and preference for nicotine was measured. 

A signifi cant preference for nicotine was 

seen on day seven. This preference decreased 

over subsequent days. However, when mice 

were segregated by preference for nicotine 

on day seven, two subpopulations emerged: 

one showed preference for nicotine over 

all days of testing (days 7–10), and the 

other showed no preference for nicotine. 

This study suggests that in outbred mice, 

naturally occurring genetic variance could 

contribute to preference for oral nicotine; 

however, follow-up studies are needed to 

determine if polymorphisms exist between 

the two subpopulations.

A study that strongly suggests that genetic 

variance infl uences oral nicotine selfadministration examined if expression 

of Cyp2a5—the homologue of the 

human gene CYP2A6, which codes for an 

enzyme involved in the metabolism of 

nicotine—is correlated with levels of oral 

nicotine self-administration.171 Because 

C57BL/6 mice show high levels of nicotine 

consumption and ST/b mice show low levels, 

F2 mice from a C57BL/6×ST/b cross were 

segregated into high and low oral nicotine 

consumers to test if levels of Cyp2a5 protein 

similarly segregated. In male F2 mice, 

high nicotine consumption was associated 

with higher levels of Cyp2a5 protein and, 

not surprisingly, faster metabolism of 

nicotine, suggesting that the genes involved 

in nicotine metabolism can infl uence oral 

nicotine self-administration. The same 

effect was not seen in female mice; thus, 

the expression of this phenotype may be 

linked to gender. The fi ndings from this 

study, combined with the other behavioral 

genetics studies reviewed here, demonstrate 

how polymorphisms can alter nicotine selfadministration and potentially infl uence 

nicotine dependence. Although genetic 

factors infl uence nicotine self-administration, 

environmental and developmental factors 

most likely interact with genetics to alter 

nicotine self-administration.

Modeling Developmental Factors 

in Nicotine Reinforcement

A relationship exists between childhood 

exposure to tobacco industry promotional 

activities and risk for initiation of tobacco use 

(for a review, see DiFranza and colleagues172). 

Thus, identifying variables that contribute 

to adolescent nicotine consumption may 

prove critical for the successful treatment 

and prevention of nicotine dependence. 

A limited number of studies in mice have 

examined factors that infl uence oral nicotine 

consumption in adolescent mice. One study 

compared oral nicotine consumption in 

outbred CD-1 mice across early (24–35 days), 

middle (37–48 days), and late (50–61 days) 

adolescence.170 Water-restricted mice were 

given two-hour access to two bottles—water 

versus a 10 mg/L solution of nicotine—for six 

days, after which the nicotine concentration 

was reduced to 7 mg/L for the next three 

days, and then to 5 mg/L for the last three 

days. The youngest group demonstrated 

a preference for nicotine; nicotine and 

water consumption was equal in the midadolescence group, and the late adolescence 

group showed a trend for avoidance of the 

nicotine solution. These results suggest that 

early adolescence may be a critical period for 

increased risk of nicotine consumption.

Two factors that may contribute to 

adolescent nicotine intake are gender 

and novelty-seeking behavior. Gender 

differences in oral nicotine consumption 

by adolescent C57BL/6 mice (35 days of 

age) were examined using a two-bottlechoice paradigm.173 Mice had access to 

the vehicle (2% saccharin) and one of 

six doses of nicotine (10, 25, 50, 75, 100, 

or 200 lg/mL) for seven days. When 

adjusted for body weight, female adolescent 

mice consumed more nicotine than did 

males. Adolescent smoking in humans can 

be associated with increased novelty-seeking 
156

4. Mouse Models and the Genetics of Nicotine Dependence

behavior or attempts to mitigate teenage 

angst and anxiety. A study in C57BL/6 

mice directly examined if individual 

differences in novelty-seeking behavior 

or anxiety correlated with oral nicotine 

self-administration.174 At postnatal day 30, 

novelty-seeking behavior and anxiety were 

assessed using the hole-board activity box. 

After testing, nicotine consumption was 

measured for 10 days using a two-bottlechoice paradigm: water versus 10 lg/L 

nicotine. No correlations existed between 

novelty seeking and anxiety. However, 

adolescent mice classifi ed as high novelty 

seeking consumed more nicotine than did 

adolescent mice classifi ed as low novelty 

seeking; no relation was found between 

anxiety levels and nicotine consumption. 

These results agree with results from human 

research that show that novelty seeking, or a 

personality trait of disinhibition, is a risk 

factor for smoking (chapters 3 and 5).

Involvement of Extra-Nicotinic 

Mechanisms

An interaction among nAChRs and other 

neurotransmitter systems, most notably 

changes in dopamine signaling, appears 

to be critical to the reinforcing effects of 

nicotine in the VTA. One study examined 

involvement of dopamine D1 receptors in 

the reinforcing effects of nicotine infused 

into the VTA.162 Four groups of C57BL/6 

mice were trained in the Y-maze intra-VTA 

nicotine self-administration paradigm. 

Mice received either the vehicle infused 

into the VTA, 10 ng of nicotine infused 

into the VTA, 100 ng of nicotine infused 

into the VTA, or 100 ng of nicotine infused 

2.3 millimeters dorsal of the VTA. The 10-ng 

and 100-ng doses of nicotine infused into 

the VTA were more reinforcing compared 

to vehicle and nicotine infusions dorsal 

to the VTA. The D1 dopamine receptor 

antagonist SCH 23390 and the high-affi nity 

a4b2 nAChR antagonist DHbE blocked the 

reinforcing effects of intra-VTA nicotine 

self-administration. These results suggest 

that nicotine activation of high-affi nity 

nAChRs (e.g., a4b2 nAChRs) in the VTA 

may modulate D1 dopamine receptor 

activity and reinforcement.

Studies suggest that both metabotropic and 

N-methyl-D-aspartic acid (NMDA) glutamate 

receptors are also involved in the reinforcing 

effects of nicotine. Sorger and colleagues 

investigated the role of the metabotropic 

glutamate receptor 5 (mGluR5) in nicotine 

self-administration in DBA/2 mice.175 DBA/2 

mice were trained to execute nose pokes for 

either saline or one of four doses of nicotine 

(0.016, 0.048, 0.16, 0.48 lg/infusion, IV selfadministration). Only the 0.048-lg dose of 

nicotine was associated with increased nose 

pokes compared to the yoked control. This 

increase was blocked by administration of 

2-methyl-6-(phenylethynyl)-pyridine, an 

mGluR5 antagonist. The involvement of 

the glutamate system in IV nicotine selfadministration was further investigated 

by examining the effects of the NMDA 

receptor channel blocker memantine on 

self-administration.176 A yoked experimental 

design was used, and Swiss mice in the 

active chamber executed nose pokes for 

either 0, 0.03, 0.06, or 0.11 lg of nicotine. 

The 0.06-lg dose of nicotine was associated 

with the greatest increase in nose pokes. 

This effect was blocked by memantine. 

Subsequently, however, the pharmacological 

specifi city of this agent has been extended 

to include a7 nAChRs,177 which could 

cloud the interpretation of this effect as 

being mediated solely through NMDA 

receptors. However, as reviewed earlier, 

the reinforcing effects of nicotine appear 

to be largely dependent on high-affi nity 

nAChRs and relatively independent of a7 

nAChRs.11,158,162,178

Clearly, the nicotinic and the glutamate 

systems interact to support selfadministration of nicotine; however, 

the mechanism underlying this interaction 

remains unclear and possibly multifaceted. 

Certainly, glutamate receptor (GluR) 
157

Monograph 20. Phenotypes and Endophenotypes

function is modifi ed by nicotine acting 

through nAChRs, as reported by multiple 

electrophysiological examinations.62,64,179–181

However, additional cell-mediated 

mechanisms, including alteration of 

GluR transport by the neurons182 and the 

modifi cation of susceptibility to proteolysis,183

must be included in considerations of how 

nicotine affects this major excitatory system.

Finally, in addition to dopamine and 

glutamate involvement in the reinforcing 

effects of nicotine, GABA may also 

be involved. The effects of the GABAB

receptor agonist baclofen on IV nicotine 

self-administration was assessed in mice; 

unfortunately, the study did not specify the 

strain of mice.184 Mice in the chamber with 

the active nicotine (0.03 mg/kg/infusion)-

associated port executed signifi cantly more 

nose pokes than did yoked controls. Baclofen 

decreased responding at the nicotineassociated port, suggesting that activation of 

GABAB receptors decreases the reinforcing 

effects of nicotine. A thorough explanation 

for the reinforcing effects of nicotine acting 

through other neurotransmitter systems, 

including dopaminergic, glutamate, and 

GABA, requires more detailed investigation. 

It remains clear, however, that modulation 

of these systems through both nAChRs and 

related polymorphisms is likely to form the 

basis of the complex genetic components 

that combine to defi ne the reinforcing 

effects of nicotine for individuals.

The cellular and molecular changes 

triggered by activation of nAChRs and 

other neuropharmacological systems that 

underlie the reinforcing effect of nicotine 

(e.g., dopamine, glutamate, and GABA 

systems) may involve altered calciummediated cell signaling. Swiss albino mice 

were trained to execute a nose poke for 0, 

0.01, 0.02, 0.03, or 0.04 mg/kg/infusion 

of nicotine.185 The dose-response curve 

was an inverted function; the 0.02- and 

0.03-mg/kg/infusion doses of nicotine 

produced signifi cant increases in nose 

pokes, with the 0.03-mg/kg/infusion 

dose producing the largest change. 

A 2.4-mg/kg dose of the nAChR antagonist 

mecamylamine blocked the reinforcing 

effects of 0.03 mg/kg/infusion of nicotine 

but had no effect on responding in the saline 

control group. In addition, the L-channel 

calcium antagonist isradipine dose 

dependently inhibited the reinforcing 

properties of 0.03 mg/kg/infusion of nicotine 

without altering baseline levels of nose 

pokes. Additional work is needed to further 

elucidate the molecular substrata of the 

reinforcing effects of nicotine because 

genetic infl uences on these substrata could 

contribute to variability in the reinforcing 

effects of nicotine.

In summary, self-administration studies 

in mice that examined the reinforcing 

effects of nicotine have demonstrated that 

mice will self-administer nicotine and have 

identifi ed the neural and genetic substrata 

involved. For example, these studies have 

demonstrated that high-affi nity nAChRs 

in the VTA are involved in nicotine selfadministration and that calcium-mediated 

cell signaling is also involved. In addition, 

these studies have shown that genetic 

variation contributes to variation in nicotine 

self-administration, and they have identifi ed 

the Cyp2a5 gene, which is involved in 

nicotine metabolism, as a gene potentially 

linked to nicotine self-administration. 

Finally, these studies have identifi ed 

potential risk factors that may, in general, 

contribute to adolescent nicotine use: 

age, gender, and risk-taking behavior.

Nicotine and Reward

Conditioned place preference (CPP) is used 

as a model to investigate the rewarding 

effects of nicotine.186 Nicotine administration 

is repeatedly paired with one chamber, 

and saline administration is repeatedly 

paired with a second. The mouse or rat 

is then given access to both chambers, 
158

4. Mouse Models and the Genetics of Nicotine Dependence

and greater time spent in the chamber 

previously paired with nicotine is taken 

as a measure of preference for nicotine. 

In addition to measuring the rewarding 

properties of nicotine, CPP also measures 

the ability to form associations between 

the effects of nicotine and a contextual 

environment. Thus, for any manipulation 

that disrupts CPP, it must be determined 

if the manipulation is altering learning or 

reward processes. Experiments using CPP to 

investigate the rewarding effects of nicotine 

in mice have identifi ed procedural variables 

that affect the development of nicotine CPP 

and have identifi ed underlying neural and 

genetic substrata involved in CPP.

Research using CPP provides a 

complementary perspective to research 

on self-administration. Although selfadministration should, in theory, refl ect the 

hedonic or rewarding impact of nicotine 

on the mouse, it may also refl ect other 

factors as well. One such factor is nicotine 

metabolism, which might affect tolerance 

to the repeated doses of nicotine used in 

self-administration studies, but be somewhat 

less relevant to the effects of acute doses 

delivered in CPP studies. Other differences 

between the self-administration and CPP 

paradigms could “select out” different 

genetic associates; for example, different 

sorts of learning are involved (Pavlovian 

versus instrumental), and only the selfadministration paradigm permits the 

organism control over drug administration. 

Thus, it is quite likely that the two 

approaches will show different data patterns 

across strains and involvement of different 

neurotransmitter systems. However, the fact 

that all addictive drugs support both drug 

self-administration and CPP acquisition 

suggests that both are sensitive to drug 

reinforcement.

External variables that can infl uence CPP 

include the dose of nicotine and prehandling. 

The effects of four different doses of 

nicotine (0.25, 0.5, 1.0, or 2.0 mg/kg) on 

CPP were tested in Swiss-Webster mice.187

Mice showed a preference for the chamber 

previously paired with 0.5 mg/kg of nicotine 

but avoided the chamber previously paired 

with 2.0 mg/kg of nicotine. No signifi cant 

preference or avoidance was seen for the 

0.25-mg/kg or 1.0-mg/kg doses. This study 

demonstrates that the effects of nicotine can 

shift from rewarding to aversive, depending 

on the dose of nicotine used in mice. 

Another study examined both the effects 

of prehandling on CPP in ICR mice and 

the effects of different doses of nicotine on 

CPP.188 CPPs for multiple doses of nicotine 

(0.1, 0.25, 0.35, 0.5, 0.7, or 1.0 mg/kg) were 

measured; only the 0.5-mg/kg dose was 

associated with CPP, but only for prehandled 

mice. Both studies suggest that a 0.5-mg/kg 

dose of nicotine is rewarding, as measured 

by CPP. In addition, prehandling can affect 

the development of CPP, but it is unclear if 

this is a strain-specifi c effect and specifi cally 

related to CPP or anxiety levels of the mice.

Strain Differences

As discussed, external variables such as 

prehandling can affect CPP, but internal 

factors such as genetics also infl uence 

nicotine CPP. Studies comparing inbred 

strains of mice and studies using selective 

breeding have shown that differences in 

nicotine CPP are associated with genotype.

In a study using selective breeding, three 

lines of mice derived from heterogeneous 

stock mice were tested in CPP: a line in 

which 0.75 mg/kg of nicotine depressed 

locomotor activity, a line in which the same 

dose increased locomotor activity, and a 

randomly bred line.189 A 0.75-mg/kg dose of 

nicotine produced CPP in the line generated 

by random breeding and the line bred for the 

stimulatory effects of nicotine on locomotor 

activity. In contrast, the same dose produced 

conditioned place aversion in the line bred 

for sensitivity to the locomotor depressant 

effects of nicotine. These results suggest that 

genes involved in the psychostimulant effects 
159

Monograph 20. Phenotypes and Endophenotypes

of nicotine may also be involved in the 

rewarding effects of nicotine. Another study 

compared CPP across inbred strains of mice 

to test if natural genetic variance contributed 

to differences in the rewarding effects of 

nicotine. Multiple doses of nicotine were 

tested for CPP in C57BL/6 mice (0.05, 0.1, 

0.3, 0.5, or 0.7 mg/kg) and in DBA/2J mice 

(0.3, 0.7, or 1 mg/kg188). The C57BL/6 mice 

showed signifi cant CPP for the 0.3-mg/kg 

dose of nicotine, but the DBA/2J mice did 

not show even a trend toward CPP. Both of 

these studies demonstrate that genotype 

contributes to phenotype for nicotine CPP.

A direct comparison between the genetic 

infl uences on CPP versus nicotine selfadministration can be made by comparing 

results from studies that contrasted these 

measures in DBA/2 and C57BL/6 mice. 

Studies previously discussed in this chapter 

found that C57BL/6 mice consumed more 

nicotine than did DBA/2 mice.6,168,169 Similar 

differences were found for CPP. These 

results suggest that C57BL/6 mice are more 

sensitive to the effects of nicotine that may 

support dependence. Furthermore, these 

results could suggest that common genes 

are involved in CPP and oral nicotine selfadministration; however, caution must be 

exercised because an extensive comparison 

across multiple inbred strains, using 

multiple nicotine doses and behavioral 

assays, is necessary to strengthen this 

argument.

Involvement of Receptor 

and Neurotransmitter Systems

In addition to identifying genetic infl uences 

on nicotine CPP, mouse studies have also 

examined receptor subtype involvement 

in CPP. The effects of the broad-spectrum 

nAChR antagonist mecamylamine, the highaffi nity nAChR antagonist DHbE, and the 

a7 nAChR antagonist methyllycaconitine 

citrate (MLA) on nicotine CPP were assessed 

in ICR mice.188 Both mecamylamine and 

DHbE signifi cantly decreased nicotine CPP 

for a 0.5-mg/kg dose of nicotine, whereas 

a nonsignifi cant trend toward attenuated 

CPP was seen with MLA. This study suggests 

that high-affi nity nAChRs, such as the a4b2 

nAChR, are involved in the rewarding effects 

of nicotine.

Another study examined the nAChR subtypes 

involved in nicotine CPP by using both 

pharmacological and genetic inhibition 

of nAChR subunits.190 Multiple nicotine 

doses were tested for CPP in (1) C57BL/6 

mice, (2) b2 nAChR subunit knockout mice 

and corresponding wild-type mice, and 

(3) a7 nAChR subunit knockout mice and 

corresponding wild-type mice. C57BL/6 

mice showed signifi cant CPP for 0.3 and 

0.5 mg/kg of nicotine but not for 0.1, 0.7, 

or 1.0 mg/kg nicotine. In b2 knockout mice, 

neither 0.5, 1.0, nor 2.0 mg/kg of nicotine 

produced CPP, but in wild-type mice, both 

0.5 and 1.0 mg/kg produced CPP. The a7 

knockout mice and wild-type mice both 

showed nicotine CPP. Further demonstrating 

a critical role of b2-containing but not 

a7-containing nAChRs in CPP, the a4b2 

antagonist DHbE blocked nicotine 

(0.5 mg/kg) CPP in C57BL/6 mice, but the 

a7 nAChR antagonist MLA had no effect on 

nicotine CPP. Thus, b2-containing nAChRs 

appear to be involved in nicotine CPP.

In addition to the nicotinic acetylcholinergic 

system, other neurotransmitter systems 

may also be involved in CPP. In mice, 

studies have suggested that the adenosine, 

endogenous cannabinoid, and neuropeptide 

systems may all be involved in the effects 

of nicotine on reward. Adenosine 2A (A2A) 

knockout mice and wild-type mice were 

tested for the development of CPP to a 0.18- 

and a 0.35-mg/kg dose of nicotine.191 Wildtype mice developed CPP for 0.18 mg/kg of 

nicotine but not for 0.35 mg/kg of nicotine. 

The A2A knockout mice did not develop CPP 

for either dose. Also, both wild-type mice 

and A2A knockout mice showed conditioned 

taste aversion to saccharin that was paired 

with a 1.75-mg/kg intraperitoneal (IP) 
160

4. Mouse Models and the Genetics of Nicotine Dependence

dose of nicotine. The A2A receptor appears 

to be involved in the rewarding effects of 

nicotine but not its aversive effects. This also 

suggests that A2A is involved in mediating 

the appetitive effects of nicotine and not in 

nicotine-based associative processes.

Another study from the same laboratory 

examined the role of the endogenous 

cannabinoid CB1 receptor in CPP for 

nicotine.191 CPP was tested for 0.04, 0.09, 

or 0.18 mg/kg of nicotine in CB1 knockout 

mice and wild-type mice. The 0.18-mg/kg 

dose of nicotine produced CPP in wild-type 

mice, but no dose of nicotine produced 

CPP in the CB1 knockout mice. The 

antinociceptive effects of nicotine, however, 

were not disrupted in CB1 knockout mice. 

Thus, CB1 receptors may modulate the 

rewarding effects of nicotine, and drugs 

altering the cannabinoid system, such as 

the CB1 antagonist rimonabant, may have 

therapeutic potential for assisting in 

smoking cessation (for a review see Siu 

and Tyndale171).

Multiple studies suggest that the rewarding 

effects of nicotine can be modulated by 

neuropeptides, perhaps through effects at 

the mu opioid receptor. CPP for 0.09, 0.18, 

or 0.35 mg/kg of nicotine was compared 

between preproenkephalin knockout mice 

and wild-type mice.192 Wild-type mice 

developed CPP for the 0.18-mg/kg dose, 

whereas the preproenkephalin knockout 

mice did not show CPP for any dose tested. 

The 0.18-mg/kg dose of nicotine increased 

dopamine levels in the nucleus accumbens 

of wild-type, but not knockout, mice. Thus, 

the endogenous enkephalin system may be 

involved in the rewarding effects of nicotine 

through altering dopamine signaling. 

Preproenkephalin stimulates mu opioid 

receptors, and consequently, mu opioid 

receptors may be involved in the effects of 

nicotine on CPP. Pharmacological studies 

and studies in mu opioid receptor knockout 

mice have directly tested if the mu opioid 

receptor is involved in the rewarding effects 

of nicotine. In NMR1 mice, nicotine CPP 

was successfully demonstrated for 1 mg/kg 

and 2 mg/kg of nicotine but not for 0.5 or 

0.75 mg/kg of nicotine.193 The mu opioid 

receptor antagonist naloxone blocked CPP 

for 1 mg/kg of nicotine, providing evidence 

for the involvement of mu opioid receptors 

in nicotine CPP. Genetic inhibition of mu 

opioid receptor function also disrupts 

nicotine CPP. No CPP for nicotine was 

seen in mu opioid knockout mice for all 

doses of nicotine tested (0.09, 0.18, and 

0.35 mg/kg), which contrasts with fi ndings 

with the wild-type mice that showed CPP 

for the 0.18-mg/kg dose but not for the 

0.09- or 0.35-mg/kg doses of nicotine.194

The mu opioid receptor does not appear to 

be involved in all of the effects of nicotine 

because deletion of the mu opioid gene did 

not alter the locomotor depressive effects 

of nicotine. The processes activated by mu 

opioid receptors, however, that are involved 

in nicotine CPP are not well understood but 

may involve changes in gene expression.

The transcription factor CREB is 

involved in learning and memory,195

and in dependence;196 mu opioid receptors 

may mediate the rewarding effects of 

nicotine through activation of CREB.190

In wild-type mice, 0.35 mg/kg of nicotine 

produced CPP, but 0.70 mg/kg of nicotine 

produced conditioned place aversion. 

The 0.35-mg/kg dose of nicotine was 

associated with increased levels of 

phosphorylated CREB in the nucleus 

accumbens and VTA. Both CPP and the 

increased levels of phosphorylated CREB 

were reduced by pretreatment with 

naloxone. In addition, mu opioid knockout 

mice did not show increased levels of 

phosphorylated CREB after treatment with 

the same dose of nicotine. These results 

suggest that mu opioid receptor activation 

of CREB may be critically involved in 

CPP. In support, the same study found the 

CREBad knockout mice did not show CPP 

for 0.35 mg/kg of nicotine but did show 

conditioned place aversion for 0.7 mg/kg of 
161

Monograph 20. Phenotypes and Endophenotypes

nicotine. Thus, cell-signaling cascades that 

activate CREB may be critically involved 

in the rewarding effects of nicotine, and 

activation of the mu opioid receptor may 

be one pathway that leads to reward-related 

increased activation of CREB. Not all effects 

of nicotine, however, involve activation 

of CREB, as demonstrated by intact, 

conditioned place aversion for nicotine in 

CREBad knockout mice.

Another transcription factor that may be 

involved in CPP is Fosb.197 Both wild-type 

and Fosb knockout mice were tested for the 

development of CPP. At 0.2 mg/kg, wild-type 

mice showed CPP, which shifted to aversion 

at doses of 0.8 and 2.0 mg/kg. In contrast, 

Fosb knockout mice did not develop CPP for 

any dose tested (0.025–2.0 mg/kg) but did 

show aversion for doses of 0.6 mg/kg and 

higher. These results show that while Fosb 

knockout mice can learn (i.e., they show 

conditioned place aversion), they do not 

seem sensitive to the rewarding effects of 

nicotine. Furthermore, the knockout mice 

also showed reduced oral intake of 50-lg/mL 

nicotine in a two-bottle-choice paradigm, 

suggesting that Fosb is involved in processes 

common to CPP and choice of nicotine 

consumption.

Two studies examined the role of the cellsignaling molecule nitric oxide in nicotine 

CPP in Swiss-Webster mice. Nitric oxide is 

critically involved in some forms of synaptic 

plasticity198,199 and may contribute to the 

addictive effects of drugs of abuse such as 

nicotine.200 In one study, mice successfully 

developed CPP for 0.5 mg/kg of nicotine,201

unless given the nitric oxide synthase 

inhibitor 7-nitroindazole (25 mg/kg). 

However, 7-nitroindazole had no effect on 

lithium-chloride conditioned place aversion, 

suggesting that the effects of 7-nitroindazole 

on CPP were not due to a generalized 

learning defi cit. Another study investigated 

if the nitric oxide precursor L-arginine would 

enhance nicotine CPP.202 Multiple nicotine 

doses (0.25, 0.5, 0.75, 1.0, and 2.0 mg/kg) 

were tested in CPP, and both the 1.0- and 

2.0-mg/kg dose produced nicotine CPP. 

Interesting, L-arginine alone also produced 

CPP at doses of 200 and 500 mg/kg, but not 

at doses of 50, 100, or 150 mg/kg. When 

ineffective doses of nicotine and L-arginine 

were paired together, CPP resulted. Both 

the nAChR antagonist mecamylamine and 

the nitric oxide synthase inhibitor L-nitroamino-methyl-ester blocked the acquisition, 

but not the expression of, CPP for the 

1.0-mg/kg dose of nicotine. Together, 

the results from these studies suggest that 

nitric oxide mediates important functions 

associated with acquisition of nicotine CPP.

In summary, mouse research shows that 

nicotine reward is highly dose dependent. 

Specifi cally, as dose is increased from 

inert levels, mice fi rst show robust CPPs, 

but ultimately, place aversions develop 

as doses are progressively increased. 

In addition, even within a strain, doses 

effective for establishing CPP can vary across 

studies. This suggests that CPP is sensitive 

to methodological and environmental 

factors, such as the construction of the 

apparatus and handling. Studies comparing 

inbred mice suggest that C57BL/6 mice 

may be particularly sensitive to the 

rewarding effects of nicotine. Further 

genetic studies are needed to elucidate 

strain differences in the rewarding effects 

of nicotine and to determine if common 

genetic substrata mediate the rewarding, 

aversive, and activating effects of nicotine. 

For instance, the stimulatory effects 

of nicotine and the rewarding effects 

of nicotine may be genetically linked. 

In addition, mouse studies have aided in 

identifying the neural substrates of the 

rewarding effects of nicotine. High-affi nity 

nAChRs, such as the a4b2 nAChR, appear 

to be critically involved in the rewarding 

effects of nicotine. The effects of nicotine 

at nAChRs may result in the activation 

of cell-signaling molecules such as nitric 

oxide and CREB that have been shown to 

be involved in drug dependence.
162

4. Mouse Models and the Genetics of Nicotine Dependence

Tolerance

The rewarding and reinforcing effects of 

nicotine are not the only effects of nicotine 

that contribute to nicotine dependence; 

physiological adaptations that occur with 

chronic nicotine administration may lead 

to nicotine dependence and tolerance. 

Tolerance is a shift in the dose-response 

curve to the right following exposure to the 

drug in question. That is, with increased 

drug exposure, and resulting tolerance, 

an increasing amount of drug is required 

to produce a given magnitude of effect.

Tolerance has been demonstrated after both 

acute and chronic nicotine administration 

for the effects of nicotine on multiple 

behaviors and physiological responses. 

Studies in mice have elucidated the neural 

and genetic substrata associated with the 

development of tolerance and have helped 

identify neural adaptations that occur 

with chronic nicotine administration 

(fi gure 4.5). Much of the research discussed 

below addresses the topic of behavioral 

tolerance—that is, tolerance to behavioral 

or physiological effects of a drug that 

is not accounted for by enhanced drug 

clearance. Animals also acquire dispositional 

tolerance, which means enhanced clearance 

or metabolism of a drug as a function of 

prior exposure. The latter phenomenon has 

been discussed in the context of Cyp2a5

expression.171 The following sections 

review both acute and chronic tolerance. 

Signifi cantly more is known about chronic 

tolerance, permitting a review of behavioral, 

genetic, and neural factors (from the level 

of the receptor to downstream cell-signaling 

cascades) involved in tolerance.

Acute Tolerance

Single injections of nicotine can produce 

tolerance to some of the direct effects of 

nicotine. The development and duration 

of such acute tolerance for the effects 

of nicotine on antinociception, body 

temperature, and motor activity were 

investigated in ICR mice.203 Mice were 

treated with 4 mg/kg of nicotine and then 

challenged with a 2-mg/kg subcutaneous 

(SC) dose. The time to maximum tolerance 

and the duration of acute tolerance varied 

across tasks; maximum acute tolerance for 

the antinociceptive effects of nicotine was 

seen at between 30 and 60 minutes, and 

recovery from acute tolerance occurred 

after 6 hours; maximum acute tolerance 

for nicotine-induced motor impairments 

occurred between 3 and 6 hours and 

dissipated by 24 hours, and maximum 

acute tolerance for nicotine-induced 

hypothermia occurred between 2 and 

4 hours and lasted 6 hours. The effect of 

intrathecal administration (i.e., injection 

into cerebral spinal fl uid at the spinal cord) 

on the development of acute tolerance for 

the antinociceptive effects of nicotine was 

also tested. Maximal acute tolerance was 

seen at 5–10 minutes, and effective doses 

for the initial induction of tolerance ranged 

between 0.5 to 1 lg. Acute tolerance was 

disrupted by the calcium channel blocker 

nimodipine administered either via SC 

or intrathecal injections, suggesting that 

changes in calcium levels contribute to 

the development of acute tolerance.

Genetics may contribute to variability in 

acute tolerance. Miner and Collins204 found 

that pretreating DBA mice with doses of a 

nicotine subthreshold for inducing seizures 

(1 or 2 mg/kg) either 15 or 30 minutes 

before testing for nicotine-induced seizures 

produced acute tolerance; by 60 minutes, 

acute tolerance was lost. In C3H mice, only 

pretreatment with the 2-mg/kg dose of 

nicotine resulted in acute tolerance and only 

when pretreatment was 7.5 minutes before 

testing for nicotine-induced seizures. These 

results suggest that genetic differences 

between DBA and C3H mice account for 

the increased sensitivity for acute tolerance 

in the DBA mice. The authors propose 

that nicotine inactivation of nAChRs may 
163

Monograph 20. Phenotypes and Endophenotypes

account for the observed acute tolerance 

and that strain differences in desensitization 

or inactivation of nAChRs may underlie the 

strain differences in acute tolerance.

Less is known about the genetic factors that 

infl uence the likelihood or magnitude of 

such acute tolerance compared with chronic 

tolerance. In addition, there is little evidence 

from the human literature that indicates a 

role for tolerance in dependence.

Chronic Tolerance

Tolerance that develops after chronic 

treatment of nicotine has also been 

demonstrated on numerous measures. 

In theory, chronic tolerance might be related 

to dependence because higher levels of 

tolerance may permit higher rates of selfadministration, which, in turn, result in 

greater effects on dependence processes. 

Although not a great deal of evidence links 

degree of chronic tolerance with tendency 

to self-administer nicotine, there are 

data showing that prolonged exposure to 

nicotine inures mice to the aversive effects 

of nicotine that they experience secondary 

to self-administration. The effects of chronic 

nicotine exposure on subsequent nicotine IV 

self-administration and the development of 

tolerance to the aversive effects of nicotine 

were assessed in DBA/2 mice.205 Nose pokes 

delivered either saline, or one of four doses of 

nicotine (0.016, 0.048, 0.16, 0.48 lg/infusion), 

to the mouse executing the nose poke and 

to a yoked control. The 0.048 lg/infusion 

dose of nicotine was associated with a 

higher rate of nose pokes by mice in the 

chamber with the active port compared to 

the yoked mice. After self-administration, 

one-half of the mice were implanted with SC 

minipumps that delivered 6.3 mg/kg/day of 

nicotine or saline for 14 days. After removal 

of the pumps, the highest dose of nicotine 

(0.48 lg/infusion) self-administered was 

aversive in mice chronically treated with 

saline but not in mice chronically treated 

with nicotine. These data suggest that 

chronic nicotine exposure renders the 

organism less sensitive to the aversive effects 

of nicotine that is self-administered.

Behavioral Analysis of Tolerance

In many tolerance studies, the experimenter 

controls nicotine administration; however, 

oral self-administration can also induce 

tolerance. Tolerance for the acute effects 

of nicotine (1 mg/kg) on the depression of 

locomotor activity and on the induction 

of hypothermia was measured in mice 

that had access for 30 days to either 2% 

saccharin, 50 lg/mL of nicotine in 2% 

saccharin, 100 lg/mL of nicotine in 2% 

saccharin, or 200 lg/mL of nicotine in 2% 

saccharin.140 The 200-lg/mL, but not the 

100-lg/mL, oral nicotine group showed 

tolerance for the effects of acute nicotine 

on both locomotor activity and body 

temperature. In another study that 

assessed the development of tolerance 

with oral nicotine self-administration, 

ICR mice had access for 42 days to either 

2% saccharin, 50 lg/mL of nicotine in 

2% saccharin, 100 lg/mL of nicotine in 

2% saccharin, or 200 lg/mL of nicotine 

in 2% saccharin.146 On day 43, tolerance 

for the effects of 2.5 mg/kg of nicotine on 

nociception and body temperature was 

measured. Tolerance was seen for all doses 

of oral nicotine with the 200-lg/mL dose 

of nicotine producing the most tolerance. 

This dose produced a plasma nicotine level 

of 15.85 ± 10.54 ng/mL. Both of these 

studies demonstrate that self-administration 

of nicotine can produce tolerance for the 

effects of nicotine on multiple behaviors.

The majority of the studies examining 

the development of tolerance in mice 

have focused on tolerance for the 

effects of nicotine on physiological 

and locomotor responses, but nicotine 

can also alter cognition. A series of 

experiments has examined how the effects 

of nicotine on learning change as nicotine 

administration is shifted from acute 
164

4. Mouse Models and the Genetics of Nicotine Dependence

to chronic administration. In C57BL/6 

mice, acute nicotine enhanced contextual 

conditioning206,207—that is, learning to 

associate a specifi c context with a stimulus 

such as a foot shock (for a review see 

Gould208). If, however, C57BL/6 mice are 

treated for 14 days with a chronic dose of 

nicotine (6.3 mg/kg/day, SC) producing the 

same plasma nicotine level as seen with 

the acute dose of nicotine (0.09 mg/kg) 

that enhanced contextual conditioning, 

no enhancement of contextual conditioning 

is seen. Thus, even though plasma nicotine 

levels were similar, the behavioral effects 

of the acute and chronic nicotine were 

not the same; acute nicotine treatment 

enhanced contextual conditioning, whereas 

chronic nicotine treatment failed to enhance 

contextual conditioning, suggesting the 

development of tolerance.209 It should be 

noted that plasma nicotine levels in mice 

treated acutely and chronically with nicotine 

(13 ng/mL) were within the range of plasma 

nicotine levels (10–50 ng/mL) demonstrated 

by smokers.210,211

The above results demonstrate the 

development of tolerance for the effects of 

nicotine on cognition. The neural adaptations 

responsible for this behavioral change are 

unknown, but studies examining tolerance 

for the effects of nicotine on locomotor 

activity and physiological responses have 

identifi ed accompanying changes in receptor 

density and function. This research is 

consistent with other research showing that 

behavioral tolerance cannot be explained by 

degree of dispositional tolerance. It must 

involve separate CNS neural adaptations 

that permit one animal to compensate 

for the disruptive effects of a drug, while 

another animal with the same level of the 

drug in its body shows greater drug effects. 

This phenomenon has, of course, many 

precedents with other drugs of abuse, with 

studies showing that most adaptation to 

drug-induced behavioral disruption is 

caused by learning mechanisms, rather 

than dispositional tolerance.212,213

Behavioral Genetics of Tolerance 

and Putative Substrata

Experiments conducted three decades ago 

provided early evidence of strain differences 

in tolerance. In one study,214 DBA/2 and 

C57BL/6 mice were compared on the 

development of tolerance to nicotine. Mice 

received three daily IP injections of 1 mg/kg 

of nicotine for two, four, or seven days. 

Genotype and gender both contributed to 

variance in developing tolerance to the effects 

of nicotine on Y-maze activity. C57BL/6 

male mice developed tolerance most rapidly, 

and DBA/2 male mice had the latest onset 

of tolerance; female mice of both genotypes 

developed tolerance at the same rate, 

but C57BL/6 female mice showed greater 

tolerance. A subsequent study examined 

the effects of chronic IV administration of 

2, 4, or 6 mg/kg of nicotine for 10 days on 

nicotine and a-bungarotoxin binding as well 

as the effects of acute nicotine on Y-maze 

activity and rears, acoustic startle, heart rate, 

respiration rate, and body temperature in 

both DBA/2 and C3H/2 inbred mice.215 DBA/2 

and C3H/2 mice differed in the development 

of tolerance, but not in 3H-nicotine binding 

or a-bungarotoxin binding, the two means 

available at that time for quantitation of 

nAChR expression.

The effects of chronic nicotine treatment 

were also extended to compare effects of 

chronic nicotine on tolerance and nAChR 

binding in C57BL/6, DBA/2, C3H/2, and 

BALB/cBy mice.5 Mice were treated with 

3 mg/kg/hour of nicotine intravenously for 

10 days. Assays included tolerance to the 

acute effects of nicotine on Y-maze activity, 

startle response, heart rate, respiratory 

rate, and body temperature. This research 

revealed substantial interspecifi c variability 

in response to chronic nicotine exposure. 

Only C3H mice developed tolerance for 

the effects of nicotine on acoustic startle. 

However, C57BL/6, DBA/2, and BALB/cBy 

mice, but not C3H mice, all showed tolerance 

to the effects of nicotine on Y-maze 
165

Monograph 20. Phenotypes and Endophenotypes

activity and body temperature. In addition, 

BALB/cBy mice showed tolerance for the 

effects of nicotine on heart rate. No strains 

showed tolerance for the effects of nicotine 

on respiratory rate, a measure of nAChR 

function in the autonomic nervous system. 

All four strains showed similar increases in 

nicotine binding in the cortex, hippocampus, 

midbrain, striatum, hypothalamus, 

and hindbrain after chronic nicotine 

treatment. Another marker of nicotine 

receptor expression, a-bungarotoxin binding, 

varied across strains in those areas of the 

brain after chronic nicotine treatment. 

DBA/2 mice showed increased binding in the 

cortex, hippocampus, and hypothalamus; 

C57BL/6 mice showed increased binding in 

the hindbrain and hippocampus; BALB/cBy 

mice showed increased binding in the 

hindbrain and hypothalamus, and C3H 

mice showed increased binding only in the 

hypothalamus. In sum, this early research 

showed considerable variability in tolerance 

development across inbred mouse strains 

and across physiological systems in response 

to nicotine exposure.

Genetic analysis was also used to examine 

the dose-dependent effects of chronic 

nicotine treatment on tolerance and 

changes in binding in A, C57BL/6, DBA/2, 

C3H/2, and BUB/Bn mice.5

 Mice were 

chronically infused with 0, 0.5, 1.0, 2.0, 4.0, 

or 6.0 mg/kg/hour of nicotine intravenously 

for 10 days and then tested for tolerance 

to the acute effects of nicotine on Y-maze 

activity and rears, acoustic startle, heart 

rate, respiration rate, and body temperature. 

C57BL/6 mice were more sensitive to the 

effects of chronic nicotine than C3H/2 and 

BUB/Bn mice in that the latter showed 

tolerance for only the highest doses tested; 

A and DBA/2 mice were intermediate. 

Changes in nAChR binding were measured 

in the cortex, cerebellum, colliculi, 

hindbrain, hippocampus, hypothalamus, 

midbrain, and striatum. All strains showed 

increased nicotine binding after chronic 

nicotine treatment, but variability across 

strains was seen for sensitivity to doses and 

for brain regions affected. For instance, 

A mice showed less change in binding across 

brain regions, and changes in binding were 

seen at higher doses, whereas C57BL/6 

mice showed changes in binding in all brain 

regions and the lowest dose of nicotineincreased binding in six of the eight regions 

tested. Changes in a-bungarotoxin binding 

associated with chronic nicotine treatment 

were also seen but to a lesser extent than 

with nicotine binding.

Interestingly, changes in receptor binding 

may not exclusively explain tolerance. 

The time course for the development of 

tolerance for the effects of nicotine on 

locomotor activity, as measured by Y-maze 

activity and rears, body temperature, and 

heart rate, were compared with the time 

course for the effects of chronic treatment 

on nAChR binding in DBA mice.14 DBA 

mice were infused with 4 mg/kg/hour of 

nicotine, and tolerance was assessed after 

1, 2, 4, 8, or 12 days of treatment. Maximal 

tolerance to the effects of an acute dose of 

0.75 mg/kg of nicotine was seen after four 

days of treatment, and the development of 

tolerance corresponded to increased binding 

of nicotine in the cortex, midbrain, hindbrain, 

hippocampus, and hypothalamus. Chronic 

nicotine treatment was also associated with 

increased a-bungarotoxin binding in the 

cortex and hippocampus, but the increase in 

low-affi nity nAChR binding occurred before 

the development of tolerance. Tolerance for 

the effects of nicotine on Y-maze locomotor 

activity and rears was lost after 8 days, 

tolerance to the acute effects of nicotine on 

body temperature was lost after 12–16 days, 

and tolerance to the acute effects of nicotine 

on heart rate was lost after 20 days. Nicotine 

binding, however, returned to control levels 

after 8 days, and a-bungarotoxin binding 

returned to control levels after only 4 days. 

These results suggest that changes in nAChR 

density may, in part, contribute to tolerance, 

but may not be the only mechanism involved 

because receptor binding returned to control 
166

4. Mouse Models and the Genetics of Nicotine Dependence

levels before all of the physiological measures 

of tolerance returned to control levels. This 

result is consistent with a great deal of other 

evidence that behavioral tolerance involves 

complex learning processes.212,213

Although the above studies collectively 

established a genetic basis for the response 

to nicotine and changes in receptor 

properties, they also preceded molecular 

studies revealing that beyond simply 

those systems detected by nicotine and 

a-bungarotoxin binding, there is a diverse 

genetic richness in the genes that constitute 

the nAChR family and that collectively 

contribute to the functional and regionally 

specifi c effects of nicotine on the organism.

Involvement of nAChR and Other 

Neurotransmitter Systems

It is important to establish which nAChRs 

are implicated in chronic tolerance 

phenomena. As reviewed earlier, a4b2 

nAChRs are involved in CPP and nicotine 

self-administration; thus, a logical question 

is whether the same receptors are involved 

in tolerance. Although early studies, 

based almost entirely upon ligand-binding 

measurements, indicated that a variety of 

receptors appear to underlie the development 

of tolerance, subsequent directed genetic 

studies have helped elucidate the nAChR 

subtypes that play a central role in the 

development and/or expression of tolerance. 

b2 nAChR subunit knockout mice treated 

chronically with 0, 1, 2, or 4 mg/kg/hour 

of IV nicotine for 10 days did not develop 

tolerance for the effects of nicotine on Y-maze 

activity and body temperature, but instead, 

showed increased sensitivity to the acute 

effects of nicotine after chronic treatment, 

suggesting that b2-containing nAChRs are 

involved in the development of tolerance for 

these measures.97 Furthermore, mice with 

a single point mutation (Leu9′ Ala9′) that 

was associated with increased sensitivity 

of a4-containing nAChRs exhibited 

heightened development of tolerance.178

Mice were treated daily with a single 15-lg/kg 

nicotine IP injection for nine days, and 

body temperature was measured. The Leu9′

mutant mice developed tolerance to the 

effects of nicotine on body temperature by 

day nine, but the wild-type mice did not 

develop tolerance. In contrast to the b2 and 

a4 nAChR subunits, which appear to be 

involved in tolerance, the a7 nAChR subunit 

may not be involved in tolerance because 

a7-null mice exhibit normal development 

of tolerance to the effects of nicotine on 

schedule reinforcement.216 Although the a7 

subunit does not appear to be as important as 

once thought in this process, caution must 

be exercised; these studies used different 

measures of tolerance, and it is possible 

that the nAChRs involved in tolerance are 

measurement specifi c.

In sum, the results of the behavioral 

genetic analysis of tolerance provide several 

important insights into the effects of 

nicotine. First, these studies demonstrate 

that genetic variation contributes to the 

development of tolerance for the effects of 

nicotine. Second, these studies illustrate 

the potential for the use of the nAChR 

subunit null mouse in that they accurately 

complement most pharmacological and 

functional studies and demonstrate how 

mutations to genes coding for nAChR 

subunits can alter sensitivity to nicotine.

As in self-administration, other nonnicotinic 

systems may interact during tolerance 

development. For instance, there is 

evidence in DBA/2 mice of an interaction 

between the nicotinic and muscarinic 

acetylcholinergic systems. DBA/2 mice were 

treated chronically with IV administration 

of 8 mg/kg/hour of nicotine, 1 mg/kg/hour 

of oxotremorine (muscarinic agonist), 

or a vehicle for 10 days.217 After chronic 

treatment, the acute effects of 2 mg/kg 

of nicotine or 0.2 mg/kg of oxotremorine 

on rotorod performance, Y-maze activity, 

heart rate, respiratory rate, and body 

temperature were measured, along with 
167

Monograph 20. Phenotypes and Endophenotypes

nicotine and a-bungarotoxin binding. 

For all tests, tolerance was seen for both 

drugs. In addition, mice chronically 

treated with oxotremorine showed crosstolerance with nicotine for nicotine-induced 

heart rate and body temperature change. 

Interestingly, mice chronically treated with 

oxotremorine showed decreased binding 

at muscarinic receptors, but no change 

in nicotine and a-bungarotoxin binding. 

In contrast, mice chronically treated with 

nicotine showed increased nicotine and 

a-bungarotoxin binding, but no change in 

muscarinic receptor binding. In addition 

to demonstrating cross-tolerance between 

muscarinic and nicotinic agonists, this study 

once again demonstrates that tolerance 

to the effects of nicotine can develop 

independent of changes in nAChR binding; 

mice treated chronically with oxotremorine 

showed tolerance for the effects of nicotine 

on heart rate and body temperature but did 

not show changes in binding at nAChRs.

The mu opioid receptor may also be involved 

in the development of tolerance to at least 

one effect of nicotine. C57BL/6 mice were 

treated chronically with nicotine (three daily 

SC injections of 1.75 mg/kg) for 12 days. 

Locomotor responses and nociception 

were measured on even-numbered days 

for 5 minutes (locomotor activity) and for 

15 minutes (nociception) after nicotine 

injection.218 After the last test, mu opioid 

binding was assessed. Tolerance was seen 

for nicotine-induced antinociception but 

not for the disruptive effects of nicotine 

on locomotor activity. Chronically treated 

mice had decreased mu opioid binding in 

the caudate-putamen and in the nucleus 

accumbens. Tolerance was also tested in mu 

opioid receptor knockout mice. These mice 

developed tolerance to the antinociceptive 

effects of nicotine faster than did wild-type 

mice. These results suggest that nicotinemediated changes in mu opioid receptor 

function may contribute to the development 

of tolerance for the antinociceptive effects 

of nicotine.

Cell Signaling

Studies in mice have demonstrated that 

chronic nicotine treatment is often 

associated with an increase in nAChR 

density but a decrease in the function of 

those nAChRs.11,64 However, such changes 

in the nAChRs do not always correlate with 

the onset and duration of tolerance. This 

suggests that effects downstream of nAChR 

activation may be involved in tolerance. 

Changes in calcium-related cell signaling 

may be involved in the development 

of tolerance. The relationship between 

calcium signaling and the development 

of tolerance for the effects of nicotine on 

locomotor activity and nociception was 

measured in ICR mice.219 Mice were treated 

chronically for 10 days with 2 mg/kg of 

SC nicotine twice daily. Tolerance was 

seen for the locomotor-impairing effects 

of nicotine and for the antinociceptive 

effects of nicotine. Mice that developed 

tolerance also showed cross-tolerance for 

the effects of BAY K 8644, a calcium channel 

agonist, and thapsigargin, which increases 

intracellular calcium concentrations, on 

locomotor activity and nociception. These 

results suggest that calcium signaling 

(possibly an a3 nAChR subtype) may be 

involved in the development of tolerance 

for some effects of nicotine. In further 

support of the involvement of calcium 

signaling in the development of tolerance 

to the effects of nicotine, drugs that alter 

calcium signaling altered tolerance.220 Mice 

treated chronically with 24 mg/kg/day 

(SC, minipump) of nicotine for 14 days 

were concurrently treated with a calcium 

channel antagonist, a calcium channel 

agonist, or a vehicle, and tolerance for 

the antinociceptive effects of nicotine was 

then measured. Twice daily injections of 

the L-type calcium channel antagonists 

nimodipine and verapamil blocked the 

development of tolerance; whereas twicedaily injections of BAY K 8644 enhanced 

the development of tolerance. In addition, 

the study found that tolerant mice had 
168

4. Mouse Models and the Genetics of Nicotine Dependence

higher levels of calcium calmodulin protein 

kinase II in the spinal cord, and infusion 

of the calcium calmodulin protein kinase 

II antagonist KN-62 into the spinal cord 

decreased tolerance for the antinociceptive 

effects of nicotine. These results strongly 

suggest that calcium-mediated cell signaling 

is involved in the development of tolerance 

for the effects of nicotine on nociception. 

The involvement of calcium in nicotine 

tolerance has also been demonstrated 

for tolerance to the anxiogenic effects of 

nicotine.221 Swiss mice treated with daily 

injections of 0.04 mg/kg of nicotine for seven 

days showed tolerance for the anxiogenic 

affects of nicotine. However, in mice that 

received nicotine injections paired with 

injections of L-type, voltage-dependent, 

calcium channel antagonists (nimodipine, 

fl unarizine, diltiazem, or verapamil), 

tolerance was blocked. Thus, while chronic 

nicotine treatment is associated with 

receptor level changes and the development 

of tolerance, changes in intracellular 

calcium cell signaling may also be critically 

involved in such tolerance development.

In summary, studies of the neural and 

cellular substrates of tolerance in mice have 

identifi ed receptor subtypes and cell-signaling 

molecules involved in tolerance. The a4-

containing and b2-containing nAChRs 

appear to be critically involved in tolerance 

to the effects of nicotine, although the role 

of a7 nAChRs may be less direct. In addition 

to nAChRs, muscarinic acetylcholinergic 

receptors and mu opioid receptors may also 

be involved in tolerance to the effects of 

nicotine. The cellular mechanisms involved 

in tolerance appear to involve calciummediated cell signaling because calcium 

channel antagonists decreased tolerance, 

and agonists increased tolerance.

It is important to bear in mind, however, 

that the functional role of tolerance to 

human nicotine dependence remains 

unclear. It is unclear that dispositional 

tolerance to nicotine222 or behavioral 

tolerance223,224 are causally determinant of 

nicotine reinforcement and dependence. 

Future research should address the extent 

to which the different types of tolerance 

are related to core features of dependence, 

such as a pervasive pattern of drug use. 

Further understanding of the neural and 

genetic substrata of tolerance, and how 

these compare with other causal infl uences 

on dependence, may elucidate the role of 

tolerance in dependence development.

Additional Directions 

for Research 

on the NicotineDependence 

Phenotype in Mice

Given the tremendous potential created by 

the availability of well-characterized mouse 

strains and both knockout and knockin 

preparations, there is a great need to use 

such tools to explore genetic infl uences on 

phenotypes that provide additional insight 

into the processes involved in nicotine 

dependence. Additional assays, both 

physiological and behavioral, should be used 

to expand understanding of the genetic 

contributors to the critical motivational 

processes of dependence.

Extended Central and Peripheral 

Effects of Nicotine Observed 

in Mice

Alteration in nAChR function may also 

provide insights into nicotine effects on 

central and peripheral components of 

complex behaviors. For example, the roles 

of a7 and a4b2 receptors are implicated in 

nicotine-induced enhancement of cognition, 

including working memory, learning, and 

attention.225 This relation is particularly 

strong in rodents; the loci of this effect 
169

Monograph 20. Phenotypes and Endophenotypes

appear to be the hippocampus and the 

amygdala.206,226–229 In C57BL/6 mice, acute 

nicotine enhanced hippocampus-dependent, 

but not hippocampus-independent, fear 

conditioning.207,229 This enhancement of 

hippocampus-dependent forms of fear 

conditioning by nicotine is mediated by 

a4b2 nAChRs. DHbE, the high-affi nity 

nAChR antagonist, blocked the nicotine 

enhancement;230 b2 knockout mice did not 

show the enhancement of hippocampusdependent fear conditioning, but a7 

knockout mice did.230,231 The mechanisms 

that modulate fear-based learning may be 

relevant to nicotine dependence, given the 

substantial evidence that affect control is a 

powerful motive for smoking in humans.232

Glutamate receptor systems are directly 

involved in learning and synaptic 

plasticity.233–235 Accordingly, one mechanism 

through which nicotine can enhance 

learning and memory, in addition to 

modulating inhibitory tone and circuitry in 

regions such as the hippocampus (above), 

is via interaction with the glutamate 

system.236 For example, chronic nicotine 

increases the phosphorylation state of 

the NR2B subunit, which correlates with 

a long-lasting component of long-term 

potentiation.237 Similarly, chronic nicotine 

self-administration in rats corresponds 

with region-specifi c increases in NR2A 

mRNA expression (e.g., the auditory 

cortex), whereas thalamic NR2B 

messenger RNA (mRNA) levels decline.238

In addition, protein levels that these 

subunits share also increased particularly 

in mesocorticolimbic regions.239 Nicotine 

can also act on dopamine cell bodies to 

regulate glutamatergic inputs to these distal 

neurons that do not experience direct nAChR 

activation.80,142,183,240,241 Finally, nicotine 

modulation of activity-dependent limited 

proteolysis of the GluR1 C-terminus has been 

described.183 Because the C-terminus of this 

AMPA-GluR subunit is critical to association 

with proteins of the synaptic spine, it is 

possible that nicotine increases GluR1 

expression through altering traffi cking of 

the receptor. The common feature of these 

studies is, however, that nicotine’s effects via 

glutamate receptor expression (even acting 

via the same receptor) may be very different, 

or even opposite, within the same learning 

or memory paradigm, depending upon the 

anatomical location of nicotine’s actions.

Nicotine infl uences on gene-transcription 

cascades could be very important and highly 

strain specifi c. Genes and their protein 

products do not work alone; they are part 

of complex metabolic cascades that impart 

a change in “state” to the cell, eventually 

resulting in a change in function or behavior 

in the organism. Researchers in the fi eld 

are aware of examples that extend from 

the regulation of the classic pathways of 

intermediary metabolism to later discoveries 

of complex cascades that regulate cell 

functions, including the induction of gene 

transcription and proteolytic cascades that 

determine cell survival or death through 

apoptosis.242 This also means that changing 

the function of just one element, possibly 

because of a dysfunction of a pathway external 

to the one being examined, can change 

how these cascades proceed and how they 

eventually infl uence ultimate end points or 

states. In mice, the administration of nicotine 

can lead to persistent c-Fos activation243,244

as well as to changes in fi broblast growth 

factor-2 mRNA,245,246 nerve growth factor 

and tyrosine kinase B in the hippocampus,247

and activation of CREB.88 In tissue culture, 

reports indicate that nicotine increases 

the corticotropin-releasing factor and, 

as noted above, inhibits lipopolysaccharide 

induction of certain infl ammatory cytokines 

(e.g., interleukin (IL)-1 and IL-8;150,248) or 

signaling through the receptor. This latter 

study suggests that nicotine inhibits the 

nuclear factor-kappa B transcriptional 

system,249 although in other cell types, 

antagonism between a7 activity and 

tumor necrosis factor-a (TNF-a)-initiated, 

ceramide-related metabolic cascades has 

been reported.144
170

4. Mouse Models and the Genetics of Nicotine Dependence

Distinguishing strain-specifi c systems that 

differ in ways relevant to nicotine dependence 

from those that vary because of unrelated 

genetic differences (e.g., the original reasons 

many of these strains were selected, such as 

H2 functions) is not straightforward. Factors 

that also infl uence nicotine dependence, 

such as drug metabolism or absorption, 

increase the complexity of the problem 

of genetic dissection. This is particularly 

true in mouse strains that are particularly 

“sensitive” (or possibly very ”insensitive”) 

to nicotine for which toxicity or seizure 

sensitivity due to particularly robust 

catabolism or clearance, versus compound 

accumulation, may mask correlations in 

seeking signaling cascades relevant to 

receptor function. The advent of microchip 

analysis of whole-genome quantitative 

transcript screening (e.g., Affymetrix) 

seems a likely future direction to begin the 

experimental dissection of the magnitude 

and specifi city of the strain response to 

defi ned drug administration.

Aging

One measure of normal age-related decline 

in the CNS is the diminishment and eventual 

dysfunction of the limbic cholinergic system 

that, in its most severe form, manifests 

in pathologies of dementia, including 

Alzheimer’s disease (AD; fi gure 4.1). 

Although studies that examine the state of 

the cholinergic neurotransmitter receptors 

in aging and dementia often focus upon 

muscarinic receptor expression, the loss 

of neuronal nAChRs precedes muscarinic 

receptor loss and is often much more 

extensive in human brains affl icted with AD 

relative to age matched controls.17,250–253

Mouse strains, like humans, exhibit a 

striking range in life span, ranging from 

two to three years in non-cancer-prone 

strains,254 and they exhibit an onset of agerelated decline in nAChR expression that is 

strain specifi c.139,147,255 One example of this 

is seen in the hippocampus of aged CBA 

and C57BL/6 mice. In both strains, the 

expression of the a4 nAChR is diminished 

with age, but this loss is much more severe 

in CBA than in C57BL/6 when compared 

with adults of the same strain.147 Also 

observed in the hippocampal CA1 region is 

a signifi cant loss of a7 nAChR expression 

by aged CBA/J but not by C57BL/6 mice. 

In contrast, the b4 nAChR is preferentially 

diminished in C57BL/6 mice. Coincident 

with the loss of the a4 nAChR in the CBA/J 

strain is a signifi cant age-related increase 

in nAChR staining of astrocytes,69,81,147

which has also been reported in cases with 

AD.256–258 These results suggest that mouse 

strains of different genetic backgrounds 

undergo dissimilar age-related changes in 

the expression of nAChRs.

The strain-related differences noted above 

have implications for how age will affect an 

animal’s response to various toxic assaults. 

For example, either nicotine or acutely 

administered TNF-a can be neuroprotective 

when applied individually, but when applied 

together, neuroprotection is abolished.142

In contrast, a4-receptor subtypes provide 

neuroprotection to assault produced by 

the toxic amyloid beta-peptide 25-35 

(Abeta 25-35).144 Therefore, loss of receptors 

containing a4 would signifi cantly increase 

susceptibility to age-associated assault by 

Abeta 25-35. At the same time, loss of a7 

activation would enhance susceptibility 

to excitotoxic challenges (e.g., NMDA) 

such as those associated with ischemic 

damage or with the presence of TNF-a, 

including reduced apoptosis. Combining 

these fi ndings suggests that in the aged 

brain, a CBA mouse is likely to be relatively 

more susceptible to Abeta toxicity, 

while the C57BL/6 is more susceptible 

to excitotoxicity. More to the point, 

these studies indicate that early genetic 

predispositions may have important impacts 

upon the lifelong dynamics of nAChR 

function, and hence, dependence processes. 

Research shows age-related changes in 

quitting success;259,260 it is possible that 
171

Monograph 20. Phenotypes and Endophenotypes

age-related sensitivities to toxins could 

affect trajectories of dependence across the 

life span. Finally, therapeutic interventions 

in patients, including the use of memantine, 

which has been proposed to inhibit 

glutamate receptors and which interacts 

with a7,177 could have widely differing 

impacts on the recipient that are consistent 

with the individual’s genetic background.

Novel Behavioral Phenotypes

Although research with mouse models has 

already yielded very valuable information 

about the nature of nicotine dependence 

and its genetic substrata, progress might 

be enhanced by use of new phenotypic 

measures that could be used along with 

manipulations of strain differences, 

knockout/knockin status, agonist and 

antagonist administration, and other 

strategies designed to implicate particular 

physiological and genetic mechanisms. 

Several behavioral paradigms may assess 

relatively more specifi c, dependence-linked 

motivational processes than are assessed 

by traditional CPP or self-administration 

paradigms. In a sense, these would represent 

phenotypes similar to the intermediate 

and mature subphenotypes discussed 

in chapter 3. One such novel phenotype 

would be to examine the ability of nicotine 

to enhance either the incentive value or 

reinforcing value of nonpharmacological 

stimuli. For instance, in regard to the 

latter effect, Caggiula and colleagues261,262

have shown that nicotine enhances the 

execution of behavior maintained by salient 

nonnicotine reinforcers. Thus, nicotine 

appears to modulate the reinforcing value 

of other stimuli. This may be one reason 

that nicotine produces such an intransigent 

dependence, despite its being a relatively 

weak primary reinforcer.261 Similarly, 

activity in mesotelecephalic dopamine 

structures could be monitored to assess how 

nicotine modulates the incentive value of 

nonpharmacological stimuli (as opposed to 

their reinforcing effects).263

Another potentially useful phenotype might 

be the increase in reward threshold for 

electrical brain stimulation produced by 

nicotine withdrawal. Research by EppingJordan and colleagues has shown that 

nicotine withdrawal elevates the magnitude 

of stimulation required to sustain reliable 

self-stimulation.264 Subsequent research 

suggests that cues associated with 

withdrawal may similarly decrease activity 

in brain reward systems via associative 

mechanisms.265 A well-defi ned association 

between cues and withdrawal may provide 

a sensitive index of the motivational 

impact of withdrawal, which appears to 

be an important determinant of ability 

to quit smoking.232 Notably, some studies 

also demonstrated little impact of the 

b2-knockout mouse on behaviors related to 

somatic signs of withdrawal,162,266 indicating 

that these behaviors are separable from 

those of reinforcement and subject to 

dissection through additional genetic 

approaches. Future development and use 

of phenotypic assessment should refl ect a 

triangulation of theories of human drug 

motivation, data on implicated genetic 

variants and their functions, and evidence 

regarding the behavioral and biological 

processes that are implicated in the various 

behavioral paradigms.

Summary

Given the tremendous potential created 

by the availability of well-characterized 

mouse strains and knockout and knockin 

preparations, it is vital that such tools 

be used to explore genetic infl uences on 

phenotypes that provide additional insight 

into the processes involved in nicotine 

dependence. The reviewed evidence shows 

that nAChR expression and function is 

customized through interplay with genetic 

background to ensure optimal modulation 

of neurotransmitter receptor functions 

important to survival and the specialized 

needs of the organism. Therefore, there is 
172

4. Mouse Models and the Genetics of Nicotine Dependence

a need to recognize that behavioral tests 

must be customized to the mouse, and care 

must be taken when fi ndings with mice are 

extrapolated to other rodents or humans. 

Such translational validation also requires 

that both similarities and differences 

in nAChR expression and function be 

considered in experimental design. Finally, 

although the potential value of mouse 

models has not yet been realized, the 

available data show that such models can 

display principal behavioral and biological 

features of nicotine dependence. In addition, 

such models have already implicated 

particular genetic variants and biological 

systems in the development and expression 

of nicotine dependence.

Conclusions

1. Substantial differences exist between 

mouse strains in their response to the 

acute or chronic administration of 

nicotine. These differences implicate 

specifi c neuronal nicotinic acetylcholine 

receptors within a broader genetic 

context, which suggests a central role 

for these genetic variants in nicotine 

dependence in humans.

2. The three most common routes 

of administration (intravenous, 

subcutaneous, and oral) for nicotine in 

rodents vary in the degree to which they 

model key features of human nicotine 

dependence, such as the behavioral 

features of self-administration and the 

acute and chronic physiological effects 

of nicotine. Each administration route 

offers advantages and disadvantages. 

Intravenous self-administration 

permits self-administration but may 

entail receptor-level response artifacts 

due to high dosages. Subcutaneous 

administration allows experimenter 

control of dosage and withdrawal over 

long time periods at a cost of precluding 

self-administration. Oral administration 

via drinking water permits chronic 

nicotine exposure and produces evidence 

of dependence, but is subject to specifi c 

possible side effects, making this issue 

an important variable in research design.

3. While mice generally are less sensitive 

to nicotine than are rats, mouse models 

now have a strong research base for 

nicotine effects. Mice are amenable 

to genetic and pharmacological 

experimental manipulation. They 

exhibit heterogeneity in strain-specifi c 

responses to nicotine, and methods 

of homologous recombination permit 

manipulation of specifi c genes. Data now 

link specifi c mouse strains to genetically 

infl uenced differences in the effects of 

nicotine exposure that can facilitate 

further study of nicotinic acetylcholine 

receptor biology in mice.

4. Mouse models link nicotine selfadministration to high-affi nity nicotinic 

acetylcholine receptors, genetic 

differences, developmental factors, 

and other potential mechanisms 

of dependence. These models have, 

in addition, linked nicotine reward in 

the form of conditioned place preference 

with genetic strain differences and 

specifi c receptor subtypes and have 

linked acute and chronic nicotine 

tolerance with other genetic and receptor 

differences. The models have also linked 

the a7 and a4b2 receptors with nicotine 

enhancement of working memory, 

learning, and attention and have shown 

strain-specifi c aging effects on nicotinic 

acetylcholine receptor expression.

5. Although substantial differences exist 

in the biology of nicotinic acetylcholine 

receptor expression and function 

between mice, other rodents, and 

humans, nascent research in mouse 

models for nicotine dependence shows 

considerable promise in furthering 

understanding of the biology and 

genetics of nicotine dependence.
173

Monograph 20. Phenotypes and Endophenotypes

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225. Levin, E. D., F. J. McClernon, and 

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hippocampal alpha 7 nicotinic receptor 

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227. Levin, E. D. 2002. Nicotinic receptor 

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228. Addy, N. A., A. Nakijama, and E. D. Levin. 

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229. Gould, T. J., O. Feiro, and D. Moore. 

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of fear of auditory and contextual cues. 

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236. Gould, T. J., and M. C. Lewis. 2005. 

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inhibition of fear conditioning. Learning & 

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237. Yamazaki, Y., Y. Jia, R. Niu, and K. Sumikawa. 

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238. Hsieh, C. Y., F. M. Leslie, and R. Metherate. 

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239. Wang, F., H. Chen, J. D. Steketee, and 

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nicotinic acetylcholine receptor subunit. 

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242. Salvesen, G. S. 2002. Caspases and 

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245. Belluardo, N., G. Mudo, G. Caniglia, 

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246. Mousa, S., and S. A. Mousa. 2006. Cellular 

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impact on cancer. Journal of Cellular 

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247. Rattray, M. 2001. Is there nicotinic 

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248. Wendell, K. J., and S. H. Stein. 2001. 

Regulation of cytokine production in human 

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nicotine and lipopolysaccharide. Journal of 

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249. Wang, H., H. Liao, M. Ochani, M. Justiniani, 

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Cholinergic agonists inhibit HMGB1 release 

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binding sites in Alzheimer’s disease cerebral 

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to nicotine in aged rat striatum: Correlation 

with a decrease in a subpopulation 

of nicotinic receptors. Journal of 

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252. Perry, E., C. Martin-Ruiz, M. Lee, M. Griffi ths, 

M. Johnson, M. Piggott, V. Haroutunian, et al. 

2000. Nicotinic receptor subtypes in human 

brain ageing, Alzheimer and Lewy body 

diseases. European Journal of Pharmacology

393 (1–3): 215–22.

253. Picciotto, M. R., and M. Zoli. 2002. Nicotinic 

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254. Goodrick, C. L. 1975. Life-span and the 

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260. Nollen, N. L., M. S. Mayo, L. Sanderson 

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smokers enrolled in a randomized, placebocontrolled trial. Journal of General Internal 

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261. Chaudhri, N., A. R. Caggiula, E. C. Donny, 

M. I. Palmatier, X. Liu, and A. F. Sved. 2006. 

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and nonpharmacological stimuli reveal 

multiple roles for nicotine in reinforcement. 

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decreases in brain reward function during 

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265. Kenny, P. J., and A. Markou. 2005. 

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decreases the activity of brain reward 

systems. Journal of Neuroscience 25 (26): 

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266. Salas, R., F. Pieri, and M. De Biasi. 2004. 

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in mice null for the beta4 nicotinic 

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187

Given the variation of smoking behavior patterns within the population, as well as their 

potential for being classifi ed as heritable traits, patterns of smoking behavior over time 

hold promise as a future basis for genetic studies of nicotine dependence. This part 

examines issues in the study of trajectories of tobacco use, ranging from the results 

of existing studies to their relationship with other trajectories, such as alcohol use or 

substance abuse.

The fi rst chapter in this part examines the literature exploring developmental trajectories 

of cigarette smoking between adolescence and adulthood, together with results from 

a population study examining these trajectories. A subsequent chapter explores 

genetic modeling issues in the study of smoking trajectories and behavior, including 

methodological and conceptual issues, statistical modeling considerations, prior genetic 

studies, and research applying an item-response theory (IRT) approach to an analysis of 

smoking trajectories. The closing chapter of this part uses an empirical example from a 

cohort study to explore whether these trajectories should be considered unique to tobacco 

or are best conceptualized as general pathways underlying substance use.

Developmental Trajectories 

of Tobacco Use and Their Relation 

to Tobacco Dependence

Part

3

189

Patterns of smoking behavior over time exhibit substantial variation, and these patterns, 

in turn, hold the potential to inform possible phenotypes of tobacco use and dependence. 

This chapter examines the literature concerning developmental trajectories of cigarette 

smoking between adolescence and adulthood. It also presents an empirical example that 

examines these trajectories by using data from the Indiana University Smoking Survey. 

Specifi c areas discussed include

■ Past studies describing smoking trajectories and their antecedents and correlates

■ Empirically identifi ed trajectories of smoking from adolescence to adulthood

■ Statistical approaches for potentially identifying unique trajectory classes from 

empirical data

■ Results from a dynamic cluster analysis of tobacco use trajectories from the ages 

of 10 to 42 years in a sample initially recruited from a midwestern school system

The data discussed in this chapter provide a framework for a three-chapter section in 

this monograph exploring aspects of cigarette smoking trajectories and their potential 

to inform further genetic research. In particular, these data point to several key areas 

for further study, linking these trajectories of smoking behavior to possible dynamic or 

developmental phenotypes of nicotine dependence.

5

Developmental Trajectories of Cigarette 

Smoking from Adolescence to Adulthood

Laurie Chassin, Patrick J. Curran, Clark C. Presson,

Steven J. Sherman, and R. J. Wirth

The analyses described herein were supported by National Institute of Health grant DA013555. The authors thank 

Jon Macy and the families of the Indiana University Smoking Survey for their assistance in data collection and 

Denise Kruszewski for assistance with literature reviews.
190

5. Developmental Trajectories of Cigarette Smoking

Introduction

In attempting to identify phenotypes 

of cigarette smoking, is it potentially 

informative to consider heterogeneity in 

trajectories of smoking from adolescence 

to adulthood? Moreover, could these 

developmental patterns be useful for genetic 

analyses of smoking behavior? This chapter 

considers developmental trajectories of 

cigarette smoking as part of a broader section 

within this monograph (chapters 5–7) 

that examines tobacco use trajectories and 

their role in informing an understanding 

of phenotypes of smoking behavior. This 

chapter reviews the literature on trajectories 

of cigarette smoking from adolescence to 

adulthood, raises methodological issues, 

and provides an empirical example of these 

trajectories in relation to aspects of adult 

smoking phenotypes.

A central premise of this monograph is 

that the adult smoking phenotype used in 

the fi eld of behavioral genetics is a crude 

and heterogeneous phenotype that is not 

ideal for genetic study (or for studies of 

etiological mechanisms more broadly). 

Efforts to refi ne this phenotype include 

distinguishing among adult smokers 

on features such as amount smoked, 

the presence or absence of particular 

dependence symptoms, failed cessation, 

maximum length of abstinence, and other 

factors, as well as on the basis of candidate 

endophenotypes (chapters 8 and 9). 

However, developmental considerations 

about the initiation, acquisition, and course 

of cigarette smoking from adolescence 

to adulthood may also contribute to an 

understanding of smoking phenotypes. 

For example, different stages of the 

smoking acquisition process (e.g., initial 

onset versus progression) differ in their 

heritability,1,2 suggesting that different 

points along smoking trajectories may 

be infl uenced by different etiological 

factors (see chapter 3). Moreover, other 

features of developmental trajectories of 

smoking—including age of onset, speed 

of acceleration in smoking rate, variability 

versus persistence in smoking over 

time, and trajectories of associated 

use of other substances (e.g., alcohol, 

marijuana)—may all be useful in defi ning 

more homogeneous phenotypes for 

genetic analysis.3,4

These research questions require data 

about adolescent origins to inform the 

identifi cation of smoking phenotypes 

in adulthood. However, the need to 

understand adult outcomes is not the only 

reason for an interest in the adolescent 

origins of smoking trajectories. For example, 

an understanding of heterogeneity in 

adolescent smoking phenotypes is itself 

important for understanding the etiology of 

adolescent smoking and for the development 

of preventive intervention targeted at 

adolescent age groups. Thus, for multiple 

reasons, it is useful to examine developmental 

aspects of smoking trajectories from their 

initial onset through adulthood to identify 

multiple pathways and the mechanisms 

underlying these pathways.

Because this is a chapter on trajectories 

of smoking behavior, and not tobacco 

dependence, it is also important to note 

questions that this chapter will not address. 

It does not cover theoretical issues in 

the conceptualization of dependence. 

This discussion is provided in chapter 3. 

It does not discuss the issues that are 

raised in modeling genetically informative 

samples; these are covered in chapter 6. 

Finally, trajectories of cigarette smoking 

do not occur in isolation but are associated 

with other forms of substance use. 

A consideration of smoking trajectories 

in combination with other substance use 

is provided in chapter 7, along with an 

empirical example of smoking and alcoholuse trajectories.
191

Monograph 20. Phenotypes and Endophenotypes

A Developmental 

Psychopathology 

Perspective: Studying 

Multiple Trajectories 

over Time

Although a comprehensive treatment of a 

developmental psychopathology perspective 

is beyond the scope of this chapter, it is 

important to embed the study of smoking 

trajectories within this broader conceptual 

and empirical context. Developmental 

psychopathology has been defi ned as the 

study of “the origins and course of individual 

patterns of behavioral maladaptation.”5(p18)

This defi nition places maladaptive behavior 

within the context of normal development, 

as well as in relation to the interplay 

between an individual’s internal and 

external contexts in which neurobiological 

development and psychosocial experience 

are proposed to infl uence each other in 

reciprocal fashion.6

A developmental psychopathology perspective 

recognizes that different infl uences may 

determine the initiation of behavior as 

opposed to the maintenance of that behavior 

(as also hypothesized by stage models of 

cigarette smoking—for example, Mayhew and 

colleagues7—and the “watershed” model in 

chapter 3). Moreover, from this perspective, 

it is hypothesized that multiple, differing 

etiological pathways may lead ultimately to 

the same outcome (equifi nality). In addition, 

it is hypothesized that any given risk factor 

may produce a range of diverse outcomes 

(multifi nality).8

A developmental psychopathology 

perspective thus leads to the study of 

multiple trajectories of behavior over time 

so as to be able to identify and explain these 

diverse patterns of development. Such 

pathways are probabilistic in nature, rather 

than reifi ed “groups,” and it is possible 

for an individual to change trajectories 

in response to some change in risk and 

protective factors. Methods used to study 

multiple trajectories include both traditional 

variable-centered approaches (in which 

predictor variables are related to some 

outcome) and person-centered approaches 

(in which relatively homogeneous 

subgroups are identifi ed and studied).

For the purposes of this monograph, an 

important question is whether thinking 

about multiple developmental pathways or 

trajectories of smoking over the life span can 

be useful for refi ning phenotypes of smoking 

to be used in genetic research. In other 

words, does it make sense to consider 

dynamic or developmental phenotypes of 

smoking? As noted by Pickles and Hill,9

the notion of “pathways” or trajectories 

is not only a rich metaphor but also one 

that raises many questions and challenges 

(including whether such pathways are 

actually empirically identifi able). For more 

information, the reader is referred to 

extended discussions in Pickles and Hill.9

Other references include Gottlieb and 

Willoughby10 for a study considering the 

comorbidity of attention defi cit hyperactivity 

disorder (ADHD) and smoking and Bergman 

and colleagues11 for an extended discussion 

of person-centered research methods.

Adolescent Cigarette Smoking 

and Tobacco Dependence

Adolescence is the developmental period 

during which smoking (and other substance 

use) is most commonly initiated. In 2005, 

9.3% of 8th graders, 14.9% of 10th graders, 

and 23.2% of 12th graders reported smoking 

in the past 30 days of a survey.12 As with 

most forms of substance use, smoking rises 

to a peak prevalence in the age period of 

18–25 years,13,14 but unlike other forms 

of substance use, which decline after the 

mid-20s, smoking is more persistent.13,14
192

5. Developmental Trajectories of Cigarette Smoking

Perhaps this is true because smoking is 

legal, addictive, and does not immediately 

impair performance.

Compared with information about the 

prevalence of adolescent cigarette smoking, 

less is known about tobacco dependence in 

adolescence because this has been a later 

focus of research attention. Two commonly 

used measurement methods involve 

modifi cations of the Fagerström Tolerance 

Questionnaire (FTQ)15 or the Diagnostic 

and Statistical Manual of Mental Disorders 

(DSM) criteria.16 These two approaches 

produce only modest concordance in 

classifying adolescents, except at high levels 

of smoking of at least 16 cigarettes per day.17

Moreover, as has been shown for other forms 

of substance-use disorders, some caution 

is warranted in applying adult dependence 

measures to adolescents.18 Whether or not 

the construct of dependence is similar for 

adults and adolescents, the functioning 

of particular items and criteria may 

differ. Additional research is needed on 

the measurement equivalence of tobacco 

dependence criteria over the life span 

(see chapter 6 for an empirical example of 

studying measurement equivalence over age).

Reported prevalence rates of tobacco 

dependence among adolescent smokers 

have varied widely depending on sampling, 

defi nitions of dependence, and defi nitions 

of adolescent smoking. A review by Colby 

and colleagues19 reports rates between 20% 

for a proxy DSM diagnosis among 12- to 

17-year-olds who smoked in the past year 

and 68% for a diagnosis based on FTQ 

criteria among 13- to 17-year-olds who 

smoked one pack or more per day. Kandel 

and colleagues17 found that the majority of 

adolescent daily smokers met criteria for 

dependence (87% according to DSM criteria 

and 63% according to Fagerström criteria). 

In addition, there were no race/ethnicity 

differences in prevalence when smoking 

intake was controlled. Using the criteria of 

the International Statistical Classifi cation 

of Diseases and Related Health Problems, 

Tenth Revision (ICD-10), O’Loughlin and 

colleagues20 found that 65.9% of 7th grade 

daily smokers were dependent. Thus, 

the majority of adolescent daily smokers 

appear to show tobacco dependence.

Whether or not they meet full diagnostic 

criteria, adolescents commonly report 

individual dependence symptoms. Kandel 

and colleagues21 found that tolerance, 

impaired control, and withdrawal were 

the most common DSM Fourth Edition

(DSM-IV) symptoms reported in a 

multiethnic sample of 6th–10th graders. 

Colby and colleagues19 reported that 

most adolescent smokers retrospectively 

recalled at least one withdrawal symptom, 

either as part of a quit attempt or during 

periods when they were restricted from 

smoking. As with adult smokers, withdrawal 

symptoms are common.22 Craving is 

the most commonly reported symptom 

(see also Rojas and colleagues23). However, 

reports of withdrawal symptoms are 

infl uenced by expectancies.24 Prokhorov 

and colleagues25 note that withdrawal 

symptoms such as irritability, depression, 

insomnia, and trouble in concentrating can 

be characteristic of adolescents in general, 

rather than specifi c to tobacco withdrawal 

(see Hughes22 for a similar point concerning 

the adult epidemiological literature).

There are some data concerning the 

amount of time and exposure required for 

adolescent smokers to develop dependence. 

Using retrospective data from the 

National Comorbidity Survey, Breslau and 

colleagues26 found that the onset of DSM

Third Edition Revised nicotine dependence 

typically occurred at least one year after 

the onset of daily smoking. Gervais and 

colleagues27 used a prospective study 

of 7th graders and reported that a 25% 

cumulative probability of attaining an ICD

tobacco dependence diagnosis occurred at 

41 months after the fi rst puff of a cigarette. 
193

Monograph 20. Phenotypes and Endophenotypes

In a longitudinal study of a multiethnic 

sample of 6th–10th graders, Kandel and 

colleagues21 found that a 25% cumulative 

probability of attaining DSM-IV nicotine 

dependence occurred 23 months after 

tobacco use onset.

In contrast to the onset of the full 

dependence diagnosis, there is a shorter 

time to the fi rst symptom of dependence. 

Kandel and colleagues21 found that 25% 

of adolescent tobacco users experienced 

DSM-IV symptoms within fi ve months of 

use. DiFranza and colleagues28 found that 

among the 40% of ever-smoking adolescents 

who reported dependence symptoms, 

the median latency from monthly smoking 

to the onset of symptoms was 21 days for 

girls and 183 days for boys.

Some researchers have suggested that 

adolescents experience dependence 

symptoms not only quickly but also at very 

low levels of consumption.21 For example, 

O’Loughlin and colleagues20 reported 

that, among 7th graders, 19.4% of weekly 

smokers met ICD-10 dependence criteria. 

Dierker and colleagues29 found a similar 

prevalence (22%) by using DSM-IV

diagnostic criteria with college freshmen 

who had smoked in the past week. 

When considering the presence of any

symptom (rather than the full diagnostic 

criteria), even higher percentages of 

adolescents report symptoms at low levels 

of consumption. For example, among a 

sample of adolescents who smoked in the 

past three months, some reported symptoms 

even though they had smoked only once or 

twice.20 Similarly, in a sample of 7th graders, 

DiFranza and colleagues28 found that the 

median frequency of smoking at the onset 

of symptoms was only two cigarettes, one 

day per week. These studies are noteworthy 

for their multiple measures and frequent 

assessments but also have sampling 

limitations in terms of somewhat low or 

unreported participation rates (a common 

problem in these kinds of studies).

Few studies have compared adolescents 

and adults in terms of the relation between 

consumption and dependence. Kandel and 

Chen30 examined a proxy measure of DSM

dependence in the National Household 

Survey on Drug Abuse data and found 

that adolescents met dependence criteria 

at lower levels of smoking intake than did 

adults. They concluded that adolescents 

are particularly vulnerable to becoming 

tobacco dependent. However, these 

differences between adolescents and adults 

might refl ect cohort rather than age effects. 

In a 2001 study, Breslau and colleagues26

suggest that members of more recent age 

cohorts who adopted smoking, despite 

widespread public knowledge about its 

negative effects, may be particularly deviant 

in personality and represent a subsample 

of the population who have a high 

probability of developing into committed 

(and dependent) smokers. Because age and 

cohort are confounded in these studies, 

it is not possible to separate these two 

interpretations, and both effects might 

be operative. Moreover, other researchers 

have suggested that, rather than a 

“hardening” of smoking, changes in tobacco 

control and prevention and in the social 

acceptability of smoking have produced 

a “softening” of smoking. A 2006 study 

indicated that in recent decades, smokers 

have decreased the number of cigarettes 

that they smoke,31 suggesting a substantial 

prevalence of light smoking. Whether 

or not smoking has been “hardening” or 

“softening” or both (i.e., becoming more 

bimodal), signifi cant changes have been 

occurring in the United States both in 

tobacco control and prevention activities 

and in social norms about smoking. Given 

the powerful role of cultural and social 

norms, tobacco policies (e.g., taxation, 

youth access laws) and changes in the 

overall prevalence of smoking in the 

United States, it is important for research 

on adolescent smoking trajectories to 

consider historical and cohort effects on 

the fi ndings.32
194

5. Developmental Trajectories of Cigarette Smoking

In general, the adolescent literature 

suggests a relation between increased levels 

of consumption and the probability of 

tobacco dependence. However, this relation 

is far from perfect, and some adolescents 

report symptoms at low levels of smoking.29

Reports of dependence symptoms at very 

low levels of intake may have multiple 

interpretations including problems of 

measurement (e.g., dependence symptoms 

being nonspecifi c), the possibility that 

tobacco dependence is a multidimensional 

construct (with only some dimensions 

related to smoking rate), and the possibility 

that there are heterogeneous subgroups of 

adolescents who are dependent on tobacco, 

some at quite low levels of consumption.19

In addition to these interpretations, 

adolescents may show dependence 

symptoms at low levels of intake because 

they are particularly sensitive (compared 

with adults) to developing tobacco 

dependence. This interpretation is consistent 

with data from rodent models that compare 

adolescent versus adult exposure to nicotine. 

Levin and colleagues33 found that female 

rats that began self-administration in 

adolescence showed signifi cantly higher 

levels of self-administration than those 

who began in adulthood. This difference 

in rate of self-administration lasted 

into adulthood. Similarly, Adriani and 

colleagues34 found that early-adolescent 

mice exhibited a spontaneous drive to 

oral nicotine (compared to water), which 

was not demonstrated by middle or late 

adolescents. In addition, early-adolescent 

nicotine exposure led to signifi cant place 

conditioning, which was not seen for either 

late-adolescent or adult exposure.

Although the mechanisms underlying these 

age differences are not well understood, 

the unique effects of adolescent exposure 

compared with adult exposure to nicotine 

self-administration may be mediated 

through differential sensitivity to nicotine 

effects. Levin and colleagues33 found that 

adolescent rats showed more hypothermia 

to nicotine than did adults at a given dose, 

although adult rats showed more activity 

reduction. Belluzzi and colleagues35

replicated these activity reduction effects 

with male rats and suggested that locomotor 

inhibition may be an aversive effect of 

nicotine that is more pronounced in 

adults than in adolescents. However, age 

differences in nicotine response may be 

further modifi ed by sex differences36 and 

by exposure to nicotine in combination 

with alcohol.37

There may also be developmental differences 

in nicotine withdrawal. For example, O’Dell 

and colleagues38 found that adolescent rats 

showed decreased sensitivity to withdrawal 

after chronic nicotine administration. 

They also suggested that nicotine exposure 

in adolescence may produce maximal 

reinforcing effects and minimal aversive 

effects, thus promoting rapid acceleration 

of self-administration. Moreover, in rats, 

adolescent nicotine exposure even for a brief 

period (intermittent doses with twice-daily 

injections) at low dosage levels (producing 

plasma concentrations as little as 1/10 of 

regular smokers) has been reported to 

produce nicotinic acetylcholine receptor 

upregulation in brain regions associated 

with nicotine dependence. This may make 

the adolescent brain particularly sensitive 

to nicotine effects.39 Taken together, these 

data suggest that adolescence may be a 

unique period of biological vulnerability, 

during which nicotine exposure produces 

particularly rapid escalation in trajectories 

of nicotine consumption that may persist 

over time as well as increased vulnerability 

for dependence because of differential 

sensitivity to nicotine effects.

Of course, signifi cant caution is required in 

generalizing from animal models to human 

adolescents, given differences in methods 

of administration, dosages, and contextual 

factors such as restrictions on access to 

tobacco, the social and peer context of 
195

Monograph 20. Phenotypes and Endophenotypes

self-administration, and self-selection into 

smoking for human adolescents. In other 

words, there are likely to be substantial 

genetic, environmental, and geneenvironment correlation and interactions 

that affect whether human adolescents 

begin to smoke as well as the timing of their 

smoking onset. Finally, even in the rodent 

model, the empirical evidence concerning 

age differences in nicotine response is not 

always clear-cut. The evidence has shown 

variation with gender, and with the task or 

paradigm that is used, as well as interactions 

with exposure to other substances.36,37,40

More needs to be learned concerning 

the mechanisms underlying these agedependent effects as well as their magnitude 

and persistence over time.

This hypothesis of age-dependent 

vulnerabilities to the effects of tobacco 

has also been proposed for other forms of 

substance use (see Spear and Varlinskaya41

for a review of alcohol data). Moreover, 

in addition to hypotheses concerning 

adolescent-specifi c vulnerabilities to 

substance-use effects, other models that 

are based in the study of neurobiological 

development suggest that adolescents 

are particularly vulnerable to risk-taking 

behaviors more broadly (which would 

include the use of tobacco and other 

substances). These models note that 

adolescents manifest a biologically driven 

disjunction between increased levels of 

novelty/sensation seeking and the lack of 

fully developed self-regulation mechanisms 

(see Steinberg42 for a review). These models 

view adolescents’ special vulnerability for 

substance use as a function of broader 

developmental characteristics rather 

than specifi c substance-use effects. These 

alternative models do not necessarily 

oppose each other in terms of explaining 

adolescence as a particularly vulnerable 

period for the initiation of cigarette 

smoking and other substance use. However, 

the notion of differential vulnerability 

to nicotine effects further predicts that 

adolescent initiation (compared with 

later onsets) will be more likely to be 

accompanied by higher consumption levels, 

steeper acceleration and greater persistence 

over time, and the development of 

dependence at lower levels of consumption.

Biologically based approaches offer 

a different (although not necessarily 

competing) interpretation from psychosocial 

models, which have also sought to explain 

why substance use is typically initiated 

in adolescence and then shows declines 

in adulthood. Psychosocial models often 

conceptualize adolescence as a high-risk 

period for substance-use initiation, because 

of adolescents’ drives for independence, 

adult status, and peer acceptance, all of 

which can be seemingly facilitated by the 

adoption of “problem behaviors” such as 

cigarette smoking and other substance 

use.43 The transition to adulthood (ages 

18–25 years) has been viewed as a time 

of increasing diversity in trajectories 

(see Schulenberg and colleagues44 for a 

review). During these years, the relative 

homogeneity and external control within 

the high school environment is replaced 

with less external structure and increased 

choices, including choices of entry into 

multiple roles (e.g., student, worker, spouse, 

and parent). The transition to adulthood is 

marked by decreased regulation by parents, 

which might lead to escalations in substance 

use, but also by increased responsibility 

for the performance of adult roles, which 

might lead to decreased substance use.45

Thus, both psychosocial and biologically 

based models provide differing, but not 

mutually exclusive, interpretations of agerelated trajectories of tobacco and other 

substance use.

Studies of Genetic Infl uences 

on Adolescent Smoking

Surprisingly, given the interest in genetic 

infl uences on tobacco use, few highquality studies have focused on the family 
196

5. Developmental Trajectories of Cigarette Smoking

aggregation of adolescent tobacco use.46

In fact, even basic data on the relation 

between adolescent smoking and parental 

smoking have been confl icting, with 

some reviews suggesting only very weak 

relations between parental smoking and 

adolescent smoking onset.47 However, 

methodological limitations prevent fi rm 

conclusions. Many studies do not directly 

measure parental smoking (relying instead 

on adolescent reports), do not differentiate 

biological parents from adoptive or foster 

parents, do not adequately defi ne the 

phenotype of parental smoking (often 

failing to differentiate between parental 

nonsmoking and former smoking),48 and 

do not consider possible effects of prenatal 

exposure (see Avenevoli and Merikangas46

for a detailed methodological critique of 

this literature).

Similarly, studies have not carefully defi ned 

the phenotype of adolescent smoking. 

Parental smoking may be more strongly 

related to adolescents’ smoking rate or 

early age of onset rather than adolescents’ 

global smoking status. Finally, studies often 

dismiss the role of parental smoking if its 

effects are eliminated when other variables 

(such as peer smoking) are entered into 

predictive models. However, such a pattern 

is consistent with the mediation of parental 

smoking effects by peer smoking and does 

not by itself argue that the relation between 

parental smoking and adolescent smoking is 

an artifact or unimportant.

The literature on twin and adoption studies 

of adolescent substance use (including 

tobacco use) has been reviewed by Hopfer 

and colleagues,49 who reported that both 

genetic and environmental infl uences 

were important. Heritability of tobacco 

use in their review ranged across studies 

from 36% to 60% and was stronger for 

tobacco than for alcohol or marijuana use. 

For example, McGue and colleagues,50 using 

the Minnesota Twin Family Study (MTFS) 

sample, reported that approximately 50% of 

the variance in smoking initiation (studied 

in late adolescence) was attributable to 

genetic infl uences, with no signifi cant 

effects of shared environment. Han and 

colleagues51 studied lifetime tobacco use in 

the MTFS and found stronger evidence of 

shared environment and a pattern that was 

suggestive of (but not signifi cant for) gender 

differences. Finally, Rhee and colleagues52

found (both for lifetime tobacco use and 

for “problem use,” as defi ned by presence 

of a dependence symptom) that female 

adolescents showed greater heritability and 

weaker shared environment effects than 

did male adolescents. They reported that 

this gender difference was inconsistent 

with the adult literature.52 A later analysis 

of retrospective life calendar data in a 

sample of male twin pairs53 suggests that 

nicotine use in adolescence shows strong 

family environment effects that decline 

in importance through young adulthood 

whereas genetic effects were weak in 

adolescence and increased with age.

As noted elsewhere in this monograph 

(chapters 2 and 6), heritability estimates for 

adolescent smoking vary depending on which 

adolescent smoking phenotypes are selected 

for study. Koopmans and colleagues54

found stronger heritabilities for amount 

of smoking than for initiation. This is 

consistent with conclusions of other reviews 

(see chapter 2 and Rende and Waldman55) 

that environmental infl uences are more 

important in determining adolescents’ 

initial tobacco exposure, whereas genetic 

infl uences are more important for 

determining reactions to that exposure. This 

pattern likely refl ects the heterogeneity of 

adolescent smoking initiation. For example, 

some adolescent initiation of smoking 

will not progress past low levels of 

experimentation.56 This developmentally 

limited experimentation may be only weakly 

related to genetic infl uence.

This supports the use of a developmental 

trajectory approach to identifying 
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Monograph 20. Phenotypes and Endophenotypes

phenotypes in which developmentally 

limited experimentation needs to be 

distinguished from other forms of 

adolescent smoking that may start earlier, 

escalate steeply over time, and persist over 

long periods. However, exceptions to these 

fi ndings should also be noted. For example, 

McGue and colleagues50 found similar 

results when studying smoking initiation 

and nicotine dependence in late adolescence. 

Maes and colleagues,57 when studying an 

adult sample, found high heritabilities and 

an overlapping contribution of genetic 

factors for tobacco initiation, persistence, 

and nicotine dependence.

The adolescent literature (compared with 

the adult literature) also shows more shared 

environment effects on tobacco use.51 White 

and colleagues,58 in a longitudinal study 

using the Australian Twin Registry, found 

that common environmental infl uences 

were the most important factors infl uencing 

adolescent smoking, although for older 

adolescents and young adults, genetic 

factors were also important. Rende and 

colleagues59 used National Longitudinal 

Study of Adolescent Health (Add Health) 

data to model current smoking, by using 

twins, full siblings, and half siblings, and 

found both signifi cant heritability and 

signifi cant shared environment effects. 

It is noteworthy that the authors found 

signifi cant shared environment effects on 

high levels of smoking frequency, a more 

“severe” phenotype than smoking initiation.

Shared environment effects may refl ect 

multiple infl uences including (but not 

limited to) general parenting behaviors, 

such as monitoring, support, and control, 

and parental socialization about smoking 

such as home smoking restrictions 

and smoke-free homes.60 There have 

been some attempts to explain shared 

environment effects as due to the effects 

of parental smoking (which might refl ect 

modeling mechanisms, greater access to 

cigarettes, greater exposure to secondhand 

smoke, greater exposure to tobacco 

promotional advertising, and/or more 

permissive attitudes of parents toward 

their adolescents’ smoking). However, 

these fi ndings are confl icting. Boomsma 

and colleagues61 found that the association 

between parental smoking and adolescent 

smoking was due to genetic factors. They 

suggested that common environmental 

infl uences might refl ect parenting behaviors 

and family environment, rather than 

parental smoking. For example, common 

environment effects might include parents’ 

home smoking restrictions. However, White 

and colleagues58 found that controlling for 

parental smoking reduced the common 

environmental effect. Differences between 

the fi ndings of these two studies may be 

methodological. White and colleagues58

used adolescent reports of parental smoking, 

whereas Boomsma and colleagues61 used 

parent reports. Interestingly, White and 

colleagues58 also found that peer smoking 

reduced the genetic effect. They suggested 

that genetic infl uences in adolescent 

smoking may act indirectly by infl uencing 

adolescents’ choice of friends (although 

these fi ndings weakened by late adolescence 

and young adulthood). These attempts to 

examine the relation of peer and parental 

smoking to the genetic and shared 

environment infl uences on adolescent 

smoking illustrate the importance of 

the gene-environment covariation in 

understanding the smoking acquisition 

process. That is, parental genotype and 

parental smoking likely covary with a 

wide variety of social environment factors 

including general parenting; parents’ 

attitudes, values, and rules about their 

adolescents’ smoking; adolescents’ exposure 

to secondhand smoke (or conversely to 

smoke-free homes); and even adolescents’ 

exposure to tobacco industry promotional 

items and advertising.

In addition to gene-environment 

covariation, there are likely to be important 

infl uences of gene-environment interactions 
198

5. Developmental Trajectories of Cigarette Smoking

on adolescent smoking, although few 

studies have examined such interactions. 

Timberlake and colleagues62 examined the 

moderating effect of religiosity on smoking 

among late adolescents/emerging adults 

(aged 18–27 years) from Add Health. They 

found that self-rated religiousness weakened 

the magnitude of genetic infl uence on 

smoking initiation (defi ned as having 

smoked an entire cigarette). Similar 

fi ndings have been reported for alcohol-use 

initiation.54 Surprisingly, however, organized 

religious activity had no moderating effect. 

Timberlake and colleagues62 hypothesized 

that organized religious activity may 

refl ect parental pressure, whereas selfrated religiousness might constitute a 

more genuine refl ection of the adolescent’s 

religious commitment. In any case, these 

results serve to highlight the potential 

importance of larger cultural and social 

environmental factors in moderating the 

magnitude of genetic effects. Given the 

lack of studies that test gene-environment 

interaction in adolescent smoking, this is an 

important area for future investigation.

Finally, several studies have investigated the 

genetic underpinnings of the association 

among different forms of adolescent 

substance use. Young and colleagues63

studied a large sample of adolescents aged 

12–18 years and defi ned the “problem” 

use of a substance by the presence of at 

least one symptom. They found that the 

correlation among substance-use behaviors 

was driven by both common genetic and 

common environment factors (as well as 

special twin environment factors) but that 

the more “severe” phenotypes (i.e., problem 

substance use as opposed to substance 

use) showed stronger genetic correlations. 

Similarly, McGue and colleagues64 found a 

highly heritable factor that accounted for 

the association among multiple forms of 

disinhibitory psychopathology (including 

substance use) among 17-year-old twins 

from the MTFS. Interestingly, earlier 

problem behavior (retrospectively assessed) 

was only weakly heritable, but the link 

between early problem behavior and 

later disinhibitory psychopathology was 

genetically mediated. From the perspective 

of developmental trajectories of smoking, 

these fi ndings suggest that a trajectory 

of stable, persistent smoking over time 

might show more genetic infl uence than 

adolescent smoking does at any one given 

point in time.

Although the literature is quite small, there 

have also been a few molecular genetic 

studies of adolescent smoking, several 

of which have focused on the dopamine 

system. Audrain-McGovern and colleagues65

followed 615 adolescents from 9th to 11th 

grade. They found no effects of SLC6A3

(dopamine transporter genetic variants) 

but found that DRD2 genetic variants were 

related to smoking progression. Specifi cally, 

adolescents with previous smoking 

experience were more likely to increase their 

smoking as a function of increased DRD2*A1

alleles, and this effect was stronger for 

adolescents with depressive symptoms. 

However, there were no effects for 

adolescent never smokers, suggesting that 

different stages of smoking progression may 

have different determinants. The authors 

suggest that DRD2 genetic variants may 

index greater reward value from smoking 

and that depressed adolescents (who lack 

other sources of positive experiences) 

may be particularly susceptible to such 

increased reward value.

However, as noted in chapter 2, DRD2 has 

been associated with numerous addictive 

and affective disorders, so these effects 

are not specifi c to tobacco. Findings from 

an Australian longitudinal adolescent 

study66,67 reported a protective effect for 

the *K4 allele of the TH gene, which is 

involved in dopamine synthesis. The authors 

hypothesize that this protective effect may 

work by increasing endogenous dopamine 

levels or by reducing the perceived reward 

of nicotine. However, their fi ndings of a 
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Monograph 20. Phenotypes and Endophenotypes

protective effect were limited to a strict 

defi nition of nicotine dependence, which 

included high frequency (more than six 

days per week), high quantity (more than 

10 cigarettes per day), shorter periods 

of abstention (smoking within one hour 

of waking), and stability (present at two 

waves of longitudinal measurement). These 

fi ndings illustrate the potential importance 

of carefully defi ned phenotypes of smoking.

Laucht and colleagues68 also studied the 

dopamine pathway and focused on the 

DRD4 exon III polymorphism associated 

with novelty seeking. They studied a sample 

of 15-year-olds from the Mannheim Study 

of Risk Children, which followed infants 

who were oversampled for obstetrical and 

psychosocial risk. They found that the 

DRD4*7-repeat allele was associated with 

greater smoking among males (including 

lifetime smoking, amount smoked, and 

earlier onset), but not among females. 

Moreover, novelty seeking mediated this 

relation, suggesting that novelty seeking 

is a potential endophenotype, at least 

among adolescent males. For females, 

however, there was an interaction between 

the DRD4*7-repeat allele and the long 

allele of 5-HTTLPR. Females who lacked 

the DRD4*7-repeat allele and who were 

homozygous for the long allele of 5-HTTLPR

smoked the most.69 This demonstrates 

both the potential importance of genegene interaction and of gender differences 

in the mechanisms underlying adolescent 

smoking. However, given the possibility 

of chance fi ndings with multiple tests, 

these interactions require confi rmation in 

multiple studies.

The short-short genotype of 5-HTTLPR has 

also been associated with increased smoking 

among adolescents. Gerra and colleagues70

found this genotype to be associated with 

smoking and with early onset (before 

15 years of age), heavy smoking (more 

than 10 cigarettes per day), as well as 

with novelty seeking, irritability, and 

underachievement. Finally, several studies 

focused on CYP2A6, which inactivates 

nicotine to cotinine. Studying adolescents 

from the longitudinal McGill University 

Study on the National History of Nicotine 

Dependence, O’Loughlin and colleagues71

defi ned nicotine dependence as more than 

three ICD symptoms. They found that those 

smokers who became dependent were more 

likely to have 1 or 2 copies of the inactive 

CYP2A6*2 or *4 variant. In contrast, 

Audrain-McGovern and colleagues72 found 

that slower metabolizers (those with 

CYP2A6 variants) had a signifi cantly slower 

growth in tobacco dependence symptoms 

from grades 9 to 12. Differences in study 

fi ndings may be due to many methodological 

differences between the studies including 

differing ages of measurement and 

defi nitions of dependence (categorical ICD

diagnoses in O’Loughlin and colleagues71

and changes over time in Fagerström-type 

symptomatology in Audrain-McGovern 

and colleagues72). A meta-analysis of 

adult studies73 failed to fi nd any relation 

between the CYP2A6 genotype and 

smoking status or amount smoked, but the 

authors also noted limitations in these 

conclusions due to the generality and 

heterogeneity of these smoking phenotypes. 

In addition, association studies have high 

false-positive rates.

In general, fi ndings from genetic studies of 

adolescent smoking echo the conclusions of 

Lessov and colleagues74 from the adult data: 

although heritable factors are important, 

there are also important common 

environmental infl uences (particularly 

on smoking initiation) as well as complex 

interactions both among multiple genes 

and between genetic and environmental 

factors. Moreover, inconsistent fi ndings 

across studies refl ect multiple factors, 

including variation in study designs and 

ascertainment, and high rates of false 

positives, but also wide variation in smoking 

phenotypes that are studied. Finally, low 

participation rates in molecular genetic 
200

5. Developmental Trajectories of Cigarette Smoking

studies can jeopardize both the internal 

and external validity of the conclusions 

(e.g., several studies66,71 had participation 

rates of 55% or less).

Developmental Trajectories

Age of Smoking Onset

To this point, age of onset has been discussed 

mostly as it relates to age-dependent 

nicotine effects in animal studies along with 

associated alterations in neural systems that, 

in turn, make it more likely that tobacco use 

will escalate and persist over time. These 

studies assign a causal role to adolescent 

exposure in producing steep acceleration 

in tobacco use. In other words, adolescence 

is thought to be a biological period of 

vulnerability during which exposure to 

tobacco increases risk for accelerating 

smoking by changing neural pathways.

However, age-dependent nicotine effects 

and associated changes in neural systems 

may not be the only reason that an early 

age of smoking onset is associated with 

acceleration and persistence. Rather, 

adolescents who begin tobacco use at 

particularly early ages may have unique 

characteristics, and their smoking may be 

maintained by different factors compared 

to those with late (after age 18) onset.13,56

Perhaps early- and late-onset smoking 

represent two distinct subgroups (or what 

are called transitional phenotypes in 

chapter 3). For defi ning endophenotypes 

and phenotypes for the genetic study of 

tobacco dependence, this is an alternative 

model of adolescent exposure in which both 

early use and rapid acceleration are caused 

by a common underlying vulnerability 

(potentially one or more endophenotypes).

A similar hypothesis concerning age of onset 

has been advanced in the developmental 

psychopathology literature concerning 

antisocial behavior75 in which childhoodonset, life-course-persistent delinquency 

is thought to be more strongly associated 

with inadequate parenting, neurocognitive 

problems, and violence. On the other 

hand, adolescent-onset delinquency is not 

characterized by these features; it is seen 

as more normative and more strongly 

linked to peer infl uence. Differences 

between childhood-onset, life-coursepersistent delinquency and adolescentonset delinquency are maintained into 

adulthood.76 Moffi tt’s75 developmental 

taxonomy of antisocial behavior 

illustrates the potential importance of 

developmental trajectories in understanding 

the heterogeneity that underlies adult 

phenotypes and when trying to create more 

homogeneous subgroups. Alternatively, 

differing ages of onset may simply refl ect a 

continuum of severity with those having the 

highest levels of risk factors showing earliest 

entry,43 or they may refl ect differences in 

environmental opportunity and access to 

cigarettes. Thus, age of smoking onset is 

likely to have multiple determinants.

In terms of smoking trajectories, early 

smoking onset has been associated with 

steeper acceleration in smoking rate, greater 

persistence over time, and greater likelihood 

of developing dependence.56,77,78 Note that 

this association does not mean that all early 

onset inevitably produces heavy smoking 

and dependence. As described later, some 

subgroups of early-onset smokers show 

experimental or developmentally limited 

patterns that do not persist over time.56,79

Nevertheless, age of onset is related to 

greater risk for persistence and heavy use. 

Moreover, early-onset smoking has been 

reported to be more strongly related to 

parental smoking, whereas later onset 

(but still under 15 years of age) was related 

to peer (but not parental) smoking.80

Similarly, a subgroup of smokers with early 

onset, steep acceleration, and persistence 

of heavy smoking over time also had the 

highest levels of smoking among biological 

parents.56 These fi ndings are consistent 

with reports that age of smoking onset 
201

Monograph 20. Phenotypes and Endophenotypes

is heritable.81,82 Moreover, Broms and 

colleagues81 found that the same genetic 

infl uences on age of smoking onset did 

not account for the amount of smoking 

or smoking cessation, suggesting that 

age of onset may have distinct genetic 

underpinnings. Ling and colleagues83

reported that a polymorphism of the 

dopamine transporter gene was associated 

with early onset of smoking and that it also 

magnifi ed the relation between early onset 

and dependence.

Age of smoking onset has also been related 

to child psychopathology (see review by 

Upadhyaya and colleagues84). For example, 

ADHD has been associated with earlier 

initiation of regular smoking,85,86 even after 

controlling for comorbidity.87 A combination 

of conduct disorder and ADHD may be 

particularly predictive.88 In contrast, 

anxiety disorders have been associated 

with delayed smoking onset,89 although 

different forms of anxiety disorder may have 

different impacts. At least for alcohol use,90

generalized anxiety disorder symptoms were 

associated with greater risk for initiation, 

whereas separation anxiety symptoms were 

associated with decreased risk.

Although some data suggest that early 

onset of smoking may constitute a unique 

phenotype in terms of showing different 

predictors than does late-onset smoking,56,80

such a conclusion is still premature. First, 

few studies have contrasted early- and 

late-onset smoking, and much of the 

data concerning age of onset comes from 

retrospective studies, which might suffer 

from forward telescoping bias. For example, 

Johnson and Schultz,91 using national 

interview data, found that older age at 

interview (within a given birth year) was less 

likely to produce a report of early smoking 

onset (see also Parra and colleagues92). 

Second, there is no specifi c age of onset 

that has been identifi ed as “early,” and this 

defi nition is likely to change within a social, 

cultural, and historical context. Third, it is 

unclear whether early-onset smoking is a 

unique phenotype distinct from other forms 

of early-onset substance use. For example, 

Yoon and colleagues93 found that multiple 

forms of early substance use (including but 

not limited to smoking before 15 years of 

age) were linked to reduced P300 amplitude, 

which itself was highly heritable. They 

suggest that a failure in top-down control 

of behavior (as manifested by reduced P300 

amplitude) may be one endophenotype 

that accounts for genetic infl uences on 

adolescent substance use more broadly 

(particularly for males). Thus, an early onset 

of smoking may also be associated with early 

onset of alcohol and other drug use, and 

these may be markers for an endophenotype 

associated with the “externalizing” spectrum 

broadly defi ned, not necessarily associated 

uniquely with tobacco use. 

Rate of Acceleration from Initiation 

to Regular Smoking or Dependence

Another feature of developmental 

trajectories that might defi ne a smoking 

phenotype is the speed at which an 

adolescent transitions from initial onset 

to regular smoking or dependence, or 

in other words, the slope of the growth 

curve of tobacco use.94 Rate of acceleration 

itself may be a phenotype that refl ects 

vulnerability to dependence on the basis of 

reactions to initial tobacco exposure as an 

endophenotype.95 Retrospective data suggest 

that smoking is generally reported by study 

participants as an aversive experience 

initially; however, there is variability in 

how participants rate the experience. 

Participants who reported their initial 

experiences as relatively more positive 

(e.g., who report relaxation or a “buzz”) 

and relatively less aversive were more likely 

to become smokers.96–98 Thus, sensitivity 

to the pleasurable effects of nicotine 

and/or insensitivity to the negative effects 

are potential endophenotypes that might 

determine the speed of smoking acquisition 

(see chapter 8 for a detailed review of this 
202

5. Developmental Trajectories of Cigarette Smoking

literature). However, the extant data are 

largely confi ned to retrospective self-reports 

of unknown dose amounts. More research 

is needed to determine whether individual 

differences in sensitivity to smoking’s 

effects predict the degree of acceleration of 

smoking acquisition.

Importantly, a subsequent study 

empirically identifi ed heterogeneity in 

trajectories of dependence symptoms 

over 36 months among novice smokers 

from a multiethnic sample of 6th–10th 

graders,99 Using latent growth mixture 

modeling, the authors found that 47% 

of the sample developed no dependence 

symptoms. In contrast, 21% developed 

symptoms rapidly (within the fi rst year), 

averaged more than two symptoms, and 

showed persistent symptoms, whereas 18% 

developed symptoms rapidly but averaged 

somewhat fewer symptoms and did not 

persist. Finally, 14% developed symptoms 

more slowly. Among those who developed 

symptoms rapidly, those who persisted 

showed signifi cantly more parental tobacco 

dependence than did those who remitted. 

Moreover, compared to those whose 

symptoms developed more slowly, those 

who rapidly developed persisting symptoms 

showed more pleasant initial sensitivity and 

more conduct disorder symptoms. These 

data suggest that rapid acceleration and 

persistence of dependence symptoms may 

be informative phenotypes.

Empirically Identifi ed 

Trajectories of 

Adolescent Smoking

Although adolescence is the typical age 

of smoking onset, there is substantial 

variability in age of onset, in steepness 

of acceleration, and in persistence over 

time. As noted above, this heterogeneity 

may refl ect different phenotypes of 

smoking, which may be infl uenced by 

different underlying mechanisms and 

endophenotypes. Researchers have begun 

to empirically examine heterogeneity in the 

course of smoking over time in longitudinal 

studies. Studies are reviewed here that 

have examined tobacco use from early 

adolescence through either adolescence 

or adulthood (see table 5.1 for a summary 

of these studies). Only studies of tobacco 

use are in this review; studies of the joint 

trajectories of tobacco and alcohol or other 

drug use are reviewed in chapter 7.

For the youngest adolescent ages, Abroms 

and colleagues100 examined trajectories of 

smoking from 6th to 9th grade by using 

growth mixture modeling with “stages” 

of smoking (ranging from no intention 

to smoke to smoking more than three 

cigarettes per month) as the outcome 

variable. Results showed fi ve trajectory 

classes: never smokers, intenders, delayed 

escalators (who averaged monthly smoking 

by 9th grade), early experimenters, and early 

users (who averaged smoking three or more 

times per month by the end of 7th grade). 

Early users were an early-onset, sharply 

accelerating (albeit small) subgroup who 

surprisingly were distinguished from never 

smokers by decreased depression (unlike 

the other groups).

In another study, Vitaro and colleagues80

distinguished among groups who started at 

11, 12, and 13 years of age, and found that 

early onset was associated with antisocial 

behavior. Colder and colleagues101 followed 

a similar age group (aged 12–16 years) 

by using growth mixture modeling. Similar 

to Abroms and colleagues,100 they also 

identifi ed an early-onset group (with onset 

between 12 and 13 years of age) that rapidly 

escalated to heavy smoking. A later-onset 

(after 14 years of age) group escalated 

less quickly and reached lower levels of 

smoking by 16 years of age. The remaining 

three groups were light smokers. AudrainMcGovern and colleagues65 identifi ed a 

never-smoker group, an experimenter 
203

Monograph 20. Phenotypes and Endophenotypes

Table 5.1 Studies of Smoking Trajectories

Authors/

Year Age Gender Ethnicity Definition of smoking

Statistical 

analysis Trajectory groups Endophenotype findings Comments

Chassin et al. 200056

11–31 years

51% male

96% NonHispanic Caucasian

0 = not currently smoking1 = up to monthly smoking2 = up to weekly smoking3 = weekly or more 

smoking, but only 10 or fewer cigarettes a day

4 = weekly or more 

smoking of 11–20 cigarettes per day

5 = weekly or more 

smoking of 20 or more cigarettes a day

Abstainer and erratic groups were defined a prioriLatent class growth analysis mixture modeling

Abstainers (60%)Erratics (.02%)Early stables (12%)Late stables (16%)Quitters (5%)Experimenters (6%)

Abstainers reported lower levels of depression and lower levels of personality risk (including extraversion and conscientiousness).Early, stable group reported higher levels of depression than other groups.

Colder et al. 2001101

12–16 years

52% female

79% Caucasian18% African American3% Asian Pacific/Asian Indian0.3% Other

1 = used to smoke, but 

now I don’t

2 = I’ve only tried a few 

puffs

3 = a few cigarettes per 

month or less

4 = less than a pack per 

week

5 = about a pack per week6 = about one-half pack 

per day

7 = 1 pack per day or more

Piecewise latent growth mixture modeling

Early, rapid escalatorsLate, moderate escalatorsLate, slow escalatorsStable, light escalatorsStable puffers

None reported. Percentages of 

sample in each trajectory group not reported. 
204

5. Developmental Trajectories of Cigarette Smoking Table 5.1 Studies of Smoking Trajectories (continued) Authors/ Year Age Gender Ethnicity Definition of smoking Statistical analysis Trajectory groups Endophenotype findings Comments Juon et al. 2002102 6–32 years 52.2% female 99% African American Frequency and quantity of smoking Multiple logistic regression Nonsmokers (37%) Former smokers (12.9%) Current smokers/late adopters (25.6%) Current smokers/early adopters (24.1%) Current smokers/early adopters were more likely to display antisocial behaviors in 1st grade and young adulthood than did nonsmokers and the other two smoker groups. Current smokers/early adopters were more likely to report both depression and drug problems than did nonsmokers. Soldz and Cui 2002103 6th–12th grade 55% female 79%–87% Caucasian 7%–9% African American 6%–9% Hispanic 0 = no cigarette use during the past month 1 = moderate use (≤40 cigarettes) during the past month 2 = heavy use (≥40 cigarettes) during past month Generalized estimating equations approach Nonsmokers (72.2%–93.5%) Light smokers (5.3%–8.9%) Heavy smokers (1.2%–20%) Class truancy was related to smoking across grades. White et al. 2002104 12–31 years 50% female 92% Caucasian Frequency of smoking in the past year and typical quantity per day Latent growth mixture modeling Multinomial logistic regression analyses Nonsmokers (39.6%) Occasional smokers (19%) Heavy smokers (1.2%–20%%) Higher sensation seeking was linked with increased probability of belonging to a smoking trajectory group as well as heavier smoking over time. 

Delinquency and depression were not found to predict smoking group membership. 
205

Monograph 20. Phenotypes and Endophenotypes

Table 5.1 Studies of Smoking Trajectories (continued)

Authors/

Year Age Gender Ethnicity Definition of smoking

Statistical 

analysis Trajectory groups Endophenotype findings Comments

AudrainMcGovern et al. 2004105

14–18 years

52% female

63% Caucasian12% Hispanic11% Asian 8% African American6% other

0 = never smoker1 = puffer (never having 

smoked a whole cigarette)

2 = experimenter (<100 

cigarettes ever)

3 = current smoker 

(smoked <20 days in last 30 days and >100 in lifetime)

4 = frequent (smoked ≥20 

days in last 30 days and >100 in lifetime)

Latent class growth modeling

Early/fast adopters (8%)Late/slow adopters (24%)Experimenters(23%)Never smokers(45%)

Adolescents higher in novelty seeking and depressive symptoms were more likely to be early/fast adopters and late/slow adopters than never smokers or experimenters. 
206

5. Developmental Trajectories of Cigarette Smoking Table 5.1 Studies of Smoking Trajectories (continued) Authors/ Year Age Gender Ethnicity Definition of smoking Statistical analysis Trajectory groups Endophenotype findings Comments Orlando et al. 2004106 13–23 years 52% male 67% Caucasian 10% African American 11% Hispanic 8% Asian 4% Other 0 = nonsmoker in past year 1 = <3 times in past year and <3 times in past month 2 = 3–10 times in past year and <3 times in past month 3 = 11± times in past year and <3 times in past month OR 3–5 times in past month 4 = 6± days in past month and <3 cigarettes per day 5 = 6± days in past month and about one-half pack per day 6 = 6± days in past month and about one-half pack per day 7 = 6± days in past month and 1 pack or more per day Latent growth mixture modeling Multinomial logistic regression analyses Nonsmokers (28%) Stable highs (6%) Early increasers (10%) Late increasers (10%) Decreasers (6%) Triers (40%) At 13 and 15 years of age, nonsmokers reported fewer deviant behaviors and internalizing symptoms than did other smoking groups. At age 23, late increasers were more likely than triers and nonsmokers to have engaged in deviant behavior in the past year. Also, at 23 years of age, nonsmokers reported fewer internalizing symptoms than early increasers and late increasers. 
207

Monograph 20. Phenotypes and Endophenotypes

Table 5.1 Studies of Smoking Trajectories (continued)

Authors/

Year Age Gender Ethnicity Definition of smoking

Statistical 

analysis Trajectory groups Endophenotype findings Comments

Stanton et al. 2004107

9–18 years

Not reported 

96% Caucasian4% Maori/ Polynesian

Count of number of cigarettes smoked in past month

Latent growth mixture modeling Multinomial logistic regression analyses

Early, rapid escalators (11.4%)Late, rapid escalators (38.8%)Late, moderate escalators (14.3%)Late, slow escalators (11.4%)Stable puffers (12.7%)Late, slow escalatorspuffers (11.4%)

Attention deficit disorder predicted early, rapid escalators, as well as late, slow escalators-puffers. Conduct disorder was an early predictor of smoking. Depression and behavior problems were a predictor of midadolescent smoking. 

Sample comprised only New Zealanders.

Vitaro et al. 200480

10–15 years

50.7% female

>90% Caucasian and French speaking

Number of cigarettes smoked during the week and during the day before data collection

Latent growth mixture modelingLogistic regression analyses

Never smokers (75.4%)11–12-year-old starters (5.7%)12–13-year-old starters (11.1%)13–14-year-old starters (7.9%)

Membership in the 11–12-year-old starter group was associated with increased antisocial behaviors. 

Antisocial behavior was analyzed as part of a composite general maladjustment score.

White et al. 2004108

10–25 years

100% male

42% Caucasian (C)56% African American (AA)2% Other/Mixed

At screening, if ever tried tobacco, even a puff, and if so, what age (age of onset)At subsequent assessments, lifetime use, past year use, and number of cigarettes smoked per day

Latent class growth modeling Hierarchical logistic regression 

Nonsmokers:C: 44.3%AA: 55.9%Occasional smokers:C: 23.7%AA: 27.3%Heavy smokers:C: 32%AA: 16.7%

None reported. Separate 

trajectories were examined for Caucasians and African Americans. 
208

5. Developmental Trajectories of Cigarette Smoking Table 5.1 Studies of Smoking Trajectories (continued) Authors/ Year Age Gender Ethnicity Definition of smoking Statistical analysis Trajectory groups Endophenotype findings Comments Abroms et al. 2005100 6th–9th grade Not reported Not reported 0 = did not smoke in past 30 days or past 12 months and had no intention of smoking in high school 1 = did not smoke in past 30 days or 12 months but intended to smoke at least 1 or 2 times in high school 2 = smoked in the past 12 months but not in past 30 days 3 = smoked 1 to 2 times in the past 30 days 4 = smoked 3 or more times in the past 30 days Latent growth mixture modeling Logistic regression to examine risk factors Never smokers (41.2%) Intenders (33.5%) Delayed escalators (8.9%) Early experimenters (13.9%) Early users (2.5%) Higher levels of deviance acceptance were associated with being an intender, an early experimenter, and an early user compared to a never smoker. Higher levels of depression decreased the likelihood of being an early user rather than a never smoker. Ages not reported. Karp et al. 2005109 12–17 years 64.8% female 100% Canadian For 3-month intervals, number of days smoked each month and average number of cigarettes smoked per day each month Individual growth curve modeling Latent class growth modeling Low initial use, gradual increase (72.4%) Low initial use, rapid increase (11.1%) Low initial use, then increase in use, then 

decrease in use (10.8%)High-intensity initial use, then decrease in use (5.7%)

Depression, novelty seeking, and impulsivity did not seem to predict class membership. 

All participants were novice smokers on entering the study.
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Monograph 20. Phenotypes and Endophenotypes

Table 5.1 Studies of Smoking Trajectories (continued)

Authors/

Year Age Gender Ethnicity Definition of smoking

Statistical 

analysis Trajectory groups Endophenotype findings Comments

Brook et al. 2006110

14–26 years

51% female

51% African American (AA)49% Puerto Rican (PR)

1 = none2 = a few cigarettes or 

less per week

3 = 1–5 cigarettes per day4 = about one-half pack 

per day

5 = about 1 pack per day6 = more than 1 pack per 

day

Latent growth mixture modeling

Nonsmokers:AA: 56% PR: 36.5%Maturing out:AA: 6.9%PR: 12.9%Late starting:AA: 19.2%PR: 18.4%Early starting:AA: 20.3% PR: 25%

None reported.

Riggs et al. 200778

12–24 years

44% female

84% NonHispanic Caucasian

Amount smoked per week Latent class 

growth analysis

Abstainers (47%)Low users (24%)Late, heavy users (16%)Early, heavy users (12%)

None reported. Late is defined 

as after 15 years of age.

Maggi et al. 2007111

Maggi 2008112

10–21 years

49.3% female

Separate models for probability of trying a cigarette and smoking frequency

Latent class growth analysis

Stable nonsmokers (48.4%)Late experimentersnonsmokers (17.2%Experimenters-daily smokers (5.8%)Late experimentersdaily smokers (4.1%)Early experimentersoccasional smokers (10.5%)Late experimenters (13.9%)

None reported. Late is defined as 

after ages 14–15 years.Sample from Canadian National Longitudinal Survey of Children and Youth.
210

5. Developmental Trajectories of Cigarette Smoking Table 5.1 Studies of Smoking Trajectories (continued) Authors/ Year Age Gender Ethnicity Definition of smoking Statistical analysis Trajectory groups Endophenotype findings Comments Bernat et al. 200879 12–19 years 49% female 85% Non- Hispanic Caucasian Frequency of smoking (from never user to smoked most days) Latent class growth analysis Nonsmokers (55%) Triers (17%) Occasional users (10%) Early, established smokers (7%) Late, established smokers (7%) Decliners (4%) None reported. Community sampling using random digit dialing; 58.5% response rate. Lessov- Schlagger et al. 2008113 13–24 years 49% female 92% Non- Hispanic Caucasian 2% Hispanic 3% Black 2% Native American Quantity smoked in the past week Latent class growth analysis Experimenters (48.5%) Late increasers (16.3%) Early increasers (15.5%) Quitters (9.2%) Persistent (10.5%) None reported. Nonadopters excluded. Late is defined as after 18 years of age.
211

Monograph 20. Phenotypes and Endophenotypes

group, a late/slow adopter group, and 

a small (8%) early/fast adopter group. 

The two adopter groups showed elevated 

novelty seeking.

Wills and colleagues114 followed students 

from 6th to 10th grade. Using cluster 

analysis, they distinguished stable 

nonsmokers and experimenters from 

three smoking groups that varied by age 

of onset: early (by 6th grade), intermediate 

(by 9th grade), and late (by 10th grade). 

Looking at psychosocial risk factors, their 

results suggested a continuum of risk in 

which early, intermediate, and late onset 

were ordered from highest to lowest risk. 

Moreover, they noted that scores on the risk 

factors changed over time, leading to an 

increased risk just before smoking onset.

Thus, rather than unique phenotypes, 

these fi ndings are most consistent with a 

continuum model of time-varying risk with 

smoking onset resulting from an increase in 

psychosocial and/or genetic risk. However, 

because of the short developmental span 

of assessment, all of the onset groups that 

were identifi ed in this study might still be 

considered “early” in developmental terms.

Finally, Karp and colleagues109 focused 

on only a subsample of participants who 

had already begun to smoke and followed 

them from an average age of 13 to 17 years. 

Because all participants were smokers, 

no differentiation of age of onset can be 

made in this study. Over this short time 

span, most of the participants (72%) 

remained at low levels of smoking. However, 

the other 28% escalated their smoking, 

divided among rapid, low, and moderate 

groups, with the rapidly accelerating group 

representing 6% of the sample. Escalating 

youth were more likely to show symptoms 

of nicotine dependence, but other predictors 

did not differentiate among the groups 

(perhaps because all of these participants 

were already smokers and most remained at 

low levels of smoking during the study).

Several studies traced trajectories to slightly 

older ages of 18–21 years. Stanton and 

colleagues107 modeled monthly smoking in 

the Dunedin study and found six trajectory 

classes. Again, there was an early, rapidly 

escalating group, but also a later (after 

13 years of age), escalating group, and both 

ended at 18 years of age with high levels of 

smoking. Note that the defi nition of “late” 

escalation was still “early” in adolescence 

(13 years of age), which might account 

for both these groups’ steep acceleration 

and high fi nal smoking rates. Early, rapid 

escalators had higher conduct problems 

at 13 years of age than did all of the other 

groups and higher depression at 15 years 

of age than all but the late, moderate 

escalators. Early, rapid escalators also had 

higher attention defi cit scores than the late, 

moderate escalators (but did not differ from 

the other groups).

Soldz and Cui103 followed a large sample 

of students through 12th grade. Using 

cluster analysis, they identifi ed nonsmokers, 

quitters, experimenters, early escalators, 

late escalators, and stable smokers. These 

fi ndings are noteworthy for identifying a 

small quitting group that was absent in the 

other studies. Psychosocial protective factors 

(e.g., church attendance, time with fathers) 

were highest in nonsmokers, compared 

with continuous smokers, who showed 

an elevated risk profi le. A similarly small 

group of decliners (4% of the sample) 

was identifi ed by Bernat and colleagues79

in a study of adolescents (aged 12–19 years). 

These “decliners” are intriguing because 

(unlike experimenter groups that are often 

identifi ed) they show high levels of smoking 

frequency. Moreover, unlike the quitters in 

the Soldz and Cui study, the decliners showed 

elevated baseline risk profi les (compared 

to nonsmokers). However, the fi ndings 

warrant replication because no measure of 

smoking quantity was considered and, unlike 

other studies, there was no “early stable” 

smoking group, so that very early onset was 

not present. Given the participation rate 
212

5. Developmental Trajectories of Cigarette Smoking

(58.5%), perhaps the highest risk adolescents 

were absent from the sample. Finally, Maggi 

and colleagues111 and Maggi112 assessed 

trajectories for participants in the Canadian 

National Longitudinal Survey of Children and 

Youth. They found that a group of occasional 

smokers were not distinguishable from daily 

smokers until the age of 21 years.

Although there have been few studies, most 

researchers agree on the existence of a 

group with early onset, steep acceleration 

(or stably high levels of smoking from an 

early age), and high fi nal levels of smoking. 

This group was also elevated, in general, 

in profi les of psychosocial risk. There 

was also substantial agreement about the 

existence of light-smoking groups, often 

associated with later onset. However, it is 

diffi cult to draw conclusions about a life 

course trajectory of smoking from studies 

that track the behavior only through high 

school. Age-related patterns of substance use 

more broadly (i.e., alcohol and illegal drugs) 

typically show adolescent initiation, peaks in 

emerging adulthood (ages 18–25 years), and 

later declines. In other words, some forms of 

substance use are developmentally limited 

and some persist.115 Thus, to adequately 

map heterogeneity in smoking trajectories 

requires studies that span the early years 

of onset to adulthood to differentiate both 

early versus late onset and developmentally 

limited versus persistent use.

Very few studies have tracked smoking 

over such long age periods. White and 

colleagues104 studied adolescents who 

were recruited through random telephone 

sampling and examined a quantity-frequency 

measure of cigarette use. They found three 

groups: a heavy smoking group that showed 

steep acceleration and heavy smoking, an 

occasional smoking group that “matured 

out” after 18 years of age, and a nonsmoking 

group. Female gender was associated with 

maturing out, and higher disinhibition was 

associated with regular smoking. However, 

adolescent risk factors did not differentiate 

the occasional and heavy groups. This lack 

of differentiation is likely due to the fact that 

the two smoking groups did not signifi cantly 

differ in age of onset and because this 

study had a relatively small sample size for 

trajectory group differentiation.

Orlando and colleagues106 tracked a large 

school-based sample from 13 to 23 years of 

age. They identifi ed nonsmokers, stable high 

smokers, early increasers (increases between 

13 and 14 years of age), late increasers 

(after 18 years of age), decreasers, and triers. 

Importantly, they found substantial onset 

after high school that has not often been 

recognized and cannot be found by studies 

that track participants only through high 

school. However, by 23 years of age, the 

different trajectory groups merged into two 

groups: low- and high-frequency smokers. 

An identical fi nding was reported by LessovSchlaggar and colleagues,113 who studied a 

smaller sample from adolescence to 24 years 

of age. Moreover, these authors113 found 

that smoking more than a few cigarettes 

per week in adolescence resulted in similar 

levels of nicotine dependence (as least 

as measured by the Fagerström Test for 

Nicotine Dependence and the Nicotine 

Dependence Syndrome Scale. These 

fi ndings suggest that early onset is not very 

informative about adult smoking outcomes. 

In contrast, Riggs and colleagues78 found 

that an early-onset-trajectory group 

showed more frequent weekly smoking 

and greater reported dependence than a 

later-onset-trajectory group at 24 years 

of age. Differences between these studies 

may refl ect the fact that the “late” groups 

in the studies by Orlando and colleagues106

and Lessov-Schlaggar and colleagues113

increased smoking after 18 years of age 

whereas the “late” group in the study by 

Riggs and colleagues78 increased smoking 

after 15 years of age. However, the question 

of whether heterogeneity in adolescent age 

and smoking course predicts adult smoking 

levels and adult nicotine dependence may 

be diffi cult to resolve when participants 
213

Monograph 20. Phenotypes and Endophenotypes

are followed only until the ages of 23 or 

24 years. It is possible that developmentally 

limited smoking had not yet declined and 

that further divergence would occur after 

the age range of the mid-20s.

Chassin and colleagues56 studied a 

large school-based sample of ages 11 to 

31 years. They removed two a priori groups 

(abstainers and a small group of erratic 

smokers who showed periods of relapse 

and remission) and empirically identifi ed 

four groups described here. Early, stable 

smokers showed middle-school onset (ages 

12–13 years) and averaged daily smoking by 

15 years of age. They attained a high level 

of smoking (averaging more than one-half 

pack per day by 18 years of age) and stayed 

stable over the study. Late-onset smokers 

did not transition to weekly smoking until 

after 18 years of age and averaged less 

than one-half pack per day at their peak. 

An experimenter group never progressed 

past weekly smoking, and a quitter group 

declined after 21 years of age (similar to 

other forms of substance-use behavior). 

The early, stable and the erratic groups 

showed the riskiest profi le on psychosocial 

factors. They were the least socially 

conventional, and their parents and peers 

were most likely to smoke. Interestingly, 

although both the early, stable and the 

experimenter groups showed early smoking 

onset, the experimenters were less likely to 

have parents who smoked (perhaps refl ecting 

the heritability of smoking persistence). 

The late, stable group showed low levels of 

early risk factors and higher levels of college 

attendance. Their late onset might refl ect 

transition out of the supervision provided 

in the parental home as well as some 

college environment factors. Surprisingly, 

a “chipper,” or very light smoker, group 

did not emerge, perhaps refl ecting its 

low prevalence in the population (or its 

correlation with late-onset smoking).

It is worth noting the similarities between 

the two studies. Both Orlando and 

colleagues106 and Chassin and colleagues56

identifi ed an early-onset, rapidly 

accelerating group, and both identifi ed 

substantial late (after high school) onset. 

Both studies also identifi ed an experimental 

group that does not progress to regular 

smoking. However, the Chassin study did 

not fi nd that the trajectories merged into 

two outcomes (high smoking and low 

smoking). Differences between the two 

studies might refl ect the ethnic distribution 

of the samples. The Chassin sample was 

almost entirely non-Hispanic Caucasian. 

There were also possible cohort effects 

in that the two studies were fi ve years 

apart in their baseline data collections. 

Finally, because the Chassin study tracked 

participants longer into adulthood, greater 

differentiation of smoking trajectories 

as a function of developmentally limited 

smoking might have been achieved.

One important issue in describing 

trajectories of smoking is potential ethnic 

differences. Four studies have focused on 

African American samples. In one study, 

Juon and colleagues102 examined data from 

the Woodlawn study that followed inner city 

(predominantly low socioeconomic status) 

1st graders to the age of 33 years. Rather 

than empirically identifying trajectories, 

they divided participants into nonsmokers, 

former smokers, late-onset smokers (after 

18 years of age), and early-onset smokers. 

Compared to late-onset smokers, early-onset 

smokers were more likely to have been rated 

as aggressive by their 1st grade teachers, 

to have moved less often, to have had more 

lax parental supervision, and to have had 

more drug problems. Thus, early onset was 

generally associated with a profi le of greater 

psychosocial risk in this study as it was with 

the other studies of ethnically diverse or 

Caucasian samples described above.

In another study, White and colleagues108

modeled trajectories of the number of 

cigarettes smoked each day separately 

for African Americans and whites in the 
214

5. Developmental Trajectories of Cigarette Smoking

Pittsburgh Youth Study, a prospective, 

longitudinal study of males that oversampled 

high-risk children and tracked them from 

ages 10 to 25 years. For each race, the three 

identifi ed groups were nonsmokers, light 

smokers, and heavy smokers. However, there 

were differences in prevalence such that 

whites began smoking earlier and reached 

higher quantities of smoking than did 

African Americans. Similarly, Blitstein and 

colleagues116 found that African Americans 

were more likely to show slow progression 

than rapid progression in their smoking. 

Brook and colleagues110 modeled trajectories 

for African American and Puerto Rican 

adolescents aged 14–26 years and identifi ed 

a group of nonsmokers, maturing-out 

smokers, late-starting smokers, and earlystarting smokers. Although few studies 

have examined ethnicity, these fi ndings 

converge in suggesting ethnic differences 

in onset and speed of progression that 

should be considered in describing smoking 

trajectories.

In short, among the few studies that 

have examined smoking trajectories from 

adolescence to adulthood, there is some 

convergence in terms of identifying an 

early-onset group that is either stably 

high or rapidly escalating, a later-onset 

group, and light-smoking groups that do 

not progress to regular smoking. However, 

there are important discrepancies, such as 

whether multiple trajectories do and do 

not diverge in their “fi nal” endpoints and 

whether conceptually expected (but possibly 

low prevalence) groups such as stable light 

smokers (i.e., chippers) can be empirically 

identifi ed. Studies have not empirically 

identifi ed relapsing and remitting groups 

because (in all likelihood) these forms of 

growth are very complex to model. Most 

important, other than the fact that earlyonset, sharply accelerating, and persistent 

groups are usually the most “at-risk” groups 

in terms of familial smoking, psychosocial 

risk, and measures of externalizing 

and internalizing problems, there is 

little evidence that any one trajectory 

group constitutes a unique phenotype 

characterized by specifi c endophenotypes. 

Indeed, some studies have not been able 

to empirically predict different smoking 

trajectories (other than differentiating 

between smoking and nonsmoking groups). 

However, very little work has been done 

to link the hypothesized preexposure 

endophenotypes (see table 5.1 and chapter 8) 

to trajectories that might constitute 

dynamic phenotypes of smoking. This is 

an important direction for future research. 

Finally, these studies have been limited to 

cigarette smoking and have not considered 

trajectories of dependence symptoms or of 

other forms of tobacco use.

Statistical Models for 

Evaluating Alternative 

Developmental 

Phenotypes of 

Smoking Behavior

To this point, multiple possible 

developmental phenotypes have been 

described that can be hypothesized to 

underlie the onset and maintenance 

of smoking behavior. For example, 

one hypothesized phenotype might be 

defi ned by age of onset, such that children 

who begin smoking at a very young age 

might constitute a unique subgroup. 

Another phenotype might be hypothesized 

to be refl ected in the latency from onset 

to regular or heavy smoking. Yet another 

phenotype might be evidenced in smoking 

persistence over age, such that individuals 

who repeatedly fail to quit (or never 

attempt to quit) form an important 

homogeneous phenotypic subgroup. 

Finally, phenotypes might be represented 

by combinations of these features that are 

captured by developmental trajectories 

that show particular ages of onset, slopes 

of acceleration, peaks of smoking rates, 
215

Monograph 20. Phenotypes and Endophenotypes

and persistence of smoking over time. These 

are just a few examples of many different 

observable behaviors or patterns of behavior 

over time that might indicate developmental 

phenotypes of smoking.

Given the heterogeneity in hypothesized 

phenotypes of interest, there is 

correspondingly no single statistical 

model that will allow for the optimal 

empirical evaluation of the viability of all 

of these phenotypes. Instead, a statistical 

model must be selected that most closely 

corresponds to the theoretical model 

of the phenotype.117,118 For example, if a 

hypothesized phenotype postulates age 

of onset as a critical component, then 

survival analysis might be the ideal strategy. 

Alternatively, if a hypothesized phenotype is 

focused on time from onset to a transition 

to a category or stage of dependence, then 

latent transition analysis might be most 

appropriate. Because this chapter focuses 

on developmental trajectories (i.e., patterns 

of smoking behavior over development) 

as potential phenotypes, statistical methods 

for clustering developmental trajectories 

are emphasized. However, this is in no way 

meant to suggest that these methods are 

the only (or even necessarily the optimal) 

way to empirically evaluate smoking 

phenotypes. Rather, the goal is to provide 

a brief review of analytic methods for 

clustering growth trajectories, to summarize 

the relative strengths and weaknesses 

of these approaches, and to provide an 

empirical example of these techniques 

through the analysis of a large longitudinal 

study of smoking behavior.

Growth Curve Modeling

A core premise of the discussion so far is 

that the identifi cation of phenotypes of 

smoking behavior might be well served by 

considering developmental trajectories of 

smoking-related behaviors and outcomes. 

This requires the collection and analysis 

of repeated-measures data, for which 

there are a plethora of analytic options. 

Traditional methods such as repeatedmeasures analysis of variance (ANOVA) 

and multivariate analysis of variance have 

long been known to be limited by strict 

underlying assumptions that are rarely 

met in practice.119 Examples include 

the requirements of complete case data, 

equally spaced assessments, and normally 

distributed repeated measures. However, 

over the past two decades, a number of 

signifi cant improvements in statistical 

models for repeated measures data have 

been introduced that overcome nearly all of 

these prior limitations. Because variations 

of these models arose within multiple 

literatures, there are various terms to which 

these are commonly referred. Examples 

include growth curve models, random 

coeffi cient models, latent curve models, 

and latent trajectory models. The evolution 

of growth curve models can be broadly 

traced to two modeling traditions: mixed 

(or multilevel) models and structural 

equation models (SEMs). Although there 

are a small number of important differences 

between models estimated within the mixed 

and SEM traditions, it is well known that 

these two approaches are isomorphic under 

a broad set of conditions.120–124

The standard growth curve model is based on 

the principle that a set of observed repeated 

measures drawn from a sample of individuals 

can be adequately reproduced as a function 

of an underlying, unobserved developmental 

trajectory.119,125,126 The functional form of 

the trajectory might be linear, curvilinear, 

or characterized by some constant level that 

does not systematically change over time. 

Fitting a growth model to the observed 

statistics results in sample estimates of 

the parameters that defi ne the underlying 

trajectory.127–129 To accomplish this, one or 

more latent factors are used to defi ne the 

functional form of the trajectory. The means 

of the latent factors are the fi xed effects

of the model and represent the estimated 

trajectory pooling across all individuals 
216

5. Developmental Trajectories of Cigarette Smoking

in the sample. The variances of the latent 

factors are the random effects of the model 

and represent the degree of individual 

variability around the fi xed effects. Finally, 

one or more covariates can be included 

to test for systematic differences in the 

parameters that govern the trajectories as 

a function of the covariates (e.g., to identify 

characteristics of individuals who begin 

at a higher level and increase at a steeper 

rate over time). There are a number of 

powerful extensions to the standard growth 

curve model, and these have been widely 

used in many studies of development and 

change (see Bollen and Curran130 for a 

comprehensive review of these models).

Growth modeling methods might provide 

one important approach to the study of 

developmental phenotypes for cigarette 

smoking. For example, a particular genotype 

or putative endophenotype score could be 

used to predict the slope of a growth curve, 

such that the genotype or endophenotype 

predicted steeper acceleration in smoking 

trajectories (see Audrain-McGovern and 

colleagues72 for an empirical example). Thus, 

if variation in acceleration of trajectories is 

a phenotype of interest, growth modeling 

provides a useful method for testing such 

phenotypes. Similarly, if early, heavy 

smoking is a phenotype of interest, then 

a particular genotype or endophenotype 

could be used to predict the intercept of the 

growth curve at a specifi c age of interest. 

Finally, the intercept and slope components 

of a developmental trajectory of smoking 

could themselves be used as predictors of a 

distal outcome such as nicotine dependence 

or some other measured characteristic. 

Taken together, growth curve models can 

be used to evaluate a number of important 

questions related to individual differences 

in developmental trajectories and their 

potential relation to smoking phenotypes 

and endophenotypes.

Despite the signifi cant advantages growth 

curve models offer, they have limitations. 

Of greatest importance for this chapter, 

a strong underlying assumption of 

the standard growth model is that all 

individuals are sampled from a single 

population. This, in turn, implies that 

the population is governed by a single 

multivariate distribution of trajectory 

parameters from which all individuals in the 

sample are randomly drawn. For example, 

a population might be characterized by 

an overall trajectory that is defi ned by 

some mean intercept and linear slope. 

Further, there is a bivariate distribution 

of individual intercepts and individual 

slopes around these mean trajectory 

values. Particular individuals might by 

characterized by intercept values that are 

larger or smaller than others or by linear 

slopes that are steeper or less steep than 

others. Importantly, though, all individual 

trajectories are assumed to be drawn from 

this single bivariate population distribution. 

In this conceptualization, phenotypes 

are viewed as arrayed on a continuum of 

severity rather than as qualitatively different 

categories that represent discontinuous, 

separate populations.

The assumption that the sample is drawn 

from a single (or homogeneous) population 

is perfectly reasonable in many research 

applications. However, this assumption 

presents a substantial limitation if there is 

theoretical reason to believe that individuals 

within a single sample may have been 

drawn from one of several populations. 

Although the standard growth model 

can be expanded to explicitly incorporate 

multiple populations,127,131 this is only 

possible if the grouping variable has been 

directly observed. Examples of observed 

grouping variables include gender, ethnicity, 

treatment condition, or observed genotype. 

However, signifi cant challenges arise if the 

grouping variable has not been, or might 

never be, directly observed. A salient 

example of this is in the study of phenotypes. 

A single sample might consist of individuals 

drawn from one of several populations 
217

Monograph 20. Phenotypes and Endophenotypes

(or phenotypic groups), yet these groups are 

inextricably mixed when using a standard 

single sample growth model. Further, 

the multiple group growth model is not 

a viable strategy because the phenotypic 

group membership is not directly observed. 

The challenge then becomes estimating 

the existence of these discrete groups 

on the basis of patterns of observed 

responses drawn from a single sample of 

individuals.132,133 Fortunately, a broad class 

of analytic methods exists that allows for the 

clustering of trajectories into two or more 

discrete groups.

Clustering Trajectories

A long and rich history drawn from 

fi elds including statistics, biostatistics, 

psychometrics, econometrics, and 

criminology has focused on the complex 

task of seeking empirical evidence for the 

existence of unobserved groups. A wide 

array of techniques have been developed 

including cluster analysis, latent class 

analysis, latent profi le analysis, fi nite 

mixture modeling, and growth mixture 

modeling. A comprehensive exploration 

of these techniques is, however, beyond 

the scope of this chapter; see Bauer and 

Curran,134,135 Muthén,136 and Nagin137 for 

reviews.

The shared foundation of these analytic 

approaches is that an apparently 

homogenous sample of individuals is in 

actuality drawn from two or more discrete 

populations. Failure to properly model 

the mixing of multiple populations (or 

population heterogeneity) can lead to 

biased or invalid conclusions about the 

structural relations that exist within any of 

the multiple populations.138 That is, fi tting 

a model to the aggregation of multiple 

populations will likely not accurately refl ect 

any one population, much less the full set. 

However, here lies the challenge: because 

population membership was not directly 

observed in the sample, the existence 

of these groups must be inferred on the 

basis of other measured characteristics 

of the sample. Key analytic tasks include 

the identifi cation of the optimal number 

of groups, the proper specifi cation of 

structural relations of the observed variables 

within each group, and the probabilistic 

assignment of each individual as a member 

of each of the multiple groups. 

Traditional clustering techniques make 

assignments of individuals to groups based 

on ad hoc measures such as the sum of 

distances or the sum of squared Euclidean 

distances from the mean (or centroid) 

of each cluster. A prominent example 

of this is the classic method of k-means 

clustering.139 This is an iterative approach 

in which variability is maximized between 

groups and minimized within groups. 

The k-means approach typically begins 

with the placement of k-points into the 

data space, where k represents the number 

of clusters. This set of points defi nes the 

initial group centroids. Next, each individual 

observation is assigned membership to 

the group that is defi ned by the closest 

centroid. Once all of the observations have 

been assigned to a cluster, a new set of 

centroids are computed on the basis of the 

individuals assigned to that group. This 

process is then repeated until the change in 

centroids from one iteration to the next is 

negligible. Although this is a straightforward 

and sometimes useful clustering procedure, 

the strong assumption of perfect reliability 

of measures, the sensitivity to outliers, and 

the ad hoc nature of class assignment has 

limited the use of this approach in practice.

Subsequent clustering methods incorporate 

likelihood-based approaches to estimation 

in which class extraction, class membership 

probabilities, and covariate relations are 

estimated simultaneously. Two closely 

related yet distinct approaches are 

increasingly used for clustering trajectories. 

The fi rst approach does not incorporate 

random effects associated with the growth 
218

5. Developmental Trajectories of Cigarette Smoking

process within class and is sometimes 

referred to as latent class growth analysis 

(LCGA) because of the shared similarities of 

this approach with traditional latent class 

analysis.140,141 The second approach allows 

for the estimation of random effects within 

each class and is sometimes referred to as 

growth mixture modeling/models (GMM) 

because of the shared similarities of this 

approach with fi nite mixture modeling.142

The historical lines of development that 

ultimately led to these methods span more 

than a century.141–149 Drawing on these 

prior developments, two individuals can 

be predominantly credited with the latest 

methods of LCGA and GMM: Daniel Nagin 

and Bengt Muthén.

LCGA has primarily been developed by 

Daniel Nagin and his colleagues.133,137,150,151

Like latent class analysis,152 LCGA assumes 

conditional independence within class. 

As such, the within-class trajectory 

model is defi ned only by fi xed effects. 

A mean trajectory is thus estimated for all 

individuals within a class, but there is no 

individual variability around these classspecifi c mean values. Posterior probabilities 

are estimated that refl ect the probability that 

each individual belongs to each of the total 

number of classes. The effects of covariates 

can be included in LCGA, but these 

infl uences are limited to either predicting 

the set of class membership probabilities, 

or predicting class membership itself if each 

individual has been assigned to a single class 

on the basis of the posterior probabilities. 

LCGA has a variety of strengths, including 

the ability to directly model continuous, 

truncated continuous, and discrete repeated 

measures, and the expansion of the model to 

forming classes on the basis of simultaneous 

trajectories of two constructs over time.

GMM has primarily been developed by Bengt 

Muthén and his colleagues.132,136,153,154 Like 

fi nite mixture modeling,142 GMM assumes 

a multivariate normal distribution of the 

observations within each class. This, in 

turn, allows for the estimation of a growth 

model within each class that is characterized 

by both fi xed and random effects. Like 

LCGA, an overall mean growth function is 

estimated within each class; however, there 

is also the ability to incorporate individual 

variability around these mean values. This, 

in turn, allows for the inclusion of covariates 

as predictors of class membership, of 

the growth process within class, or both. 

One of several close ties between the two 

techniques is that restricting the withinclass variability to zero in GMM is an 

equivalent parameterization to LCGA. 

Further, if only a single class is extracted 

in GMM, this is equivalent to the standard 

single group latent curve model. And if 

class membership is directly observed, then 

GMM is equivalent to the standard multiple 

group latent curve model (e.g., Bollen and 

Curran,130 Chapter 6). More standard growth 

models can thus be viewed as restricted 

parameterizations of the more general 

GMM.154 Growth mixture models also 

offer a variety of strengths including the 

incorporation of continuous, nonnormal, 

and discrete outcomes, as well as a number 

of advantages provided by the general SEMs 

(e.g., multiple indicator latent factors and 

formal tests of mediation).

The LCGA and GMM approaches to 

estimating population heterogeneity 

in longitudinal trajectories represent 

an exciting advance that has salient 

implications for the study of phenotypes in 

that these analytic techniques offer a close 

correspondence to the theoretical model 

of different developmental trajectories as 

phenotypes of smoking behavior. However, 

because these methods are new, there is 

still much to learn about their performance 

under a variety of research conditions. 

Further, as with any advanced statistical 

technique, a number of challenges are 

encountered when fi tting these models to 

longitudinal data in practice,134,135,137,155–157

and both the advantages and challenges 

must be understood.
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Monograph 20. Phenotypes and Endophenotypes

This chapter has already articulated many 

of the potential strengths associated with 

alternative techniques for clustering 

developmental trajectories over time. 

However, important issues must be 

considered when using these techniques. 

Because of space constraints, it is not 

possible to present a comprehensive 

discussion of all of these issues; see Bauer 

and Curran,134,135,155 Muthén,136,154,156

Nagin,133,137 and Nagin and Tremblay157

for more detailed explorations. Instead, 

several specifi c issues are explored that are 

particularly salient in the empirical study of 

smoking phenotypes.

Theoretical Distinctions between 

Discrete and Continuous Phenomena 

Possibly one of the most challenging issues 

immediately encountered when considering 

the use of clustering methods to empirically 

study smoking phenotypes is fundamentally 

philosophical. There has been a centuriesold confl ict over the very nature of taxa 

and continua and the intersections between 

the two. The primary issue at hand is 

whether phenotypes are characterized as 

discrete, continuous, or some intersection 

of the two.158–163

For example, consider two distinct 

phenotypes that are based upon a set of 

repeated observations taken on a sample 

of individuals over time. The fi rst might 

be defi ned by individuals characterized by 

an early onset (i.e., intercept) and steep 

acceleration (i.e., slope) of use, and a second 

by individuals characterized by a later 

onset and less steep acceleration of use. 

Any given individual uniquely belongs to one 

phenotype or the other. In contrast, consider 

the same set of individuals observed on the 

same set of repeated observations. However, 

instead of distinctly belonging to one of two 

groups, the developmental growth process 

is characterized by a continuous bivariate 

distribution of intercepts and slopes; some 

individuals begin earlier and others later, 

and some increase more steeply and others 

less so. This latter situation is the same as 

that described earlier for the single-group 

growth curve model. Although from this 

single-group model some arbitrary cutoff 

might be defi ned that forms two groups, 

in reality the developmental process operates 

across a smooth (but not necessarily 

normal) continuum. This is not to say that 

some cutoff would not be of potential use 

(e.g., as in clinical diagnoses), but it would 

be theoretically invalid to conclude that two 

distinct groups exist in the population.

Although some argue that a potentially 

fl awed model might still provide a useful 

summary of a set of observed data,164

the misattribution of discrete versus 

continuous processes is particularly 

challenging in the search for potential 

phenotypes.155 That is, if the goal is to 

identify observable characteristics that 

might identify an underlying genotype, 

the arbitrary creation of discrete groups in 

the presence of true continua would be of 

limited use. Great care must be taken from 

both theoretical and empirical perspectives 

in the accumulation of evidence for or 

against the existence of discrete phenotypes. 

As explored further below, several 

conditions might lead to the spurious 

identifi cation of multiple classes when in 

actuality none exist. Appreciation of these 

potential alternative explanations for the 

identifi cation of multiple classes will aid in 

building a cumulative science.

Static Versus Dynamic Clustering

Once it has been determined that there is 

a theoretical foundation for the positing of 

multiple classes, the next challenge is to 

determine whether groups will be based on 

static or dynamic methods of clustering. 

Whereas static clusters are typically based 

on data derived from a single cross-sectional 

assessment, dynamic clusters are based on 

longitudinal data assessed repeatedly over 

time. From a static perspective, only the 
220

5. Developmental Trajectories of Cigarette Smoking

characteristics of an individual at a given 

fi xed point of development are informative 

with regard to their association with a 

particular phenotype. A salient example 

is a simple assessment of the presence or 

absence of nicotine dependence, although 

other static phenotype groups have been 

posited. Importantly, the characteristics 

of the trajectory that an individual may 

have traversed to arrive at that particular 

point in development are not of interest 

(or, at a minimum, the information has no 

predictive utility).

Given the notion of equifi nality (as seen in 

“watershed” or stage models of smoking; 

e.g., in chapter 3) it is possible that 

regardless of the multiple pathways that 

led initially into variation in smoking 

onset and acceleration, the only relevant 

phenotypes of nicotine dependence are the 

“mature” phenotypes that defi ne variation 

in ultimate nicotine dependence and 

inability to abstain. If so, then dynamic 

phenotypes such as trajectories of smoking 

behavior are of potential but time-limited 

interest and are ultimately replaced by 

other static phenotypes. In contrast, 

a dynamic perspective not only considers 

the characteristics of an individual at a 

particular point in development but also 

explicitly considers the path that individual 

followed through the years leading up to 

the particular fi xed point. For example, 

although three individuals might all 

report nicotine dependence at 25 years 

of age, one may have reached that point 

with an initial onset in early adolescence, 

one with an onset in late adolescence, 

and one with an onset in early adulthood. 

These very different ages of onset may aid 

in the identifi cation of developmentally 

informed phenotypes of smoking behavior, 

the distinction of which would have been 

wholly occluded if only considering nicotine 

dependence at 25 years of age. An example of 

the importance of dynamic phenotypes was 

proposed by Shaw and colleagues,165 who 

found that it was the trajectory of change in 

the thickness of the cerebral cortex rather 

than simply the thickness itself that was 

related to intelligence. From a statistical 

standpoint, the implementation of static 

and dynamic clustering techniques can 

lead to fundamentally different groupings 

of the same sample of individuals. As such, 

the selection of the optimal analytic 

approach has signifi cant implications for 

both the inferences drawn from a given 

research study and for the development of 

an integrated understanding of empirical 

fi ndings across existing literatures. 

The ultimate empirical reconciliation would 

primarily depend on the identifi cation 

of meaningful genotypic differences as a 

function of static versus dynamic clustering.

Estimation of Within-Class Variability

Assuming an interest in the estimation of 

dynamic clusters, the next challenge is to 

determine which analytic approaches best 

correspond to the theoretical model under 

study. Whereas LCGA does not incorporate 

random variability in the growth process 

within class, GMM can include or omit these 

within-class random effects. The estimation 

of random effects for the growth process 

offers several advantages, including the 

incorporation of one or more predictors 

of the random growth parameters within 

class.136 However, the omission of random 

effects also offers several advantages, 

including greater stability of estimation 

and correspondence to the hypothesized 

homogeneity within class.137 This is an 

important decision because the inclusion 

or exclusion of within-class random effects 

for the growth parameters can directly 

infl uence the number of classes that are 

extracted from the same sample data.135

One obvious strategy would be to fi t models 

with and without within-class random 

effects and compare the correspondence 

between the two. If any observed differences 

are minimal, then the choice between 

the two parameterizations is less salient. 

However, in reality it is likely that the 
221

Monograph 20. Phenotypes and Endophenotypes

solutions will differ, possibly substantially 

so. Moreover, the available theoretical 

models of smoking behavior are not well 

enough developed to determine whether 

random effects should be estimated within 

each class. As such, it falls on the applied 

researcher to consider the advantages and 

disadvantages of these approaches and make 

an informed and justifi able decision.

Importance of Nonnormality

As with the fi nite mixture models upon 

which it is based, GMM makes a strong 

assumption of within-class multivariate 

normality for both the repeated measures 

and the random trajectories. This 

assumption, in turn, dictates that the 

marginal distributions of the repeated 

measures (that is, the distribution of the 

measures for the fully aggregated sample) 

be nonnormally distributed. This is a 

straightforward result of mixing two or 

more normal distributions; under all but 

a small number of atypical conditions, 

the distribution of the mixture of two or 

more normal distributions must itself be, 

by defi nition, nonnormally distributed.148

This assumption is what allows for the 

very extraction of multiple classes from a 

single sample. The complex nonnormal 

distribution for the aggregated sample can 

be approximated by the extraction of two 

or more normal distributions defi ned by 

different means and variances. Indeed, this 

is the most direct tie between GMM and the 

classic fi nite mixture model.135

Yet, this assumption poses a vexing problem, 

particularly when applying GMM within 

many areas of substance-use research. 

Namely, it has been shown that not only 

is marginal nonnormality a necessary 

condition for multiple class extraction but 

also is a suffi cient condition.134 Computer 

simulation studies have shown that when 

modestly nonnormal data are generated 

from a single homogeneous population, 

GMM identifi es multiple groups 100% of the 

time.134 Of course, this is precisely what the 

model is intended to do; multiple normal 

within-class distributions are estimated to 

approximate the more complex nonnormal 

aggregate distribution. A fundamental error, 

however, would be to conclude that multiple 

groups exist within the population when the 

optimal fi tting multiple class model resulted 

solely as a function of the nonnormal 

aggregate distribution.155

This issue poses a key challenge when 

applying these techniques to the study 

of smoking phenotypes. Given the very 

nature of the construct under study, many 

observable measures of smoking behavior are 

not going to follow a normal distribution. 

But how does one know if this nonnormality 

is due to the inappropriate aggregation of 

data drawn from multiple classes, or instead, 

is simply an accurate refl ection of the 

distribution of the construct? No analytic 

method was found that will distinguish 

which of these two conditions most likely 

accounts for the observed nonnormality 

of the measures under study. Further, it is 

unclear how GMM might best be used to 

empirically test for population heterogeneity 

when it is highly likely (if not nearly certain) 

that multiple classes will be extracted 

on the basis of the marginal distribution 

alone. More specifi cally, how is a research 

hypothesis subjected to potential falsifi cation 

when the outcome is known before the test 

is conducted?

As with prior challenges, it is commonly 

recommended that theory be used as a guide 

under such circumstances. However, it is 

not always clear how this might actually be 

accomplished. In some sense, this presents a 

basic Aristotelian syllogism: if the aggregate 

data are nonnormally distributed, multiple 

classes will be extracted regardless of 

population heterogeneity; smoking-related 

measures are nonnormally distributed; thus, 

multiple classes will be extracted when fi tted 

to smoking-related measures, regardless 

of population heterogeneity. As addressed 
222

5. Developmental Trajectories of Cigarette Smoking

later, an important strategy is to avoid the 

reifi cation of class extraction from a single 

sample of data. A triangulation of fi ndings 

from multiple studies using multiple 

outcomes may offer the best strategy when 

searching for evidence of smoking-related 

phenotypes.

Proper Model Specifi cation

Just as multiple classes can be extracted 

to approximate a nonnormal aggregate 

distribution, multiple classes can also be 

extracted to “absorb” the bias introduced by 

the estimation of a misspecifi ed model.135

The model misspecifi cation might arise 

from the incorrect parameterization of 

the functional form of the trajectory, from 

the exclusion of one or more structural 

parameters, or from the omission of 

nonlinear relations among two or more 

constructs. Regardless of source, it has 

been shown that an incorrect model fi tted 

to data drawn from a single population 

can result in the identifi cation of multiple 

classes when none truly exist.135 Given the 

ubiquity of misspecifi ed models in applied 

research,166 the potential for spurious class 

extraction related to model misspecifi cation 

poses another key challenge when applying 

these methods to the study of smoking 

phenotypes.

Alternative Spans of Study

Given that the focus of this chapter has 

been on identifying potential smoking 

phenotypes from the estimation of 

developmental trajectories, an obvious 

challenge is the impact of the developmental 

span under study. Of course, alternative 

developmental spans of study do not 

typically pose a challenge within a given 

study. That is, most applications will 

use all of the repeated observations that 

are available for analysis. However, this 

poses a much greater challenge when 

attempting to identify consistent fi ndings 

from the existing literature. Consider two 

hypothetical developmental phenotypes 

of smoking: one that consists of a late 

onset, modest acceleration, stable plateau, 

and a rapid decline to a low level of use, 

and the other consisting of precisely the 

same pattern except for a rapid decline to 

complete cessation. If one study were to 

follow a sample up to the point of decline, 

whereas another were to follow a sample 

past the point of decline, the resulting 

classes would likely be quite different 

between the two studies.167,168 The fact 

that different groups are obtained for 

different spans of measurement does not 

negate the validity of the obtained groups. 

As expected theoretically, individuals can 

change their trajectories with a change 

in risk or protective factors, and certain 

trajectory groups would not be hypothesized 

to appear until certain ages. For example, 

developmentally limited or late-onset forms 

of substance use cannot be distinguished 

until well into adulthood. However, 

changes in identifi ed trajectory groups 

with changes in developmental span pose 

a salient challenge when one attempts to 

draw a broader understanding about the 

characteristics of the underlying population 

from multiple studies covering multiple 

developmental stages. A similar problem may 

occur in terms of alternative frequencies of 

repeated assessments. That is, assessments 

that occur frequently (e.g., daily, weekly, or 

monthly) can capture complex fl uctuations 

in the outcome variable that will be lost 

when assessments occur more rarely over 

longer intervals (yearly or less than yearly).

Inclusion Versus Exclusion 

of Abstainers

A long-standing issue that arises in almost 

any study of substance use is how to best 

handle stable abstainers (see chapter 6 for 

a more detailed discussion). One (often 

unsatisfactory) option is to simply delete 

these from the analysis. This is clearly not 

ideal given both the discarding of valuable 

data and the introduction of a biased sample 
223

Monograph 20. Phenotypes and Endophenotypes

relative to the population from which it was 

drawn. A second option is to treat abstainers 

as a unique class before the execution of the 

clustering analysis. Thus, abstainers are an 

“observed” class whose existence need not be 

estimated; they are then added to the other 

classes that are identifi ed via the clustering 

techniques. Although preferable to omitting 

these data entirely, the possibility remains 

that some abstainers truly are using but 

either denied this use or misrecorded 

their responses, or that a true abstainer is 

quite close to becoming a fi rst-time user. 

Treating all of these as complete abstainers 

does not allow for the possibility of these 

other issues. Finally, newer techniques 

have been developed that allow for a hybridtype modeling approach in which one 

model is fi tted to a 0/1 dichotomy of no 

use versus use, and another model is fi tted 

simultaneously to those who are reporting 

use.169 These techniques are quite promising 

but need to be more fully explored.

Summary

There are several important hypothesized 

developmental phenotypes of smoking 

behavior, each of which can be empirically 

evaluated by using one of a number of 

analytical methodologies. Techniques 

such as survival analysis, latent transition 

analysis, and growth curve modeling 

can be used with varying degrees of 

success to empirically evaluate these 

predictions. This chapter has focused on 

one such approach—namely, methods 

for clustering developmental trajectories. 

As described earlier, these methods offer 

multiple potential advantages for the 

study of phenotypes of smoking behavior. 

However, it is also important to closely 

consider challenges that arise when using 

these methods in practice. This chapter 

considers some of the particularly important 

issues that arise when studying smoking 

phenotypes on the basis of the clustering 

of developmental trajectories over time. 

The goal is to highlight these challenges 

so that future applications of trajectory 

clustering techniques may be cognizant of 

these issues and proceed in a thoughtful 

and careful manner.

An Empirical Example: 

Trajectories in the 

Indiana University 

Smoking Survey

To provide an empirical example of 

these issues raised by modeling smoking 

trajectories, a series of models are 

presented using data from the Indiana 

University Smoking Survey. The focus 

is on a comparison of two time windows 

(ages 10 to 32 years and 10 to 42 years), 

relating static indicators of adult nicotine 

dependence to clusters that are dynamic 

(developmental smoking trajectories 

estimated via LCGA). These analyses allow 

for three important comparisons. First, 

the same sample can be used to directly 

compare the optimal number of groups 

based on a 22-year developmental window 

with the optimal number of groups based 

on a 32-year developmental window. 

This will demonstrate whether different 

phenotypic groups might be identifi ed if the 

same sample were followed over a longer 

developmental period. Second, the stability 

of group membership can be examined. 

That is, the extent to which an individual 

assigned to a particular phenotype in the 

shorter window remains a member of that 

same phenotype in the longer window can 

be assessed. Finally, the relation between 

adult nicotine dependence and trajectory 

group membership examines whether 

these trajectory groups have any systematic 

implications for nicotine dependence. Taken 

together, these analyses will offer a concrete 

empirical demonstration of both several key 

advantages and several specifi c challenges 

that are encountered when using these 

techniques in practice.
224

5. Developmental Trajectories of Cigarette Smoking

The Indiana University Smoking Survey 

is an ongoing, cohort-sequential study of 

the natural history of cigarette smoking 

that began in 1980.13,56,170,171 Between 1980 

and 1983, all consenting 6th–12th graders 

in a county school system in the Midwest 

completed annual surveys (total N who were 

assessed at least once = 8,487). The sample 

included 10 cohorts that correspond to the 

graduating classes of 1980–1989. Follow-ups 

were conducted in 1987 (73% retention, 

N = 6,234, ages 15–25 years), 1993 (73% 

retention, N = 6,223, ages 21–32 years), 

and 1999 (71% retention, N = 6,068, ages 

27–37 years), as well as 2005 (70% retention, 

N = 5,931, ages 32–42 years). Because the 

sample was 96% non-Hispanic Caucasian, 

ethnic differences were not considered.

Sample representativeness has been described 

in detail elsewhere.13,171 Demographically, 

the sample is similar to the community from 

which it was drawn: 64% marriage rates 

in this sample compared with 66% among 

adults of similar ages in the Midwest,172

and 97% high school graduation rates in the 

sample compared with 92% among adults 

of similar ages in the Midwest.173 At the last 

completed follow-up (1999), the smoking 

rate in the sample was 26%, the same rate 

found statewide.174 Thus, the sample is 

representative of its community; that is, 

predominantly white and well educated.

Attrition biases have been discussed in 

detail elsewhere.171,175 For each follow-up, 

those who were lost were compared with 

those who were retained in terms of their 

earlier data. Dropouts were more likely to 

be smokers and have more positive attitudes 

and beliefs about smoking. They also had 

parents and friends who were more likely to 

smoke (effect sizes ranging from r 2 of .01 to 

.02). Because of the consistent pattern of 

fi ndings, some caution is warranted when 

making generalizations.

For these analyses, two subsamples of 

participants were selected. First, trajectories 

were modeled using the fi rst six waves of 

measurement, selecting participants who 

had been measured at least once (ages 

ranged from 10 to 32 years, and 51% were 

males). Next, these results were compared 

with trajectories obtained from considering 

the entire available data (waves 1–8, age 

range = 10–42 years, 51% male).

Procedures

Adolescent data were collected with groupadministered questionnaires in school. 

In 1987, these procedures were followed 

for cohorts who were still in high school. 

For cohorts who had graduated from 

high school (and for all participants in 

1993 and after), a survey was sent by mail 

and followed up by telephone interviews 

if questionnaires were not returned. 

Participants were paid $15 for mailed 

surveys, and in 1999, they also were entered 

into a lottery for prizes of $200. At the 2005 

follow-up, mailed surveys with telephone 

interview follow-ups were again used with 

participant fees ($30) and lottery incentives. 

Measures

Smoking Level

Smoking level was determined by two items. 

Participants reported their current smoking 

status as “never smoked, not even a single 

puff”; “smoked once or twice ‘just to try’ 

but not in the last month”; “do not smoke, 

but in the past I was a regular smoker”; 

“smoke regularly but no more than once 

a month”; “smoke regularly, but no more 

than once a week”; or “smoke regularly 

and more than once a week.” Participants 

also reported the number of cigarettes 

they typically smoked each day (from 0 to 

20 or more). To improve the validity of selfreported smoking status in adolescence, 

a bogus pipeline was used from 1981 to 

1983. As reported elsewhere,171 a study using 

an unannounced bioassay with a subsample 
225

Monograph 20. Phenotypes and Endophenotypes

of the participants supported the validity of 

the self-reports.

For these analyses, responses were 

combined into a six-level variable to refl ect 

current smoking at each measurement 

wave, refl ecting both frequency and quantity 

of smoking as follows: 0 = not currently 

smoking (nonsmokers, ex-smokers, those 

who had smoked once or twice, but not 

in the past month); 1 = up to monthly 

smoking; 2 = up to weekly smoking; 

3 = weekly or more smoking, but only 10 or 

fewer cigarettes per day; 4 = weekly or 

more smoking of 11–20 cigarettes per day; 

and 5 = weekly or more smoking of 20 or 

more cigarettes each day. In some cases, 

responses to these items were ambiguous, 

and additional items were consulted 

concerning the number of cigarettes 

smoked yesterday and the time since the 

last cigarette was smoked.

Adult Tobacco Dependence

Tobacco dependence was measured with the 

Fagerström measure as well as by examining 

two individual items: number of cigarettes 

smoked in a typical day and time to fi rst 

cigarette in the morning. 

Family History of Smoking

Family history of smoking was based on 

participants’ reports of lifetime smoking 

among their biological parents at waves 

5 and 6. Family history was scored positive 

if at least one biological parent was reported 

to be a smoker.

Data Analysis

Individual, time-specifi c, smoking behavior 

scores were modeled over ages 10–32 years 

(using waves 1–6) and ages 10–42 years 

(using waves 1–8) with LCGA. However, 

several groups were constructed a priori. 

Individuals who reported never having 

smoked a single puff or having smoked 

once or twice “just to try” but never 

progressing beyond this category at any 

measurement were defi ned a priori as 

continuous abstainers (N = 4,642 in waves 

1–6, and N = 4,298 in waves 1–8). Individuals 

who were never measured as smokers but 

only as “ex-smokers” were defi ned a priori 

as stable quitters (N = 672 in waves 1–6, 

and N = 669 in waves 1–8). Individuals 

who reported periods of smoking, quitting, 

and then smoking again were defi ned a priori 

as a “relapsing/remitting” group (N = 535 

in waves 1–6, and N = 874 in waves 1–8). All 

other participants were clustered empirically. 

All LCGA model estimation was performed 

using Proc Traj133,176 as available for 

Statistical Analysis Software (SAS). A series 

of latent class models were estimated 

ranging from one to seven classes assuming 

a censored normal (0, 5) response 

distribution. No user-supplied start values 

were used in the initial models. A number 

of criteria were used to select the “best” 

model for the two age ranges. The criteria 

included an overall reduction in the 

Bayesian Information Criterion177 and Akaike 

Information Criterion178,179 to be less than 

100 for the next largest class as well as for 

the model to remain stable (with the addition 

of and changes to subsequent start values) 

and consistent with substantive theory. 

Model selection for waves 1–6 and 1–8 was 

done independently of each other.

Once the appropriate models were chosen, 

the modal probability of class membership 

was used to place individuals into a 

particular class (the class for which their 

membership probability was highest). 

These classes serve as the independent 

variables in all further analyses. Class or 

“group” membership was used to predict 

several outcomes including Fagerström 

dependence, number of cigarettes smoked 

in a typical day, time to fi rst morning 

cigarette, and family history of smoking. 

Each outcome was evaluated independently.
226

5. Developmental Trajectories of Cigarette Smoking

For dichotomous outcomes, logistic 

regression was employed and parameter 

estimates were obtained using SAS’s Logistic 

procedure. Each model consisted of dummycoded class membership variables (0 for not 

in the class, 1 for in the class). Abstainers 

were zero on all membership variables. 

Linear contrasts were then used to test for 

signifi cant differences between each class’s 

ability to predict the outcome. Continuous 

outcomes were predicted with ANOVAs.

Results

LCGA Results

A fi ve-class solution was found to be 

optimal for waves 1–6. As seen in fi gure 5.1, 

the fi ve classes were experimenters (4.5%); 

developmentally limited smokers (3.4%); 

early-onset, persistent smokers (7.4%); 

high-school-onset, persistent smokers 

(9.9%); and late-onset, persistent smokers 

(6.4%). As noted earlier, a priori groups were 

stable abstainers (54%), stable quitters (8%), 

and relapsing/remitters (6%). Experimenters 

began smoking early (around 11 years 

of age), but never smoked more than 

occasionally, and generally quit smoking 

by 21 years of age. Developmentally limited 

smokers started smoking around the age 

of 16 years, smoked regularly but averaged 

around 10 cigarettes per day at their peak, 

and gave up smoking by 27 years of age. 

Early-onset, persistent smokers typically 

started around the age of 11 years and 

increased their smoking quickly to a peak 

of more than one-half pack per day, which 

they maintained over development. Highschool-onset, persistent smokers started 

around 14 years of age, quickly increased 

their smoking, and although never quite 

as heavy as the early group, continued 

to smoke heavily. Late-onset, persistent 

smokers started around the age of 18 years 

and quickly became moderate smokers.

Figure 5.1 Five-Class Solution for Waves 1−6

0

1

2

3

4

5

Age

10 15 20 25 30 35 40 45

Smoking level

Note. Smoking level was determined by participants’ reported frequency and quantity of smoking, expressed numerically as 

follows: 0 = not currently smoking (nonsmokers, ex-smokers, those who had smoked once or twice, but not in the past month); 

1 = up to monthly smoking; 2 = up to weekly smoking; 3 = weekly or more smoking, but only 10 or fewer cigarettes per day; 

4 = weekly or more smoking of 11–20 cigarettes per day; and 5 = weekly or more smoking of 20 or more cigarettes each day. 

Dotted lines denote observed trajectories. Solid lines denote model-implied trajectories. Experimenters are denoted by squares;

developmentally limited smokers by “x”s; early-onset, persistent smokers by circles; high-school-onset, persistent smokers by 

triangles; and late-onset, persistent smokers by diamonds.
227

Monograph 20. Phenotypes and Endophenotypes

As seen in table 5.2, the gender makeup 

of the groups was generally evenly split, 

except for the late-onset, persistent 

group, which had signifi cantly more 

males compared to every other group 

except for the relapsing/remitting groups. 

However, there was a stronger relation 

between group membership and education. 

When measured at wave 6, the sample of 

participants reporting education data had 

approximately 36% of individuals with 

BA degrees or higher. The least educated 

group comprised the early-onset, persistent 

smokers (3.4% of whom had completed a 

college degree). They signifi cantly differed 

from all other groups. Other groups 

with relatively low levels of educational 

attainment were the high-school-onset 

group (14.4% with BAs or higher) and the 

relapsing/remitting group (13.5% with 

BAs or higher), who signifi cantly differed 

from all other groups but not from each 

other. The abstainers were the most highly 

educated group (47.9% with BAs or higher), 

and they signifi cantly differed from all other 

groups. Late-onset and developmentally 

limited groups were also relatively well 

educated (approximately 38% with BAs or 

higher) and did not signifi cantly differ from 

each other.

A six-class solution was found to be optimal 

for waves 1–8. As seen in fi gure 5.2, the 

six classes were experimenters (4.2%); 

developmentally limited smokers (4.7%); 

successful quitters (2.4%); early-onset, 

persistent smokers (8.4%); high-schoolonset, persistent smokers (8.7%); and 

late-onset, persistent smokers (3.1%). 

Compared to the waves 1–6 model, 

this model produced one additional 

class: successful quitters. As noted 

earlier, the a priori groups were stable 

abstainers (50.4%), stable quitters (7.8%), 

and relapsing/remitters (10.24%). Similar 

to the experimenters who were identifi ed 

in waves 1–6, this experimenter group 

began smoking around the age of 11 years, 

never smoked more than occasionally, 

and, in general, quit smoking by the age 

of 22 years. Similarly, the developmentally 

limited group generally started around the 

age of 16 years, smoked more on average 

than did the experimenters, (but less than 

one-half pack at their peak), and gave up 

smoking by the age of 30 years. As in the 

waves 1–6 model, the early-onset, persistent 

group typically started smoking around the 

age of 11 years, increased their smoking 

behavior quickly, and maintained the 

highest level of smoking over the course of 

Table 5.2 Trajectory Group Sizes and Relationship with Gender and Educational Attainment

Waves

N %

Gender

(% female)

College degree 

or higher (%)

1–6 1–8 1–6 1–8 1–6 1–8 1–6 1–8

Abstainers 4,642 4,298 54.39 50.36 49.59a 50.58ab 47.85 56.50a

Stable quitters 672 669 7.87 7.84 48.21a 46.64ab 35.00ac 47.73b

Developmentally 

limited

288 398 3.37 4.66 45.83ab 43.97ab 38.37a 59.31a

Experimenters 381 362 4.46 4.24 51.44a 55.25a 28.01c 41.60b

Early onset 628 715 7.36 8.38 48.57a 48.04ab 3.44 6.90

Successful quitters — 208 — 2.44 — 57.21ab — 38.71b

High school onset 844 744 9.89 8.72 50.89a 44.62b 14.42b 24.32c

Late onset 544 266 6.37 3.12 38.60bc 30.83 37.78a 45.89b

Relapse/remit 535 874 6.27 10.24 45.05ac 46.00ab 13.45b 26.17c

Total 8,534 100 48.59 36.19 45.75

Note. Groups that share subscripts do not signifi cantly differ from one another (p < .05).
228

5. Developmental Trajectories of Cigarette Smoking

the study (more than one-half pack per 

day). High-school-onset, persistent smokers 

(as described in the waves 1–6 model) 

began to smoke around the age of 16 years 

and smoked fairly heavily over development 

(almost as much as the early-onset group). 

The late-onset, persistent group identifi ed 

in the waves 1–8 model tended to start 

a little later (21 years of age) than in 

the waves 1–6 model (18 years of age). 

Moreover, this group generally increased 

more slowly and maintained a relatively 

low level of smoking behavior. Successful 

quitters (the group that did not emerge in 

the waves 1–6 model) started slightly later, 

around the age of 12 years, smoked fairly 

heavily for many years, and eventually quit 

in adulthood (by 37 years of age).

As shown in table 5.2, the pattern of gender 

and education differences was quite similar 

to the waves 1–6 model. The groups were 

generally evenly split in gender, with 

the percentage of females in a group 

ranging from 30.83% (late onsetters, who 

signifi cantly differed from all other groups) 

to 57.21% (successful quitters who did not 

differ from any other group except for late 

onsetters).

As with the waves 1–6 model, a strong 

relation was found with educational 

attainment. When measured at wave 8, 

45.8% of the sample who reported their 

educational attainment had completed a 

BA degree or higher. The trajectory group 

with the highest educational attainment 

was the developmentally limited smokers 

(59.3% with BAs or higher), and they did 

not signifi cantly differ from the abstainers 

(56.5%). Other groups with relatively high 

levels of educational attainment were the 

stable quitters (47.7% with BAs or higher), 

experimenters (41.6% with BAs or higher), 

successful quitters (38.7% with BAs or 

higher), and late-onset smokers (45.9% with 

Figure 5.2 Six-Class Solution for Waves 1−8

0

1

2

3

4

5

Age

Smoking level

10 15 20 25 30 35 40 45

Note. Smoking level was determined by participants’ reported frequency and quantity of smoking, expressed numerically as 

follows: 0 = not currently smoking (nonsmokers, ex-smokers, those who had smoked once or twice, but not in the past month); 

1 = up to monthly smoking; 2 = up to weekly smoking; 3 = weekly or more smoking, but only 10 or fewer cigarettes per day; 

4 = weekly or more smoking of 11–20 cigarettes per day; and 5 = weekly or more smoking of 20 or more cigarettes each day. 

Dotted lines denote observed trajectories. Solid lines denote model-implied trajectories. Experimenters are denoted by squares;

developmentally limited smokers by “x”s; quitters by pluses (+); early-onset, persistent smokers by circles; high-school-onset,

persistent smokers by triangles; and late-onset, persistent smokers by diamonds.
229

Monograph 20. Phenotypes and Endophenotypes

BAs or higher), and these groups did not 

signifi cantly differ from each other. As in 

the waves 1–6 model, the lowest level of 

educational attainment was found for 

the early-onset, persistent group (only 

6.9% with a BA degree or higher), and this 

group signifi cantly differed from all others. 

The high-school-onset group (24.3% with 

a BA or higher) and the relapsing/remitting 

group (26.2% with a BA or higher) were also 

relatively less educated and signifi cantly 

differed from all other groups, but not from 

each other.

Stability of Classifi cation across 

the Models

In general, group membership remained 

fairly consistent across analyses. As can be 

seen in table 5.3, a large proportion of the 

sample (81.9% of the total sample, 64.6% of 

the empirically classifi ed subsample) was 

classifi ed in the same group across the 

different age span models. Moreover, 

differences in classifi cation across models 

were theoretically reasonable. Of those 

who were stable abstainers in waves 1–6, 

93% were still abstainers 10 years later, 

and those who changed categories were 

most likely to become late-onset, persistent 

smokers or successful quitters. Of those 

who were stable quitters in waves 1–6, 

82% were stable quitters 10 years later, 

and those who were not were categorized 

as relapsing/remitters. Of those who 

were developmentally limited smokers 

in waves 1–6, 62% were classifi ed in the 

same group 10 years later, and those who 

were not were most likely to be classifi ed 

as relapsing/remitters. Of those who were 

experimenters in waves 1–6, 77% were 

classifi ed in the same group 10 years later, 

and those who were not were most likely 

to be classifi ed as relapsing/remitters. 

Of those who were early-onset, persistent 

smokers in waves 1–6, 90% were classifi ed 

in the same group 10 years later, and those 

who were not were most often classifi ed as 

successful quitters or experimenters.

Table 5.3 Stability of Classifi cation across the Waves 1–6 and Waves 1–8 Models

Waves 1–6

Waves 1–8 trajectory group

Abstainers

Stable 

quitters

Developmentally 

limited Experimenters Early onset

Successful 

quitters

High school 

onset Late onset

Relapse/

remit Total

Abstainers 4,298 115 3 3 0 3 76 128 16 4,642Stable quitters 0 554 0 0 0 0 0 0 118 672Developmentally limited

0 0 178 16 0 5 28 1 60 288

Experimenters 0 0 5 292 6 1 4 0 73 381Early onset 0 0 0 24 563 24 0 0 17 628Successful quitters 0 0 0 0 0 0 0 0 0 0High school onset 0 0 65 27 146 137 433 0 36 844Late onset 0 0 147 0 0 38 203 137 19 544Relapse/remit 0 0 0 0 0 0 0 0 535 535

Total 4,298 669 398 362 715 208 744 266 874 8,534
230

5. Developmental Trajectories of Cigarette Smoking

The largest difference between the two 

models is seen in the successful quitting 

group, which did not emerge in the earlier 

years. The waves 1–8 successful quitting 

group was drawn primarily from the highschool-onset and late-onset smokers in 

waves 1–6. This fi nding indicates that a later 

onset of smoking is associated with greater 

likelihood of successful cessation.

In a parallel fi nding, there was somewhat 

less stability in the waves 1–6 high-schoolonset smokers, with 51% classifi ed in 

the same group 10 years later. Those 

who changed groups were likely to be 

classifi ed as either early onset (refl ecting 

the similarities between middle school 

and high school onset) or successful 

quitters (refl ecting those who succeeded 

in cessation).

Finally, there was low stability in the 

waves 1–6 late-onset smokers, with only 

25% being classifi ed as late onset in the 

waves 1–8 model. Those who were lateonset smokers in the waves 1–6 model 

were most likely to be classifi ed as high 

school onset in the waves 1–8 model 

(37%), refl ecting the ambiguity of their 

onset at the age of 18 years as somewhat 

like adolescents and somewhat like adults. 

Moreover, a substantial number of the 

waves 1–6 late-onset smokers (27%) were 

classifi ed as developmentally limited 

smokers in the waves 1–8 model, refl ecting 

the fact that they stopped smoking by 

adulthood. Conversely, the late-onset group 

that emerged in the waves 1–8 model was 

drawn mostly from the waves 1–6 lateonset smokers and abstainers and showed 

a later age of onset than did the fi rst model 

(22 years of age rather than 18 years).

Family History Analysis

Logistic regression models that related 

trajectory group membership to family 

history of smoking produced similar results 

for the waves 1–6 and waves 1–8 groups. 

In both models, the lowest likelihood of 

having a smoking parent was for late-onset 

smokers, abstainers, and developmentally 

limited smokers, who did not signifi cantly 

differ from each other. In the waves 1–6 

model, the highest likelihood of having a 

smoking parent was for early-onset, highschool-onset, and relapsing/remitting 

groups, who differed from the other groups, 

but not from each other. In the waves 1–8 

model, the highest likelihood of having 

a smoking parent was for the early-onset 

and the successful quitter groups, who 

signifi cantly differed from abstainers and 

late-onset smokers, but not from each 

other (table 5.4).

Indicators of Adult Nicotine 

Dependence: Amount Smoked, Time 

to First Cigarette, and Fagerström 

Dependence Diagnoses

For both the waves 1–6 and 1–8 models, 

the groups who smoked at the end of 

the trajectory (early onset, high school 

onset, late onset, and relapsing/remitting) 

were compared on indicators of nicotine 

dependence measured at wave 8 (ANOVAs and 

logistic regressions in table 5.5). (Note that 

this analysis examines current dependence 

at wave 8, but does not identify the timing 

of onset of dependence). The fi ndings for 

the two models were quite similar. For both 

waves 1–6 and 1–8 groupings, the earlyonset, high-school-onset, and late-onset 

groups signifi cantly differed from each other 

in both models on all indicators. The earlyonset group showed the highest percentage 

of tobacco dependence (more than one-half 

of the group); this percentage was strikingly 

higher than the late-onset group (12%–19% 

in the second model). Similarly, the earlyonset group smoked at high levels (averaging 

one pack per day), whereas the late-onset 

group was closer to “chippers” (averaging 

seven cigarettes per day for the waves 1–8 

late-onset group). Also paralleling these 

fi ndings, the early-onset group was more 
231

Monograph 20. Phenotypes and Endophenotypes

likely to smoke the fi rst cigarette of the day 

immediately upon awakening (39%–41% of 

these groups in the two models), whereas less 

than 10% of the late-onset groups did this.

Differences between the waves 1–6 and waves 

1–8 models involved the relapsing/remitting 

group. For the waves 1–6 model, this group 

resembled the high-school-onset group 

on all indicators, whereas it signifi cantly 

differed from the early- and late-onset group 

on these same indicators. In the waves 1–8 

model, the relapsing/remitting group was 

distinctly and signifi cantly different from 

all of the other groups and was the second 

lowest (to the late-onset group) in indicators 

of nicotine dependence.

Discussion

These fi ndings demonstrate both the 

potential utility and some of the challenges 

involved with empirically identifying 

multiple developmental trajectories of 

smoking. Analyses revealed meaningful 

heterogeneity in trajectories that would be 

Table 5.4 Relationship of Trajectory Group Membership to Family History of Smoking

Waves 1–6 Waves 1–8

N

With at least 1 

smoking parent 

(%) N

With at least 1 

smoking parent 

(%)

Abstainers 3,359 67.40a 3,154 66.93a

Stable quitters 452 71.68ab 451 71.40ac

Developmentally limited 269 68.77ab 353 69.12ac

Experimenters 310 73.23bc 290 74.48abc

Early onset 327 85.02d 390 83.85bc

Successful quitters — — 164 86.59bd

High school onset 622 81.19cd 519 78.23cd

Late onset 460 67.61ab 217 63.59a

Relapse/remit 432 80.32bcd 693 77.20cd

Total % with a smoking parent 71.27

Total N 6,231

Note. Groups that share superscripts do not signifi cantly differ from one another (p < .05).

Table 5.5 Relationship of Trajectory Group Membership to Smoking Dependence Indices 

in Wave 8

Waves 1–6 Waves 1–8

Dependent 

using 

FTND ≥ 6 

(%)

Number of 

cigarettes 

smoked in 

1 day

Smoke first 

cigarette 

of day 

immediately 

(<5 min.) (%)

Dependent, 

using 

FTND ≥ 6 

(%)

Number of 

cigarettes 

smoked in 

1 day

Smoke first 

cigarette 

of day 

immediately 

(<5 min.) (%)

Early onset 59.0a 22.1a 41.4a 54.4a 22.0a 39.4a

High school onset 34.0b 17.1b 22.0b 31.7b 16.3b 19.1b

Late onset 18.8c 12.0c 10.7c 11.9c 6.7c 6.9c

Relapse/remit 34.7b 16.5b 19.5b 25.3d 13.6d 13.5d

Overall 38.8 17.6 24.9 34.6 16.1 21.4

Note. Groups that share superscripts do not signifi cantly differ from one another (p < .05). FTND = Fagerström Test for Nicotine 

Dependence. 
232

5. Developmental Trajectories of Cigarette Smoking

relevant for genetic studies. Results also 

showed that static assessment at any one 

time point has limitations. For example, 

early-onset smoking is correlated with 

steep acceleration and high persistence. 

Examining only a single age point of onset, 

however, would not reveal this fi nding 

because some of these early onsetters will 

merely experiment and not progress to 

regular smoking. However, a developmental 

trajectory that combines early onset, steep 

acceleration, and high persistence produces 

the highest risk for adult dependence 

and shows high levels of family history 

of smoking. This suggests a phenotype of 

interest for genetic analysis. The very low 

educational attainment of this group also 

suggests the presence of other risk factors 

(as identifi ed in other studies, table 5.1). 

Thus, the low educational attainment may 

refl ect the effects of endophenotypes that 

undermine educational success, such as 

conduct problems, impulsivity, behavioral 

undercontrol, and attention defi cit. It is also 

important to assess the possible role of low 

socioeconomic status (which also constrains 

educational attainment).

Although the early-onset, persistent group 

is clearly at highest risk, the differences 

between the early-onset and high-schoolonset groups appear to be quantitative and 

dimensional, rather than demonstrating a 

qualitatively distinct etiological pathway 

of smoking acquisition. These trajectories 

show both steep acceleration and persistence 

at high levels of smoking, although the 

early-onset group was somewhat elevated 

in indicators of adult dependence and 

somewhat lower in educational attainment. 

Thus, the available data suggest that there is 

simply a difference in severity between the 

early-onset and high-school-onset groups 

(although a consideration of predictors of 

trajectories might reveal other patterns).

Moreover, even among adolescents whose 

onset of smoking is early in adolescence, 

some became successful quitters (albeit a 

small prevalence). These successful quitters 

had family histories of smoking equivalent 

to the early-onset group, suggesting that 

a simple genetic explanation of cessation 

is likely to be insuffi cient. The similarities 

between the early-onset and successful 

quitting groups in family history is 

consistent with a stage model demonstrating 

multifi nality, so that similar factors may 

have led the quitters and persistent groups 

to initiate smoking, but different factors 

(possibly arising in adulthood) ultimately 

determine successful smoking cessation. 

Interestingly, the groups differed in 

educational attainment, so perhaps social 

contextual and intrapersonal factors related 

to success in higher education may provide 

some way to distinguish successful quitters 

from early-onset, persistent smokers.

Although the early-onset and high-schoolonset groups appear to represent continuous 

distributions of risk, the late-onset group 

appears more qualitatively distinct. In fact, 

in many ways, late-onset group members 

resembled abstainers. They have low levels 

of familial smoking and high levels of 

educational attainment. They also show low 

levels of adult smoking and dependence. 

In these ways, they resemble tobacco 

“chippers,” who may be relatively less 

vulnerable to tobacco dependence. Thus, 

whatever mechanisms produced their lateonset smoking, these mechanisms may be 

different from those that underlie early and 

high school smoking acquisition. This lateonset group has been underrecognized and 

understudied, unless they are considered to 

be the same as “chippers” (light smokers).

The fi ndings also demonstrate several 

challenges that arise in attempting to 

empirically identify multiple trajectories 

of smoking. For example, although the 

groups obtained from waves 1–6 and 

1–8 were substantially similar in terms 

of classifi cation, the solutions were not 

identical. Changes in group classifi cation 

occur with changes in the ages under 
233

Monograph 20. Phenotypes and Endophenotypes

study, sample characteristics, variations 

in smoking measures and other factors, 

and these changes make it challenging 

to examine the robustness of trajectories 

derived across different studies. However, 

it is also important to note that some of the 

differences between the waves 1–6 and 1–8 

models refl ect meaningful developmental 

changes, and that trajectories measured 

at different stages of the life course 

would be expected to differ. For example, 

trajectories of developmentally limited or 

late-onset smoking cannot emerge until a 

suffi cient age span is measured. Moreover, 

individuals can change trajectory groups 

with a meaningful change in their smoking 

behavior (i.e., individuals may smoke at high 

levels but then become successful long-term 

quitters). Future research might explore 

such meaningful developmental changes 

by using methods developed to identify 

“regime switching” (i.e., switching between 

trajectory groups180).

Moreover, the fi ndings were produced by a 

combination of approaches in which some 

groups were defi ned a priori and others were 

derived empirically. For example, a wholly 

empirical approach could not differentiate 

individuals who were always measured as 

abstainers from those who were always 

measured as ex-smokers since both groups 

would need to have identical scores to 

enter into the model. Also, these analyses 

did not examine modeling solutions that 

incorporate within-class variability, and all 

of these modeling decisions will affect the 

trajectory groups produced.

Finally, although not necessarily a limitation 

of this approach, these analyses did not 

address many questions that go beyond 

the scope of a single chapter. For example, 

tobacco use was not examined in forms 

other than cigarette smoking (which may 

affect patterns and trajectories of smoking). 

In addition, prospective predictors of these 

trajectories (other than family history of 

smoking) were not examined nor were 

trajectory group memberships related to 

hypothesized endophenotypes.

Future Research 

Directions

The literature review and empirical 

example provided in this chapter point 

to several directions for future research. 

The fi rst task is a better specifi cation of the 

relation between trajectories of smoking 

behavior and the development of nicotine 

dependence, as well as the relation between 

adolescent and adult trajectories. Given 

stage models (such as the “watershed” 

model in chapter 3), developmental 

trajectories of smoking acquisition may 

better be considered as “transitional” 

phenotypes whose etiological determinants 

differ from those that underlie the 

phenotypes of nicotine dependence. Thus, 

developmental phenotypic information 

may be very important at particular 

stages in the smoking trajectory to mark 

diverse etiological mechanisms underlying 

acquisition, but these diverse pathways 

may become relatively less important 

in the presence of tobacco dependence. 

In addition, the measurement equivalence 

of tobacco dependence symptoms over the 

life span requires further study to determine 

the similarities and differences between 

tobacco dependence in adolescence and 

adulthood. More research is needed on the 

heterogeneity in time course and predictors 

of the transitions from initial exposure to 

dependence. Moreover, further research 

is required to understand the age-specifi c 

effects of initial nicotine exposure, which 

have shown a signifi cant relation between an 

early age of onset and steeper acceleration 

over time. The mechanisms underlying this 

relation require further exploration in both 

animal and human models. 

Another important question is whether 

a particular individual feature of a 

trajectory (e.g., age of onset or steepness of 
234

5. Developmental Trajectories of Cigarette Smoking

acceleration) is the important phenotype 

or whether it is more useful to consider an 

entire trajectory group. The probabilistic 

nature of empirically identifi ed trajectories 

(which change with different measures 

and age spans and from which individuals 

may enter and exit over time) creates a 

confl ict with the goal of defi ning ”true” and 

unchanging groups for genetic analysis. 

Moreover, different research approaches are 

needed to determine whether phenotypes 

are best considered as categorical “groups” 

or as representations of an underlying 

continuous dimension. For future research 

on multiple trajectories of smoking, an 

accumulating literature on the subject 

will help to determine whether empirically 

identifi ed trajectory groups are reliable 

across different samples, and whether there 

are important ethnic differences in these 

groups. Moreover, very few studies relate 

these trajectories to measured genotypes 

or that examine trajectories in genetically 

informative samples (see chapter 7 for 

an example). More studies are needed to 

understand these areas. It may also be useful 

to consider tobacco within a context of 

other substance use (chapter 7) or broader 

“externalizing” disorders because the 

underlying genotype may refl ect a broader 

tendency to disinhibition rather than a 

specifi c risk for tobacco dependence. Along 

with this consideration, studies are needed 

that relate smoking trajectories to indicators 

of hypothesized endophenotypes and to 

indicators of tobacco dependence.

Finally, practical issues must be considered 

in this type of research. Unless reliable 

and valid methods can be developed to 

retrospectively reconstruct trajectories, 

these must be derived from costly 

longitudinal studies. Moreover, within 

longitudinal studies, there are tradeoffs between intensity and frequency of 

measurement intervals. For example, more 

frequent and more intense measurements 

provide greater resolution of transition 

points but require greater participant 

commitment and greater fi nancial 

resources. These studies also require large 

sample sizes if heterogeneity in trajectories 

is of interest (especially given the high 

prevalence of nonsmoking). A useful 

approach may be to target important ages 

or high-risk groups or to use accelerated 

longitudinal designs. One potentially helpful 

strategy may be to take advantage of existing 

longitudinal data sets by adding measures 

of endophenotypes (as well as genetic data) 

that are not time varying or age dependent. 

These represent some of a number of open 

questions that remain to be addressed 

through future research.

Summary

As demonstrated in this chapter, 

a consideration of developmental trajectories 

of cigarette smoking has potential for 

refi ning phenotypes of smoking for genetic 

analysis and for illuminating etiological 

mechanisms. Research has demonstrated 

meaningful heterogeneity in age of onset, 

slope of acceleration, and peaks and 

persistence of use. These features have been 

found to be signifi cantly related to some 

hypothesized endophenotypes as well as to 

indicators of tobacco dependence. In a very 

small number of studies, these trajectory 

features have also been related to genetic 

variability. However, this literature is 

still in a very early stage. It is premature 

to conclude that smoking trajectories 

(or even individual features of trajectories) 

will constitute important phenotypes of 

smoking.

At the same time, the study presented here 

demonstrates that developmental aspects of 

trajectories of smoking acquisition may be 

useful in refi ning phenotypes of smoking. 

There is evidence (including the evidence 

provided in the empirical example) that 

heterogeneity in trajectories is related 

to indicators of nicotine dependence in 

adulthood. Identifying these trajectories may 
235

Monograph 20. Phenotypes and Endophenotypes

help to illuminate the multiple etiological 

pathways that underlie the development of 

tobacco dependence. The next chapters in 

this section extend this work to genetically 

informative designs (chapter 6) and to the 

consideration of dual trajectories of tobacco 

and alcohol use in a genetically informative 

design (chapter 7).

Conclusions

1. Previous studies (and the empirical 

example presented in the chapter) 

have identifi ed multiple developmental 

trajectories of tobacco use from 

adolescence to adulthood. These 

trajectory groups, which vary in age 

of onset, rate of acceleration, and 

persistence of smoking over time also 

vary in their antecedents and correlated 

risk factors. These trajectories may be 

informative as developmental phenotypes 

for genetic studies of tobacco use.

2. Statistical approaches such as latent 

class growth analysis and growth 

mixture modeling can be useful in 

evaluating developmental trajectories of 

smoking behavior. However, challenges 

in using these approaches include the 

handling of within-class random effects, 

the impact of a nonnormal aggregate 

distribution on the classes extracted, 

the need for proper model specifi cation 

and parameterization, the span of 

evaluated data, and the impact of 

abstainers on the model.

3. Analysis of a 25-year cohort-sequential 

study of smoking behavior identifi ed six 

distinct trajectories of smokers across 

eight waves of data collection. These 

trajectory groups were experimenters; 

developmentally limited smokers; earlyonset, persistent smokers; high-schoolonset, persistent smokers; late-onset, 

persistent smokers; and successful 

quitters, with a priori groups of stable 

abstainers, stable quitters, and relapsing/

remitters. Trajectory group membership 

was related to educational attainment, 

family history of smoking, and indicators 

of nicotine dependence.
236

5. Developmental Trajectories of Cigarette Smoking

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245

Genetic studies have provided strong evidence that heritable factors generate individual 

differences in smoking behavior. Shared environmental factors appear to play a larger 

role in tobacco use at earlier ages. Improved modeling techniques hold the potential 

to better differentiate between genetic and environmental factors in tobacco use. This 

chapter examines genetic modeling issues in the study of smoking trajectories and 

behavior, including

■ Methodological and conceptual issues such as inferring potential dependence 

and trajectories in nonsmokers, issues in measurement invariance, and the use 

of epidemiological methods in genetically informative studies

■ Statistical modeling considerations such as the use of structural equation 

modeling (SEM) to assess whether covariation between traits is due to genetic 

or environmental causes, the identifi cation of genetic latent classes, and the 

analysis of molecular genetic data from linkage and association studies

■ A review of prior genetic studies of smoking behavior, including twin and extended 

twin family studies, multivariate genetic studies, and molecular genetic studies

■ A study applying an item response theory (IRT) approach to an analysis of 

smoking trajectories with data from the Virginia Twin Registry, examining 

tobacco initiation, regular tobacco use, and items on the modifi ed version of 

the Fagerström Tolerance Questionnaire (FTQ)

The IRT study in this chapter underscores the importance of assessing measurement 

invariance in establishing the heritability of nicotine dependence and its variation 

with gender.

6

Genetic Modeling of Tobacco Use 

Behavior and Trajectories

Hermine H. Maes and Michael C. Neale

The analyses described herein were supported by Public Health Service grant RR008123 and National Institute of Health 

grants CA085739, CA093423, DA011287, DA016977, DA018673, MH001458, MH049492, MH065322, MH068521, and a 

grant from the Virginia Tobacco Settlement Foundation. Mx development was previously supported by Public Health 

Service grants RR008123 and National Institute of Health grant MH001458. Data were kindly provided by Dr. Kenneth 

S. Kendler and the Mid-Atlantic Twin Registry. 
246

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

Introduction

This chapter examines issues in studying 

the heritability of tobacco use behavior 

and trajectories and their methodological 

implications for future genetic research in 

nicotine dependence. Its goal is to follow 

the discussion of tobacco use trajectories 

in chapter 5 to examine what can be 

learned about these trajectories through 

studies using genetically informed data. 

Areas discussed include methodological 

and conceptual issues, a review of existing 

genetic studies of smoking, and the results 

from a multivariate genetic analysis of 

nicotine dependence using the Virginia 

Twin Registry.

A key question in many epidemiological 

studies is the extent to which parents 

infl uence their children. For example, one 

may ask whether parental cigarette smoking 

in and of itself increases the chance that 

their children will smoke. At the simplest 

level, one might compare the proportion of 

smokers in parents whose children smoke to 

the parents whose children do not. A higher 

rate of smoking in the parents of smokers 

may be taken as evidence that behavioral 

modeling is operating; that is, children have 

learned their behavior from their parents. 

Alternatively, it might be thought that the 

secondhand smoke ingested by the child 

of a smoker kindles the smoking habit. 

In practice, however, such conclusions may 

be unwarranted, because—except in the case 

of adoption—parents share genetic factors 

with their children. Genetically informative 

research designs, such as data collected 

from monozygotic and dizygotic twins, 

or from adopted and biological relatives, 

permit a closer inspection of the nature of 

parent-child resemblance, and indeed, of any 

association between a putative risk factor 

and an outcome. In principle, any random 

effect, such as variation in level or slope in 

a growth curve model, or membership in 

a particular latent trajectory class, may be 

partitioned into genetic and environmental 

components. However, the value of data 

collected from family members does not end 

here. In addition to the potential to resolve 

genetic and environmental components of 

variance, it is possible to measure covariance 

between variables that cannot be measured 

with data from unrelated individuals. 

For example, one can test whether liability 

to initiate smoking is related to quantity 

smoked or propensity to become nicotine 

dependent. Such information is of particular 

value when one considers whether to 

expend efforts on the prevention of tobacco 

initiation or on the alteration of trajectories 

of tobacco consumption once initiation 

has occurred. Therefore, this chapter 

provides a review of these methods, with 

a view to integrating both molecular and 

nonmolecular approaches into the same 

framework.

First, some of the methodological and 

conceptual issues in tobacco use research 

are considered. A statistical framework is 

then discussed within which these issues 

may be tackled. The approach is general 

enough to encompass both latent trait and 

latent class models and is suited to a wide 

variety of both genetic and nongenetic 

analyses. This methodological review is 

followed by a substantive one, considering 

the fi ndings of genetic studies of smoking 

initiation on nicotine dependence. The fi nal 

section applies multivariate genetic analysis 

of tobacco use and nicotine-dependence 

symptoms to data collected from relatives 

in the Virginia Twin Registry. The results 

are described in more detail than those of 

published studies because these results 

integrate a focus on assessing the phenotype 

with the traditional partitioning of the 

variance of that phenotype into genetic and 

environmental sources. The analyses also 

take into account that nicotine dependence 

is contingent on smoking initiation and 

progression to regular smoking.
247

Monograph 20. Phenotypes and Endophenotypes

Methodological 

and Conceptual Issues

One of the problems with studying tobacco 

use is that many of the symptoms and signs 

of abuse or dependence are contingent.

Thus, it is not possible to observe the rate 

of increase in use of cigarettes in those 

who have never smoked. Whether it is 

correct to regard the nonsmoker’s increase 

in cigarette consumption as zero is an 

empirical question. There is an assumption 

that a nonsmoker does not experience 

symptoms of nicotine dependence. However, 

in trying to understand the population 

from an epidemiological perspective, it is 

often better to ask the question of whether 

a nonsmoker would have experienced 

symptoms of dependence if he or she had 

initiated cigarette use. Certain research 

designs permit such inferences. For example, 

data from pairs of siblings might show that 

nicotine-dependence symptoms are more 

common in individuals with a sibling who 

has also become a tobacco user than in 

those with a sibling who has not. Such data 

imply a relationship between initiation and 

dependence. Ordinarily, with data collected 

from unrelated individuals, it is typically not 

possible to assess the relationship between 

initiation and dependence symptoms because 

dependence data are missing in those who 

have not initiated. Therefore, the modeling 

of this contingent type of data is described.

A similar issue arises with the analysis of the 

relationship between age at onset of tobacco 

use and its sequelae, such as trajectory. 

While it is possible to compare trajectories 

of those who initiate at a young age to 

those who initiated at a later age, it remains 

impossible to examine the trajectories of 

those who have not initiated use. Again, 

a research design that includes data collected 

from relatives provides a framework within 

which the relationship between age at onset 

and liability to use may be estimated. In this 

context, it becomes possible to tease apart 

factors that infl uence initiation, which, 

in turn, infl uences trajectory, from those 

that infl uence trajectory only.1

 In addition, 

it may prove useful to examine substance 

use as a function of time onset rather than 

of chronological age.2

One of the impediments to research on 

behavioral and psychological traits, such as 

tobacco use, is that behavior is intrinsically 

diffi cult to measure. For the most part, 

the quantifi cation of daily tobacco use 

is limited to an ordinal scale (0, 1–5, 

6–10, 11–20, 20+), and the assessment of 

symptoms of dependence is typically only 

binary (e.g., do you fi nd it diffi cult to cut 

down?). Many of the more modern models 

for the analysis of growth or change have 

been developed on the assumption that 

measurement has been at the interval level. 

For the most part, it is not wise to simply 

pretend that the data have been measured 

on a continuous scale and proceed with data 

analysis as usual. However, it is often possible 

to extract continuous-level information from 

ordinal data by modeling it appropriately,3

although at the cost of additional computer 

time. Yet, even given an appropriate 

analytical framework for ordinal data, things 

can go wrong at the measurement level. 

For example, a questionnaire item—do you 

fi nd it diffi cult to wait for your fi rst cigarette 

of the day—may provide a good indicator of 

dependence for those attending high school 

if smoking at home is not permitted. Those 

who no longer live at home may never have 

to wait, and therefore, the question loses 

its relevance as a measure of dependence. 

Such failures of measurement invariance

are important to detect and should be 

controlled wherever possible.4–7 That is, it is 

important to distinguish change in behavior 

or symptoms over time from change in 

the way that the measurement instrument 

works. This chapter examines this issue of 

measurement invariance with data from 

twins assessed with the FTQ.8
248

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

Many statistical frameworks are constructed 

around the assumption that the population 

is homogeneous in some respect. Thus, 

a simple regression equation, y = 0 + 1 x,

implies that the effect of the independent 

variable x on the dependent variable y is 

the same for all subjects in the sample. 

In practice, however, it is possible that the 

strength of the regression—for example, 

between liability to initiate tobacco 

use and liability to progress to nicotine 

dependence—varies as a function of other 

variables. Such moderation of relationships 

may occur as a function of either variables 

that have been measured, such as age 

or gender, or of variables that have not 

been measured, such as an unidentifi ed 

polymorphism at a particular region of 

the genome or the quantity of secondhand 

smoke experienced as a child.

Much progress has been made in tying 

together statistical methods used in 

epidemiological studies of unrelated 

individuals with those in use with genetically 

informative studies. For example, analyses 

of growth curves, measurement invariance, 

factor analysis, and latent class analysis all 

have been adapted and extended for use 

with data collected from relatives. Multilevel 

analysis might be considered to be almost 

ubiquitous in the study of relatives in 

that the family provides a level. However, 

it is clear that several areas have yet to 

be implemented for use in family data. 

For example, factor mixture modeling and 

growth curve transition modeling are in 

need of further development. While technical 

challenges remain (e.g., the likelihood of 

longitudinal ordinal data collected on a large 

pedigree may involve numerical integration 

over a very large number of dimensions), 

this is an area of active research. The future, 

with improvements in computer architecture 

and software that exploit it, holds much 

promise for furthering the understanding 

of genetic and environmental factors in the 

etiology, development, and interaction of 

complex traits.

Statistical Framework

Structural Equation Modeling

The majority of statistical modeling of 

genetically informative data is carried out 

within the framework of SEM. In its basic 

form, SEM involves the specifi cation of two 

types of variables: (1) observed variables 

that have been directly measured and 

(2) latent variables that have not been 

directly measured. Two types of relationship 

between these variables may be specifi ed: 

linear regression and covariance. This type 

of model may be represented as a path 

diagram9–11 in which observed variables are 

shown as boxes, latent variables are shown 

as circles, regression paths are drawn as 

single-headed arrows from the independent 

variable to the dependent variable, and 

covariance paths are shown as double-headed 

arrows. Any description of the model, be it a 

simple list of the paths involved, or matrices 

thereof, or a correctly drawn path diagram, 

is mathematically complete and can be used 

to derive predicted covariances between 

variables. Three extensions of this framework 

are becoming popular. One is the depiction 

of means,12 usually drawn as a triangle that 

has a constant value of one, which enables 

specifi cation of mean structure as well as of 

covariances.13 The second is the specifi cation 

of “defi nition variables,” which are values 

attached to specifi c paths in the diagram. 

These may specify a different predicted 

covariance structure for every subject in 

the sample.14 They are thus of value in the 

specifi cation of models for data that were 

collected at different sets of ages, as opposed 

to the unlikely scenario that, for example, 

all subjects were assessed precisely on their 

10th, 12th, and 15th birthdays.15 The third 

extension is that the population may be 

described as a mixture of two or more 

subpopulations in which different mean 

and/or covariance structures exist. This third 

addition subsumes latent class and latent 

profi le analyses as special cases; growth curve 
249

Monograph 20. Phenotypes and Endophenotypes

mixture modeling is a popular example.16,17

This framework is referred to as “extended 

structural equation modeling” (i.e., XSEM).

Originally, SEM was devised for the analysis 

of data that were distributed according to 

the multivariate normal distribution, and it 

is still used in this way in many applications. 

The addition of mean structures, which 

may differ according to group or defi nition 

variables, makes the method appropriate 

for the analysis of data that are distributed 

according to a conditional multivariate 

normal distribution (the data from the 

sample as a whole will not be normally 

distributed if there are group mean 

differences). Applications to binary or 

ordinal data have become popular, because 

such data are commonly encountered in 

behavioral and other research. For the most 

part, these methods shift the distributional 

assumption to a level above the actual 

measurement, such that it is assumed that 

there is an underlying normal distribution of 

liability in the population, but that it is only 

possible to discriminate whether a given 

subject falls in a particular range or band of 

this distribution. For example, if a subject 

indicates that he or she has tried smoking 

cigarettes, as do some 60% of subjects, then 

the subject’s liability is assumed to be in 

the top 60% of the distribution, or above 

a threshold of –0.253 measured in z-score 

units. It turns out that with ordinal data 

with at least three categories, it is possible 

to estimate the same parameters as in the 

continuous case by fi xing the fi rst threshold 

at zero, the second threshold at one, and 

estimating the mean and variance of the 

measure instead.3

 It is also possible to 

fi t growth curves to binary item data,18,19

although item-specifi c variances are 

confounded with item means in this case.

Structural Equation Model 

for Twin Data

The basic path diagram for the analysis of 

data collected from pairs of monozygotic 

and dizygotic twins—the most widely used 

genetically informative design20—is shown 

in fi gure 6.1. The diagram includes three 

variance components: additive genetic 

factors (A), which correlate perfectly 

between monozygotic twins and .5 between 

dizygotic twins; common or shared 

environmental factors (C), which correlate 

perfectly between twins regardless of 

their zygosity; and random or specifi c 

environmental factors (E), which are those 

infl uences unique to each member of a 

twin pair (including measurement error 

and genotype by specifi c environment 

interaction). The key to identifying the 

parameters of this model (the regression 

paths a, c, and e) is the availability of three 

statistics: the variance, the covariance 

between monozygotic twins, and the 

covariance between dizygotic twins. These 

data, together with the equal environment 

assumption (for more details, consult, 

for example, Loehlin and Nichols,21

Rose and colleagues,22 and Kendler and 

colleagues23 for theoretical and empirical 

reasons that the equal environment 

assumption is unlikely to be violated), allow 

unique estimates of the parameters to be 

obtained. Alternatively, one could include 

a dominance parameter (D) instead of 

shared environment; the effects of both are 

confounded in the classical twin design. It is 

important to note that the classical twin 

study is really just a starting point for the 

genetic epidemiological investigation of a 

trait. Extending the design to include, for 

example, adoptees, parents and offspring, 

half siblings, or more distant relatives allows 

for resolving a greater variety of genetic and 

environmental parameters, such as genetic 

nonadditivity and assortative mating,24

which are assumed to be zero when fi tting 

the ACE model. Other assumptions include 

no genotype by environment correlation or 

interaction. The power of the classical twin 

study has been described in detail for the 

continuous case25 and the ordinal case.26

Of note is that for ordinal data, three times 

the sample size is needed for equivalent 
250

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

power to the continuous case when the 

threshold is at the optimal 50%, and this 

ratio increases rapidly for more extreme 

thresholds. In general, the twin study has 

more power to reject false models when the 

true world involved shared environmental 

effects than when familial aggregation was 

genetic. While false models that involve 

no familial aggregation are easy to reject, 

models including incorrectly specifi ed 

sources of resemblance (e.g., AE instead of 

CE) are diffi cult to reject.

The basic ACE model can be 

straightforwardly extended to multivariate 

or longitudinal data. In the multivariate 

context, it becomes possible to partition 

covariation into the same components as is 

variation. Thus, one can establish whether 

two traits covary primarily because the 

same genetic factors infl uence both or 

because the same environmental factors 

do so. In addition, it is possible to detect 

relationships between variables that do not 

covary within an individual but, in fact, share 

genetic and environmental factors whose 

infl uences counterbalance—for example, 

a correlation of +.7 due to environmental 

factors but –.7 because of genetic factors. 

This same partitioning of covariation 

between traits may be applied to the same 

trait measured on repeated occasions to 

address whether development and change 

have primarily genetic or environmental 

origins. Four specifi c extensions to this 

model are considered below.

Extensions of the Basic Twin 

Model

Extended Twin Family Studies

While twin studies provide an excellent 

design to disentangle genetic and shared 

environmental infl uences, several 

assumptions are made, and only a limited 

number of sources of variance can be 

estimated simultaneously (A, C, and E or A, 

D, and E, with C and D being confounded). 

Three statistics provide the information for 

the partition: the total phenotypic variance, 

Figure 6.1 Basic Path Diagram for the Analysis of Data Collected from Pairs of Monozygotic 

and Dizygotic Twins

A C E

T1

1.0 1.0 1.0

a e c

A C E

T2

1.0 1.0 1.0

a e c

1/.5 1

Note. The correlation between additive genetic factors is fi xed at either 1.0 or 0.5, according to whether the twins are monozygotic

or dizygotic. A = additive genetic; C = common or shared environment; E = specifi c or unique environment; a, c, e = regression paths; 

T1 = twin 1; T2 = twin 2.
251

Monograph 20. Phenotypes and Endophenotypes

the monozygotic covariance, and the 

dizygotic covariance. Data from other types 

of relatives provide additional, qualitatively 

different statistics, which (subject to 

identifi cation of the model) permit 

estimation of additional sources of variance. 

Conceptually, this approach is similar to 

that used in plant and animal breeding 

experiments in which different types of cross 

provide information about different types 

of genetic effect.27 Early contributions to 

developing methods for the analysis of data 

from human populations were provided 

by Jencks,28 Eaves and colleagues,29,30

and Fulker.31

Extending the twin design to include siblings 

allows a test of whether twins resemble 

each other more than do regular siblings. 

The usual way to model the addition of 

siblings is as a special twin environment 

variance component, T, for which twins 

(monozygotic or dizygotic) are specifi ed to 

correlate perfectly, while siblings are specifi ed 

to correlate with zero. Several potential 

contributors to a variance component are 

specifi ed in this way. The most obvious 

source is twins who share trait-infl uencing 

environmental factors to a greater extent 

than do siblings. A second possibility is that 

twins infl uence each other, although typically 

this would result in different total variances 

of monozygotic, dizygotic, and siblings. 

A third potential contributor is interaction 

between age or cohort and the variable 

under study. Nontwin siblings are commonly 

measured at different ages and may therefore 

have reduced similarity compared with 

siblings measured at the same age and time. 

The addition of half siblings or adoptive 

siblings also permits estimation of genetic 

dominance as well as shared environmental 

infl uences—two sources that are confounded 

in the classical twin study.

Further extensions, such as including 

parents of twins, provide a test for the 

presence of assortative mating (process of 

mate selection based on the phenotype) and 

cultural transmission or whether parents 

infl uence their children’s behavior through 

environmental pathways in addition to 

passing on their genes. Different mechanisms 

could account for environmental 

transmission: (1) parents can infl uence 

the environment of their children directly; 

this is referred to as phenotypic cultural 

transmission (or P [phenotype] to C [shared 

environment] transmission); and (2) the 

parental environment directly infl uences 

the children’s environment, which is known 

as social homogamy (C to C transmission). 

Similarly, assortment, evidenced through 

signifi cant marital correlations, can 

be a function of the phenotypes of the 

spouses (phenotypic assortative mating). 

Alternatively, social homogamy may result 

in spousal concordance, or direct infl uence 

between the spouses may lead to increased 

similarity over time. The extended twin 

kinship model, which extends the classical 

twin study with not only siblings and parents 

but also spouses and children of twins, was 

developed32 for simultaneous estimation of 

additive and dominance genetic as well as 

unique and shared environmental (cultural 

transmission, nonparental, special twin) 

factors in the presence of assortment. 

The specifi cation includes phenotypic 

cultural transmission and phenotypic 

assortative mating.33 It is important to note 

that the correlation between parents and 

their children alone provides information to 

sort out whether parents directly infl uence 

their children’s smoking behavior in that 

they also share genes with one another. 

However, a design that includes additional 

types of relatives (with differing degrees of 

genetic similarity), such as monozygotic and 

dizygotic twins, allows one to disentangle 

genetic from environmental transmission 

and “controls for” the genetic relatedness 

of parents and offspring.

Another design that also allows for 

disentangling genetic from environmental 

transmission is the children of twins (COT) 

design, which collects data from adult twin 
252

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

pairs and their children (and possibly their 

spouses). One specifi c application relevant 

to tobacco use research is the comparison 

of the prevalence of smoking initiation 

in children and the parent-offspring 

correlation as a function of the smoking 

status of the parents: either nonsmoker, 

former smoker, or current smoker.

Multivariate Factor Model

A basic model for multivariate data collected 

from twins is shown for one member of a 

twin pair in fi gure 6.2. This model, known 

as a “latent phenotype” or “common 

pathway” model,34,35 includes three latent 

phenotypes (factors) that infl uence all the 

observed measures (shown in squares). 

It is a natural extension of a psychometric 

common factor model to twin data. All the 

covariation between twins’ items occurs 

through correlations between the additive 

genetic (A) and common environment 

(C) latent variables in twin 1 and their 

counterparts in twin 2. These correlations 

are fi xed, in accordance with genetic theory, 

at 1.0 for monozygotic twins for both A and 

C, and at .5 and 1.0 for A and C, respectively, 

in dizygotic twins. Note that residual or 

“measure-specifi c” covariation between an 

observed measure and that of the co-twin 

may occur through the A and C paths shown 

at the bottom of the fi gure. Also note that the 

variance components A, C, and E for factor 1 

may correlate with their counterparts for 

factors 2 and 3. Thus, there is an analog of 

the oblique factor model in psychometrics.

An important submodel of this three-factor 

model is one in which the path coeffi cients 

Figure 6.2 Three-Factor Latent Phenotype Model

AC E

1.0 1.0 1.0

aF1 eF1 cF1

F1

AC E

1.0 1.0 1.0

aF3 eF3 cF3

F3

AC E

1.0 1.0 1.0

aF2 eF2 cF2

F2

P1T1 P2T1 P3T1 P4T1 P5T1

A CE C E A CE A CE A CE A

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Note. A = additive genetic; C = common or shared environment; E = specifi c or unique environment; a, c, e = regression path 

coeffi cients; F1–F3 = latent phenotypes; P1–P5 = observed phenotypes; T1 = twin 1.
253

Monograph 20. Phenotypes and Endophenotypes

aF2 , aF3 , cF1 , cF3 , eF1 , and eF2 are fi xed to zero, 

and the coeffi cients (aF1 , cF2 , and eF3) are 

fi xed to unity. This submodel, known as the 

“independent pathway” or “biometric factor” 

model,34,35 estimates loadings from variance 

components that are specifi ed as having only 

one source of variation. Another important 

submodel is one in which only one latent 

phenotype is specifi ed; that is, F2 and F3

are omitted. This model is often called the 

“common pathway” model, because the 

genetic and environmental components 

(at the top of the diagram) are combined 

into a latent common factor before they 

affect the measured variables.

Causal Contingent Common 

Pathway Model

To analyze contingent data, such as 

the presence of symptoms of nicotine 

dependence, for which nicotine use is 

prerequisite, a simplifi ed bivariate model 

is used. The model is necessarily simplifi ed 

because not all the data that would ordinarily 

identify a multivariate model for twin data 

are available. There is no information on the 

within-person covariance between initiation 

and progression because there is no 

variation in initiation when progression is 

observed. However, it is possible to estimate 

the strength of this relationship in twin data 

because the co-twin data provide a proxy 

form of information about the relationship 

between progression and initiation. Thus, 

this information comes from discordant 

pairs; that is, one twin progresses from 

initiation but the co-twin does not progress. 

The diagram in fi gure 6.3 shows progression 

regressed onto initiation. Each variable has 

its own A, C, and E components, which are 

specifi c to either initiation or progression. 

All covariance between these two variables 

is assumed to arise via the regression path. 

This model has a number of extensions, 

including the multivariate case and more 

than two-stage phenomena.36,37 The key here 

is the use of twins to overcome the problem 

of systematically missing data, which is 

exploited in the application below in the 

section “Item Response Theory Approach: 

Application to Virginia Twin Registry Data.” 

Ordinarily, it is not possible to identify the 

loading of a binary initiation variable on a 

common factor when the remaining items 

that load on the factor (e.g., measures of 

dependence) are contingent on it. However, 

when data are collected from twins, and 

when the factors correlate, the model is 

identifi ed.38

Figure 6.3 Causal Contingent Common Pathway Model

A C E

I

T1

1.0 1.0 1.0

a1 e1 c1

A C E

PT1

1.0 1.0 1.0

aP eP cP

b

Note. A = additive genetic; C = common or shared environment; E = specifi c or unique environment; a, c, e, and b = regression path 

coeffi cients; I = initiation; P = progression; T1 = twin 1.
254

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

Item Response Theory

In both clinical practice and research, it is 

common practice to collect data on the 

presence or absence of multiple symptom 

criteria for a given disorder or trait. 

The item-level data are often collapsed into 

either a single affected versus unaffected 

classifi cation or summarized into a score 

by summing the endorsed symptoms. 

For example, the Fagerström criteria 

are widely used to provide an nicotinedependence score (either FTQ or Fagerström 

Test for Nicotine Dependence [FTND]) or a 

binary dependence diagnosis. Both types 

of summary statistic have problems. In the 

binary classifi cation case, much of the 

available information is not utilized. The sum 

score approach assumes that the criteria are 

equally important. These assumptions can 

be tested in an item response framework. 

In addition, one can evaluate the role of 

potential covariates, such as gender and 

age, on the measurement of the phenotype 

of interest. For genetic studies, failure of 

these assumptions can lead to erroneous 

conclusions about the genetic architecture of 

the trait of interest. Lubke and Neale39 noted 

that studies of genotype by environment 

interaction, including genotype by age and 

genotype by gender interactions, are subject 

to potential confounding of measurement 

artifacts when sum scores or diagnoses are 

analyzed. Since changes in heritability over 

time or across groups are fundamental to 

the genetic analysis of trajectories, it is a 

crucial fi rst step to assess whether one is 

measuring the same construct at different 

times and with the same accuracy. Therefore, 

these methods are applied in the section 

below, “Item Response Theory Approach: 

Application to Virginia Twin Registry Data.”

Genetic Latent Growth Curve Models

Of particular interest to those studying 

trajectories of tobacco use is the information 

provided by structured latent growth curve 

(LGC) models. These models are described 

in detail in chapter 5. Their treatment here 

is brief and focuses on their extension to 

genetically informative data,40 nonlinear 

models, and switching. By way of preamble, 

the authors of this chapter support “putting 

the individual back in growth curves” as 

proposed by Mehta and West.15 That is, 

arbitrary categorization of subjects into 

age bands (e.g., 10 years old, 11 years old) 

should be avoided if possible, and the analysis 

should proceed at the raw data level with 

subjects’ actual ages at testing. This method 

eliminates the biases that can accrue when 

there is variation between subjects’ ages 

at a particular occasion of measurement. 

The LGC model is essentially a factor model 

with some specifi c restrictions. In the 

linear case, it is hypothesized that there is 

random variation in two factors: initial level 

and slope. These factors may be correlated. 

A natural extension of this model for 

genetically informative data is, therefore, 

to apply the variance and covariance 

partitioning to these latent variables. Thus, 

with twin data, one expects the variation in 

the level and slope factors to arise from the 

action of genetic and environmental (C or E) 

factors, and the covariance between level and 

slope can be partitioned in the same way. 

In addition, one can partition the residual 

occasion-specifi c variance into the three 

usual A, C, and E sources. Note, however, that 

this departs from the idea that the residual 

variance is purely random measurement 

error; such a model would eliminate the 

A and C components and may be fi tted 

to explicitly test this hypothesis. Initial 

attempts to model growth data collected 

from relatives41 used a two-stage approach 

in which individual growth curves were 

estimated (to obtain person scores for level 

and slope), followed by biometric analysis 

of the scores themselves. This is a practical 

approach that is suitable when all subjects 

are measured at equal intervals. However, 

when data are missing or there is variation 

in the intervals between measurements, the 

individual growth curves will vary in their 

accuracy. The initial summary step does not 
255

Monograph 20. Phenotypes and Endophenotypes

capture these differences in precision and 

therefore may yield biased or inaccurate 

estimates of the biometrical parameters. 

It is for this reason that one should prefer, 

whenever possible, single-step analysis of 

what has been measured.42

A critical issue in LGC modeling is the 

assumption that growth is linear. While 

it is likely that the majority of variation 

in many traits will be captured by this 

component, it is unlikely to be the case for 

all traits, especially those measured over 

a wide range of ages. This was recognized 

as early as the eighteenth century by 

Malthus,43 who developed mathematical 

equations for alternative growth curves. 

Fundamental work by Browne44 provided 

methods to fi t such nonlinear growth curves 

to data. Perhaps due, in part, to limitations 

of some of the popular software packages, 

such nonlinear growth curves have not 

proved popular.44 Fitting such models is 

not technically diffi cult, even for the case 

of data from relatives45 with very long time 

series; these are typically handled by using 

time series analysis.46 Ecological momentary 

assessments, which may contain thousands 

of repeated measures for each individual, 

present an obvious technical challenge. 

Research in this area typically extracts 

summary statistics in a two-stage approach. 

While practical, there may be unwarranted 

or undesirable assumptions in such an 

approach, which a more direct analytic 

method could avoid if it became practical.

The assessment of tobacco use patterns is 

no different from most other behavioral and 

psychological domains in that it typically 

begins with a collection of binary or ordinal 

items. The analysis of such measures 

represents a serious challenge for growth 

curve modeling because the methods were 

developed for continuous data. Two main 

challenges present themselves. One is that 

computing the likelihood of ordinal data is 

typically done by integrating the multivariate 

normal distribution. In a growth curve 

model with m occasions of measurement, 

multiple integrals must be computed 

over as many dimensions as there are 

occasions, which becomes computationally 

intensive with more than 10 dimensions. 

This problem is more acute with data 

from relatives; pairs of twins doubles the 

number of dimensions of integration, 

and larger pedigrees (e.g., size f ) further 

exacerbate the problem to mf integration. 

Worse still, when measures of dependence 

are being derived from a set of p items, 

mpf dimensional integration is needed. 

It is nonetheless possible to apply models 

for both mean and covariance structure 

(of which LGCs are an example) to ordinal 

data, as described by Mehta and colleagues3 

and Wirth and Edwards.47 The second 

key issue in the analysis of multivariate 

data (such as a measure derived from a 

number of questionnaire items) is that it 

is very important to assess measurement 

invariance.5 Analysis of sum scores could 

provide misleading results infl uenced 

by variance specifi c to any of the items 

rather than by the factor itself. Conversely, 

analysis of individual items subsumes 

factor variance and item-specifi c variance 

for which patterns of familial resemblance 

(and relative magnitude of variance 

components) may differ.

An addition to modeling of growth curve 

mixture models is the notion of switching48

in which individuals may belong to different 

trajectory groups at different times. 

The specifi cation of these models is not 

straightforward, because it is necessary to 

consider all possible latent states in which 

an individual might be at each occasion of 

measurement. With r trajectory classes and 

s occasions of measurement, there are r s

possible states for each individual and, thus, 

r s components to the mixture distribution. 

The situation is exacerbated when the model 

is extended to data collected from pairs of 

relatives in that r 2s components are required. 

One may therefore envisage analysis of 

relatively few occasions of measurement 
256

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

with this approach. Nevertheless, this 

approach has some attraction for the 

analysis of data on nicotine use. Transitions 

between user and nonuser classes are of 

key importance in the study of the uptake 

and cessation of tobacco use. In future 

work, it is hoped to extend the model to the 

genetic epidemiology of the probability of 

transitions between different latent states.

Genetic Latent Class Models

Historically, latent class models and factor 

models developed separately. Factor models 

can be traced to the work of Spearman.49

Latent class analysis was developed in the 

mid-twentieth century.50,51 Although its 

use has been less widespread than that 

of latent trait models (which have been 

very popular for the last 20 years), it is 

still a popular method.52 Under certain 

circumstances, latent class models and 

factor models are equally able to account for 

mean and covariance structure;53 they have 

distinct conceptual frameworks and can 

be distinguished by analysis of raw data.39

In the latent class model, the population is 

regarded as a mixture of subgroups, whose 

item response probabilities (or item means 

and variances in the continuous case, known 

as the latent profi le model) vary between the 

groups. Within each subgroup, the items are 

specifi ed to be uncorrelated (the assumption 

of conditional independence). The model 

is one example of a fi nite mixture model;54

along with other such models, it is 

becoming popular in many areas.

Eventually, structural equation models 

and latent class models were combined 

in a single comprehensive model.55–60

Slightly different combined models have 

been proposed with names including 

“fi nite mixture structural equation model,” 

“mixtures of conditional mean- and 

covariance-structure models,”55 and “fi nite 

mixture confi rmatory factor models.”58

In what follows, the combined model is 

referred to as the “factor mixture model” 

(FMM). The FMM features two types of 

latent variables—namely, a latent class 

variable and one or more continuous 

factors within each class. The continuous 

factors have several observed indicators 

(e.g., items of a questionnaire), which can 

be binary, ordinal, or continuous. The FMM 

is therefore a model for multivariate 

data. Muthén and Asparouhov40 describe 

application of an FMM to data collected 

from twins. These models may be fi tted 

with either Mx or Mplus.

Several genetic latent class models were 

described by Eaves and colleagues.61

In these models, the conditional 

independence assumption is retained, both 

within individuals and across relatives. 

Complexity arises in the modeling of 

familial resemblance for class membership. 

Several choices are possible. A simple 

Mendelian model of a diallelic major locus 

that controls class membership (AA versus 

Aa versus aa genotypes corresponding to 

three latent classes) would yield a pattern of 

identical class membership for monozygotic 

twin pairs with frequencies p 2, 2pq, and 

q 2, where p = 1 – q is the frequency of 

allele A in the population. The dizygotic 

proportions of class membership are more 

complex, involving pairs discordant for 

class membership, but are straightforward 

to derive. It is also possible to construct 

a two-class concatenation of this single 

locus model, where genotypes AA and Aa

are both associated with class 1, while aa is 

associated with class 2. Eaves and colleagues 

also describe more complex models that 

specify a binary environmental factor that 

interacts with the major locus to generate 

four classes. The environmental factor is 

allowed any degree of association between 

relatives, according to the pattern

a2 + d ab – d

ab – d b2 + d

where a = 1 – b is the frequency of the 

fi rst environmental condition, and d is 
257

Monograph 20. Phenotypes and Endophenotypes

the association parameter for familial 

resemblance, which has to satisfy the range 

constraint

0 < (ab – d) / [(a2 + d) (b2 + d)]1⁄2 < 1

Such nonlinear inequality constraints are 

easily specifi ed in Mx or MPlus, although 

no implementation of the model was found. 

Other specifi cations of familial resemblance 

for class membership are possible. 

Gillespie and Neale62 described a fi nite 

mixture distribution model for genotype 

by environment interaction in which a 

major locus, a continuous threshold model, 

a shared environment, or a nonshared 

environment factor controlled group 

membership. This area is underdeveloped 

in genetic modeling, particularly in view 

of developments such as growth curve 

mixture modeling.48,56,63,64

Molecular Genetic Analysis

Linkage Analysis

The focus of structural equation modeling 

of data has largely been on testing 

the signifi cance and quantifying the 

contributions of genetic and environmental 

latent sources of variance to individual 

traits or the comorbidity of traits. This is 

referred to as either “basic” or “advanced 

genetic epidemiology.” The 1990s saw 

a huge upswing in the analysis of data 

collected from molecular genetic studies, 

which continues to increase to this 

day. These studies attempt to establish 

whether measured specifi c genetic variants 

contribute to variation in the trait of interest 

and thus identify the actual genes involved. 

There are two main types of molecular 

genetic study: linkage and association. 

Linkage analysis uses related individuals to 

evaluate the correlation between similarity 

at a genetic locus with similarity of the trait 

value. Association studies mostly employ 

unrelated individuals and compare the 

frequency of genetic variants at a locus in 

cases and controls. Typically, these analyses 

are repeated for a range of locations across 

the genome, either using a candidate gene 

approach or by scanning the genome. While 

traditionally a limited set of markers across 

the genome was included, genome-wide 

association studies now employ chips with a 

million loci. This section describes in brief 

the connection between structural equation 

modeling and linkage analysis, setting 

the stage for the integration of models for 

gene action with growth curves or stages of 

tobacco use trajectories.

Linkage analysis is closely analogous 

to the analysis of twin data. In practice, 

the molecular biologist assays several 

markers along the genome. Originally, 

these markers were chosen to be highly 

polymorphic, such that there were some 

15–20 different alleles at a “microsatellite” 

locus, and some 300–400 loci were placed 

at approximately equal intervals along the 

genome. Today, a larger number of twoallele loci are usually assayed, using single 

nucleotide polymorphism (SNP) genotyping 

technologies. In either case, the idea in 

linkage analysis is to assess how many 

alleles a pair of siblings (for example) share 

at a particular location along the genome. 

Sib pairs can then be classifi ed into those 

sharing zero, one, or two alleles identical 

by descent (IBD) at the locus. The possible 

IBD confi gurations for sib pairs can be 

tabulated by labeling parents’ alleles as AB

for the father and CD for the mother.65,66

Their possible offspring are AC, AD, BC,

and BD; the possible pairwise combinations 

of these offspring are shown in table 6.1. 

The cells of this table indicate the number of 

alleles shared IBD by each of the 16 possible 

sib pair types. Since each combination 

is expected to be equally frequent, the 

expectation is that one-fourth of the pairs 

will be IBD 2, one-half will be IBD 1, and 

one-fourth will be IBD 0.

Detection of linkage occurs when IBD 2 pairs 

are more similar than IBD 1 pairs, who in 
258

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

turn are more similar than IBD 0 pairs. This 

is very much the same as the twin study 

apart from three important exceptions. 

First, rather than fi tting an ACE model, 

an estimate is made of the contributions 

to the variance of the genetic variants at a 

specifi c locus, the quantitative trait locus 

(QTL), the residual familial factors (F), and 

unique environmental factors (E), sometimes 

referred to as the QFE model. Second, the 

IBD 0 pairs correspond to unrelated pairs, 

such as adopted children reared in the same 

family. Third, the information about IBD 

sharing is imperfect because the markers 

do not unambiguously classify sib pairs into 

those sharing 0, 1, or 2 alleles IBD. There 

are two main approaches to overcoming 

this limitation. One is to use an estimate 

of p, the proportion of alleles shared IBD, 

and p is specifi ed as the covariance between 

the variance component that represents 

the effect of the QTL. Alternatively, and 

mathematically more consistent, the 

imperfect classifi cation can be represented 

as a mixture distribution.67,68 The likelihood 

of a sibling pair’s phenotypes can be written 

as the weighted sum of three likelihoods: 

the IBD status is zero, one, or two. In either 

approach, one uses the defi nition variable 

approach described above in the subsection 

on “Structural Equation Modeling” to 

specify the model. The signifi cance for 

linkage is evaluated by the logarithm of odds 

(LOD) score, a statistic that represents the 

likelihood of the odds of linkage over the odds 

of no linkage. Criteria have been established 

to classify results as suggestive, signifi cant, 

or confi rmed evidence for linkage.69

In practice, most linkage analysis is 

conducted with specialized software such 

as Merlin70 or GENEHUNTER.71 However, 

such programs are designed for the analysis 

of a single trait. Fortunately, they permit 

export of IBD probabilities that can be used 

in other software for modeling multivariate 

or longitudinal data or simply modeling 

traits that are assessed by using a collection 

of binary or ordinal items. For example, 

it is possible to conduct a linkage scan for 

quantitative trait loci that cause variation in 

level or slope of a growth curve model.

Association Analysis

Association analysis is in principle simpler 

than linkage analysis in that it can be 

conducted with groups of cases and controls. 

Conceptually, the idea is to compare between 

groups the allele frequency at a particular 

locus. From a statistical point of view, 

this is a simple comparison that can be 

conducted using a v2 test. However, certain 

pitfalls have the potential to generate false 

positives or false negatives. One is population 

admixture in which there exist two or more 

subpopulations whose allele frequencies 

differ and whose trait mean values differ for 

entirely different reasons. Several approaches 

exist to control for such admixture 

(or stratifi cation). One is to obtain a set of 

alleles in noncoding regions of the genome 

to assess whether there is stratifi cation.72

A second is to use data collected from 

relatives.73 Since families come from the 

same stratum of the population, any allelephenotype association observed within 

families cannot be due to population 

stratifi cation. An additional advantage of 

the family-based research design is that 

it permits joint analysis of linkage and 

association information, which in turn assists 

with fi ne mapping of quantitative trait loci.74

There is much focus on genome-wide 

association studies, which have become 

practical to conduct with the advent of 

inexpensive SNP chips. These microarray 

Table 6.1 Number of Alleles Shared 

Identical by Descent for a Pair 

of Full Siblings

AC AD BC BD

AC 2110

AD 1201

BC 1021

BD 0112

Note. Parental genotypes are AB and CD.
259

Monograph 20. Phenotypes and Endophenotypes

chips permit the assaying of a very large 

number (500,000, for example) of SNPs 

across the genome. Such density permits 

exploitation of linkage disequilibrium in 

which short strands of DNA are transmitted 

intact with low chance of recombination. 

Thus, it becomes possible to identify 

very small regions likely to contain a 

polymorphism that accounts for variation 

in a trait. Much of the software development 

in this area is targeted at the rapid analysis 

of this large number of data points and with 

handling the high type 1 statistical error 

rates that ensue. Redden and Allison75 note 

that assortative mating can increase the risk 

of type 1 error in association studies. Again, 

the focus is single trait oriented rather than 

multivariate. However, it has been noted 

that association data have considerable 

potential to resolve alternative pathways 

between phenotypes.76 The integration of 

association data into a more sophisticated 

modeling framework is straightforward in 

principle, but much remains in the way 

of opportunities to develop and test the 

models. For example, in a latent growth 

curve mixture model, one might specify that 

alleles at a locus affect the mean of the level 

or growth factors. Alternatively, one might 

specify that an individual’s class membership 

probabilities are a function of genotype. 

Thus, one would explicitly model the 

allele effects as another model parameter. 

A simpler two-step approach would be to 

assess allele frequencies between those 

classifi ed as belonging to one or another 

class. This latter method would have the 

advantage of analytic simplicity at the cost 

of losing the information about the precision 

of the class membership classifi cation.

Review of Genetic 

Studies of Smoking

The role of genes and environment in 

initiating smoking has been the subject of a 

growing number of twin and family studies 

and several reviews.77,78 Evidence from these 

studies generally points to an important role 

of genetic factors in explaining individual 

differences in starting to smoke. In addition 

to additive genetic factors, however, shared 

environmental factors also contribute 

signifi cantly to the variation, especially 

in adolescent samples. The literature is 

reviewed here from a range of perspectives, 

with the aim of providing a better 

understanding of the process of developing 

the smoking habit and subsequent 

dependence. As shown in epidemiological 

studies, approximately 50% of the 

individuals who start to smoke continue 

to do so and go on to become dependent 

on nicotine. Prevalence rates for adults in 

2006 suggest that 42% have ever smoked in 

their lifetime and 24% of men and 18% of 

women still were smoking.79 One obvious 

question is whether the factors that lead to 

individuals starting to smoke also contribute 

to whether they persist in their smoking 

behavior. First, this section reviews the 

most prominent twin studies on adolescent 

smoking. Second, additional information 

is considered that can be obtained from 

extending the classical twin design to other 

relatives—for example, parents, siblings, 

and spouses. Third, the focus is on studies 

that have included measures of smoking 

initiation and progression to discern the role 

of genes and environment to the different 

stages of the smoking process. Finally, 

molecular studies of adolescent smoking are 

reviewed to show how the direct assessment 

of molecular genetic polymorphisms can 

enhance understanding of the trajectory 

from initiating the smoking habit to 

nicotine dependence.

Twin Studies of Adolescent 

Smoking

Eight published papers were identifi ed 

that report results from twin studies on 

smoking behavior in adolescence. The fi rst, 

by Boomsma and colleagues,80 reported 

data on 1,600 Dutch adolescents aged 

13–22 years, concluding that the majority 
260

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

of interindividual variation in smoking 

behavior was due to shared environmental 

factors (59%), with 31% attributed to 

genetic factors. Results, however, were 

not consistent across age groups, with 

heritability estimates decreasing with age 

in males but increasing in females. When 

age was included in the analyses, 9% of the 

variance could be accounted for by age, 

reducing the proportion explained by shared 

environmental factors to 50%. Furthermore, 

the shared environmental factors differed 

between males and females (correlation 

between shared environmental factors, 

rC = .65). A follow-up study including more 

than 2,600 pairs of Dutch adolescents81

showed a more consistent trend of an 

increasing role of genetic factors in smoking 

behavior from ages 12 to 22 years, with a 

corresponding decline in the contribution of 

shared environmental factors. Up to age 17, 

heritability was not signifi cantly different 

from zero. However, 33% (95% confi dence 

interval [CI], 31%–54%) of the variance 

was attributed to genetic factors in young 

adult females, and 66% (95% CI, 43%–86%) 

in males. Again, shared environmental 

factors, which accounted for the majority 

of the variance in adolescence, appeared 

partially different for males and females. 

Similar results were obtained in a sample 

of 1,419 16-year-old Finnish twin pairs.82

Shared environmental factors accounted 

for the majority of the variance in smoking 

behavior (having smoked 50 cigarettes or 

more)—75% in males and 63% in females. 

Heritability was estimated at 17% and 

30%, respectively. In analyzing FinnTwin12 

smoking data from twins and their 

classmate controls, heritability (h2) was 11% 

and shared environment could be split into 

familial infl uences (49%) and school-based 

neighborhood effects (24%).83,84

Data from 16-year-old twins (N = 159) 

studied in the fi rst wave of the Virginia 

Twin Study of Adolescent Behavioral 

Development85 suggested that additive 

genetic factors accounted for 65% (95% CI, 

10%–93%) of the variance in liability 

to lifetime smoking and 60% (95% CI, 

0%–93%) for current tobacco use, with 

nonsignifi cant contributions of shared 

environmental factors (18% and 21%, 

respectively). While the prevalence of 

smoking was statistically different for 

males and females, the contribution of 

genetic and environmental factors did 

not differ by gender. Gender differences 

were also not statistically signifi cant in 

analyses of 500 17- to 18-year-old twin 

pairs from the Minnesota Twin Family 

Study,86 resulting in estimates of 36% 

for the heritability of tobacco use and 

44% for shared environmental factors. 

When analyzed separately by gender, the 

predominant source of variance was genetic 

(59%) for males and shared environmental 

(71%) for females. An updated report87

on a slightly larger sample (N = 626) with 

primarily additional female twins showed 

a heritability of 56% for ever having used 

tobacco and a smaller contribution of shared 

environmental factors (30%). Genetic 

factors were the predominant source of 

variance in males (48%) and females (62%). 

The contributions of both genes (38%) and 

shared environment (52%) were signifi cant 

in data from 682 twin pairs (306 biological 

siblings and 74 adoptive sibling pairs) aged 

12 to 19 years assessed by the Center for 

Antisocial Drug Dependence in Colorado.88

Again, gender differences were not 

statistically signifi cant. The slightly larger 

role of the shared environment is consistent 

with the inclusion of younger adolescents.

Shared environmental factors were also the 

predominant (52%) source of variation in 

smoking initiation in a sample of 414 samegender twin pairs aged 13–18 years from the 

Australian Twin Registry (ATR).89 Heritability 

reduced from 22% to zero when the model 

was adjusted for smoking by peers and 

parents. While shared environmental factors 

were more important in males, and genetic 

factors accounted for the largest proportion 

of variance in females, the gender difference 
261

Monograph 20. Phenotypes and Endophenotypes

was not signifi cant. Data from two follow-up 

waves showed a gradual shift from shared 

environmental to genetic infl uences, with 

each accounting for about 35% of the 

variance in 18- to 25-year-olds, consistent 

with results from other studies. A 2005 study, 

using a genetically informative subsample 

of 2,142 sibling pairs, aged 11–20 years, 

participating in two waves of the National 

Longitudinal Study of Adolescent Health,90

presented heritability estimates for smoking 

frequency of 52% and between 28% and 

35% for high levels of smoking frequency. 

The role of shared environmental factors was 

greater for high levels of smoking frequency 

(25%–38%) than for overall smoking 

frequency (7%). In contrast to previous 

studies, smoking frequency was used rather 

than a measure of smoking initiation.

As discussed in a review of twin and 

adoption studies of adolescent substance 

use,91 shared environmental infl uences 

appear stronger in younger adolescents, 

whereas genetic infl uences are more 

substantial in older adolescents and young 

adults (fi gure 6.4). However, it is possible 

that the earliest stage of cigarette smoking 

(i.e., fi rst experimentation) is mostly due 

to environmental factors, whereas later 

stages (conditioned on previous exposure 

to nicotine) are more likely to be due to 

genetic factors. The issue with many studies 

is that the presence of smoking initiation 

is determined by a somewhat vague item, 

such as, “Have you ever smoked?” It is 

possible that this question is more likely 

to be interpreted by a younger adolescent 

(i.e., one closer to fi rst exposure to 

cigarettes) as “whether they’ve ever tried 

even a single cigarette,” while by older 

adolescents or young adults as meaning 

onset of regular smoking. Studies that use 

such a vague measure of initiation with 

a broad age range may inadvertently be 

measuring different behaviors (i.e., different 

stages of the smoking habit) in early 

adolescents compared with those subjects 

Figure 6.4 Estimates of the Contributions of Additive Genetic (a2) and Shared Environmental 

(c2) Factors to Smoking Initiation by Sample, Age, and Gender in Published 

Studies of Adolescent Twins

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Proportion of variance explained

0.1

OZ mf

18-25

NL m

17-25

NL f

17-25

MN m

17

MN f

17

OZ mf

16-21

VA mf

16

FI f

16

FI m

16

NL m

15-16

NL f

15-16

OZ mf

13-18

CO m

12-19

CO f

12-19

NL m

12-14

NL f

 12-14

Samples, gender, and age range

a2

c2

Note. NL = Netherlands Twin Register; f = female; CO = Center for Antisocial Drug Dependence in Colorado; m = male; OZ = Australian 

Twin Registry; FI = Finnish Twin Registry; VA = Virginia Twin Study of Adolescent Behavioral Development; MN = Minnesota Twin 

Family Study.
262

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

in young adulthood. It should also be 

noted that these comparisons are based on 

estimates from studies with varying sample 

size and thus varying precision. Ideally, 

a meta-analysis should be undertaken that 

appropriately accounts for these differences. 

Alternatively, a mega-analysis that combines 

the raw data of several related studies 

would provide more accurate estimates of 

the potentially changing role of genes and 

environment across adolescence.

Extended Genetic Epidemiology

Extended Twin Family Studies 

of Transmission of Smoking

Relatively few studies have either included 

or analyzed data collected from other 

relatives. Rhee and colleagues reported 

results of fi tting a model to data on 

twins, nontwin siblings, and adoptees.88,92

The major fi nding was that the proportions 

of variance associated with the special twin 

environment and with genetic dominance 

were small. Data from the Netherlands Twin 

Register suggested signifi cant assortment 

between spouses, with the correlation 

between husband and wife for “currently 

smoking” larger than for “ever smoking.”80

Furthermore, there was no evidence that 

parental smoking encouraged smoking in 

their offspring, as resemblance between 

parents and offspring was signifi cant, 

but rather low, and could be completely 

accounted for by genetic relatedness. 

If included, cultural transmission estimates 

were negative. Similar results were obtained 

for data on twins and their parents from the 

Finnish Twin Registry,82 showing signifi cant 

assortment (husband-wife correlation = .42), 

and low but signifi cant parent-offspring 

correlations.

Nongenetic analyses of data on 3,906 twins 

confi rmed signifi cant associations between 

the smoking behavior of the twin with that 

of the co-twin. Odds ratios, ranked highest 

to lowest, were given when an individual 

had a smoking monozygotic co-twin, 

a smoking same-gender dizygotic co-twin, 

or a smoking opposite-gender dizygotic 

co-twin—suggesting a role for both genetic 

factors and gender. In addition, associations 

were also signifi cant for smoking behavior 

of parents, siblings, and friends, and were 

gender dependent (stronger associations for 

same-gender smoking family members).93,94

In fact, the risk to initiate smoking when 

having friends who smoke was similar to 

that of having a smoking co-twin and greatly 

exceeded that of having a parent who smokes.

Similarly, data from the Virginia 30,000 

Study, including about 15,000 twins and 

their fi rst degree relatives (parents, siblings, 

spouses, children), showed little evidence 

for the role of parents in infl uencing the 

smoking behavior of their children through 

other than genetic pathways.95,96 Analyses of 

these extended twin kinship data supported 

the role of additive genetic factors, 

accounting for more than one-half of the 

variance in smoking initiation, partly due 

to the consequences of assortative mating, 

which was highly signifi cant. About 20% of 

the variance was accounted for by specifi c 

environmental factors. Furthermore, 

the contributions of shared environment 

and special twin environment were both 

signifi cant. The environmental paths from 

the parents to their children were estimated 

to be negative, but this was not signifi cant. 

Note that these analyses were based on data 

from different generations of adults and 

should ideally be performed on data sets of 

adolescent twins augmented with parents.

Multivariate Genetic Studies

Only a few studies have investigated whether 

the same genetic or the same environmental 

factors account for the co-occurrence of 

several smoking behaviors. Genetic analyses 

of data from young adult Australian twins97

reporting any cigarette use were undertaken 

to examine whether there are genetic 
263

Monograph 20. Phenotypes and Endophenotypes

factors specifi c to nicotine withdrawal 

after controlling for factors for smoking 

progression and quantity smoked. Signifi cant 

genetic overlap was found for smoking 

progression, quantity smoked, and nicotine 

withdrawal, but evidence for specifi c genetic 

infl uence to nicotine withdrawal remained. 

An extension of the causal contingent 

common (CCC) pathway models (see also 

the subsection below, “Progression from 

Smoking Initiation to Nicotine Dependence”) 

was used to explore the interrelationship 

of smoking age at onset, cigarette 

consumption, and smoking persistence.98

Smoking initiation was operationalized as 

an ordinal variable with three categories—

nonsmokers, late-onset smokers, and 

early-onset smokers—assuming a single 

underlying distribution and thus referred 

to as age at onset. This allows the authors 

to fi t a full multivariate model, rather than 

the CCC pathway model, according to Heath 

and colleagues,99 and partition both the 

variation and covariation into genetic and 

environmental contributions. The authors 

found signifi cant heritability for all three 

phenotypes in males and females and slightly 

higher genetic correlations in males than 

in females. The relationship of smoking 

age of onset, cigarette consumption, and 

smoking persistence was also mostly due to 

shared genetic infl uences. A similar analysis 

of age at initiation, amount of smoking, 

and smoking cessation was done on data 

from adult Finnish twins.100 The study 

found that genetic factors were important in 

amount of smoking and smoking cessation, 

but these were largely independent of genetic 

infl uences on age at initiation.

Progression from Smoking Initiation 

to Nicotine Dependence

Most individuals who initiate smoking 

progress to regular smoking, and many 

become dependent on nicotine.79 It is, 

therefore, important to evaluate whether 

the same factors infl uence whether someone 

starts to smoke and whether one continues 

to smoke. Reports that analyze measures 

of persistence or dependence without 

taking initiation into account assume that 

the dimensions underlying initiation and 

progression are independent (if only smokers 

are included) or assume that persistence is 

an extreme version of initiation on the same 

single liability dimension (if nonsmokers are 

included but score zero on the progression 

measures). Heath and colleagues101

recognized this and developed alternative 

models to test these assumptions. First, 

studies are reviewed that estimated the role 

of genes and environment on the measure 

of dependence without taking initiation into 

account. McGue and colleagues87 reported 

no gender differences in the role of genetic 

and environmental factors for nicotine 

dependence in a sample of 626 17-year-old 

twin pairs, with genes accounting for 44% 

and shared environment for 37% of the 

variance. Although Rhee and colleagues88

found no signifi cant gender differences for 

initiation, shared environmental factors 

were signifi cant for tobacco use and problem 

use in males but not in females, explaining 

45%–48% of the variance in a sample 

of more than 1,000 twins and siblings. 

Heritability estimates were 24%–26% in 

males and 95% in females, respectively.

As far as known, only one study has 

simultaneously analyzed data on smoking 

initiation and persistence in a juvenile 

sample. Koopmans and colleagues102

published analyses from 1,676 Dutch 

adolescents. They found separate smoking 

initiation and quantity dimensions, which 

were not completely independent. The total 

heritability of quantity smoked was estimated 

at 86%. Five studies were found of smoking 

initiation and progression in adults. Data 

from 4,000 male twin pairs from the Vietnam 

Era Twin (VET) Registry103 found that 

genetic and shared environmental factors 

accounted for 50% and 30%, respectively, of 

the variance in liability to initiate smoking. 

However, no evidence for shared environment 

was found for factors specifi c to persistence, 
264

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

for which variation was estimated to be 

70% additive genetic. Signifi cant heritability 

for nicotine dependence (60%) was also 

found in a follow-up study of 3,356 male 

VET Registry pairs.104 Using nonmetric 

multidimensional scaling, Heath and 

colleagues105 found that the etiologic factors 

that determined which individuals were at 

risk of becoming smokers differed from those 

that infl uenced age of smoking initiation. 

The role of genes and shared environment in 

the onset of smoking differed by cohort and 

gender, and only genetic factors accounted 

for twin resemblance in the age at which 

smoking onset occurred.

As described above, Kendler and colleagues1

developed a model that estimates the 

correlation between liability to smoking 

initiation and liability to nicotine dependence 

and applied it to data on 1,898 female twins 

from the Virginia Twin Registry. Results 

indicated that etiological factors that 

infl uence initiation and dependence, while 

overlapping, are not perfectly correlated. 

Thus, genetic factors contributed 72% to 

variance in liability to nicotine dependence, 

of which 69% also infl uence initiation and 

31% are unique to nicotine dependence. 

Madden and colleagues106 fi tted a similar 

correlated liability dimensions model 

to data from large samples of male and 

female same-gender twins from three 

countries—Australia (1,535 pairs), Sweden 

(5,916 pairs), and Finland (4,438 pairs)—

further subdivided by age bands. The authors 

also found that familial infl uence on risk for 

persistence in smoking cannot be entirely 

explained by the same factors responsible 

for risk of smoking initiation. Total genetic 

variance for smoking persistence ranged 

between 39% and 48% in women and 

42% and 45% in men, of which only 7%–35% 

was accounted for by factors in common with 

initiation. Although shared environmental 

factors contributed signifi cantly to 

smoking initiation, there were no 

signifi cant additional shared environmental 

contributions to smoking persistence.

Maes and colleagues37 extended the liability 

models to include smoking initiation, 

regular tobacco use, and nicotine 

dependence, and applied them to data 

on both female and male twins from the 

Virginia Twin Registry. Results showed that 

the liabilities to all three stages of smoking 

behavior were correlated, with 80% of 

the variance in liability shared between 

initiation and regular use, and 50% between 

regular use and nicotine dependence.37

The heritability of nicotine dependence was 

estimated at 62%, of which 24% was specifi c 

to nicotine dependence, 10% shared with 

regular tobacco use, and the remaining 28% 

shared with smoking initiation as well. Data 

on 1,572 Dutch adult twins also showed 

that the smoking initiation dimension is not 

independent from the nicotine-dependence 

dimension.107 As shown in other data sets, 

shared environmental factors contributed 

signifi cantly to the variance in liability to 

initiation but not to nicotine dependence, 

which was strongly (75%) infl uenced by 

genetic factors (fi gure 6.5).

Assessment of Nicotine Dependence

Almost all studies that measure nicotine 

dependence use either a sum score or a 

binary diagnosis. The most widely used 

measures are the FTQ8 items and the criteria 

based on the Diagnostic and Statistical 

Manual of Mental Disorders (DSM),108 either 

of which may be dichotomized by imposing 

a threshold for affection status. The latter 

approach reduces the information available, 

which, in genetic studies, would typically 

result in reduced statistical power.26,109

Using sum scores assumes that the scale 

of measurement is invariant and that the 

underlying liability is unidimensional. 

The FTQ correlates with other proposed 

measures of nicotine dependence such as 

carbon monoxide, nicotine, and cotinine 

levels.8 However, the nicotine rating and 

inhalation items were found to be unrelated 

to biochemical measures, and a revised 

scoring was proposed, the FTND.110 Both 
265

Monograph 20. Phenotypes and Endophenotypes

the FTQ and the FTND were highly reliable, 

and internal consistency was greater for the 

FTND than for the FTQ.111 Retrospectively 

assessed FTQ-FTND scale scores also have 

acceptable reliability.112 Furthermore, the 

six FTQ items were positively correlated with 

cotinine values in adolescent smokers.113

Several studies have attempted to evaluate 

the dimensionality of nicotine dependence 

using factor analysis of either the FTQ 

or the FTND items. An exploratory factor 

analysis of FTND in young adult smokers 

resulted in two factors.114 The fi rst factor, 

labeled “smoking pattern,” included items 

assessing the number of cigarettes smoked 

per day, time to fi rst cigarette, diffi culty 

refraining from smoking, and smoking when 

ill. The second factor, labeled “morning 

smoking,” consisted of two items measuring 

whether one smokes more in the morning 

and whether the fi rst cigarette is most 

satisfying, Confi rmatory factor analysis, 

however, only confi rmed the fi rst factor. 

Similar factors resulted from an exploratory 

factor analysis of FTND in an adult sample, 

with a third factor related to the brand of 

cigarettes when all eight FTQ items were 

included.115 These analyses were repeated 

in the drug abuse patient sample, with 

similar results, except that time to fi rst 

cigarette loaded on both factors.116 Factors 

were named “persistence in maintaining 

nicotine levels during waking hours” and 

“urgency in restoring nicotine levels after 

nighttime abstinence.” A confi rmatory 

analysis in hospital patients confi rmed that 

the items of the FTND were best modeled 

as two correlated factors with a crossloading.117 Furthermore, a four-item single 

factor (“daytime smoking factor”) fi tted 

the data reasonably well. This confi rms 

previous studies showing that both the 

four-item and the Heavy Smoking Index 

Figure 6.5 Estimates of the Contributions of Additive Genetic Factors in Common with 

Initiation (a2i), Total Additive Genetic (a2), and Shared Environmental (c2) Factors 

to Smoking Persistence by Sample, Gender, Age, and Measure of Persistence

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Proportion of variance explained

0.1

NL mf

18-50

ND

VA mf

18-60

ND

VE m

36-55

ND

VE m

30-50

ND

ASF m

36-46

per

ASF f

36-46

per

ASF m

26-35

per

ASF f

26-35

per

ASF m

18-25

per

ASF f

18-25

per

MN mf

17

ND

CO f

12-19

PU

CO m

12-19

PU

CO f

12-19

use

CO m

12-19

use

Samples, gender, age range, and measures

a2i

a2

c2

Note. The fi rst fi ve studies do not take initiation into account. CO = Center for Antisocial Drug Dependence in Colorado; m = male; 

MN = Minnesota Twin Family Study; f = female; ASF = Australian, Swedish, and Finnish Twin Registries; VE = Vietnam Era Twin 

Registry; VA = Virginia Twin Registry; use = tobacco use; PU = problem tobacco use; per = persistent tobacco use; ND = nicotine

dependence.
266

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

(based on two FTND items: the number 

of cigarettes smoked per day and the time 

to fi rst cigarette) represent the FTND 

well.118,119 Few studies have compared 

the questionnaire-based FTQ-FTND 

measures with those based on structured 

interviews (i.e., DSM, the International 

Statistical Classifi cation of Diseases 

and Related Health Problems), and have 

found only moderate concordance,120,121

which may indicate that they tap into 

different aspects of nicotine dependence. 

Only one analysis was found that was 

based on factor analysis/item response of 

nicotine-dependence measures using a 

genetically informative sample. In a genetic 

factor analysis of nicotine-dependence items 

measured in adult Australian twins, item 

covariation was best captured by two genetic 

but one shared environmental factor for 

both women and men; however, item factor 

loadings differed by gender.122 None of these 

studies included initiation as an item. Later 

in this chapter, results are presented from 

a genetic item analysis of adult Virginia 

twin data that include both initiation and 

regular smoking in the analysis. As nicotinedependence symptoms were only assessed 

in individuals who had initiated smoking 

and become regular smokers, it is shown 

here how including these conditional items 

affects the estimates of factor loadings and 

thresholds.

Genetic Latent Growth Curves 

and Latent Class Analysis

Although the epidemiological literature 

on growth curve and latent class analysis 

of smoking behavior is rapidly expanding 

(chapter 5), genetically informative 

applications of this type of analysis were 

not identifi ed.

Molecular Genetic Studies of Smoking

Besides the extensive literature on genetic 

epidemiological studies of smoking behavior 

and nicotine dependence, the literature 

on gene-fi nding approaches for nicotine 

dependence is increasing rapidly, refl ecting 

the general trend in the genetic analysis 

of complex traits. The major results from 

linkage and association studies on smoking 

behavior and nicotine dependence are 

briefl y summarized below. Given that very 

few molecular genetic studies of smoking 

behavior have included adolescent subjects, 

results are provided from adult samples.

Linkage Studies

In 2003, three linkage scans of smokingrelated measures had been completed. 

Since then, at least seven more have 

been published and others are under way. 

The fi rst genome scan was conducted using 

a sample of 130 sibling pairs concordant for 

nicotine dependence from Christchurch, 

New Zealand (CNZ) and a replication 

sample from Richmond, Virginia.123 Several 

publications have resulted from data made 

available to investigators participating 

in Genetic Analysis Workshops (GAW). 

As part of GAW11, data on 105 families 

from the Collaborative Studies on Genetics 

of Alcoholism (COGA) were examined for 

linkage for smoking-related traits, including 

smoking initiation, and habitual smoking, 

defi ned as ever smoking at least one pack 

(20 cigarettes) daily for six months or 

more.124 Data from a genome scan with 

330 extended families participating in the 

Framingham Heart Study (FHS) were made 

available to investigators participating in 

GAW13, resulting in several reports on 

maximum cigarettes per day (maxcig)125

on a typical day.

Several genome scans have been performed 

with samples initially selected for 

phenotypes other than smoking. As part 

of a Netherlands Twin Study of Anxious 

Depression (NETSAD) collaborative project, 

a genome scan was performed on 646 sibling 

pairs in 212 families for the three smoking 

phenotypes: smoking initiation, maxcig, and 
267

Monograph 20. Phenotypes and Endophenotypes

age of fi rst cigarette.126 A scan for regular 

and persistent tobacco use was performed 

with data collected from a community 

sample of Mission Indians as part of a larger 

study exploring risk factors for substance 

dependence.127 Another scan was conducted 

using a Yale University sample originally 

collected for linkage analyses of anxiety 

disorders, which also included a measure 

of cigarette smoking.128 Similarly, data 

on tobacco use and nicotine dependence 

were available for a sample ascertained 

for affected sibling pair linkage studies of 

cocaine or opioid dependence.129 In the latter 

study, analyses were conducted separately 

for subjects with European American versus 

African American descent.

A number of studies have been published on 

samples specifi cally ascertained for smoking 

behavior. A linkage study focused entirely 

on a sample of African American origin from 

the Mid-South Tobacco Family (MSTF)130

cohort, with assessments of tobacco use and 

nicotine dependence. Swan and colleagues131

performed a genome-wide screen for nicotine 

dependence susceptibility loci on tobacco 

use data collected from families obtained 

through participants in the Smoking in 

Families Study (SMOFAM). Saccone and 

colleagues132 analyzed a smoking quantitative 

trait in Australian and Finnish families 

with at least one heavy smoker. In 2006, the 

fi rst study in linkage analysis for smoking 

initiation and cigarette consumption 

was published that incorporates gender 

differences by using Australian twin families 

(ATR).133 None of the reported linkage scans 

of smoking-related phenotypes have included 

data from adolescents.

Published linkage scans have resulted in 

only a few regions that have exceeded levels 

of genome-wide signifi cance.134 Saccone 

and colleagues132 reported the largest LOD 

score (5.98) for nicotine use on chromosome 

22q12. The second highest LOD score (4.22) 

was found for maxcig on chromosome 20 at 

72 centimorgans (cM),132 which replicates 

an earlier result in the FHS sample.135,136

A similarly high LOD (4.17) was reported 

for chromosome 10 between 92 and 94 cM 

for quantity smoked130 in the MSTF sample. 

Furthermore, this result was supported by 

suggestive linkage in the same location for 

three other nicotine dependence measures. 

This region was part of a broader region 

initially reported by Straub and colleagues123

for which the highest LOD score (1.28) for 

nicotine dependence was obtained in the 

CNZ sample. A modest signal (LOD 2.16) was 

also found for a location close to this region 

(80 cM) for FTND in a European American 

sample.129 An LOD score of 3.71 was found 

for smoking rate in the FHS sample on 

chromosome 11 at 70 cM.135 This result has 

not been replicated so far, although a modest 

LOD score of 1.64 was found at 87 cM for 

heavy smoking.124 Suggestive evidence for 

linkage (LOD = 3.04) was also reported at 

95 cM on chromosome 5 for FTND.129

At least 12 other 10-cM chromosomal 

regions contain positive fi ndings from 

at least two different samples; however, 

neither reach criteria for signifi cant linkage. 

For chromosome 5, Vink and colleagues137

reported an LOD of 2.09 at 205 cM for age 

at fi rst cigarette in NETSAD, and Saccone 

and colleagues125 obtained an LOD of 

1.02 at 100 cM for maxcig in FHS. Five 

reports converged on locations between 

50 and 65 cM on chromosome 6 with LOD 

scores ranging from 1.1 to 3 for different 

tobacco use phenotypes.126,127,133,137,138

Four reports converge on an area on 

chromosome 7 between 140 and 164 

cM.127,131–133 Two regions on chromosome 8 

showed some evidence for linkage: one 

between 24 and 31 cM for maxcig and 

nicotine dependence in the FHS and 

SMOFAM, the other between 110 and 115 cM 

for regular tobacco use in the Mission 

Indian and FHS samples. The largest region 

identifi ed, with seven “hits,” is in a 25-cM 

region (91–116 cM) on chromosome 9 for 

phenotypes ranging from lifetime smoking 

to nicotine dependence. An additional 
268

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

region on chromosome 9 (between 165 and 

172 cM) also showed modest to suggestive 

evidence for linkage for ever smoking 

(COGA sample) and maxcig (in the FHS). 

Two positive reports were found for an area 

between 38 and 43 cM on chromosome 11 in 

the MSTF and the ATR. On chromosome 13 

(41–42 cM), two positive linkage signals 

were found for quantity smoked, one in 

FHS and the other in MSTF. LOD scores 

between 1.29 and 3 were reported for the 

exact same location on chromosome 14 

(88 cM) in three independent samples 

(COGA, NETSAD, and the FHS). Another 

region with support from at least two 

samples includes locations 127 and 135 cM 

on chromosome 15 for smoking rate (FHS) 

or ever smoking (COGA).129 Given the 

range of phenotypes, methods, selection 

criteria, and sample sizes, the accumulated 

data have at least identifi ed regions of 

interest for susceptibility loci for nicotine 

use phenotypes. Collaborations and metaanalyses might assist in resolving some of 

these fi ndings.139

Association Studies

The number of association studies of 

candidate genes for smoking initiation and 

nicotine dependence has grown steadily. 

A search identifi ed only 10 studies published 

before 2000 and fi ve or less papers per year 

from 2001 to 2003. Yet, in 2004, 13 papers 

were published on the subject, a trend that 

has continued with 17 papers in 2005 and 

15 in 2006, bringing the total to more than 

70 articles.

Several reviews have summarized the 

fi ndings.140–146 They can be broadly divided 

into four categories: (1) metabolism of 

nicotine, (2) nicotine receptors, (3) the 

dopaminergic reward system, and (4) the 

serotonergic reward system. Obvious 

candidate genes are those that infl uence 

the metabolism of nicotine, such as 

the cytochrome P-450 (CYP) system. 

Interest has focused on CYP2A6, which is 

involved in the metabolism of nicotine to 

cotinine. At least 10 out of 14 studies show 

signifi cant associations to smoking behavior, 

primarily smoking status and quantity, 

with 3 reporting positive associations with 

nicotine dependence. The second group of 

candidates are genes involved in sensitivity 

to nicotine, the major addictive substance 

in tobacco. Evidence from mouse knockouts 

suggests that the gene coding for the 

nicotinic acetylcholine receptor beta2-

subunit (CHRNB2) is necessary for the full 

reinforcing properties of nicotine. Four 

studies of humans did not fi nd association 

between CHRNB2 and smoking initiation or 

nicotine dependence. However, a nominally 

signifi cant allelic and genotypic association 

was found for CHRNB2 and three other 

nicotinic cholinergic receptors and smoking 

initiation.147 Furthermore, some evidence 

suggests variation in the CHRNA4 gene may 

be associated with reduced risk for nicotine 

dependence. Two other receptors (CHRNB1

and CHRM1) have also been implicated in 

the risk for nicotine dependence.

A third group of studies has examined the 

association of smoking with variations in 

genes involved in the dopamine system, 

motivated by fi ndings that the mesolimbic 

dopaminergic system appears to play a 

signifi cant role in the reinforcing effects 

of addictive drugs, including nicotine. 

A number of studies have examined 

the association between several aspects 

of smoking behavior and variants in 

the dopamine receptors and a repeat 

polymorphism in the dopamine transporter 

protein (DAT/SLC6A3). About two-thirds 

of the fi ndings for DRD2 suggested an 

association with smoking status. Evidence 

for an association of DAT with smoking 

behavior was even stronger: fi ve out of 

six reports presented signifi cant positive 

fi ndings. Analyses of other dopamine 

receptors (DRD4, DRD5) have largely 

produced nonsignifi cant results. A number 

of studies have examined genes related 

to dopamine synthesis or degradation. 
269

Monograph 20. Phenotypes and Endophenotypes

Mostly signifi cant associations have been 

reported for DOPA decarboxylase (DDC) 

and dopamine b-hydroxylase (DbH) with, 

respectively, three out of three and three 

out of four studies showing signifi cant 

results. Several studies have examined 

polymorphisms in the monoamine 

oxidase (MAOA, MAOB), catechol-Omethyl transferase (COMT), and tyrosine 

hydroxylase (TH) genes with mixed results.

The fourth group of genes examined in 

association studies of smoking involves 

the serotonin system on the basis of 

evidence that nicotine withdrawal may be 

modulated by serotonergic transmission. 

The most studied gene in this system is the 

serotonin transporter 5-HTT, particularly 

the functional polymorphism 5-HTTLPR,

which is implicated in alcoholism and major 

depression. These studies have produced 

confl icting results, with one-half of the 

reports indicating a signifi cant association. 

Variation in another serotonin system gene, 

TPH, has been associated with smoking 

behavior in three out of fi ve reports. 

Finally, other genes have been tested for 

associations with smoking behavior, such 

as the phosphatase and tensin homolog 

gene (PTEN), and the cholecystokinin gene 

(CCK), but so far these results have not 

been replicated.

In addition, two genome-wide association 

studies of nicotine dependence have 

nominated several novel genes while also 

identifying known candidate genes.148,149

In summary, although this research area 

is in an early stage, and may be limited by 

several methodological weaknesses, various 

trends are starting to emerge. Most studies 

did not examine nicotine dependence 

directly; they used smoking status as the 

outcome. Sample sizes have tended to be 

relatively modest, the statistical criteria have 

been liberal, and multiple testing has been 

common. Therefore, the chance that these 

fi ndings contain false positive results is high.

Item Response Theory 

Approach: Application 

to Virginia Twin 

Registry Data

This section applies the psychometric factor 

model to data on nicotine initiation and 

dependence collected from twins. The model 

is described above in the subsection “Item 

Response Theory.” These analyses are novel 

in that initiation and dependence are being 

analyzed together, exploiting the information 

on co-twins’ dependence as a function of a 

twin’s initiation status. The model tests for 

measurement noninvariance of nicotine 

dependence as a function of age and gender 

and their interaction.

Subjects

Participants in the present investigation 

were drawn from two longitudinal studies 

of adult twins, conducted in parallel;1

the fi rst consisted of female-female twin 

pairs (FF) and the second of male-male 

and male-female twin pairs (MMMF). Each 

sample was obtained from the populationbased Virginia Twin Registry, which is now 

part of the Mid-Atlantic Twin Registry. 

The fi rst study was of zygosity determination 

and was based on questionnaire responses 

and DNA polymorphisms when required.150

Telephone interviews were collected 

from 1,846 individuals in the FF study 

and from 4,959 individuals in the 

MMMF study. The fi nal sample includes 

1,503 monozygotic males, 1,085 dizygotic 

males, 1,078 monozygotic females, 

768 dizygotic females, and 2,371 dizygotic 

opposite-gender twin pairs.

Measures

Interviews for both the FF and MMMF 

studies were highly homologous. In the 

MMMF study, all common forms of tobacco 
270

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

self-administration (cigarettes, cigars, pipe 

tobacco, chewing tobacco, snuff) were 

assessed, whereas FF study participants 

were asked only about cigarettes. The focus 

was on tobacco initiation (TI), regular 

tobacco use (RTU), and items on the 

modifi ed version of the FTQ.8 TI was defi ned 

according to the responses to the questions 

“have you ever smoked cigarettes?” and the 

follow-up query “not even once?” RTU was 

defi ned as the use of an average of at least 

seven cigarettes per week for a minimum 

of four weeks. Individuals who met criteria 

for RTU were given the FTQ. This scale 

consists of eight items; three are scored on 

a two-point scale (number of cigarettes per 

day, inhale, nicotine level of cigarette brand) 

and fi ve on a one-point scale (fi rst cigarette 

soon after waking, diffi culty refraining 

when forbidden, smoking when ill in bed, 

smoking most in morning, fi rst cigarette 

most satisfying). The revised FTND110 scale 

includes only six items (inhale and nicotine 

level were dropped), with two other items 

scored on a three-point scale (number of 

cigarettes per day, fi rst cigarette soon after 

waking).110 It should be noted that the FTQ 

and FTND scales are not universally agreedupon defi nitions of nicotine dependence, 

and results obtained with other measures—

that is, DSM criteria108—could vary.

Methods

IRT models were used to estimate parameters 

that represent the “locations” of items on a 

latent continuum. The model describes the 

probability of a discrete response to an item 

as a function of a person parameter (their 

location on the latent trait) and one or more 

item parameters. In the two-parameter case, 

one parameter represents the location, and in 

the case of attainment testing, is referred 

to as the “item diffi culty.” The second 

parameter estimates the discrimination of 

the item—that is, the degree to which the 

item distinguishes between persons who 

have different scores on the latent trait. This 

second parameter characterizes the slope of 

the item characteristic curve. The diffi culty 

parameter relates to the location of the 

curve on the continuum. These models 

can be extended to data on pairs of twins, 

and the trait variance can be partitioned 

into sources due to additive genetic, shared 

environmental, and specifi c environmental 

factors. The parameterization of these 

models is similar to that of the common 

pathway/latent phenotype model,20 which 

allows for variance partitioning at two 

levels: (1) the latent trait—that is, nicotine 

dependence, and (2) the residual item 

variances. The parameters of the genetic 

IRT model thus include item discrimination 

parameters (which correspond to factor 

loadings), item diffi culties (which 

correspond to thresholds), and genetic and 

environmental parameters of the items and 

construct. As is typical for twin analyses, 

factor loadings are constrained to be the 

same for monozygotic and dizygotic twins. 

This assumption seems reasonable because 

it is unlikely that zygosity has a main effect 

on the measurement of the latent trait; 

however, it could be evaluated empirically 

by testing for measurement invariance of 

factor loadings as a function of zygosity. 

In the present analysis, item thresholds were 

also constrained to be equal across zygosity. 

Again, this assumption could be relaxed 

to test for possible sibling interaction, 

which results in differences in thresholds 

by zygosity.151 All analyses were performed 

using the Mx statistical modeling package;14

Mx scripts are available on the Mx website.152 

Note that for identifi cation purposes, the 

variance of the factor was fi xed to one 

(but allowed to differ as a function of the 

covariates) and an estimate was made of 

all factor loadings rather than arbitrarily 

fi xing one factor loading to one. This has 

implications for the choice of model testing 

for measurement invariance.

Results

The twin sample contained 6,805 individuals; 

55% were male and 44% were female. 
271

Monograph 20. Phenotypes and Endophenotypes

The mean age was 36.2 (standard deviation 

8.6) years with a range of 20.4–59.5 years. 

Overall, 78% reported lifetime TI, and 54% 

had smoked regularly and thus completed 

the FTQ. Consistent with previous 

analyses, which included TI and RTU when 

estimating the contributions of genetic 

and environmental factors to nicotine 

dependence in the CCC pathway model, 

TI and RTU were included together with 

the eight FTQ items, assuming neither 

independence nor unidimensionality of 

TI and nicotine dependence. Results were 

compared by using the traditional eight FTQ 

items, allowing for multiple thresholds as 

necessary, and the revised six FTND items. 

Results showed that factor loadings were 

consistently higher when including TI and 

RTU compared to those from analyses that 

(1) included TI alone and (2) included neither 

of the conditional variables (fi gure 6.6A). 

Similarly, prevalences were consistently 

lower when including TI and RTU, properly 

adjusting these parameters for the fact that 

only a selected sample was given the FTQ 

(fi gure 6.6B). Of note is that the genetic and 

environmental parameters were biased when 

not taking TI and RTU into account. These 

fi ndings were observed for males and females.

When comparing results from analyses 

including the FTQ scoring with those 

including only the six FTND items, the two 

items not included in the FTND showed 

a different pattern of factor loadings 

and prevalences than the other items 

(fi gures 6.7A and 6.7B). The inhale item 

showed very high prevalence, resulting in 

little variance; the high-nicotine-level item 

exhibited the lowest factor loadings and 

clear difference in prevalence by gender. 

Therefore, the presentation of the results of 

the genetic analyses with IT, RTU, and the 

six FTND items is limited here, although 

the results using the full FTQ items did not 

differ substantially.

A strict order of model testing was followed 

and measurement properties were evaluated 

before testing alternative genetic models. 

One of the common hypotheses to test 

with twin data for females and males is 

whether the contributions of genes and 

environment are the same (in magnitude 

and nature) between both genders. However, 

if differences exist in the assessment of the 

phenotype in the two genders, then false 

conclusions may be drawn from genetic 

analyses if these measurement differences 

are not taken into account.4

 For example, 

one might conclude that the heritability 

for the latent phenotype of interest is 

signifi cantly greater in females than in 

males, when in fact there are signifi cant 

gender differences in the factor loadings 

and/or thresholds, but not in the sources 

of individual differences. Accordingly, 

a series of homogeneity and heterogeneity 

models were fi tted to evaluate the degree 

of measurement invariance (see Neale 

and Cardon20 for a detailed description of 

heterogeneity models).

Homogeneity models assume that the 

contributions of genes and environment 

to the variance (both at the level of the 

factor, and at the item level, that is, residual 

variances) are equal for both genders. 

First, a measurement invariant model was 

used in which factor mean and variance, 

factor loadings, and thresholds were the 

same by gender and age. Then the factor 

mean and/or factor variance were tested for 

difference by gender and age (given the large 

age distribution of the sample) and their 

interaction. Further testing was conducted 

to determine signifi cant effects of the 

covariates on the factor loadings in addition 

to the factor mean or on the item thresholds 

in addition to the factor variance. Given that 

one estimates all factor loadings and fi xes 

the factor variance, one cannot at this stage 

estimate all factor loadings in addition to the 

factor variance. Finally, the most saturated 

measurement model was fi tted allowing for 

covariate effects on both the item thresholds 

and factor loadings. This series of tests was 

then repeated for heterogeneity models, 
272

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

which allow the magnitude of the genetic 

and environmental contributions to differ 

by gender. A comparison of the two series 

of models provides a gender heterogeneity 

test for the role of genes and environment. 

Table 6.2 presents selected results from these 

genetic analyses of nicotine dependence.

When fi tting the homogeneity models 

(columns 2–4) to the adult nicotinedependence data, signifi cant effects were 

found of gender and age on both the factor 

mean and factor variance (models 2 and 3). 

Differences by gender and age were then 

tested at the item level—that is, differences 

Figure 6.6 Estimates of Factor Loadings (A) and Thresholds (B) of Nicotine-Dependence 

Items in Female Twins from the Virginia Twin Registry

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Factor loadings

0.1

High

nicotine

Regular Initiation Cigarettes Waking Refrain Illness Morning First best Inhale

smoking

Nicotine-dependence items

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Prevalence rates

0.1

High

nicotine

Regular Initiation Cigarettes Waking Refrain Illness Morning First best Inhale

smoking

Nicotine-dependence items

FTQ_f IRFTQ_f IFTQ_f

B.

A.

Note. Separate lines depict the effect of including tobacco initiation (TI) and regular tobacco use (RTU) items. FTQ = Fagerström 

Tolerance Questionnaire; f = female; IFTQ = FTQ items plus initiation; IRFTQ = FTQ items plus initiation and regular tobacco use.
273

Monograph 20. Phenotypes and Endophenotypes

in thresholds and factor loadings. Results 

indicated that thresholds were signifi cantly 

different by age (model 4) and gender 

(model 5). Furthermore, factor loadings 

differed signifi cantly by age (model 6) and 

between males and females (model 7). When 

testing was conducted for measurement 

invariance of the factor loadings allowing for 

differences in thresholds by gender, age and 

their interaction, only gender differences in 

factor loadings were found to be signifi cant 

(model 9). Similar results were obtained 

when heterogeneity models were fi tted 

(columns 5–7). The gender heterogeneity 

Figure 6.7 Estimates of Factor Loadings (A) and Thresholds (B) of Nicotine-Dependence 

Items Plotted by Gender and Measurement Instrument (FTQ or FTND Scale)

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Factor loadings

0.1

High

nicotine

Regular Initiation Cigarettes Waking Refrain Illness Morning First best Inhale

smoking

Nicotine-dependence items

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Prevalence rates

0.1

High

nicotine

Regular Initiation Cigarettes Waking Refrain Illness Morning First best Inhale

smoking

Nicotine-dependence items

IRFTQ_f IRFTND_f IRFTND_m IRFTQ_m

A.

B.

Note. FTQ = Fagerström Tolerance Questionnaire; FTND = Fagerström Test for Nicotine Dependence; IRFTQ = FTQ items plus 

initiation and regular tobacco use; f = female; IRFTND = FTND items plus initiation and regular tobacco use; m = male.
274

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

tests (last three columns, 8–10) compare 

the corresponding homogeneity and 

heterogeneity models. For the measurement 

invariant models (model 1), as well as 

for models with limited measurement 

variance—that is, age variant and gender 

invariant (models 2, 4, and 6)—the gender 

heterogeneity tests are signifi cant, 

suggesting that the contributions of genes 

and environment to the factor and the items 

differ signifi cantly. However, when allowing 

for gender differences at the measurement 

level (thresholds and factor loadings), 

the combined genetic and environmental 

parameters—that is, at the factor and item 

level—did not differ signifi cantly between 

males and females, resulting in homogeneity 

model 9 as the best fi tting model (by the 

Akaike Information Criterion [AIC]).

A further exploration was made of whether 

the difference in fi t between homogeneity 

and heterogeneity models 9, although not 

signifi cant, was explained by differences 

in the genetic and environmental 

contributions to the factor variance or 

to the residual item variances. A model 

allowing for different magnitudes of genetic 

and environmental contributions to the 

latent construct (but equating genetic and 

environmental parameters at the item level 

between the genders) (model 10) further 

signifi cantly improved the overall fi t of 

the model over model 9 and resulted in a 

lower AIC. The converse—different variance 

components at the item level but not at 

the factor level (model 11)—did not result 

in improvement of fi t over model 9. Thus, 

the overall conclusion is that signifi cant 

gender differences exist at the measurement 

level (both thresholds and factor loadings). 

If one is prepared to assume that the same 

factors are operating in males and females 

of different ages, and that the measurement 

Table 6.2 Results from Fitting Measurement Noninvariance and Gender Heterogeneity Models 

to Nicotine Initiation and Dependence Data Collected from Twins

Homogeneity models Heterogeneity models Gender heterogeneity test

–2LL ep AIC –2LL ep AIC Dv2 df p

1. Invariance 52474.92 38 -24729.1 52436.76 57 -24729.2 38.16 18 0.00

Factor mean and factor variance

2. Age 52075.79 42 -25120.2 52039.78 61 -25118.2 36.01 18 0.01

3. Gender 52075.19 42 -25120.8 52044.87 61 -25113.1 30.33 18 0.05

Item thresholds and factor variance

4. Age 52259.44 49 -24922.6 52222.72 68 -24921.3 36.72 18 0.01

5. Gender 51976.54 49 -25205.5 51952.14 68 -25191.9 24.40 18 0.14

Factor mean and factor loadings 

6. Age 52046.74 49 -25135.3 52014.43 68 -25129.6 32.31 18 0.02

7. Gender 52017.26 49 -25164.7 51989.19 68 -25154.8 28.07 18 0.06

Item thresholds and factor loadings

8. Age 51765.04 70 -25375.0 51747.36 89 -25354.6 17.68 18 0.48

9. Gender 51742.11 70 -25397.9 51724.14 89 -25377.9 17.97 18 0.46

Submodels of model 9

10. GE variance 

of factor 

51732.84 73 -25401.2 9.27 2 0.01

11. GE variance 

of items

51733.48 86 -25374.5 8.63 16 0.93

Note. –2LL = minus twice the log-likelihood of the data; ep = number of estimated parameters; AIC = Akaike Information Criterion; 

Dv2 = difference chi-square statistic; df = degrees of freedom; p = probability; GE = genetic and environmental.
275

Monograph 20. Phenotypes and Endophenotypes

noninvariance is due to differential 

sensitivity of certain items, then it would 

appear that the genetic and environmental 

factors have different magnitudes of effect 

at the factor level, but not at the item level. 

Age also has a signifi cant effect on the 

thresholds but not on the factor loadings. 

If these differences in measurement 

had been ignored, it would have been 

wrongly concluded that the genetic and 

environmental contributions were different 

for males and females not only at the factor 

level but also at the item level.

The information about the contributions of 

the individual items to the latent construct 

of nicotine dependence is best viewed using 

ICCs in which the slope of the curve refl ects 

the factor loading or indicates how well the 

item discriminates people who have nicotine 

dependence from those who have not. 

The threshold corresponds to the point of 

infl ection of the curve that marks the level 

at which individuals have a 50% chance 

of endorsing the item and relates to the 

endorsement frequency of the items. 

Thus, the higher the factor loading, the 

steeper the curve; the higher the threshold, 

the more to the right of the underlying 

liability distribution is the curve. As these 

measurement parameters may be moderated 

by gender and age, the curves will depend 

on the particular values of the covariates. 

Figure 6.8 shows the ICCs for 20-year-old 

females.

The curves have fairly good coverage in 

that from –2 to 3 SDs there is likely to be 

variation in the response patterns. Below 

–2 SDs, almost all respondents would likely 

Figure 6.8 Estimates of Nicotine-Dependence Item Characteristic Curves for 20-Year-Old 

Females

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Item response probability

0.1

3.0

Liability to nicotine dependence

-3.0 -2.6 -2.2 -1.8 -1.4 -1.0 -0.6 -0.2 0.2 0.6 1.0 1.4 1.8 2.2 2.6

Initiation

Regular smoking

Cigarettes

Waking

Illness

First best

Refrain

Morning

Note. The initiation and regular smoking items are farthest on the left, refl ecting that more people initiate smoking and become 

regular smokers than exhibit dependence symptoms. The least endorsed items are “smoking when ill” and “smoking most in the 

morning.” “Smoking when ill” is also one of the most discriminating items, together with “fi rst cigarette soon after waking” and 

“number of cigarettes per day.”
276

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

respond in the lowest category on all items. 

At +3 SDs, responses would be almost all 

in the highest response category, although 

some 15% may be expected not to do so 

for the “fi rst best” and “morning” items. 

Because of its relatively fl at slope, the “fi rst 

best” item would also be most likely to be 

responded to positively at the low end of the 

scale. In a sum score approach, this item 

would therefore perform inconsistently and 

might be considered for deletion. 

When curves were compared by the level 

of the covariates, the ICCs for males are 

shifted to the left compared to those for 

females, refl ecting the more frequent 

endorsement of most of the items by males 

than by females. Similarly, curves for older 

individuals are shifted to the left of those 

of younger individuals. The slopes of the 

curves differ only by gender, and all but 

one (fi rst cigarette soon after waking) is 

steeper for males than females. The same 

information is gleaned from fi gures 6.9A and 

6.9B, which depict the factor loadings and 

thresholds, respectively.

Separate lines represent the different levels 

of the categorical covariates. For continuous 

covariates, such as age, estimates are 

shown for minimum and maximum of 

the range of the covariate in the sample. 

The remaining two panels of fi gure 6.9 

present the estimates of the genetic 

variance (heritability) of each of the items 

separately for the heritability through the 

common factor and the residual heritability. 

The heritability of the latent factor is also 

shown. Note that the latter was signifi cantly 

different in males and females, explaining, 

respectively, 80% and 58% of the variance. 

Thus, the heritability of the items resulting 

from the latent factor also differed by gender 

and refl ects the factor loadings of the items.

Interestingly, the pattern of genetic 

contributions to the residual variance 

of the items, independent of the latent 

nicotine-dependence factor, is quite distinct, 

with initiation and regular smoking 

exhibiting the largest genetic variance 

specifi c to them. This is consistent with 

previous results from fi tting CCC pathway 

models to these data, which suggested that 

the genetic factors for TI, RTU, and nicotine 

dependence were correlated, but not identical 

dimensions, and that specifi c genetic factors 

infl uence each stage of the smoking behavior 

continuum. Shared environmental factors 

contributed about 20% to the latent nicotinedependence factor in females but were 

negligible in males (not shown). They also 

accounted for zero to 8% of the residual item 

variances. Specifi c environmental factors 

explained about 20% of the factor variance 

in males and females, and between 18% and 

32% of the residual item variances, except 

for “smoking most in morning” and “fi rst 

cigarette most satisfying,” which accounted 

for about 65% of the variance.

Study Conclusions

This analysis has shown the importance 

of taking the assessment of nicotine 

dependence into account when estimating 

the role of genetic and environmental factors 

in the liability to nicotine dependence. 

When measurement invariance of nicotine 

initiation and dependence by age and 

gender was assumed, signifi cant gender 

heterogeneity was found in the contributions 

of genes and environment to both age and 

gender at the factor level and the item level. 

However, when measurement invariance 

was accounted for, the overall gender 

heterogeneity test was not signifi cant, 

suggesting no differences in the magnitude 

of genetic and environmental infl uences 

in males and females. Model fi t further 

improved when these infl uences were 

allowed to differ at the factor level but not 

the item level. One could argue that when 

measurement is not invariant by gender, 

the common factor is measuring something 

different, or at least the latent factor is 

measured on a different metric in males 

and females, which makes it diffi cult to 
277

Monograph 20. Phenotypes and Endophenotypes

interpret results of differences in heritability 

of the common factor. Although caution 

in interpretation is needed, it is argued 

that by allowing for limited measurement 

differences—due to differential sensitivity 

of items by gender—inferences about 

heterogeneity of heritability by gender at 

the latent factor become more meaningful. 

Figure 6.9 Estimates of Factor Loadings (A), Thresholds (B), Genetic Variance Components 

Due to the Factor (C), and Residual Item-Specifi c Genetic Variance Components 

(D) in Virginia Twin Registry Males

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Prevalence rates

0.1

Initiation Regular Cigarettes Waking Refrain Illness Morning First best

smoking

Nicotine-dependence items

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Factor loadings

0.1

Initiation Regular Cigarettes Waking Refrain Illness Morning First best

smoking

Nicotine-dependence items

20-year-old female 20-year-old male 60-year-old male 60-year-old female

Female Male

A.

B.
278

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

In the current application, factor loadings 

are slightly shifted upward in males versus 

females and prevalences are consistently 

higher in males and older individuals. 

In situations in which some items have 

substantially higher factor loadings and/or 

thresholds in one gender and other items 

in the other gender, comparing gender 

heterogeneity at the factor level may 

become problematic.

Second, it appears that an item response 

framework is to be preferred over a sum 

score approach in which differential item 

functioning would be obscured and each 

item weighted equally, regardless of their 

correlation with the latent construct 

to be measured. Finally, as was shown 

previously in using the CCC pathway model 

to estimate the heritability of nicotine 

dependence, and repeated in the current 

Figure 6.9 Estimates of Factor Loadings (A), Thresholds (B), Genetic Variance Components 

Due to the Factor (C), and Residual Item-Specifi c Genetic Variance Components 

(D) in Virginia Twin Registry Males (continued)

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Proportion of variance

0.1

Nicotine-dependence items

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

1.0

Proportion of variance

0.1

Initiation Regular Cigarettes Waking Refrain Illness Morning First best

smoking

Nicotine-dependence items

Regular Cigarettes Waking Refrain Illness Morning First best

smoking

Factor Initiation

Male

Female

Male/female

C.

D.
279

Monograph 20. Phenotypes and Endophenotypes

analysis, it is important to include smoking 

initiation and regular smoking to obtain 

unbiased estimates of the factor loadings 

and thresholds. Factor scores from such 

an analysis should provide a more accurate 

quantitative phenotype that will improve 

the ability to fi nd and replicate susceptibility 

genes for nicotine dependence.

Previous studies of the heritability of 

nicotine dependence reported estimates 

between .4 and .7, with little evidence 

for gender differences. The current 

analysis estimated heritability of nicotine 

dependence to be .8 in males and .6 in 

females. These estimates are signifi cantly 

different. Estimates of factor loadings 

and thresholds increased or decreased up 

to .2 units when measurement variance 

was allowed and conditional variables 

such as initiation and regular smoking 

were included. At this point, one can only 

speculate about whether this difference 

proves to be relevant in the search for 

specifi c genes or environments that 

infl uence smoking behavior and whether it 

might guide prevention efforts.

Limitations

Although the twin study is one of the 

most powerful designs to estimate the 

contributions of genetic and environmental 

factors to a phenotype of interest, several 

assumptions are made. One of the most 

often voiced criticisms of the classical twin 

study is the equal environments assumption, 

which states that the degree to which traitrelevant environments are shared is the 

same for monozygotic and dizygotic twins. 

In one of the few formal examinations 

of the validity of the equal environment 

assumption in twin studies, it appeared 

not to be violated for regular smoking.153

Some assumptions can be tested, that is, 

the random mating can be evaluated when 

data on spouses are available. A common 

way to include such data with the twin 

design is by extending it with their parents. 

The twin-parent design also allows one 

to disentangle genetic transmission from 

environmental transmission, which are 

confounded in nuclear family parentoffspring correlations. Other designs, 

however, such as adoption studies or COT 

designs may be more powerful to sort out 

transmission from parents to offspring.

Another limitation of studies is the vague 

assessment of the phenotype. Typically, 

smoking initiation is assessed with simple 

questions such as, “Have you ever smoked?” 

However, how initiation is operationalized 

for data analysis varies considerably, or 

the same question may be interpreted 

differently by younger and older individuals 

or vary by the status/stage of an individual’s 

smoking behavior. With regard to nicotine 

dependence, the FTND is widely used, 

although it is not considered the gold 

standard. Typically, a sum score is derived, 

although factor analyses have suggested 

that factor loadings vary considerably and 

that two factors might better account for 

the item correlations. There also appears 

to be limited overlap between the FTNDbased and DSM-based assessment of nicotine 

dependence.

Except for six studies of smoking behavior in 

adolescents, the majority of the research has 

focused on adult samples. It is likely incorrect 

to assume that the same results would be 

obtained with adolescent samples. Additional 

complications arise, however, when using 

data on adolescents in that adolescents have 

not passed through the main period of uptake 

of smoking behavior. Thus, the data are left 

censored and may require approaches based 

on survival analysis.

While most studies have included both 

males and females, this is not true for 

ethnicity, and no adolescent studies on 

non-Caucasian populations were found. 

Furthermore, the vast majority of studies 

on adults are based on Caucasian samples. 
280

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

Redden and Allison75 note that assortative 

mating can increase the risk of type 1 error 

in association studies; accordingly, this is 

a risk for association studies of nicotine 

initiation and dependence.

Summary

The main goal of this chapter is to provide a 

review of methods for, and applied analyses 

of, the genetic epidemiological study of 

nicotine dependence. A secondary aim is 

to demonstrate that data collected from 

relatives provide qualitatively different 

information, which can be used to overcome 

certain limitations of data collected from 

unrelated individuals. In the process, the 

ability to assess the relationship between 

initiation and dependence by estimating 

parameters of a factor model applied to data 

on nicotine initiation as well as the FTND, 

was exploited. In addition to providing 

the reader with a general impression 

of the genetic epidemiology of nicotine 

dependence, this review has identifi ed a 

number of further opportunities for model 

development and data analysis.

Data from relatives do not merely provide 

a way to partition variation into genetic 

and environmental components. Especially 

important for the study of tobacco use, 

abuse, and dependence is the potential to 

examine the association between initiation 

and subsequent progression. The proxy 

information gleaned from comparing the 

rate of progression in pairs of relatives 

who are concordant for initiation to 

the rate in those who are discordant for 

initiation allows a number of hypotheses 

and assumptions to be tested. At the most 

basic level, one can assess measurement 

invariance assumptions, which address 

whether dependence items perform equally 

well at measuring the latent trait of nicotine 

dependence in males and females and at 

different ages. To some extent, this is a sine 

qua non of epidemiological research into 

complex behavioral traits. In the absence 

of measurement invariance tests, one 

cannot draw unambiguous conclusions 

about development, trajectories, or even 

the effi cacy of treatments.

Conclusions

1. Data from twin studies suggest that 

shared environmental factors are 

the predominant source of familial 

resemblance in liability to smoking 

initiation in young adolescents, while 

additive genetic factors appear more 

important in older adolescents.

2. Results from extended twin designs 

show that signifi cant assortative mating 

exists for smoking initiation and that the 

parent-child correlations can be almost 

entirely accounted for by genetic factors. 

This implies a limited environmental 

infl uence of parental smoking initiation 

on smoking initiation in their children.

3. In contrast to the signifi cant role of 

shared environmental factors in smoking 

initiation, the liability to smoking 

persistence and nicotine dependence 

appears to be primarily accounted for by 

additive genetic factors. Furthermore, 

the liabilities to initiation and 

progression appear to be substantially 

correlated. Molecular genetic studies 

may be expected to fi nd some genetic 

variants that contribute specifi cally to 

initiation—some that are specifi c to 

dependence and some that contribute 

to both.

4. Future development and applications of 

genetic latent growth curve models and 

genetic latent class models promise to 

improve the understanding of the role 

of genes and environment in smoking 

trajectories and transitions from 

nonsmoker to smoking dependence.

5. The search for susceptibility loci for 

smoking-related traits, either through 
281

Monograph 20. Phenotypes and Endophenotypes

linkage or association studies, has not 

identifi ed any convincing replicated 

fi ndings. However, several genomic 

regions and several candidate genes have 

been found to be associated with smoking 

behavior in more than one study.

6. Improving the assessment of nicotine 

initiation and dependence by allowing 

for differences in measurement by age 

and gender and taking conditionality 

into account might provide more 

accurate estimates of the contributions 

of genes and environment to different 

stages of smoking.

7. Meta-analyses or mega-analyses of 

studies of smoking phenotypes—both 

genetic epidemiological and molecular 

genetic—should prove useful in 

summarizing the available data and 

results. Possibly, certain data sets may 

produce results that are outliers, and 

controlling for their effects would permit 

fi ner resolution between hypotheses and 

more accurate parameter estimates.
282

6. Genetic Modeling of Tobacco Use Behavior and Trajectories

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289

The relationship between developmental trajectories of smoking and other substance use 

may provide clues for a genetic vulnerability to nicotine dependence, which, in turn, may 

inform smoking phenotypes for genetic analysis. This chapter examines the evidence 

base for linkages between substance-use trajectories as well as the results of an original 

empirical study examining smoking and alcohol use over time across a cohort group of 

male twins from Finland. Areas discussed include

■ Common versus specifi c liability to substance-use disorders

■ Covariate relationships between smoking and other substance-abuse trajectories

■ Conjoint trajectories of smoking and other substances, including alcohol, 

marijuana, and polysubstance use

Available evidence points to the possible existence of general underlying factors for 

substance-use and tobacco-specifi c pathways, both of which may link to genetic 

phenotypes. Moreover, the cohort study examined in this chapter supports the 

existence of heritable genetic traits for general substance-abuse trajectories on the 

basis of comparisons between monozygotic and dizygotic twins. The link between such 

trajectories and a genetic basis for nicotine dependence remains an area for further study.

7

Trajectories of Tobacco Use 

from Adolescence to Adulthood: 

Are the Most Informative Phenotypes 

Tobacco Specifi c?

Kristina M. Jackson, Kenneth J. Sher, Richard J. Rose, and Jaakko Kaprio

The analyses described herein were supported by National Institute of Health grants AA017242, AA11998, AA13938, 

and AA13987. The data collection from Finn Twin16-25 was supported by National Institute of Health grants AA00145, 

AA08315, AA12502, and by the Academy of Finland Centre of Excellence on Complex Disease Genetics.
290

7. Trajectories of Tobacco Use from Adolescence to Adulthood

Introduction

As described in chapter 5 of this volume, 

an unresolved question is whether phenotypic 

information is best conceptualized as 

tobacco specifi c or as describing a broader 

spectrum of substance use or disinhibition. 

Accordingly, the goal of this chapter is to 

examine the utility of considering joint 

trajectories of tobacco and other substance 

use, drawing on trajectories of tobacco 

and alcohol use as an empirical example. 

Moreover, as discussed in chapters 5 and 

6, almost no empirical work exists on the 

heritability of these trajectories. After a 

review of the literature, joint trajectories of 

alcohol and tobacco use with a genetically 

informative (twin) sample are characterized, 

and the extent to which these trajectories 

overlap across substance is described. 

Findings from the empirical example will 

have implications regarding the extent to 

which trajectories are unique to tobacco 

or whether they can be conceptualized as 

general pathways of substance use.

Resolving the question of tobacco-specifi c 

phenotypes versus general substance use is 

critical for identifying key etiological and 

maintaining processes, developing theorybased prevention programs, and allocating 

resources for prevention activities. Broad, 

substance-general phenotypes could refl ect 

underlying shared individual vulnerability 

factors (e.g., affective dysregulation, impaired 

self-control, reward seeking, conventionality) 

or common environmental infl uences (peer 

affi liation, substance availability) on use. 

Individual and environmental factors might 

act alone or may operate in combination 

for promoting or inhibiting multiple forms 

of substance use. In contrast, tobaccospecifi c phenotypes could refl ect individual 

differences in sensitivity to the rewarding and 

punishing effects of nicotine (and associated 

variables inherent in smoking) alone and 

in interaction with cultural variables and 

tobacco control and prevention policies.

Importance of Studying 

Substance-Use 

Comorbidity

A wealth of literature supports the high 

concurrent use of nicotine with other 

substances. This is particularly true for 

cigarette smoking and alcohol use,1–4 but also 

for use of tobacco with marijuana and other 

drugs.5–7 During adolescence, smoking is 

highly associated with use of other substances 

such as alcohol, marijuana, and other 

drugs,8–17 and tobacco use often precedes both 

alcohol-use18 and substance-use disorders, 

including alcohol dependence.19

By using a nationally representative sample, 

onset and persistence of drinking and 

smoking in adolescence were each predicted 

by prior use of the other substance.20 This 

smoking-drinking association persists into 

emerging adulthood.21–23 One nationally 

representative college student sample revealed 

that over 98% of smokers reported prior-year 

drinking, and those who initiated regular 

smoking at an early age were at greatest 

risk for drinking. Likewise, current smoking 

and regular smoking were overrepresented 

among those who drank, particularly at high 

or risky levels.24 Compared with nonsmokers, 

young adults with tobacco dependence and 

nondependent smokers had increased odds 

of being diagnosed with an alcohol or illicit 

drug disorder.25

The health consequences of tobacco use 

in conjunction with other substance use 

can be severe. Concurrent use of tobacco 

and alcohol acts synergistically to produce 

greater health risks than expected from 

the additive effects of each substance,2

including elevated rates of esophageal,26,27

laryngeal,28–30 and oral cancers.29,31,32

Although the case for considering 

tobacco use in conjunction with other 

substance use for estimating health risks 
291

Monograph 20. Phenotypes and Endophenotypes

(i.e., consequences) is now well established, 

consideration of tobacco use in the context 

of other substance use may be just as 

important for understanding etiological 

processes. Extant research has suggested 

several possible mechanisms underlying 

substance-use comorbidity. Directional 

(perhaps causal) associations include crosstolerance and cueing as well as reciprocal 

antagonism; for example, individuals 

may use nicotine to counteract alcohol’s 

debilitating effects on cognitive skills.33,34

Alternatively, a common-vulnerability 

model suggests that different substances 

share important third-variable precursors 

and hence are likely to co-occur. Using 

prospective data, Jackson and colleagues35

demonstrated that the prospective 

association between tobacco- and alcoholuse disorders could be explained by a general 

traitlike tendency to use both substances 

as opposed to directional associations 

between the two. Such underlying 

tendencies to use both substances appear 

Phenotypes Based on Comorbidity and Course

Comorbidity has traditionally been viewed as a cross-sectional phenomenon—that is, the existence 

of two or more conditions occurring at a single point in time (even when sequencing information 

is used to classify one condition as “primary” and the comorbid condition as “secondary.”a

Implicit in the traditional approach is that each comorbid condition is adequately characterized 

as a static entity. Subsequent data (described elsewhere in this chapter), however, emphasize the 

importance of the course of single disorders or conditions, suggesting that comorbidity should 

be viewed in the context of the longitudinal course of each co-occurring condition. Despite the 

surge of longitudinal research on comorbidity, however, “too little attention has been given to the 

implications of diagnostic course...both singly and across related disorders.”b(p.956)

Although the explicit diagnostic criteria sets introduced in the third edition of the Diagnostic and 

Statistical Manual of Mental Disorders and subsequent revisionsc,d,e represent a major advance in 

psychiatric phenotype defi nition by rejuvenating the Kraepelinian approach to diagnosis, these 

criteria represent only a partial embrace of a Kraepelinian approach that equally emphasized 

syndrome description by using specifi c behavioral indicators and longitudinal course.b

 To a large 

extent, formal diagnostic nosology has not kept up with either developmental theory or data that 

highlight the importance of considering both longitudinal course and co-occurring comorbidity 

as informative phenotypes. Corresponding (e.g., parallel) courses suggest similar developmental 

timing of use across substances. Developmental transitions such as change in living situation or 

attainment of traditional roles associated with career and family may exert common infl uence 

on use of different substances. Understanding the extent to which pattern of use of different 

substances overlap can provide the foundation for understanding factors contributing to 

substance use, abuse, and dependence and can suggest the existence of particular subtypes that 

may benefi t from targeted prevention or treatment efforts.

a

Schuckit, M. A., R. M. Anthenelli, K. K. Bucholz, V. M. Hesselbrock, and J. Tipp. 1995. The time course of 

development of alcohol-related problems in men and women. Journal of Studies on Alcohol 56 (2): 218–25.

b

Widiger, T. A., and L. A. Clark. 2000. Toward DSM-V and the classifi cation of psychopathology. Psychological 

Bulletin 126 (6): 946–63.

c

American Psychiatric Association. 1980. Diagnostic and Statistical Manual of Mental Disorders: DSM III.

3rd ed. Washington, DC: American Psychiatric Association.

d

American Psychiatric Association. 1987. Diagnostic and Statistical Manual of Mental Disorders: DSM-III-R.

3rd rev. ed. Washington, DC: American Psychiatric Association.

e

American Psychiatric Association. 1994. Diagnostic and Statistical Manual of Mental Disorders: DSM-IV.

4th ed. Washington, DC: American Psychiatric Association.
292

7. Trajectories of Tobacco Use from Adolescence to Adulthood

to be, at least partially, genetic in origin. 

Given the high statistical association 

between tobacco and alcohol use and the 

fi nding that there appears to be a shared, 

genetically transmitted vulnerability to use 

both substances, it is possible and perhaps 

even likely that the most informative 

smoking phenotypes for genetic analysis 

will be those that simultaneously consider 

smoking and other substance use as 

associated features.

Common Versus 

Specifi c Liability 

to Substance-Use 

Disorders

Models Supporting a Common 

Underlying Substance-Use Factor

An infl uential model of substance 

use—the gateway theory—suggests that 

use of tobacco, alcohol, and drugs follows a 

progressive sequence of involvement from 

licit to illicit substances. In general, the 

sequence starts with use of alcohol and/or 

tobacco products, followed by marijuana 

use, proceeding to other illicit drug use 

(see Kandel36 for a review). Thus, the idea that 

tobacco use is comorbid with use of other 

substances is refi ned whereby tobacco serves 

as a “gateway” to use of other drugs; that is, 

smoking is necessary but not suffi cient for 

subsequent substance use. This sequencing 

is robust to gender, ethnicity/culture, and 

age of initiation, and has been demonstrated 

in numerous cross-sectional and prospective 

analytic approaches, including prevention 

trials.36 Research demonstrating that early 

smoking18,37–39 leads to subsequent alcohol 

and drug use also supports the gateway 

theory. However, some research has led to 

conclusions that a common-factor model 

based on propensity and opportunity to use 

substances serves as a more parsimonious 

alternative.40

In contrast to the gateway theory, a body 

of research suggests that the associations 

among smoking, drinking, and marijuana 

and other substance use are a function 

of a common factor of substance-use 

vulnerability. This idea has received 

attention from various camps, initially 

by Jessor and Jessor in their problem 

behavior theory (PBT).41 PBT conceptualizes 

substance use as one of a number of 

behaviors (also including delinquency 

and precocious sexual activity) associated 

with a deviant lifestyle in rejection of the 

conventional values of society. This theory 

stands up to replication using different 

samples42,43 and long-term follow-up.44

Applications of this theory show robust 

support for PBT.45,46

A number of researchers have added to the 

evidence that a common factor underlies 

substance use and other problem behaviors. 

A body of studies by Krueger and colleagues 

using both quantitative47,48 and behaviorgenetic48,49 approaches support a common 

externalizing dimension underlying 

substance dependence, antisocial behaviors, 

and a disinhibited personality. This work, 

however, only considers alcohol, marijuana, 

and other drug dependence; information 

regarding the degree to which smoking 

loads on a common externalizing factor is 

lacking. Consistent with the work by these 

authors, McGue and colleagues suggest 

that a common trait of disinhibition 

underlies use of alcohol, tobacco, and 

illicit drugs as well as other problem 

behaviors.50,51 Supporting this idea, indices 

of behavioral undercontrol (e.g., constraint, 

novelty seeking, psychoticism,52,53 as well 

as conduct disorder and attention defi cit 

hyperactivity disorder [ADHD]54,55 increase 

the risk of alcohol, tobacco, and marijuana 

use in adolescents. King and colleagues56

demonstrated prospective relationships 

between childhood externalizing disorders 

(conduct disorder, oppositional defi ant 

disorder, ADHD), internalizing disorders 

(major depressive disorder, and for girls 
293

Monograph 20. Phenotypes and Endophenotypes

only, overanxious disorder and separation 

anxiety disorder), and substance use in early 

adolescence. Externalizing psychopathology 

predicted having tried alcohol, nicotine, 

and marijuana by 14 years of age as well 

as regular and advanced experience with 

these substances. Internalizing disorders 

showed much weaker effects, with only 

major depression at 11 years of age 

showing a signifi cant relationship with 

substance use at 14 years of age. Hence, 

a large and growing body of empirical 

literature implicates the existence of general 

mechanisms linking adolescent problem 

behavior and disinhibitory psychopathology 

in adulthood.

Further, Lynskey and colleagues57 presented 

evidence that much of the association 

between smoking, drinking, and marijuana 

use in adolescence could be explained by a 

factor representing individual vulnerability 

to substance use. Newcomb and colleagues58

demonstrated that alcohol, marijuana, and 

other drug use are indicators of a common 

substance-use factor, and the infl uence 

of risk factors on use of these substances 

is mediated through the common factor. 

This factor was also evident across different 

developmental stages.59 In addition, 

Vanyukov and colleagues60 showed that a 

substantial proportion of the variance in 

liability to use substances is shared between 

substances.

Finally, the idea of a common substanceuse factor is supported by models of 

substance-use behavior that have been 

shown to generalize across different types 

of substances. These include Petraitis and 

colleagues’61 integrative theory of triadic 

infl uence (see also Flay and Petraitis62) and 

the social development model of Catalano, 

Hawkins, and colleagues63,64 as well as West’s65

synthesis of different models of addiction.

However, some research has failed to 

identify a common factor that underlies 

substance use and other problem 

behaviors.66,67 Willoughby and colleagues68

identifi ed a substance-use factor distinct 

from other problem behaviors such as 

delinquency and aggression. Likewise, White 

and Labouvie69 showed in a community 

sample that substance use (including 

alcohol, tobacco, and illicit drug use) and 

delinquency represented two dimensions of 

problem behavior. In addition, in contrast 

to work suggesting a common underlying 

substance-use factor, several studies suggest 

that use of different types of substances 

may be better represented by separate 

(but correlated) factors. These studies 

demonstrate that use of alcohol loads on 

a factor separate from smoking70 or drug 

use,71,72 although Zhang and colleagues72

demonstrated that drinking, drug use, and 

delinquency loaded strongly on a higherorder deviance factor. Likewise, Dembo 

and colleagues73 noted some specifi city of 

alcohol use beyond a general deviance factor 

that included marijuana use (as well as 

delinquency).

Osgood and colleagues74 proposed that 

associations between various deviant 

behaviors can be attributed to general 

deviance during adolescence at a point 

at which behaviors such as substance 

use and sexual activity are much less 

normative; however, as youth age, behaviors 

become more acceptable and show greater 

specifi city. The fi nding by Resnicow and 

colleagues75 that substance use loaded 

on the same factor as carrying a weapon 

and stealing, but not more normative 

school problems and (low) positive 

behaviors, supports this notion. White76

noted that certain problem behaviors 

cluster at different developmental periods. 

White suggested that youth experiment 

with different problem behaviors across 

adolescence but that many of these 

behaviors do not become established 

behavior patterns. In summary, there 

appears to be a strong general factor 

indicating susceptibility to varied forms 

of substance use, but there remains 
294

7. Trajectories of Tobacco Use from Adolescence to Adulthood

considerable substance-specifi c residual 

vulnerability, and evidence for overlap with 

other problem behaviors is more limited.

Shared Genetic Risk

Consistent with the phenotypic work 

supporting a common general dimension 

of substance use and evidence for common 

correlates across substances, a good portion 

of genetic risk for substance-use disorders 

is carried through one major common 

factor.77 Twin data provide ample evidence 

for a general underlying genetic risk factor 

that increases liability to use tobacco and 

alcohol, including measures of alcohol 

volume,78–81 intoxication,82 and alcohol 

dependence.83 Comparable fi ndings have 

been shown for tobacco dependence and 

alcohol dependence84,85 (also see Volk and 

colleagues86 for evidence of specifi city of 

genetic effects). Extending this work further, 

there is evidence for a common genetic 

infl uence mediating concurrent tobacco, 

alcohol, and marijuana use87 and problem 

use,88 with tobacco and marijuana showing 

the strongest genetic overlap. In a study 

by Yoon and colleagues,89 P3 amplitude, 

shown to be highly heritable in adolescent 

boys, is associated with various indices of 

use of different substances (e.g., early use, 

frequency of use, maximum use across 

cigarettes, alcohol, and illicit drugs). This 

fi nding extends the work demonstrating that 

a common genetic vulnerability underlies 

substance use in general.

Also consistent with the problem behavior 

theory, although not directly relevant 

to understanding smoking phenotypes, 

some studies have documented common 

genetic factors underlying alcohol and drug 

dependence.49,90–92 In general, Hettema and 

colleagues79 noted that common genetic 

liability may be attributable to variation 

in genetically infl uenced personality traits 

(e.g., sensation seeking) or variation in 

biological substrates, which may include 

genetic infl uences on variation in the reward 

system.93 Consistent with evidence for a 

common genetic infl uence, genetically 

informed family studies also show familial 

transmission of smoking, alcohol use, 

marijuana use, and illicit drug use.94–96

Approaches to Research 

on Substance-Use Prevention

Evidence of a common externalizing factor 

suggests the design of relatively generic 

prevention strategies that target multiple 

problem behaviors.51 Numerous studies 

that have adopted a general approach in 

prevention of adolescent substance use 

show evidence of reduced use of alcohol, 

tobacco, and marijuana across multiple 

community, school-based, and highrisk populations97–103 and reduced use of 

illicit drugs;104 however, see Brown and 

colleagues105 for failure to detect program 

effects specifi cally for smoking.

However, it is important to note that 

several macro-level environmental factors 

can infl uence the availability and social 

acceptability of specifi c substances. Local 

and cultural social norms can differ across 

substances. For example, for the last several 

decades in the United States, the college 

environment has promoted heavy episodic 

drinking but not regular smoking as a 

normative behavior.106 Moreover, formal 

alcohol and tobacco prevention and control 

policies (e.g., taxation, minimum age laws, 

advertising bans) can be applied to both 

substances in a roughly equal manner 

or differentially, and the nature of this 

balance presumably has implications for 

overall comorbidity and the relative degree 

of common versus unique environmental 

infl uence across substances. Other 

substance-control policies for tobacco 

use (e.g., smoking bans; see Hopkins and 

colleagues107) and alcohol use (e.g., social 

host and dram shop liability laws, zoning 

of outlets; see Wagenaar and colleagues108) 

are substance specifi c and presumably 

unique. This larger environmental context 
295

Monograph 20. Phenotypes and Endophenotypes

highlights the importance of considering 

a range of environmental variables that 

might condition both manifest comorbidity 

and the relative contribution of genes, 

environments, and their interaction within 

a given population.

Review of Trajectory 

Literature

Chapter 5 of this volume reviews the 

literature on smoking trajectories; 

consequently, this literature is summarized 

here only to the extent that it is relevant to 

this discussion. First, the large and growing 

literature on trajectories of alcohol and 

marijuana/drug use is described in greater 

detail. Then, literature characterizing the 

associations between trajectories of one 

substance and use of other substances is 

reviewed, considering associations with 

time-invariant substance use as well as 

course of co-occurring substance use.

Trajectories of Alcohol 

Involvement

Consistent with theoretical work on 

course of alcohol involvement109–111 and 

complementing a wide body of subtyping 

literature in the alcohol fi eld,112 a large 

number of studies have characterized 

the developmental course of drinking 

over adolescence and young adulthood. 

Although results with respect to the specifi c 

characterization of course and associated 

prevalences vary somewhat from study 

to study, investigations are consistent in 

identifying four broad classes that vary in 

age of onset, magnitude and direction of 

slope, and severity of use: a nonuser/stable 

low-user course, a chronic high-use course, 

a decreasing course, and an escalating 

course. Not unexpectedly, trajectories 

derived from adolescent samples tend 

to detect courses typifi ed by escalation, 

whereas samples that include young adults 

reveal decreasing courses. For example, 

studies that follow adolescent drinking often 

show two groups of escalators that differ 

in age of onset and slope.113–116 In contrast, 

studies examining drinking in young 

adult samples are more likely to detect a 

decreasing or “developmentally limited” 

course.117–119 Some studies assessing a 

sample across the developmental transition 

from adolescence to young adulthood 

also identify a “fl ing” or “time-limited” 

trajectory,118,120,121 which, in studies using 

a younger or older sample, may manifest 

itself as an increasing or a developmentally 

limited course.

Although most of these studies have focused 

on a broad developmental span covering a 

number of years, a few studies have explored 

alcohol involvement over the course of a 

single year122–124 to resolve more-fi ne-grained 

changes in drinking behavior over shorter 

intervals. These fi ne-grained studies identify 

patterns of use primarily characterized by 

slope (e.g., stable behavior versus behavior 

that escalates or declines over time).

Drinking course has been defi ned along 

indices of alcohol involvement such as 

heavy/binge drinking,113,115,116,118–121,125

quantity/frequency,114,126,127 problem 

drinking,128,129 alcohol dependence,130 or a 

composite of drinking items.131 Subsequent 

work examined congruence of trajectories 

across different indices of alcohol 

involvement (alcohol-use disorder, alcohol 

dependence, alcohol consequences, heavy 

drinking, and alcohol quantity/frequency117). 

Consistent with the existing body of 

literature, there was similarity in trajectory 

shapes (i.e., courses) across diverse indices, 

although predicted prevalences varied 

across measure.

Trajectories of Marijuana 

Involvement

Far fewer studies have characterized 

developmental course for frequency of 
296

7. Trajectories of Tobacco Use from Adolescence to Adulthood

marijuana use,132–136 but fi ndings have 

generally been consistent with regard to 

course shape and prevalence, with the 

majority of individuals being classifi ed as 

abstainers/nonusers (ranging from 41% to 

82%, with higher rates among adolescent 

samples). All studies identifi ed a chronic 

high group marked by early onset and 

heavy use. A later-onset, escalating course 

was observed in three of the fi ve studies. 

Not surprisingly, this group was the largest 

in the study with the longest time frame 

(covering ages 13–23 years). Additional 

groups were marked either by moderate 

occasional use or by reduced use.

Trajectories of Polysubstance 

Use

Some researchers assume but do not 

explicitly test comparability of substanceuse course by using indices based on 

a composite of multiple substances at 

the point of trajectory identifi cation. 

Using a large adolescent sample, Wills 

and colleagues137 classifi ed substanceuse trajectories by using a composite of 

frequency of alcohol use, heavy alcohol use, 

tobacco use, and marijuana use. Although 

one-half of the sample consisted of nonusers, 

there were sizable subgroups characterized 

by experimentation and varying degrees of 

escalation. Clark and colleagues138 identifi ed 

course of substance-use-disorder symptoms 

based on retrospective report by using a 

sample of young adult males diagnosed 

with a substance-use disorder. Trajectories 

of substance-use problems varied across 

severity and onset age, with groups ranging 

from early-onset, severe, to improved 

(decrease), to mild or minimal problems. 

In addition, on the basis of indices of onset 

and intensity, Labouvie and White139 detected 

three substance-specifi c trajectories (heavy 

smoking, heavy alcohol use, and heavy 

drug use) as well as a common-substance, 

adolescent-limited course, suggesting both 

specifi city and commonality across courses 

of different types of substance use.

Associations between 

Substance-Use 

Trajectories and Other 

Substance Involvement

A number of studies indicate that smoking 

courses can be differentiated as a function of 

involvement with other substances (alcohol, 

marijuana, and other drug use) as measured 

at a single time point (e.g., as a baseline 

correlate or as an outcome), and smoking 

behavior (measured at a single time point) 

can differentiate alcohol and marijuana 

courses. These studies advance comorbidity 

research by considering the dynamic 

nature of at least one of the substances, 

but the arbitrary nature of which variable to 

consider as primary, and which as covariate, 

highlights the need for a true multivariate 

(i.e., multisubstance) approach to deriving 

trajectories.

Smoking Trajectories 

with Alcohol and Marijuana Use

Smoking behavior that is characterized 

by early onset and heavy use is robustly 

associated with marijuana use and, to 

a less consistent degree, with alcohol 

involvement. This is true when looking 

at substance-use correlates at baseline, 

as outcomes, or as time-varying covariates 

that track the smoking courses. White 

and colleagues140 demonstrated that 

smokers endorsed more frequent alcohol, 

marijuana, and other drug use at baseline 

than did nonsmokers, although substance 

use did not differentiate between heavy/

regular smoking and occasional smoking. 

Wills and colleagues141 showed that 

both (heavy) drinking and marijuana 

use tracked smoking frequency during 

early- to mid-adolescence. That is, those 

smokers characterized by early onset 

showed greatest use, and nonsmokers the 

lowest use, with experimenters showing 
297

Monograph 20. Phenotypes and Endophenotypes

low but still elevated rates compared 

with nonsmokers. In addition, Brook and 

colleagues142 found that early-onset smokers 

with continuous use over adolescence and 

emerging adulthood were more likely to 

be diagnosed with alcohol dependence and 

illicit drug dependence than nonsmokers 

and smokers who had later onset of 

smoking or who showed reduced use over 

time. Moreover, late-starting smokers were 

more likely to be diagnosed with drug 

dependence than were nonsmokers. Finally, 

Juon and colleagues143 showed that drug 

abuse/dependence during adulthood was 

highest for those assigned to a smoking 

class on the basis of reported use and age 

of onset and was lowest for those classifi ed 

as nonsmokers.

After identifying four courses of smoking 

in adolescence (early adopters, late adopters, 

experimenters, and never smokers), 

Audrain-McGovern and colleagues144

examined lifetime alcohol and marijuana 

use both as baseline predictors and as 

time-varying covariates. All smoking 

courses differed from never smokers on 

alcohol and marijuana use, and both early 

and late adopters showed greater use of 

marijuana (and alcohol for late adopters 

only) than did experimenters. For the most 

part, early and late adopters did not differ 

from one another as a function of other 

substance use.

Orlando and colleagues145 characterized 

courses defi ned by smoking frequency over 

adolescence and emerging adulthood. They 

also tracked heavy drinking and marijuana 

over the study interval. No differences 

were observed in heavy drinking at 13 or 

18 years of age, but at 15 years of age 

both the stable high and early, increasing 

courses showed greater drinking than 

did all other groups. Drinking rates were 

lowest for nonsmokers, with rates for late 

increasers and experimenters (“triers”) 

falling between nonsmokers and those with 

declining smoking rates. A similar pattern 

was observed for marijuana use, but it was 

more consistently associated with smoking 

across time (at 13, 15, and 18 years of age). 

Early adulthood alcohol and drug problems 

showed similar patterns, with those 

characterized by a stable high smoking 

course and by an early-onset, increasing 

course most likely to report substanceuse problems (by 23 years of age) and 

nonsmokers or triers least likely to report 

problems. Using the same data, but limiting 

the sample to women and extending 

outcomes to 29 years of age, revealed the 

same patterns.146

Soldz and Cui147 identifi ed the extent to 

which substance use tracked courses of 

adolescent past-month smoking quantity. 

They portrayed a pattern of marijuana use 

that very closely paralleled smoking, with 

similar fi ndings for alcohol use. Continuous 

smokers had the highest rates of marijuana 

and alcohol use, and early-smoking 

escalators also started at low or moderate 

levels but escalated rapidly to high rates of 

drinking and marijuana use. Experimenters 

and late escalators were also similar, both 

showing escalating use of marijuana and 

alcohol toward the end of high school. 

In addition, smoking quitters showed more 

substance use than did nonsmokers but 

only minimally so (indicating a pattern of 

experimentation).

Finally, using data from the prospective 

Dunedin sample, Stanton and colleagues148

showed that alcohol and marijuana 

use tracked smoking patterns over 

preadolescence and adolescence, with 

highest rates of substance use among 

rapid escalators. Again, indicators of 

alcohol use (past-month drinking, 

intention to get drunk) were less associated 

with smoking than was marijuana use. 

In sum, those with smoking trajectories 

characterized by early initiation or 

elevated use tend to report greater baseline 

substance use and subsequent problems or 

abuse/dependence.
298

7. Trajectories of Tobacco Use from Adolescence to Adulthood

Drinking Trajectories 

with Smoking and Marijuana/

Drug Use

Few of the many studies characterizing 

course of alcohol involvement examine 

smoking or marijuana-use correlates. 

Windle and colleagues121 showed an 

association with heavy drinking course for 

men only; moderate or high heavy drinking 

(but not very high heavy drinking) was 

associated with heavier baseline smoking. 

Men with high or very high drinking also 

were more likely to report marijuana use 

at baseline. In contrast, women were more 

likely to report baseline marijuana use if 

they belonged to an infrequent or timelimited drinking course. D’Amico and 

colleagues123 showed that adolescents who 

were consistently heavy drinkers over the 

course of a year reported higher rates of 

smoking and marijuana use and initiated 

smoking, regular smoking, and marijuana 

use at a younger age than did nonheavy 

drinkers or individuals whose drinking 

increased over the course of the year.

Hill and colleagues115 demonstrated that 

those whose heavy drinking began early 

and was persistently high were more likely 

to use drugs in early adolescence; likewise, 

reported lifetime history of drug use 

obtained during adolescence was higher for 

those with courses marked by early drinking 

experience.113 In addition, those with 

increasing rates of heavy drinking were most 

likely to be diagnosed subsequently with 

drug-use disorder,115 whereas non-heavydrinking individuals were less likely than any 

heavy drinking group to develop subsequent 

drug abuse.113 Finally, Schulenberg and 

colleagues119 showed that time-varying 

measures of illicit drug use very closely 

paralleled heavy drinking trajectories, and 

Wiesner and colleagues149 found that regular 

drinkers (those with chronic high alcohol 

use) were overrepresented with regard to 

marijuana and other drug use.

Marijuana Trajectories 

with Smoking and Alcohol Use

Studies characterizing marijuana 

trajectories suggest that those with an 

early onset show increased likelihood of 

being diagnosed with a lifetime alcoholuse disorder,136 as well as increased alcohol 

use at study outset134,136 or study end132

and hard drug use at study end.132,133 These 

studies also were more likely to report 

early onset for drinking and smoking.134

Correspondingly, the low- or nonusing 

marijuana groups showed the lowest 

rates of smoking and drinking. Alcohol 

involvement was also high among those 

whose marijuana use declined over time. 

Occasional users tended to fall in the 

middle for drug use,133 and those whose 

marijuana use increased to a high rate 

reported heavy drinking at study end.132

Although not formally testing concordance 

between the two courses, Schulenberg 

and colleagues135 demonstrated that both 

frequency of smoking and binge drinking 

closely tracked courses of marijuana during 

the developmental period (ages 18–24 years) 

under consideration.

Modeling Conjoint 

Trajectories 

of Substance Use

Researchers have begun exploring the 

extent to which various risk behaviors 

or disorders “travel together” over 

time, with an emerging literature that 

uses a developmental framework to 

examine co-occurrence of use of different 

substances. The available body of work 

is described, with acknowledgment that 

this is a rapidly evolving fi eld. Table 7.1 

presents characteristics of this literature, 

describing for each study the nature of 

the sample, the developmental period 

under investigation, the number of waves, 

the measures from which trajectories were 
299

Monograph 20. Phenotypes and Endophenotypes

derived, the trajectory group structure and 

prevalence, and the type of analytic model.

Tobacco and Alcohol

Four studies were found that have modeled 

trajectories of both smoking and drinking. 

Orlando and colleagues155 extracted fi ve 

classes (and an a priori nonusing class) 

from indicators of drinking and smoking 

frequency when the two substances were 

modeled together in a single model. They 

demonstrated that, for the most part, 

smoking and drinking during adolescence 

and emerging adulthood tracked one 

another. A large group of normative users 

was observed (consisting of experimental 

smokers and moderate drinkers). Additional 

groups included those who exhibited 

chronic high use of both tobacco and 

alcohol, two groups whose substance 

use increased over time, and those who 

maintained their alcohol use but quit 

smoking. There was no evidence for a 

group of smokers whose drinking remitted, 

suggesting that smoking may be an 

indicator of a more severe form of drinking. 

Belonging to an early substance-use class 

was predicted by factors such as disrupted 

nuclear family, lower parental education, 

poor grades, and being white. In addition, 

nonusers and normative users revealed 

better overall health and life satisfaction, 

higher college graduation rates, fewer 

delinquent and violent behaviors, and fewer 

alcohol and drug problems.

A similar study was conducted using panel 

data from the Monitoring the Future 

project.154 Group membership was identifi ed 

on the basis of both smoking and (heavy) 

drinking. Perhaps because the large sample 

size (N > 32,000) permitted identifi cation of 

classes with relatively low prevalence, seven 

groups were identifi ed, including nonusers, 

chronic high users, those who smoked 

but did not drink, those who consumed 

alcohol but did not smoke, and three classes 

whose drinking was moderate but whose 

pattern of smoking differed (moderate, late 

onset, or decreasing). Hence, unlike the 

Orlando and colleagues study,155 a group of 

individuals who smoked but did not drink 

was observed. This may be due to the age 

under investigation, with the Orlando study 

targeting individuals earlier in adolescence 

(13 years of age versus 18 years) and 

tracking behavior until 23 years of age 

(versus 26 years); drinking rates tend to drop 

off in mid-adolescence but smoking tends 

to be more stable. Jackson and colleagues154

demonstrated that some risk factors were 

relatively unique to the substance being 

predicted (e.g., parent education, gender, 

and race) and may exhibit additive effects 

in predicting smoking and drinking. 

In contrast, religiosity was a risk factor 

common to both smoking and drinking. 

Perhaps of greatest interest, alcohol 

expectancies and delinquency showed a 

“masked” effect whereby their association 

with smoking could be attributed to 

smoking’s association with drinking.

Using a high-risk college sample, Jackson 

and colleagues35 identifi ed fi ve classes 

derived on the basis of both tobacco and 

alcohol involvement (specifi cally, tobacco 

dependence and alcohol-use disorders). 

Consistent with the Orlando study155 and 

Jackson and colleagues,154 an earlier study 

by Jackson and colleagues35 observed a 

chronic high class for both substances, 

a class characterized by alcohol involvement 

but not tobacco involvement, and a 

nondiagnosing class. In addition, as in their 

later study,154 Jackson and colleagues35

observed a group diagnosed with tobacco 

dependence but not with alcohol-use 

disorder. Of note, a second class of 

individuals who were alcohol involved but 

not diagnosed with tobacco dependence 

was identifi ed; however, diagnoses with 

alcohol-use disorders declined over time, 

consistent with a “maturing out” effect 

that has been observed in young adulthood. 

Predictors that were common to both 

substances included a family history of 
300

7. Trajectories of Tobacco Use from Adolescence to Adulthood Table 7.1 Overview of the Literature on Conjoint Trajectories of Substance Use Study Sample Developmental period Number of waves Measures Number and characteristics of class How is conjoint substance use characterized? Jackson et al. 200035

N = 449 college 

student sample

Young adult (ages 18–24 years)

5 Tobacco dependence 

(TD)Alcohol-use disorder (AUD)

Nondiagnosers (60%)Chronic AUD (6%)Developmentally limited AUD (16%)Chronic TD (11%)Comorbid AUD and TD (7%) 

Dual-trajectory model

White et al.2000127

N = 1,380 community sample

Adolescence/early adulthood(ages 15–28 years)

4 Smoking volume

Alcohol volume(volume = frequency × quantity)

Smoking:

Low (52% female/56% male)Moderate (36%/37%)Heavy (12%/7%)

Alcohol:

Low (18% female/17% male)Late/increase (27%/25%)Moderate (38%/35%)Heavy (17%/23%) 

No explicit comparison but evidence for common predictors

Guo et al.2002150

N = 786 children in Seattle Social Development Project 

Adolescence/early adulthood(ages 10–21 years)

5 Frequency of smoking

Frequency of binge drinkingFrequency of marijuana useFrequency of illicit drug use

Tobacco:

Chronic smokers 1%Escalators 8%Late onsetters 11%Experimenters 7%Nonsmokers 73%

Alcohol:

Chronic bingers 3%Escalators 4%Late onsetters 23%Nonbingers 70%

Marijuana:

Early high 3%Escalators 4%Late onsetters 4%Nonusers 89%

Illicit drug:

Early onsetters 7%Late onsetters 4%Nonusers 89%

No explicit comparison but evidence for common outcomes
301

Monograph 20. Phenotypes and Endophenotypes

Table 7.1 Overview of the Literature on Conjoint Trajectories of Substance Use (continued)

Study Sample

Developmental 

period

Number 

of waves Measures Number and characteristics of class

How is conjoint 

substance use 

characterized?

Chassin et al.2004151

N = 586 community 

sample

Adolescence/early adulthood(ages 13–23 years)

6 Alcohol volume 

(volume = frequency × quantity)Frequency of smokingFrequency of drinking

Growth mixture model: 3 classes (plus a priori abstainers, 11%): low (light drinking/rare drug use; 24%); moderate (moderate drinking/experimental drug use; 45%); heavy (heavy drinking/heavy drug use; 20%)

Dual-trajectory model

Chung et al.2004152

N = 110 outpatient treatment sample (aged 16–25 years)

Course over a single year posttreatmentCompute monthly abstention rates based on timeline follow-back technique

12 Number of consecutive 

abstinent days per month for drinking and other drug use

Alcohol:

High abstinence (53%)Decreasing abstinence (10%)Increasing abstinence (16%)Low abstinence (21%)

Other drug:

High abstinence (59%)Decreasing abstinence (12%)Increasing abstinence (14%)Low abstinence (15%)

Cross-classification: v2(9, 

N = 110) = 80.74, 

p < .001 (

j = .49)

Flory et al.2004153

N = 481 Project DARE

Adolescence/early adulthood (age 12 through age 19–21 years)

6 Frequency of drinking

Frequency of marijuana use

Alcohol:

Early onset (17% men/25% women)Late onset (64%/57%)Nonusers (19%/18%) 

Marijuana:

Early onset (6% men/12% women)Late onset (56%/42%)Nonusers (39%/46%) 

Cross-classification: v2(4, 

N = 236) = 78.82, 

p < .001 (men; 61% on diagonal)v2(4, 

N = 234) = 69.37, 

p < .001 (women; 50% on diagonal) 

Jackson et al.2005154

N = 32,087 Monitoring the Future panel data

Late adolescence/early adulthood(ages 20–26 years)

4 Smoking quantity

Frequency of binge drinking 

Nondrinker/nonsmoker (56%)Chronic (6%)Chronic drinker (14%)Chronic smoker (8%)Moderate drinker/developmentally limited smoker (5%)Moderate drinker/late onset-smoker (5%)Moderate drinker/smoker (6%)

Dual-trajectory model
302

7. Trajectories of Tobacco Use from Adolescence to Adulthood Table 7.1 Overview of the Literature on Conjoint Trajectories of Substance Use (continued) Study Sample Developmental period Number of waves Measures Number and characteristics of class How is conjoint substance use characterized? Orlando et al. 2005155

N = 5,608 schoolbased substance 

program for substance abuse prevention

Adolescence/early adulthood(ages 13–23 years)

6 Frequency of smoking

Frequency of drinking 

A prior nonusing class (4%)Normative users (55%)Smoking quitters/drinking maintainers (6%)Steady increasers (13%)Early increasers (12%)Early, high (9%)

Dual-trajectory model

Tucker et al.2005156

N = 4,245 (smoking)N = 3,889 (drinking) N = 3,185 (marijuana)school-based program for substance abuse prevention 

Adolescence/early adulthood(ages 13–23 years)

6 Frequency of smoking

Frequency of binge drinkingFrequency of marijuana use

Tobacco: 

Abstainers (28%)Persistent light use (55%)Stable high use (26%)Decreasers (9%)Steady increasers (14%)Early increasers (9%)

Alcohol:

Abstainers (32%)Persistent light use (54%)Early, high (22%)Steady increasers (23%)Increase/decrease (bingers/fling) (13%)

Marijuana:

Abstainers (45%)Persistent light use (53%)Stable occasional light users (17%)Early, high (5%)Steady increasers (25%)

Cross-classification: greatest overlap among abstainers
303

Monograph 20. Phenotypes and Endophenotypes

Table 7.1 Overview of the Literature on Conjoint Trajectories of Substance Use (continued)

Study Sample

Developmental 

period

Number 

of waves Measures Number and characteristics of class

How is conjoint 

substance use 

characterized?

Jackson et al.2008157

N = 32,087 

Monitoring the Future panel data

Late adolescence/early adulthood(ages 20–26 years)

4 Smoking quantity

Frequency of binge drinkingFrequency of marijuana use

Tobacco:

Nonheavy smoker (69%)Chronic (12%)Developmentally limited (6%)Late onset (5%)Moderate (8%)

Alcohol:

Nonheavy drinker (64%)Chronic (12%)Developmentally limited (16%)Late onset (7%)

Marijuana:

Nonheavy user (81%)Chronic (7%)Developmentally limited (9%)Late onset (3%)

Cross-classification: Tobacco vs. alcohol: v2(12, 

N = 31,853) = 

2,474.41, p < .001, 

U = .28, 

Cramer’s V = .16Tobacco vs. marijuana: v2(6, 

N = 31,872) = 

3,683.51, p < .001, 

U = .34, 

Cramer’s V = .20Alcohol vs. marijuana: v2(9, 

N = 31,869) = 

4,172.32, p < .001, 

U = .36, 

Cramer’s V = .21

Audrain-McGovern et al. Forthcoming

N = 998 high school students

Late adolescence (age 14–20 years)

6 Smoking categories 

(based on frequency and quantity)Frequency of marijuana use

Regular users (11%)Late escalators (8%)Slow escalators (23%)Fast escalators (2%)Cigarettes only (21%)Abstainers (33%)

Sequential process model
304

7. Trajectories of Tobacco Use from Adolescence to Adulthood

alcoholism and expectancies about the 

effects of alcohol (suggesting the possibility 

of common expectancies across substance). 

However, being male and exhibiting 

behavioral undercontrol was a predictor 

that was specifi c to alcohol-use disorders, 

and childhood stressors only predicted 

comorbid tobacco dependence and alcoholuse disorder.

Muthén159 reanalyzed the same data by 

using a different analytic technique (general 

growth mixture modeling versus the use of 

latent class analysis). Muthén identifi ed three 

classes of alcohol-use disorders and three 

classes of tobacco dependence and estimated 

joint probabilities between the classes. 

The results of these analyses corresponded 

to the fi ndings in Jackson and colleagues35: 

the fi ve trajectory groups in Jackson and 

colleagues35 were represented by the fi ve most 

prevalent joint probabilities in Muthén.159

Although White and colleagues127 modeled 

the developmental course of both smoking 

and drinking over adolescence and young 

adulthood, they did not explicitly compare 

concordance across the two substances. Both 

smoking and drinking showed low, moderate, 

and heavy courses; in addition, for drinking, 

a later-onset course made up one-quarter of 

the sample. The authors found evidence for 

both common (parental warmth) and specifi c 

(parental smoking, for tobacco use; parental 

drinking, for alcohol use) predictors of 

smoking and drinking. In conclusion, these 

four studies show that tracking trajectories 

of multiple substances not only illustrates 

the pattern of concurrent substance use 

over adolescence and young adulthood but 

also can permit better understanding of 

mechanisms that are common versus unique 

to use of a given substance.

Tobacco and Marijuana Use

Using a sequential process model, AudrainMcGovern and colleagues158 characterized 

conjoint trajectories of smoking and 

marijuana use over adolescence and 

emerging adulthood. With the exception of a 

class characterized by smoking only, courses 

of cigarette and marijuana use tracked one 

another; these were marked by abstention, 

regular use, or slow, fast, or late escalation. 

Of interest to this chapter, the regular 

smokers and the fast escalators tended to 

have greater marijuana use than did the 

other groups.

Tobacco, Alcohol, 

and Marijuana/Other Drugs

Several studies have extended the analysis 

of conjoint substance-use course by also 

considering marijuana or other drug use. 

Unlike the work focusing on only tobacco 

and alcohol use, these studies have each 

modeled course of each substance separately 

and then examined concordance between 

substances to ascertain the extent to which 

patterns of substance use change together.

Again using the Monitoring the Future 

panel data, Jackson and colleagues157

examined smoking, (heavy) drinking, and 

marijuana use and identifi ed similar classes 

across substance that included nonusers, 

chronic high users, later-onset users, and 

decreasing users; for smoking only, there 

was also a class of moderate users. Smoking, 

drinking, and marijuana use tracked each 

other over time, with concordance between 

trajectories of marijuana and tobacco use 

as high as the association between tobacco 

and alcohol use. Early users of alcohol 

and marijuana were most likely to smoke 

moderately or heavily, even for those whose 

drinking decreased over young adulthood, 

underscoring the highly addictive nature 

of smoking. Delinquency and alcohol 

expectancies were the strongest predictors 

of general comorbidity; gender, race, 

religiosity, and parent education emerged 

also as signifi cant predictors. Delinquency 

and alcohol expectancies both accounted for 

confi gurations of comorbid chronic high 

use, although expectancies failed to explain 
305

Monograph 20. Phenotypes and Endophenotypes

combinations of smoking and marijuana 

use, supporting some specifi city of 

expectancies to alcohol use. That delinquent 

behavior accounts for combinations of 

comorbidity characterized by early onset 

and persistently high use corroborates 

research suggesting common vulnerability 

underlying substance use.

Building on their earlier work, Tucker 

and colleagues156 compared trajectories of 

smoking, (heavy) drinking, and marijuana 

use over adolescence and emerging 

adulthood. The greatest overlap was 

among abstainers but also among those 

characterized by increasing or early high 

use. Adult psychosocial and behavioral 

functioning was associated with class 

membership similarly across substances. 

Nonusers were at lowest risk for adverse 

outcomes (e.g., stealing, violence), and those 

whose substance use increased steadily to 

very high use were at greatest risk. However, 

in contrast to smoking, those who began 

using marijuana early but declined over 

time were not distinguishable from those 

with steady increasing use.

Using indices of cigarette smoking, (heavy) 

drinking, marijuana use, and illicit drug 

use, Guo and colleagues150 tracked each 

substance over early to late adolescence. 

Each substance showed a large nonusing 

class and groups with onset either early or 

at a later point. In addition, chronic users 

were observed for smoking, drinking, and 

marijuana use, and an additional class of 

experimenters was observed for smoking 

only. Although explicit comparisons between 

substances were not conducted, the extent 

to which associations between course and 

sexual risk-taking behaviors at 21 years 

of age were common versus unique to 

substance was examined. Chronic and lateronset alcohol and marijuana use, but not 

cigarette or hard drug use, were associated 

with risky sexual behavior, whereas early 

cigarette and alcohol use, but not early 

marijuana or hard drug use, increased risk, 

suggesting greater specifi city than might be 

expected from theories of general adolescent 

problem behavior.

Alcohol and Marijuana/Other 

Drugs

Although not directly relevant to this 

chapter, work examining trajectories of 

alcohol and marijuana/drug use provides 

additional evidence that longitudinal 

phenotypes of substance use are relatively 

common across substances. Chassin and 

colleagues151 demonstrated that trajectories 

of alcohol/drug use in adolescence and 

young adulthood tracked concurrent 

alcohol- and drug-use disorders such that 

those with heavy use were most likely 

to be diagnosed with a substance-use 

disorder. Likewise, Flory and colleagues153

demonstrated substantial overlap among 

courses of alcohol and marijuana use, 

although a number of alcohol users 

were nonusers of marijuana. Etiological 

correlates and young adult outcomes 

were common to both substances, with 

little evidence of specifi city. Finally, using 

retrospective reports of days abstinent in 

a clinical sample of adolescents and young 

adults, Chung and colleagues152 documented 

moderate concordance (j = .49) between 

courses of alcohol and drug use in the 

year following treatment whereby change 

in alcohol use typically paralleled change 

in drug use, although there was evidence 

that some individuals abstained from one 

substance but not the other.

Review of Results

On the basis of fi ndings from studies that 

jointly model course and comorbidity, 

several conclusions can be advanced. 

First, despite the diverse course shapes 

and different course prevalences that were 

identifi ed by each study, it is reassuring that 

in each case, relatively high concordance 

was observed between corresponding 
306

7. Trajectories of Tobacco Use from Adolescence to Adulthood

trajectories (e.g., chronic high smoking with 

chronic high drinking), as were common 

correlates of course across substance. 

Second, both common and specifi c factors 

that underlie concurrent substance use 

were observed. As noted by Jackson and 

colleagues,154 identifying risk factors that 

distinguish among courses of comorbidity 

can provide construct validity for the 

trajectories and can not only illuminate 

the nature of comorbidity but also can 

provide a better understanding of each 

substance. For example, one could compare 

risk factors for courses characterized by 

heavy use of one substance and low use of 

the co-occurring substance. Jackson and 

colleagues154 identifi ed different patterns of 

association between risk factors and paths of 

co-occurring tobacco and alcohol use that 

suggested additive effects, synergistic effects, 

and masked (confounded) effects.

Third, it is apparent from this work that 

individuals who remit from alcohol and 

marijuana use frequently remain smokers. 

This may be, in part, because tobacco is 

so highly physically and psychologically 

Why Might It Be Useful to Use Course as a Phenotype?

Developmental course might serve as a valuable phenotype for biometric models. Researchers 

have been using latent growth modelinga,b to model developmental course by using genetically 

informative (twin) data. Although work conducted in 1986 by McArdlec

 and Plomind introduced 

the idea of capturing the heritability of developmental change, 20 years passed before the 

heritability of latent variables refl ecting level (intercept) and growth was demonstrated by 

applying latent growth models to the study of genetic infl uences.e,f Carlson and Iaconoe

 suggested 

that intercept and slope factors may serve as developmental phenotypes that indicate the extent 

of genetic vulnerability for continuity or change in a given behavior. However, although this work 

is informative with regard to the heritability of initial level (at a given age) and change from that 

level over an extended observation period, these parameters do not capture individuals who are 

particularly “at risk” by virtue of membership in a developmental course that is marked by both 

high initial level and chronic continued use. A latent variable that characterizes membership in 

some developmental course could be a valuable phenotype in that it classifi es individuals by their 

level of and change in substance use as well as the timing of onset or initiation. The integration 

of mixture models into genetic models is under way,g

 although thus far this work considers 

only a single behavior (i.e., alcohol use). Determining the degree to which these phenotypes are 

substance specifi c represents a logical next step in the genetic study of addictive behavior. 

a

Curran, P. J., and A. M. Hussong. 2003. The use of latent trajectory models in psychopathology research. 

Journal of Abnormal Psychology 112 (4): 526–44.

b

Muthén, B. 2001. Latent variable mixture modeling. In New developments and techniques in structural 

equation modeling, ed. G. A. Marcoulides and R. E. Schumacker, 1–33. Mahway, NJ: Lawrence Erlbaum.

c

McArdle, J. J. 1986. Latent variable growth within behavior genetic models. Behavior Genetics 16 (1): 163–200.

d

Plomin, R. 1986. Multivariate analysis and development behavioral genetics: Developmental change as well as 

continuity. Behavior Genetics 16 (1): 25–43.

e

Carlson, S. R., and W. G. Iacono. 2006. Heritability of P300 amplitude development from adolescence to 

adulthood. Psychophysiology 43 (5): 470–80.

f

Finkel, D., C. A. Reynolds, J. J. McArdle, and N. L. Pedersen. 2005. The longitudinal relationship between 

processing speed and cognitive ability: Genetic and environmental infl uences. Behavior Genetics 35 (5): 

535–49.

g

Muthén, B., T. Asparouhov, and I. Rebollo. 2006. Advances in behavioral genetics modeling using Mplus: 

Applications of factor mixture modeling to twin data. Twin Research and Human Genetics 9 (3): 313–24.
307

Monograph 20. Phenotypes and Endophenotypes

addictive; it also may be that once an 

individual has reached adulthood, alcohol 

and marijuana use are much less compatible 

with day-to-day adult responsibilities.

Finally, although several courses for 

tobacco and alcohol use were not associated 

with risk factors or adverse outcomes 

(e.g., escalating and decreasing courses), 

it appears that membership in any course 

indicating marijuana use increases risk of 

many negative correlates of substance-use 

behavior, suggesting some specifi city to 

substances, at least with regard to those that 

are licit versus illicit.

Empirical Example 

of Modeling 

Co-Occurring Courses 

of Substance Use

In this section, an empirical example is 

presented of the modeling of conjoint use of 

multiple substances by using data from the 

Finn Twin16-25 study.160 For simplifi cation, 

only two substances are considered: 

cigarette smoking and alcohol consumption; 

models can be extended to consider more 

than two substances.150,156,157

Because this is only an illustration of the 

methodological approach, some simplifying 

decisions and assumptions were made: 

(1) Analysis was limited to data from twin 

brothers so that gender moderation was 

not an issue. Finnish girls mature earlier 

and initiate drinking at earlier ages than 

do boys in matched birth cohorts,161,162 and 

environmental contributions to individual 

differences in pubertal development differ 

across genders.163 Limiting the analysis to 

males attenuates the differential effects of 

pubertal maturation. (2) Trajectory analyses 

were conducted on the full sample of twins 

as individuals without consideration of 

the twin design. As such, the standard 

errors of parameters are underestimated, 

and confi dence intervals are narrower than 

if the sampling design were taken into 

account. However, the actual parameter 

estimates are not biased.* Furthermore, 

prior studies suggest that it is reasonable 

to generalize from twin to nontwin 

samples.164,165 (3) Finally, although analytic 

techniques permit missing data under 

the assumption that data are missing at 

random, missing data were not modeled to 

facilitate model convergence. Ascertainment 

of Finnish twins at the baseline of 16 years 

of age was essentially exhaustive and 

unbiased,166 and individual response rates 

were ≥ 90% through the third assessment at 

18 years of age.167 But compliance declined 

at the fourth wave of assessment and more 

so among adult male twins. Consequently, 

it is acknowledged that generalization to 

the general population is constrained in 

this regard.

Method

Participants and Procedure

Finn Twin16-25 is a population-based, 

longitudinal twin study that includes 

fi ve twin birth cohorts obtained from the 

Finnish national population registry and 

consists of all twins born in Finland between 

the years 1975 and 1979,166,168 with both 

co-twins alive and resident in Finland at 

16 years of age. Within 90 days of their 

16th birthday, 3,065 twin pairs received 

mailed questionnaires. They were then 

followed up at the ages of 17 years, 18.5 years 

(age range 18–19 years), and 25 years 

(age range 23–27 years; response rate 83%); 

this fi nal assessment generally corresponds 

to a period of maturing out/cessation of 

alcohol and tobacco use. Zygosity was 

*

Although it is possible to correct for dependence in Mplus by using the complex statement, the parameters 

do not change, and in fact, including this statement did not change the trajectory prevalence or structure.
308

7. Trajectories of Tobacco Use from Adolescence to Adulthood

determined from validated questionnaires 

completed by both co-twins and parents at 

baseline.168 Data on smoking and drinking, 

across all four waves of assessment, were 

available for 1,132 male twins from brotherbrother twin pairs of known zygosity; after 

deleting twins missing some data from their 

co-twins, 970 twins remained, forming 

485 male twin pairs: 213 monozygotic and 

272 dizygotic. This sample of twin brothers 

was used for all analyses.

Measures

Baseline questionnaires assessed frequency 

of alcohol use and frequency of smoking 

as well as other measures of substance use 

(including age at initiation, experimentation 

with cigarettes, and number of cigarettes 

smoked so far) and other health behaviors. 

The measures of drinking and smoking 

frequency were used in the analyses 

reported here.

Smoking

At 16 years of age, frequency of smoking was 

assessed with a single measure that asked, 

“Which of the following best describes your 

present smoking habits?” Response options 

included (1) I smoke once or more daily; 

(2) I smoke once or more a week, but not 

every day; (3) I smoke less often than once a 

week; (4) I am trying to or have quit smoking; 

and (5) I have never smoked. At the later ages, 

the set of fi ve alternatives was expanded to 

six options (17 years of age) or seven options 

(ages 18 and 25 years) to better distinguish 

individual differences in density of smoking. 

To derive consistent measures over the four 

assessments, variables were recoded into the 

following four response options: (0) I have 

never smoked; (1) I smoke less than once a 

week or am trying to quit; (2) I smoke once 

or more a week but not daily; and (3) I smoke 

once or more daily. Items were rescored so 

that high scores indicate frequent smoking. 

Figure 7.1 (top) shows smoking prevalence 

over the four waves.

Alcohol Use

Frequency of drinking was assessed at 

all waves using a single measure asking 

how often the respondents use alcohol. 

Response options included (1) daily, 

(2) couple of times a week, (3) once a week, 

(4) a couple of times a month, (5) about 

once a month, (6) about once every two 

months, (7) 3–4 times a year, (8) once a year 

or less, and (9) I don’t drink any alcohol. 

For consistency with the smoking items, 

variables were recoded into four response 

options: (0) I do not use alcohol at all, 

(1) drink less than once a month, (2) drink 

at least once a month but less than weekly, 

and (3) drink at least once a week. Items 

were rescored so that high scores indicate 

frequent drinking. Figure 7.1 (bottom) 

shows the prevalence of drinking over the 

four waves.

Analytic Procedure

To identify trajectories, a mixture 

modeling procedure—general growth 

mixture modeling/models (GGMM)— 

was used.15,159,169,170 GGMM is a form of 

latent growth modeling, but it includes 

an unobserved categorical variable that 

models variability around the latent 

growth factors via discrete homogeneous 

classes of individuals (versus representing 

variability with a parameter, as in growth 

modeling). Basically, these models combine 

the continuous nature of a latent growth 

curve model with the categorical nature of 

group membership in a single estimation 

procedure. Rather than obtaining a 

trajectory of drinking for each individual in 

the study, as might be observed via latent 

growth modeling, multilevel modeling, 

or generalized estimating equations, 

GGMM groups individuals into meaningful 

“clusters” or “classes.”

Typical latent growth curve models 

assume that respondents come from the 

same population, with the same basic 
309

Monograph 20. Phenotypes and Endophenotypes

Figure 7.1 Prevalence of Smoking (A) and Drinking (B) across the Four Study Waves

0

10

20

30

40

50

Frequency of smoking (%)

16 17 18.5 22–27

0

10

20

30

40

50

Frequency of drinking (%)

16 17 18.5 22–27

I have never smoked

I smoke less than once a week or am trying to quit

I smoke once or more a week

I smoke once or more daily

I do not use alcohol at all

I drink less than once a month

I drink at least once a month but less than weekly

I drink at least once a week

Age (years)

Age (years)

A.

B.

Note. Analyses for the four study waves are based on these collection dates: age 16, Wave 1, 1991–1995; age 17, Wave 2, 

1992–1996; age 18.5, Wave 3, 1994–1998; age 22–27, Wave 4, 2000–2003.
310

7. Trajectories of Tobacco Use from Adolescence to Adulthood

growth function with respect to starting 

point (intercept) and growth (slope; with 

individual variation represented by the 

intercept and slope factor variances). 

GGMMs, however, allow for different 

populations to have unique intercepts 

and slopes. In essence, GGMM estimates 

a unique latent growth curve (with 

individual variability) for each underlying 

population. This technique has some 

important advantages over other techniques 

used to derive developmental courses 

of substance use (e.g., cluster analysis, 

latent class analysis) in that it treats 

group membership as a latent (error free) 

variable (unlike cluster analysis) and 

accounts for the temporal ordering of 

prospective data (unlike traditional latent 

class analysis). Although GGMM is the 

model used here, other techniques can 

model change (e.g., regime switching171 and 

latent transition analysis).172 For example, 

in regime switching, individuals transition 

(or “switch”) among groups. For the 

purposes of this example, the more 

frequently applied trajectory-analysis 

technique of GGMM is used.

The GGMMs were based on a basic latent 

growth model. The base model included 

intercept and both linear and quadratic 

slopes. The intercept was centered at time 1 

(by virtue of a zero loading on the slope 

factors at time 1). Linear and quadratic 

slope factor loadings were set according 

to the interval between assessments 

(roughly 0, 1, 2.5, and 8.8). For the sake 

of a simplifi ed example, no within-class 

variability was permitted.*

 The smoking and 

drinking variables were treated as four-level 

ordinal variables. Models were estimated 

with automatically generated random 

start values with 100 initial-stage random 

sets of starting values and 10 fi nal stage 

optimizations. All models were estimated 

using Mplus 4.10.173

Two sets of analyses were conducted. 

The fi rst was to model smoking and 

drinking independently. That is, a GGMM 

was estimated for smoking and, in a 

separate analysis, a GGMM was estimated 

for drinking. Then, the association between 

the trajectories of smoking and drinking 

was examined. In the second set of analyses, 

smoking and drinking were modeled 

simultaneously in a multivariate procedure. 

Figure 7.2 portrays the underlying GGMM 

for the two sets of analyses. The top panel 

shows two GGMMs for drinking and 

smoking; the bottom panel shows the 

multivariate procedure.

Results

First, associations between drinking and 

smoking at each of the assessments were 

examined. As table 7.2 indicates, smoking 

and drinking are highly intercorrelated, 

particularly during the adolescent years. 

In addition, smoking and drinking are highly 

associated across twins, with twin 1 smoking 

moderately associated with twin 2 drinking 

at the ages of 16, 17, and 18 years (r = .37, 

.33, and .27, respectively) but less so at 

25 years of age (r = .06; note that correlations 

for twin 1 drinking and twin 2 smoking 

were comparable). Not unexpectedly, the 

associations were stronger for monozygotic 

twins (r = .42, .41, .36, and .17 at ages 16, 

17, 18, and 25 years, respectively) than for 

dizygotic twins (r = .34, .26, .20, and –.04, 

respectively).

Identifi cation of Trajectories

As recommended by Muthén,174 model fi t 

was evaluated using a likelihood ratio test 

for relative improvement in fi t—namely, 

the Vuong-Lo-Mendell-Rubin likelihood ratio 

(VLMR LR) test.175,176 An information criteria 

fi t index was also considered (Bayesian 

*

Although it would have been preferable to estimate within-class variability, model convergence was 

greatly facilitated by constraining within-class variances to zero.
311

Monograph 20. Phenotypes and Endophenotypes

Figure 7.2 Underlying General Growth Mixture Model for Characterizing Trajectories 

of Smoking and Trajectories of Drinking (A) and for Characterizing Conjoint 

Trajectories of Smoking and Drinking (B)

Time 4

smoking

Time 3

smoking

Time 2

smoking

Time 1

smoking

Smoking

intercept

Smoking

linear

Smoking

quadratic

Time 4

drinking

Time 3

drinking

Time 2

drinking

Time 1

drinking

Conjoint

smoking/drinking

class membership

Drinking

linear

Drinking

quadratic

Drinking

intercept

GGMM for smoking

GGMM for drinking

Drinking

class

Drinking

intercept

Drinking

linear

Drinking

quadratic

Time 4

drinking

Time 3

drinking

Time 2

drinking

Time 1

drinking

Time 1

smoking

Time 2

smoking

Time 3

smoking

Time 4

smoking

Smoking

class

Smoking

quadratic

Smoking

linear

Smoking

intercept

1 1 0 8.8 1 2.5

0 77.4 1 1 1 6.2

77.4 0 0 8.8

1 2.5

1

1 1 1 1 6.2

77.4 0 0 8.8

1 2.5

1

1 1 1 6.2 1

77.4 0 0 8.8

1 2.5

1

1 1 1 1 6.2

B.

A.

Note. GGMM = general growth mixture model/modeling. Factor loadings, set according to the interval between assessments, 

are shown for each growth factor. Correlations between intercept and slope factors were estimated but are not presented in the 

fi gure. For the dual-trajectory model, correlations were estimated between corresponding slope factors across substance.
312

7. Trajectories of Tobacco Use from Adolescence to Adulthood

Information Criterion [BIC])177 as well as 

class interpretability (the extent to which an 

additional class provided unique information) 

when determining number of classes.

Extracting Courses for Alcohol Use 

and for Tobacco Use

The fi rst approach was to characterize 

course of smoking and course of drinking in 

two separate analyses. For each substance, 

one- through six-class models were tested 

(see table 7.3 for fi t indices). For smoking, 

the VLMR LR test suggested that four 

classes were suffi cient, although the Akaike 

Information Criterion (AIC) and the BIC 

supported a six-class model. The four-class 

solution was selected for its interpretability 

and parsimony; the fi fth class divided the 

moderate class into two moderate classes 

that primarily differed on intercept; and 

the sixth class was characterized by a very 

sharp escalation, but only contained 1% 

of the sample. For drinking, the six-class 

model showed the best fi t in terms of the 

AIC, BIC, and VLMR LR. However, the 

sixth class did not offer much additional 

information, essentially splitting the earlyonset, chronic heavy-drinking group and 

the moderate adolescent/heavy adult group 

into three groups that primarily differed on 

intercept. As a result, the fi ve-class model 

was selected. 

For courses of smoking, group membership 

for each was characterized by the following 

trajectories: (1) nonsmokers and lowfrequency smokers (50%); (2) stable 

moderate smokers (23%); (3) delayed-onset 

smokers (7%); and (4) early-onset, chronic 

heavy smokers (20%). Figure 7.3 (top) shows 

frequency of smoking as a function of class 

membership. For courses of drinking, group 

membership for each was characterized by 

the following trajectories: (1) nondrinkers 

and low-frequency drinkers (6%); (2) stable 

moderate drinkers (8%); (3) delayed-onset 

drinkers (10%); (4) moderate adolescent/

heavy adult drinkers (47%), and (5) earlyonset, chronic heavy drinkers (29%). 

Figure 7.3 (bottom) shows frequency of 

drinking as a function of class membership.

To evaluate concordance between courses of 

tobacco and alcohol use, a cross-tabulation of 

group membership for smoking and drinking 

was created (i.e., a 4 × 5 table) and measures 

of association were calculated. Given the 

lack of independence with twin pairs, the 

p-value is reported for the design-based v2

.

178

For this analysis, group membership was 

assigned using posterior probabilities—that 

is, assigning an individual to the class to 

which he or she was most likely to belong. 

As shown in table 7.4, smoking and drinking 

were associated: v2(12, N = 970) = 221.85, 

p < .001; U = .48; Cramer’s V = .28.

Table 7.2 Correlations across Smoking and Drinking at Each of the Four Waves for the 

Full Sample

Behavior/age

Smoking Drinking

Age 16 Age 17 Age 18 Age 25 Age 16 Age 17 Age 18 Age 25

Smoking–age 16 — 

Smoking–age 17 .80 —

Smoking–age 18 .76 .82 —

Smoking–age 25 .59 .64 .70 —

Drinking–age 16 .46 .41 .38 .30 —

Drinking–age 17 .39 .44 .39 .34 .69 —

Drinking–age 18 .31 .34 .34 .32 .54 .66 —

Drinking–age 25 .13 .15 .13 .20 .41 .46 .55 —
313

Monograph 20. Phenotypes and Endophenotypes

To identify specifi c patterns of comorbidity 

that accounted for the concordance between 

courses of tobacco and alcohol use, a 

fi rst-order confi gural frequency analysis 

technique was used.179 Although there were 

20 (4 × 5) different potential trajectories 

of smoking and drinking, some of these 

particular combinations of smoking and 

drinking were more likely to occur than 

chance (“types”) and some were less likely 

to occur than chance (“antitypes”). This was 

done by testing observed versus expected 

cell frequencies in the smoking-drinking 

contingency table shown in table 7.4. Using 

Lehmacher’s approximation to the binomial 

probability (with Küchenhoff’s correction for 

continuity),179 signifi cant types and antitypes 

were identifi ed on the basis of a cell v2 value 

6.64, which indicates signifi cance at p < .01 

for a single degree of freedom. From these 

types and antitypes (denoted in table 7.4 by 

up and down arrows, respectively), several 

Table 7.3 Fit Indices and Likelihood Ratio Tests for Relative Improvement in Fit for Smoking, 

Drinking, and Dual Smoking and Drinking

Number of 

classes Test of model fit Smoking Drinking

Smoking and 

drinking

1 AIC

BIC

VLMR LR

Entropy

8973.74

8998.13

N/A

N/A

9607.94

9632.33

N/A

N/A

18581.69

18630.46

N/A

N/A

2 AIC

BIC

VLMR LR

Entropy

7068.13

7112.02

p < .0001

.92

8661.23

8705.13

p < .0001

.82

16091.76

16174.68

p < .0001

.91

3 AIC

BIC

VLMR LR

Entropy

6645.32

6708.72

p < .0001

.88

8251.50

8314.91

p < .0001

.80

15541.20

15658.26

p = .2986

.90

4 AIC

BIC

VLMR LR

Entropy

6563.85

6646.77

p < .0001

.87

8141.28

8224.19

p = .0062

.74

15137.59

15288.78

p = .0008

.88

5 AIC

BIC

VLMR LR

Entropy

6522.93

6625.36

p = .1355

.82

8095.42

8197.84

p = .0089

.76

14837.60

15022.94

p = .0003

.88

6 AIC

BIC

VLMR LR

Entropy

6503.06

6624.99

p = .2774

.84

8054.69

8176.62

p = .0011

.74

14696.31

14915.79

p = .3274

.85

7 AIC

BIC

VLMR LR

Entropy

— — 14583.52

14837.15

—a

.84

8 AIC

BIC

VLMR LR

Entropy

— — 14505.21

14792.97

—a

.83

Note. N = 970. AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; VLMR LR = Vuong-Lo-Mendell-Rubin 

likelihood ratio test for k versus k +1 classes; N/A = not applicable; — = model could not be estimated.

a

Likelihood ratio test would not converge properly.
314

7. Trajectories of Tobacco Use from Adolescence to Adulthood

Figure 7.3 Trajectories of Smoking (A) and Drinking (B) Mean smoking

Class Membership (age in years)

16

0

1

2

3

17 18 19 20 21 22 23 24 25

Nonsmokers or low-quantity smokers (50%)

Stable moderate smokers (23%)

Delayed-onset smokers (7%)

Early-onset, chronic heavy smokers (20%)

Mean drinking

Class Membership (age in years)

16

1

2

3

17 18 19 20 21 22 23 24 25

Nondrinkers or low-quantity drinkers (6%)

Stable moderate drinkers (8%)

Delayed-onset drinkers (10%)

Moderate adolescent/heavy adult drinkers (47%)

Early-onset, chronic heavy drinkers (29%)

0

A.

B.

Note. In the top graph (A), the y-axis indicates (0) I have never smoked, (1) I smoke less than once a week or am trying to quit, 

(2) I smoke once or more a week but not daily, and (3) I smoke once or more daily. In the bottom graph (B), the y-axis indicates 

(0) I do not use alcohol at all, (1) I drink less than once a month, (2) I drink at least once a month but less than weekly, and 

(3) I drink at least once a week. Data for analyses were collected between 1991–2003.
315

Monograph 20. Phenotypes and Endophenotypes

conclusions can be drawn. Early-onset, 

chronic heavy smokers were most likely 

to be early-onset, chronic heavy drinkers 

and least likely to be moderate- or delayedonset drinkers. Stable moderate smokers 

were also likely to be early-onset, chronic 

heavy drinkers. Those with a delayed onset 

in smoking also showed a delayed onset for 

drinking, suggesting that these patterns 

of use track one another. Finally, non/low 

smokers were most likely to be stable 

moderate drinkers or to show delayed onset 

of drinking.

Extracting Conjoint Courses 

of Tobacco and Alcohol Use

Next, courses of smoking and drinking were 

identifi ed in a single multivariate analysis. 

One- through eight-class models were tested 

(see table 7.3 for fi t indices). The eight-class 

model demonstrated the best fi t on the basis 

of the AIC and BIC, but the eighth class was 

not substantively meaningful (partitioning 

a single group into two groups that differed 

only in level of frequency). As such, the 

seven-class model was selected; fi gure 7.4 

presents the developmental courses for 

the conjoint trajectories of smoking and 

drinking. Group membership for each was 

characterized by the following trajectories: 

(1) non/low drinkers and smokers (5%); 

(2) non/low drinkers, stable moderate 

smokers (2%); (3) delayed-onset drinkers, 

non/low smokers (25%); (4) early, chronic 

high drinkers, non/low smokers (18%); 

(5) early, chronic high drinkers, stable 

moderate smokers (19%); (6) delayed-onset 

drinkers and smokers (11%); and (7) early, 

chronic high drinkers and smokers (19%).

Comparison of Approaches 

to Studying Conjoint Use

Two methods are presented to examine 

concurrent smoking and drinking: 

(1) modeling course of each substance 

separately and examining concordance 

between the substances, and (2) extracting 

a single factor of latent group membership 

from both smoking and drinking 

measurements. For the most part, similar 

conclusions could be reached from these 

two analyses. The three most prevalent 

Table 7.4 Cross-Tabulations of Frequency and Cell Proportions of Group Membership for 

Smoking and Drinking for the Full Sample

Drinking

Smoking

Nonsmokers 

and low freq. 

Stable 

moderate 

Delayed 

onset

Early onset,

chronic heavy Marginals

Freq. % Freq. % Freq. % Freq. % Freq. %

Nondrinkers and 

low frequency

40 4.1 11 1.1 3 0.3 8 0.8 62 6.4

Stable moderate 64 6.6 8 0.8 3 0.3 5 0.5 80 8.2

Delayed onset 73 7.5 7 0.7 14 1.4 1 0.1 95 9.8

Moderate 

adolescent/

heavy adult 

248 25.6 105 10.8 34 3.5 65 6.7 452 46.6

Early onset, 

chronic high

58 6.0 95 9.8 12 1.2 116 12.0 281 29.0

Marginals 483 49.8 226 23.3 66 6.8 195 20.1 970

Note. Freq. = frequency. v2(12, N = 970) = 221.85, p < .001; U = .48; Cramer’s V = .28. Numbers with up arrows () indicate values 

that are signifi cantly greater (p < .01, based on a cell v2 value of 6.64 with 1 degree of freedom) than would be expected by chance 

(“types”). Numbers with down arrows () indicate values that are signifi cantly less (p < .01) than would be expected by chance 

(“antitypes”).
316

7. Trajectories of Tobacco Use from Adolescence to Adulthood

groups in the dual-trajectory model 

(delayed-onset drinkers, non/low smokers; 

early, chronic high drinkers, stable 

moderate smokers; and early, chronic high 

drinkers and smokers) were identifi ed as 

types according to the contingency table 

(table 7.4). In addition, the delayed-onset 

drinker and smoker class, which was 

somewhat prevalent (11%), was identifi ed as 

a type. The two smallest classes in the dualtrajectory model—the non/low drinkers and 

smokers and the non/low drinkers and stable 

moderate smokers—were not identifi ed as 

types in the contingency table. The only 

discrepant fi nding was that of the early, 

chronic high drinker and non/low smoker 

group. Although this group was rather 

prevalent in the dual-trajectory model, it was 

actually an antitype in the contingency table. 

However, there was a signifi cant type for the 

stable moderate drinking group and non/low 

smoking group. Given that the levels of 

drinking frequency of the early, chronic high 

drinkers and the stable moderate drinkers 

had converged by 25 years of age, this 

fi nding is not so anomalous.

In sum, the two approaches showed 

consistency in identifying distinct 

phenotypes of smokers and drinkers that 

may be valuable for genetic study. However, 

clear differences exist in methodological 

approach, and it is faulty to assume that 

classes that “exist” in one approach will be 

mirrored in the other.

Trajectories as Informative 

Phenotypes

To establish the value of these trajectories 

as informative phenotypes for genetic 

study, the extent to which membership 

in the trajectories was concordant for 

twin 1 and twin 2 was considered, followed 

by an examination of agreement as a 

function of zygosity. Table 7.5 shows the 

concordance between twin 1 and twin 2 

for the four smoking courses (top) and for 

Figure 7.4 Trajectories of Conjoint Drinking (left side) and Smoking (right side)

0

1

2

3

Mean drinking

or smoking

Drinking (age in years) Smoking (age in years)

16 17 18 19 20 21 22 23 24 25 16 17 18 19 20 21 22 23 24 25

Non/low drinkers and smokers (5%)

Non/low drinkers, stable moderate smokers (2%)

Delayed-onset drinkers, non/low smokers (25%)

Early, chronic high drinkers, non/low smokers (18%)

Early, chronic high drinkers, stable moderate smokers (19%)

Delayed-onset drinkers and smokers (11%)

Early, chronic high drinkers and smokers (19%)

Note. The y-axis for drinking indicates (0) I do not use alcohol at all, (1) I drink less than once a month, (2) I drink at least once a 

month but less than weekly, and (3) I drink at least once a week. The y-axis for smoking indicates (0) I have never smoked, (1) I smoke 

less than once a week or am trying to quit, (2) I smoke once or more a week but not daily, and (3) I smoke once or more daily. Data for 

analyses were collected between 1991–2003.
317

Monograph 20. Phenotypes and Endophenotypes

the fi ve drinking courses (bottom) for the 

full sample (collapsed across zygosity). 

Concordance was high for smoking class 

membership: v2(9, N = 485) = 280.12, 

p < .001; U = .76; Cramer’s V = .44; j = .45 

(95% confi dence interval [CI], .39–.51). 

Concordance was equally high for drinking 

class membership: v2(16, N = 485) = 490.31, 

p < .001; U = 1.01; Cramer’s V = .50; j = .46 

(95% CI, .40–.53). Not unexpectedly, twin 

pairs showed overlap for corresponding 

classes (i.e., signifi cant types represented 

by the cells along the diagonal of table 7.5; 

several signifi cant antitypes along the 

off-diagonal).

Next, cross-substance twin concordance 

was explored; that is, the associations 

between twin 1 smoking and twin 2 drinking 

and vice versa were examined (table 7.6). 

Developmental course of smoking in one 

twin and course of drinking in the other 

twin showed a moderate association: 

v2(12, N = 485) = 71.45, p < .001; U = .38; 

Cramer’s V = .22 (for twin 1 smoking and 

twin 2 drinking; the converse association 

was nearly identical). Given the strength 

of the cross-twin agreement (table 7.5) for 

smoking (U = .76; Cramer’s V = .44) and for 

drinking (U = 1.01; Cramer’s V = .50), these 

cross-substance associations are notable.

Finally, the extent to which twin 1 

membership in the conjoint smokingdrinking course was concordant with twin 2 

membership was examined. Concordance 

for the dual trajectories was very good: 

v2(36, N = 485) = 719.81, p < .001; 

U = 1.22; Cramer’s V = .50; j = .41 (95% CI, 

.36–.47). Interestingly, when considering 

the likelihood-based parameters (U and 

Cramer’s V), cross-twin agreement for 

the conjoint trajectories was higher than 

concordance for each substance modeled 

individually; the magnitude for the kappas 

was nearly identical.

Next, concordance within and between 

substances as a function of zygosity was 

examined. For smoking, monozygotic 

twins showed stronger class membership 

agreement, v2(9, N = 213) = 190.36, 

p < .001; U = .95; Cramer’s V = .55; j = .57 

(95% CI, .48–.66), than did dizygotic twins, 

v2(9, N = 272) = 109.49, p < .001; U = .63; 

Cramer’s V = .37; j = .36 (95% CI, .27–.44). 

The nonoverlapping confi dence intervals 

on the kappa coeffi cients suggest that the 

stronger concordance for monozygotic twins 

was signifi cant.

A similar pattern was observed for drinking: 

monozygotic twins showed higher 

concordance, v2(16, N = 213) = 341.81, 

p < .001; U = 1.27; Cramer’s V = .63; j = .58 

(95% CI, .49–.67), than did dizygotic twins, 

v2(16, N = 272) = 185.83, p < .001; U = 0.83; 

Cramer’s V = .41; j = .36 (95% CI, .28–.45). 

Again, nonoverlapping confi dence intervals 

on the kappa coeffi cients suggest signifi cant 

differences in concordance for monozygotic 

versus dizygotic twins.

Finally, using the conjoint trajectories, 

cross-twin concordance was higher for 

monozygotic twins, v2(36, N = 213) = 463.73, 

p < .001; U = 1.48; Cramer’s V = .60; j = .55 

(95% CI, .47–.63), than for dizygotic twins, 

v2(36, N = 272) = 298.29, p < .001; U = 1.05; 

Cramer’s V = .43; j = .31 (95% CI, .24–.38), 

again with evidence that the concordance 

was signifi cantly higher among monozygotic 

twin pairs.

Summary of Empirical Example

In the example from Finn Twin16-25, 

the general techniques involved in 

characterizing developmental course of 

the use of two co-occurring substances 

is illustrated and preliminary evidence of 

genetic infl uences underlying conjoint 

substance use is presented. Four trajectories 

of smoking during adolescence and 

young adulthood are identifi ed, including 

nonsmokers, stable moderate smokers 

(perhaps “chippers”),180 and two groups 

that exhibited high smoking by 25 years of 
318

7. Trajectories of Tobacco Use from Adolescence to Adulthood Table 7.5 Cross-Tabulations of Frequency (Cell Proportion) of Group Membership for Twin 1 Versus Twin 2 (across Zygosity) for Smoking (top) and for Drinking (bottom) Twin 1 smoking Twin 2 smoking Nonsmokers and low frequency Stable moderate Delayed onset Early onset, chronic heavy Marginals Frequency % Frequency % Frequency % Frequency % Frequency % Nonsmokers or low frequency 181 37.3 32 6.6 11 2.3 10 2.1 234 48.2 Stable moderate 32 6.6 57 11.8 4 0.8 20 4.1 113 23.3 Delayed onset 21 4.3 3 0.6 11 2.3 4 0.8 39 8.0 Early onset, chronic heavy 15 3.1 21 4.3 1 0.2 62 12.8 99 20.4 Marginals 249 51.3 113 23.3 27 5.6 96 19.8 485 Twin 1 drinking Twin 2 drinking Nondrinkers and low frequency Stable moderate Delayed onset Moderate adolescent/ heavy adult Early onset, chronic high Marginals Frequency % Frequency % Frequency % Frequency % Frequency % Frequency % Nondrinkers or low frequency 20 4.1 4 0.8 3 0.6 3 0.6 0 0 30 6.2 Stable moderate 3 0.6 14 2.9 4 0.8 14 2.9 3 0.6 38 7.8 Delayed onset 4 0.8 5 1.0 26 5.4 14 2.9 1 0.2 50 10.3 Moderate adolescent, heavy adult 4 0.8 17 3.5 11 2.3 153 31.6 43 8.9 228 47.0 Early onset, chronic high 1 0.2 2 0.4 1 0.2 40 8.2 95 19.6 139 28.7 Marginals 32 6.6 42 8.7 45 9.3 224 46.2 142 29.3 485 Note. For top: v2(9, 

N = 485) = 280.12, p < .001; 

U = .76; Cramer’s V = .44; j = .45 (95% confi dence interval [CI], .39–.51) (across zygosity). For monozygotic twins (cross-tabulations not shown), 

v2(9, 

N = 213) = 190.36, p < .001; 

U = .95; Cramer’s V = .55; j = .57 (95% CI, .48–.66). For dizygotic twins (cross-tabulations not shown), v2(9, 

N = 272) = 109.49, p < .001; 

U = .63; Cramer’s V = .37; 

j = .36 (95% CI, .27–.44). For bottom: v2(16, 

N = 485) = 490.31, p < .001; 

U = 1.01; Cramer’s V = .50; j = .46 (95% CI, .40–.53) (across zygosity). For monozygotic twins (cross-tabulations not shown), 

v2(16, 

N = 213) = 341.81, p < .001; 

U = 1.27; Cramer’s V = .63; j = .58 (95% CI, .49–.67). For dizygotic twins (cross-tabulations not shown), v2(16, 

N = 272) = 185.83, p < .001; 

U = 0.83; Cramer’s V = .41; 

j = .36 (95% CI, .28–.45). Numbers with up arrows () indicate values that are signifi cantly greater (p < .01, based on a cell v2 value of 6.64 with one degree of freedom) than would be expected by chance (“types”). Numbers 

with down arrows () indicate values that are signifi cantly less (p < .01) than would be expected by chance (“antitypes”). Not all numbers add to 100% because of rounding.
319

Monograph 20. Phenotypes and Endophenotypes

Table 7.6 Cross-Tabulations of Frequency (Cell Proportion) of Group Membership for Twin 1 Smoking by Twin 2 Drinking (top) and Twin 2 

Smoking by Twin 1 Drinking (bottom)

Twin 2 drinking

Twin 1 smoking

Nonsmokers 

and low frequency Stable moderate Delayed onset

Early onset,

chronic heavy Marginals

Frequency % Frequency % Frequency % Frequency % Frequency %

Nondrinkers and low frequency 16 3.3 8 1.6 2 0.4 6 1.2 32 6.6Stable moderate 26 5.4 8 1.6 2 0.4 6 1.2 42 8.7Delayed onset 34 7.0 5 1.0 6 1.2 0 0 45 9.3Moderate adolescent/heavy adult 118 24.3 56 11.6 21 4.3 29 6.0 224 46.2Early onset, chronic high 40 8.2 36 7.4 8 1.6 58 12.0 142 29.3Marginals 234 48.2 113 23.3 39 8.0 99 20.4 485

Twin 1 drinking

Twin 2 smoking

Nonsmokers 

and low frequency Stable moderate Delayed onset

Early onset,

chronic heavy Marginals

Frequency % Frequency % Frequency % Frequency % Frequency %

Nondrinkers and low frequency 14 2.9 10 2.1 3 0.6 3 0.6 30 6.2Stable moderate 27 5.6 5 1.0 2 0.4 4 0.8 38 7.8Delayed onset 39 8.0 4 0.8 3 0.6 4 0.8 50 10.3Moderate adolescent/heavy adult 130 26.8 51 10.5 14 2.9 33 6.8 228 47.0Early onset, chronic high 39 8.0 43 8.9 5 1.0 52 10.7 139 28.7Marginals 249 51.3 113 23.3 27 5.6 96 19.8 485

Note. v2(12, 

N = 485) = 74.08, p < .001; 

U = .39; Cramer’s V = .23 (top) and v2(12, 

N = 485) = 71.45, p < .001; 

U = .38; Cramer’s V = .22 (bottom). For top (twin 1 smoking by twin 2 drinking): for monozygotic 

twins (tables not shown), v2(12, 

N = 213) = 34.63, p < .001; 

U = .40; Cramer’s V = .23; for dizygotic twins, v2(12, 

N = 272) = 57.47, p < .001; 

U = .46; Cramer’s V = .27. For bottom (twin 1 drinking by twin 2 

smoking): for monozygotic twins (tables not shown), v2(12, 

N = 213) = 36.71, p < .001; 

U = .42; Cramer’s V = .24; for dizygotic twins, v2(12, 

N = 272) = 49.58, p < .001; 

U = .43; Cramer’s V = .25.

Numbers with up arrows () indicate values that are signifi cantly greater (p < .01, based on a cell v2 value of 6.64 with one degree of freedom) than would be expected by chance (“types”). Numbers 

with down arrows () indicate values that are signifi cantly less (p < .01) than would be expected by chance (“antitypes”). Not all numbers add to 100% because of rounding.
320

7. Trajectories of Tobacco Use from Adolescence to Adulthood

age but whose smoking was distinguished 

during adolescence, with one group 

initiating use at a much earlier age than the 

other group. Five trajectories of drinking 

were characterized, including the same 

patterns of use as those identifi ed for 

smoking as well as an additional one that 

refl ected high use by 25 years of age but 

moderate use at study initiation. It was 

demonstrated that longitudinal phenotypes 

of smoking and drinking showed similar 

patterns of change, particularly for those 

with onset at an early age and those who 

exhibited delayed onset but still heavy use 

by young adulthood. In addition, smokers 

who began at an early age were also likely to 

initiate heavy drinking at some later point; 

this could be evidence for a directional 

relation between smoking and drinking, 

or perhaps it might be due to contextual 

variables that permitted the younger 

adolescent access or opportunity to smoke 

but not drink. Non/low smokers generally 

exhibited some drinking, consistent with 

norms in Finland for high-density drinking, 

often to intoxication.181,182

In addition to examining the relation 

between developmental courses of 

two substances, conjoint courses were 

characterized represented by both smoking 

and drinking behaviors. Some groups were 

identifi ed that might be expected on the 

basis of the results from the single-substance 

trajectories (i.e., early-onset, chronic high 

users of both substances; delayed-onset users 

of both substances; non/low smokers who 

drank with low frequency), as well as some 

additional groups that were discriminated 

on the basis of smoking and drinking 

(i.e., non/low drinking with stable moderate 

smoking; early-onset, chronic high drinkers 

who were non/low smokers or moderate 

smokers; delayed-onset drinkers who were 

non/low smokers).

For both approaches, the question was 

asked as to whether there was preliminary 

evidence for genetic infl uences underlying 

course of substance use, as well as common 

infl uences underlying the courses of 

conjoint substance use. Concordance 

between twin pairs differed as a function 

of zygosity, with monozygotic twins 

showing greater concordance for smoking 

and for drinking than did dizygotic twins. 

Of importance, the conjoint trajectories 

revealed even greater concordance than the 

single-substance trajectories, underscoring 

the value of utilizing substance-use 

phenotypes that capture as much 

information as possible.

That greater concordance for trajectories 

of substance use among monozygotic 

twin brothers was found suggests genetic 

infl uences, but it must be emphasized 

that genetic effects suggested by these 

analyses may refl ect gene-environment 

correlations, arising from genetically 

conditioned differences in susceptibility to 

environmental exposure rather than from 

independent genetic effects. It should also be 

emphasized that these analyses necessarily 

make the usual assumptions underlying twin 

comparisons, including the assumption that 

outcome-relevant environmental experience 

does not differ between monozygotic and 

dizygotic twin brothers. Substance use is 

infl uenced by siblings’ shared experiences 

and their reciprocal interactions,183 and 

greater similarities in smoking and drinking 

trajectories of monozygotic twin brothers 

may, in part, refl ect their greater frequency 

of social contact and greater overlap in 

peer networks.184 Social contact among 

adult Finnish twin brothers accounts for 

signifi cant variance in their patterns of 

alcohol consumption, but modeling the 

effect of social contact does not markedly 

reduce estimates of genetic variance; 

instead, it reduces the variance otherwise 

attributed to unmeasured (and unshared) 

residual environmental sources.183

Accordingly, the inference made here that 

genetic infl uences contribute to different 

trajectories of substance use appears to be 

an appropriate one.
321

Monograph 20. Phenotypes and Endophenotypes

This is the fi rst study to consider the 

extent to which courses of substance use 

might be heritable and to offer evidence 

that pathways of substance use may be 

genetically infl uenced. Given that there 

is value in using longitudinal phenotypes 

such as these, it is important to consider 

how genetic research might use these 

phenotypes. Membership in a given 

developmental trajectory, captured by a 

single categorical latent variable, refl ects 

age of onset and severity as well as change 

(slope) in use of a substance; moreover, 

membership in a trajectory characterized by 

concurrent use of two (or more) substances 

simultaneously provides information for 

multiple substances. Previously, research 

that sought to examine these constructs had 

to model four separate pieces of information. 

Although latent growth models do provide 

information regarding onset, severity, and 

course, they refl ect “average” change and 

fail to capture homogeneous groups or 

subtypes. To explore the heritability of class 

membership, the variance components of 

the underlying variability can be modeled 

(e.g., with Cholesky decomposition 

models) by using a series of dummy 

codes that represent the nominal classes 

(or polychoric correlations if the classes 

lie on an underlying continuum). These 

analyses might build on work by Eaves and 

colleagues,185,186 which examined the extent 

to which patterns of pairwise concordance 

and discordance in latent class membership 

differed between monozygotic and dizygotic 

twin pairs. A quantifi able estimate of the 

genetic contribution to the risk of taking 

different pathways in development is an area 

for further development. In addition, certain 

groups might be selected as “extreme” 

groups that can be genotyped in a more 

effi cient manner than genetic analyses that 

must consider the entire sample.

This study has demonstrated the utility of 

using a latent variable refl ecting course 

characterizing use of multiple substances. 

However, researchers must use theory to 

guide analyses with the goal of comparing 

subtypes that are of theoretical interest. 

For example, a researcher might select two 

courses of smoking that are characterized 

by similar age of onset but different 

slope or level of severity (or vice versa) 

and conduct comparisons between these 

courses. For concurrent use of substances, 

a researcher may wish to compare 

courses represented by a single substance 

with courses represented by multiple 

substances (e.g., a course characterized by 

high smoking and low drinking versus a 

course characterized by high smoking and 

high drinking). If the genetic infl uence 

underlying the latter is no stronger than 

the former, one might infer presence of a 

common underlying genetic infl uence.

The methodological issues that arise when 

characterizing course of multiple substances 

should be noted. First, the investigator 

should decide what analytic approach to 

take—that is, whether to simultaneously 

model multiple latent growth factors 

(e.g., one for each substance) in a single 

multivariate analysis or whether to derive 

courses for each substance separately and 

then model conjoint use by estimating 

concordance between each substance-based 

trajectory.187 Each approach has advantages.

The fi rst approach (i.e., the multivariate 

approach) explicitly models comorbidity 

and its change over time. It is also more 

parsimonious than the second approach. 

For example, if one considers four courses 

of smoking and fi ve courses of drinking 

(as suggested in the preceding univariate 

analyses), there are 20 possible combinations 

of smoking and drinking. However, the 

analyses presented here suggest no more 

than seven dual trajectories. That is, using 

multiple univariate (one substance at a 

time) approaches to model comorbidity, 

the investigator can be modeling forms of 

comorbidity that are unlikely to exist in 

nature but are implied by bringing together 

univariate solutions.
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7. Trajectories of Tobacco Use from Adolescence to Adulthood

However, the virtue of the univariate 

approach is that it provides estimates of 

trajectories that are specifi c to a target 

outcome (e.g., smoking only) and thus 

are not infl uenced by aspects of the 

comorbid behavior not directly relevant 

to the substances under consideration. 

For example, one might expect differing 

determinants of a comorbid course than of 

a single-substance course (e.g., availability 

of both substances; social norming of 

both smoking and drinking behavior). 

In addition, a common genetic infl uence 

is likely for multiple problem behaviors 

other than substance use.48,49 As a result, 

adequately specifying the phenotype 

underlying use of both substances becomes 

increasingly complex. Finally, this 

essentially univariate approach provides 

estimates of comorbidity (e.g., concordance) 

that are similar to more traditional crosssectional approaches (e.g., a likelihoodbased measure or a measure of agreement 

such as Cohen’s kappa). It is noted that both 

approaches become more challenging when 

three or more substances are considered 

both illustratively and, especially in 

the multivariate case, computationally. 

It is reassuring, however, that the two 

approaches yielded similar fi ndings in the 

empirical example.

In addition, the empirical example fails 

to resolve other aspects of substance 

involvement such as average and maximum 

quantity consumed and substance-use 

disorders and problems. In prior work,117

it was shown that classes based on different 

facets of drinking behavior can show 

similar course shapes (i.e., corresponding 

intercepts and slopes) but different course 

prevalences and low-to-moderate cross-class 

memberships (i.e., assignment to “similarlooking” classes on the basis of different 

input variables). As such, the present 

example is a simplifi cation, and distinctions 

may be observed between different 

aspects of smoking behavior in terms of 

developmental course.

In a related way, trajectory shape and 

prevalence may differ as a function of the 

developmental period under consideration. 

In chapter 5, the authors characterize 

trajectories over a broader age span 

(ages 10–32 years) than in the present 

example (ages 16–25 years); the authors 

were able to extract fi ve latent classes 

as well as identify three a priori groups. 

Many of the trajectories observed in 

that chapter correspond to this one, 

including an early-onset, persistent group; 

a moderate/experimenter group; and a 

group of abstainers making up roughly 

one-half the sample. However, in contrast 

to the fi ndings presented here, chapter 5 

identifi es two distinct delayed-onset groups. 

It is likely that the present delayed-onset 

group—those who began smoking at 

about 17 years of age—maps onto the two 

delayed-onset groups in chapter 5, with 

onset at ages 14 and 18 years, respectively. 

In addition, whereas smoking by 25 years 

of age was equally high for the early- and 

delayed-onset groups in the present 

example, in chapter 5 the delayed-onset 

groups failed to “catch up” to the earlyonset, persistent group by 32 years of age. 

Finally, the present chapter did not identify 

a group of smokers who had quit; it is not 

unlikely that had the participants been 

followed for an additional decade or so, 

a corresponding quitter group would have 

been observed.

Another methodological consideration 

concerns modeling age of onset for 

simultaneous processes. There is an 

exciting class of models in which trajectory 

classes can be derived on the basis of 

growth mixtures, but initiation serves 

as the intercept (i.e., course is modeled 

separately from age).188 However, there 

appear to be conceptual and estimation 

challenges extending such “initiation-based 

intercept models” to multiple substances; 

courses of multiple substances may show 

comparable trajectory structure but 

mismatched onsets.
323

Monograph 20. Phenotypes and Endophenotypes

In addition, when considering the 

association between two substances, it is 

important to consider the extent to which 

an association is due to a group of constant 

nonusers or abstainers. Prescott and 

Kendler81 raised the question of whether 

much of the genetic covariation between 

tobacco and alcohol use may be due to 

the large group of abstainers; they found 

that shared (genetic) variation between 

tobacco and alcohol use was much reduced 

when abstainers were removed. Tucker and 

colleagues156 noted that the greatest overlap 

across substances was among abstainers. 

Interestingly, when excluding abstainers 

from the present analyses, virtually no 

reduction was observed in cross-twin 

association for the conjoint trajectories: 

v2(25, N = 485) = 618.88, p < .001; U = 1.18; 

Cramer’s V = .53; j = .42 (95% CI, .37–.48); 

this was true within zygosity as well.

An alternative approach to examining 

genetic infl uences on variability on 

course involves two-stage genetic models 

that distinguish between initiation and 

progression of use;189,190 integration of 

these models with the developmental 

approach might yield the most informative 

phenotypes. It seems likely that the two 

approaches (i.e., two-stage genetic models 

that independently estimate effects on 

initiation and effects on progression, 

conditional on initiation, and genetic 

models of growth mixtures or other types 

of trajectories) will yield different types of 

insights or phenomena. For example, the 

two-stage genetic models seem especially 

useful for identifying risk factors that are 

specifi c to various phases of substance-use 

careers.191 The growth mixture approach 

offers an opportunity to derive empirically 

based complex phenotypes that capture 

associated clinical features, course, and 

developmental references.

It is important to note that although 

course is an essential dimension for 

characterizing behavior or disorder, it is 

not necessarily a “genetic” one. Although 

some degree of chronicity is almost 

certainly related to the degree of genetic 

risk, genetically identical individuals who 

are affl icted with the same largely genetic 

condition can show marked variation in 

course.192 As is true in all forms of genetic 

modeling, inclusion of more explicit 

measures of the environment—both fi xed 

(e.g., early toxic exposure) or time varying 

(e.g., environments supportive or suppressive 

of substance use, various role occupancies)—

can only serve to sharpen an assessment of 

the environment and better understand key 

characteristics such as course.

Finally, these analyses were based on 

a Finnish sample of twin brothers; 

generalizability to nontwins and other 

cultures with different genetic backgrounds, 

cultural infl uences surrounding tobacco 

and other drug use, and formal alcohol and 

tobacco prevention and control policies 

may not be straightforward. However, prior 

work160,193,194 shows that overall patterns of 

trajectories are quite similar in Finland to 

those studied elsewhere.

Summary

The goal of this chapter is to explore the 

extent to which developmental courses of 

substance use are nonspecifi c or whether 

there are developmental phenotypes that 

are unique to tobacco use. The review of 

the extant literature and the empirical 

example suggest that there is evidence for 

both of these notions. The identifi cation 

of comparable overlapping developmental 

pathways for smoking and drinking 

supports the idea of an underlying 

general factor indicating common liability 

(perhaps genetic) to the use of multiple 

substances. Yet, identifi cation of groups 

with divergent trajectories of multiple 

substances (e.g., moderate or chronic high 

drinking by nonsmokers; both abstention 

and early-onset, chronic drinking by 
324

7. Trajectories of Tobacco Use from Adolescence to Adulthood

moderate smokers) suggests substancespecifi c pathways. As the example in this 

chapter clearly shows, both common 

and specifi c developmental pathways can 

coexist. A worthwhile goal for future genetic 

research would be to examine the extent 

to which different combinations of course 

are genetically infl uenced. For example, 

one might expect, based on Prescott and 

Kendler81 and Tucker and colleagues156

(although perhaps not from the example 

in the present chapter), that membership 

in the low-using/abstaining course for 

both groups would be highly genetically 

infl uenced and that membership in a course 

marked by low smoking and delayed-onset 

drinking might be more environmentally 

infl uenced. Clearly, the opportunities for 

identifying highly genetically infl uenced 

substance-use behaviors are considerable.

As summarized earlier, a body of research 

demonstrates evidence of shared genetic 

risk for use of different substances. However, 

much of this work relies on lifetime 

substance use or dependence. If one wishes 

to distinguish among syndromes that are 

chronic, episodic, developmentally limited, 

or reactive and transient, it is critical to 

prospectively characterize the course of 

substance use and problems. Although 

much work has described the developmental 

course of single substances over the period 

from adolescence to adulthood, researchers 

have now begun to simultaneously consider 

multiple substances. Studies that jointly 

consider comorbidity and course will 

permit researchers to determine the extent 

to which trajectories unique to a single 

substance versus those refl ecting substance 

use more generally best identify longitudinal 

phenotypes for genetic study. If it can be 

shown that phenotypes represented by 

broader substance-use trajectories are 

equally or more heritable than singlesubstance trajectories, both phenotypic and 

genetic work can proceed more effi ciently. 

Findings would also have implications for 

whether researchers should take a more 

generic approach in the prevention and 

treatment of substance-use disorders. 

It is hoped that this chapter will inspire 

researchers to conduct work that reveals 

the optimal longitudinal phenotype for 

understanding genetic effects on substance 

use and substance-use disorders.

Conclusions

1. Studies examining the developmental 

course of multiple substances have 

shown relatively high concordance 

between identifi ed trajectories despite 

diverse course shapes and different 

course prevalences.

2. Membership in a given developmental 

trajectory, which can be captured by 

a single categorical latent variable, 

represents age of onset and severity 

as well as change (slope) in use of a 

substance; moreover, membership in a 

trajectory characterized by concurrent 

use of two (or more) substances 

simultaneously provides information 

for multiple substances.

3. Developmental course might serve as a 

valuable phenotype for biometric models, 

and determining the degree to which 

a phenotype of developmental course 

is substance specifi c is valuable for the 

genetic study of addictive behavior.

4. Evidence using twin data indicates that 

courses of substance use are genetically 

infl uenced, with monozygotic twins 

showing greater concordance for smoking 

and for drinking than do dizygotic twins. 

The genetic contribution to the risk of 

taking different pathways in development 

represents an area for further study.

5. Conjoint trajectories of drinking and 

smoking reveal even greater concordance 

than do single-substance trajectories, 

suggesting greater heritability for courses 

extracted from several substances. This 

underscores the value of considering 
325

Monograph 20. Phenotypes and Endophenotypes

substance use across multiple domains 

when constructing phenotypes for 

research and perhaps even for clinical use. 

However, extending the concept of the 

components of developmental substanceuse phenotypes raises new questions 

such as, Which substances? What aspects 

of substance use or its consequences? 

Which periods of development? Thus, 

the fi ndings show the value of extending 

the concept of substance-use phenotypes 

but not necessarily optimal phenotypes 

that “carve nature at its joints.”

6. If resources are limited for genetic 

analyses, focusing on those with the 

most “extreme” phenotypes marked 

by both high initial level and chronic 

continued use may represent an effi cient 

strategy for identifying genes associated 

with more problematic forms of 

substance use.
326

7. Trajectories of Tobacco Use from Adolescence to Adulthood

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337

Endophenotypes serve as intermediary measures that have the potential to provide 

an indirect link between genes, smoking behaviors, and nicotine dependence. 

Endophenotypes may help serve as a basis for future studies to identify genetic liability 

markers for nicotine dependence. Research to establish the validity, heritability, 

and reliability of such measures is a promising area for further study.

The fi rst chapter of this part examines the evidence base for several candidate 

endophenotypes for nicotine dependence risk at or before initial nicotine exposure, 

including approach, avoidance, and control-related variables, as well as measures of 

initial response to nicotine exposure. A subsequent chapter explores endophenotypes 

for nicotine dependence in chronic smokers; aspects include motivational, sensory, 

and cognitive measures as well as craving and impulse control.

Endophenotypes

Part

4

339

8

Endophenotypes for NicotineDependence Risk at or before 

Initial Nicotine Exposure

Janet Audrain-McGovern, Joel T. Nigg, and Kenneth A. Perkins

Characteristics present before or at the time of nicotine exposure may play a key role in 

identifying individuals at genetic risk for nicotine dependence. This chapter examines the 

evidence base for several candidate endophenotypes for nicotine-dependence risk at or 

before smoking and nicotine exposure, including the following:

■ Approach-related smoking risk variables based on psychological traits such as 

impulsivity, novelty seeking, and extraversion, using laboratory measures for 

aspects of reinforcement and reward

■ Avoidance-related smoking risk variables based on psychological factors such 

as neuroticism, stress, depression, and anxiety, using laboratory measures 

including personality trait measures, peripheral nervous system (PNS) effects, 

and neuroendocrine response to cortisol

■ Control-related smoking risk based on psychological variables such as attention 

defi cit hyperactivity disorder (ADHD), conduct disorders, aggression, and 

hostility, using laboratory measures including response inhibition, event-related 

potential (ERP) P300 amplitude, attention, and alertness

■ Measures of initial response to nicotine exposure, including reinforcement and 

reward measures of initial sensitivity to nicotine, as well as initial sensitivity to 

affective and mood responses to nicotine

Although available evidence shows a link between many of these variables and 

smoking behavior, further research is needed to establish possible nicotine-dependence 

endophenotypes from a standpoint of predictive validity, biological plausibility, reliability, 

and heritability.

The analyses described herein were supported in part by National Institute of Health grants AA12217, CA096836, 

CA109250, DA05807, DA19478, and DA021032.
340

8. Endophenotypes for Nicotine Dependence Risk

Introduction

This chapter examines potential 

endophenotypes for risk for (1) initiating 

and progressing in smoking and 

(2) responding to the initial nicotine 

exposure. First, it briefl y surveys major 

within-person risk factors for smoking 

initiation and progression. It then assesses 

these from the perspective of potential 

endophenotypes via a conceptual model 

of neural circuits that may be relevant 

to smoking initiation and progression, 

particularly with regard to a general risk 

pathway. A general risk pathway indicates 

a vulnerability that may be shared between 

nicotine and other drugs; hence, some 

overlap can be expected in the domains of 

interest here with those being studied for 

other drugs such as alcohol. This approach 

is well justifi ed in view of behavioral 

genetic evidence of shared genetic liability 

to the misuse of nicotine, alcohol, and 

other drugs,1–3 although the degree of 

shared genetic factors may vary with age.4

However, some endophenotypes may be 

relatively more general and linked to initial 

attraction to many types of substances 

(e.g., reward dependence), and others may 

have greater specifi city to trying nicotine 

(e.g., attentional dysfunction).

The second part of this chapter considers 

processes occurring in the early stages of 

nicotine exposure that may increase the 

likelihood of further exposure to nicotine 

and subsequent nicotine dependence; 

it looks at potential endophenotypes 

at that infl ection point, shifting to a 

pharmacological response model and a 

more drug-specifi c pathway. A drug-specifi c 

model is justifi ed at this infl ection point 

by evidence that pharmacological response 

may infl uence selection of drug use over 

time. The fi nal section of the chapter 

discusses the state of the research and offers 

recommendations for future investigation. 

Endophenotypes

An explanatory gap between candidate genes 

and the presence of symptoms of nicotine 

dependence necessitates new approaches 

to identifying genetic liability markers. 

Smoking risk is an area of study overlapping 

with numerous complex disorders and 

traits with which it is correlated. Therefore, 

a useful strategy may be to identify valid 

and reliable intervening constructs to link 

candidate genes and nicotine dependence, 

as has been suggested for behavioral 

traits and disorders generally.5–8 The fi eld 

holds relative consensus that genetic and 

environmental risk for substance use 

includes a general risk factor (not specifi c 

to one drug) and drug-specifi c factors.9

Intervening constructs need to be identifi ed 

both at the general level (where they will 

be shared among several drugs) and at the 

nicotine-specifi c level.

These intervening constructs, referred to as 

endophenotypes, can be neurophysiological, 

biochemical, endocrinological, 

neuroanatomical, cognitive, behavioral, 

or neuropsychological, as long as the 

endophenotype ultimately enhances the 

genetic signal for the disorder’s causal 

processes.10 Most behavioral measures, 

although providing useful clues, are usually 

considered less parsimonious than most 

cognitive or biological endophenotypes given 

the (presumed) extra steps needed to link 

them to genes or the proteins for which they 

code. Further, because genes infl uencing 

behavioral and addictive disorders are 

presumed to operate in the brain, and to be 

detectable by probes of brain activity (such 

as cognition), cognitive and physiological 

measures that can be validated in relation to 

neural systems are attractive candidates.

Consequently, a neural networks 

perspective is useful to analyze potential 

endophenotypes. Such a model can be 
341

Monograph 20. Phenotypes and Endophenotypes

adopted to examine behavioral, cognitive, 

and physiological endophenotypes that may 

be related to smoking initiation discussed in 

the fi rst half of this chapter. Nicotine is not 

a drug of universal exposure. Thus, factors 

that differentially infl uence initiation of use, 

including genetics, are critical. At the same 

time, once smoking has been initiated, the 

pharmacological response to the nicotine 

presumably becomes a key factor in an 

adolescent’s subsequent smoking behavior 

and progression to nicotine dependence. 

Therefore, the second part of this chapter 

moves to a lower (more molecular) level of 

analysis and considers a pharmacological 

perspective on smoking progression in 

conjunction with trait measures.

Assuming an endophenotype can be 

validated, it can provide a potentially 

powerful tool for identifying individuals 

at genetic risk of initial nicotine use 

and of going on to nicotine dependence 

(becoming nicotine dependent and 

staying nicotine dependent), and it can 

also clarify phenotypic heterogeneity.11

That is, complex traits such as smoking 

and nicotine dependence are probably 

due to numerous genes in several 

pathways, interacting with each other and 

the environment. Endophenotypes are 

intended to represent more defi ned and 

quantifi able measures that are thought to 

involve fewer genes and fewer interacting 

pathways, which ultimately result in the 

activation of a narrower set of neuronal 

circuits.11 Because endophenotypes, when 

valid, are more proximal biologically 

to the putative genetic infl uences, they 

may be more sensitive measures for 

genetic studies of nicotine dependence.12

No endophenotypes have been validated 

for smoking risk; this chapter examines 

candidate markers that may hold promise 

as potential endophenotypes.

Several criteria have been advanced 

to evaluate the validity of a putative 

endophenotype.7,10,13 The criteria used to 

evaluate a potential endophenotype for 

nicotine dependence include (1) predictive 

validity; that is, it is related to a smoking 

phenotype of interest (initiation or 

progression); (2) biological plausibility; 

that is, it can be linked to specifi c neural 

pathways or actions, which can relate 

directly to candidate genes; (3) reliability; 

and (4) heritability. Bivariate heritability is 

not evaluated in this chapter because data are 

lacking on its relation to smoking initiation 

and progression. For more discussion of the 

criteria for an endophenotype, see Waldman 

and colleagues.13,14

This chapter derives its concept of 

“endophenotype,” which has been 

criticized as underspecifi ed, from other 

studies. For example, Szatmari and 

colleagues15 suggest that responses 

that are often considered as potential 

endophenotypes can be conceptualized 

as one of three “subtypes,” only two of 

which would be true endophenotypes: 

(1) component phenotype, (2) intermediate 

phenotype, and (3) covariate.15 Component 

phenotypes capture only one aspect of a 

multidimensional disorder of interest; 

they may or may not be a necessary part of 

the disorder, but they are not a suffi cient 

determinant (i.e., alone they do not fully 

capture the disorder). Building on the logic 

of these authors, component phenotypes 

can be viewed as a portion of the disorder 

phenotype but not part of the causal chain 

to it. Intermediate phenotypes, by contrast, 

refer to a mechanism believed to be part 

of the causal chain to the disorder; this is 

the original meaning of “endophenotype” 

provided by Gottesman.10 An intermediate 

phenotype is expected to refl ect a 

predisposition for the disorder in unaffected 

family members as well as in those already 

affected. The third subtype, covariates, are 

really not endophenotypes at all; they are 

factors related to the disorder of interest 

but not components of it and certainly not 
342

8. Endophenotypes for Nicotine Dependence Risk

causal. Part of the goal of research in this 

area is to determine into which of these 

subtype categories a candidate measure 

actually falls (usually it is unknown until 

investigated). Of most interest in this 

chapter are markers suspected to be the 

second type (intermediate phenotypes), 

although in fact some of these may turn 

out to be covariates. The reason for 

emphasizing intermediate phenotypes 

is that this chapter is focused on those 

at risk for nicotine dependence but not 

yet “affected” with the disorder. The next 

chapter, on putative endophenotypes 

for dependence after chronic exposure 

(i.e., in those already “affected”), focuses 

on component phenotypes.

Rationale for Investigating 

Nicotine-Dependence Risk 

Endophenotypes

Like most complex traits, smoking 

behavior is the result of genetic and 

environmental infl uences.16 Heritability 

studies of adolescent twins estimate that 

at least 33% of the variance in smoking 

initiation (ever smoking), more than 80% of 

the variance in smoking rate, and 44% of 

the variance in nicotine dependence may 

be attributable to genetic factors.1,17–19

Genetic factors may be more important 

in discriminating those adolescents who 

become nicotine dependent from those who 

simply initiate and do not progress beyond 

limited experimentation.18,20

Evidence for smoking heritability has 

encouraged a growing number of studies 

examining the role of candidate genes 

involved in nicotine metabolism and drug 

reward in adolescent smoking and nicotine 

dependence. Most of the candidate gene 

studies have focused on genes directly 

related to nicotine’s biological action. 

For example, such studies indicate that 

genetic variation in enzymes responsible 

for nicotine metabolism (i.e., CYP2A6) 

infl uences the likelihood of becoming 

nicotine dependent and the rate of 

progression in nicotine dependence 

among adolescents.21,22 However, these two 

studies differ in their fi ndings, and it is 

not clear whether faster or slower nicotine 

metabolism confers risk for nicotine 

dependence. However, with regard to the 

nonspecifi c component of the risk path, 

studies have also linked polymorphisms 

in genes in the dopamine reward pathway 

to an increased likelihood of smoking 

progression,23 greater smoking among male 

adolescents,24 and a reduced likelihood of an 

adolescent being nicotine dependent.25,26

Two genome-wide association studies 

have pointed to several novel genes that 

discriminated among adults who smoke 

regularly but did not become nicotine 

dependent and those who smoke regularly 

and became nicotine dependent.27,28 There 

appears to be some overlap between 

polymorphisms that distinguish individuals 

who became dependent on other substances 

from those who did not.28 Likewise, a 

later candidate gene study found that the 

nicotinic receptor subunit gene CHRNA5

distinguished between adults who smoke 

regularly but did not become nicotine 

dependent and those who smoke regularly 

and became nicotine dependent.29 These 

fi ndings have been replicated in fi ve 

subsequent studies of adults.30–34 Studies 

also provide support for the importance 

of other nicotinic subunits identifi ed 

in genome-wide association studies 

(e.g., CHRNB3).29,31,32 No studies were found 

that have prospectively evaluated the role 

of nicotine receptors in the emergence of 

nicotine dependence in adolescents.

Furthermore, a range of psychological and 

psychosocial moderators likely interplay 

with genetic vulnerability in regard to drug 

use, including smoking. For example, Dick 

and colleagues35 reported that genetic effects 

on adolescent smoking were moderated by 

parenting behavior. The specifi c nature of 
343

Monograph 20. Phenotypes and Endophenotypes

these interactive gene effects remains to 

be mapped with regard to the general and 

specifi c risk streams. However, initial clues 

are tantalizing. One study found that the 

dopamine receptor D2 (DRD2) gene interacts 

with other vulnerability factors, such as 

depression, to potentiate adolescent smoking 

progression.23 In contrast, protective factors, 

such as team sport participation, appear 

to interact with genes in the dopamine 

reward pathway (i.e., DRD2 and dopamine 

transporter SLC6A3) to prevent adolescent 

smoking progression.36

Gene-by-gene interactions can also be 

considered. For example, genetic variation in 

the serotonin pathway (i.e., the short allele 

of the serotonin transporter 5-HTTLPR) has 

been linked to increased smoking among 

adolescents.37 However, a higher level of 

smoking was seen among girls who were 

homozygous for the long allele of 5-HTTLPR

and who lacked the dopamine receptor 

DRD4*7-repeat allele.38 These two fi ndings 

may refl ect the moderating effects of one 

gene on another or possibly methodological 

differences between the studies.

Despite the recognition of these general 

outlines of the problem and these 

interesting initial genetic fi ndings, it has 

proven diffi cult to identify candidate genes 

with replicable associations with adolescent 

smoking phenotypes; that is, several of the 

studies above disagree on the genotype 

that confers risk. As discussed in chapter 5, 

disparate fi ndings may be partially explained 

by differences in study methodology and 

smoking phenotypes under investigation. 

At the same time, the methodological 

problems in identifying and measuring 

liability in those who have not yet initiated 

use are nontrivial.39,40 Endophenotypes in 

the context of prospective designs are a 

crucial tool in this regard. 

Similar to most work in the fi eld, the model 

discussed here assumes at least three 

infl ection points leading to eventual 

dependence, of which two (initiation 

and initial response) are covered in this 

chapter and one (persistence) is covered 

in chapter 9. It is assumed that genetic 

infl uences on these three infl ection points 

are at least in part distinct. For one thing, 

it is likely that risks for initiation may fall 

partially into the general substance-use 

pathway and partially into a specifi c pathway 

involving attraction to nicotine, whereas a 

greater degree of drug-specifi c factors may 

be involved in initial response. However, 

initiation is an obvious prerequisite for 

progression and then dependence to 

emerge. In turn, numerous factors place 

an individual at risk for smoking initiation, 

progression to regular smoking, and 

nicotine dependence.41 Smoking obviously 

occurs in a psychosocial context in which 

nicotine availability is a necessary but not 

suffi cient condition. Those psychosocial 

contexts are bypassed here so as to focus 

on avenues to understanding genetic 

predisposition to risk in the individual.

Candidate Neural Systems 

as Guides to Smoking and 

Nicotine-Dependence Risk 

Endophenotypes

In the temperament-based model, 

major circuits include the following: 

(1) A dopaminergic, appetitive, frontal-limbic 

circuit is related to approach behaviors, 

surgency, extraversion, novelty seeking, 

and impulsivity.42 It is well recognized that 

behaviors associated with these traits are 

related to drug-use risk generally,43 so they 

are also relevant to smoking initiation 

risk. (2) A neural circuit anchored in 

amygdala and associated stress response 

circuitry is related to neuroticism, anxiety, 

stress response, fearfulness, and perhaps 

depression. These may include drug-specifi c 

as well as general risk characteristics 

inasmuch as nicotine may serve to relieve 

negative affect in a unique manner. 

(3) A frontal-thalamic-striatal circuit, 
344

8. Endophenotypes for Nicotine Dependence Risk

including dorsolateral prefrontal cortex 

and orbital prefrontal cortex, is related 

to effortful control, deliberative behavior, 

working memory, and neuropsychological 

executive functions. It is related to ADHD 

and inattention, and indirectly, to control of 

emotion. Additional neural and personality 

traits can be invoked to address hostility, 

as noted later. 

Although this does not exhaust the 

neural mechanisms to be considered 

(in particular, those that are drug specifi c 

such as cholinergic systems in relation 

to nicotine use), they provide a starting 

point for organizing this list of behavioral 

and psychological markers that are likely 

to be part of a general risk pathway. They 

also provide a basis for bridging to more 

direct behavioral and cognitive probes of 

these same neural systems. What follows, 

therefore, outlines a multilevel-analysis 

perspective on key neural systems related 

to the behavioral markers above. In each 

case, an attempt is made to carry this 

out to the point of describing operational 

measures—that is, low-level experimental 

measures that can serve as endophenotypes 

for future studies.

Figure 8.1 outlines the basic conceptual 

framework as hypothetically linked to 

both behavioral and biological (i.e., central 

nervous system [CNS] and PNS) levels 

of analysis; potential linkages to other 

laboratory measures are noted here. 

This framework, presented in more 

detail in Nigg,42 draws on a handful of 

key formulations44–50 and is similar to a 

detailed presentation by Zuckerman.51 This 

perspective assumes a small set of reactive 

response systems and a regulatory/control 

system that comes under increasing 

volitional control with development. 

These systems underlie temperament and 

personality and are directly relevant to both 

psychopathology and self-control in children 

and adolescents. These systems are relevant 

to consideration of the general risk pathway; 

the degree to which they carry drug-specifi c 

risks will remain speculative here. 

The behavioral traits are assumed to refl ect 

a set of partially discrete neurobiological 

systems anchored at the level of the CNS 

in frontal-limbic neural networks and 

stress response systems and, at the PNS 

level, with reactivity of sympathetic and 

parasympathetic systems. Whereas the 

distinction between temperament and 

personality is debated in the fi eld, that 

issue is bypassed here to focus on the 

conceptual behavioral and neural systems. 

The behavioral traits are known to be 

relatively stable across similar incentive 

conditions and to refl ect reliable individual 

differences across development,52,53 although 

these effects are modest in size over 

long periods of time and include periods 

of substantial change in personality.54

Yet, importantly, early trait scores, mediated 

by later trait scores, can predict onset of 

substance use.55,56

The hierarchical framework begins with 

reactivity of two basic incentive systems—

approach and avoidance42—which are 

related to reactivity of autonomic as well 

as neural systems.44 These are bottom-up 

systems. The framework then proceeds to 

top-down control, the ability to effortfully 

regulate responses as well as emotion and 

attention. Finally, all of these mechanisms 

infl uence attention and are moderated by 

arousal level.

Note again that the domains portrayed in 

fi gure 8.1 are of general importance to 

behavioral regulation; they are implicated 

in key psychopathologies (especially ADHD, 

conduct disorder, and mood disorders) 

and substance-use disorders as well as 

in risk factors for nicotine dependence. 

In the second half of this chapter, the focus 

is shifted to nicotine-specifi c processes. 

Therefore, the discussion begins here by 

outlining a conceptual neural model that 

will allow an organization of potential 
345

Monograph 20. Phenotypes and Endophenotypes

Figure 8.1 Hierarchical Structural Model and Hypothesized Physiological Concomitants

Basic Evolutionary StructureBasic Higher Order Trait StructureRefined Four-Factor StructurePsychological/Emotional Response

Withdraw

NN

Fear Anxiety

C

Effortful ControlReactive Control

A

ApproachEEPositive Approach

Response Facilitation Passive AvoidanceCNS ProbesPNS ProbesCognitive Representation

EEG Profile (L)Cortisol ReactivitySympathetic (electrodermal)Reactive Response to Potential PunishmentLearned CuesNovelty Response

Active AvoidancePFC Activation

Parasympathetic (RSA)

EmpathyDispositional Sympathy

Executive FunctionsCognitive ControlStrategic ResponseLonger Term Reward

ApproachEEG Profile (R)

Alternative (affective) Terminology Negative Emotionality Regulation Positive EmotionalityPrimary Neural Anchor Amygdala/Limbic Circuit Frontal-Striatal Amygdala Limbic-Frontal Reward Circuit

Sympathetic (heart rate)Reactive Response to Reward CueConditioning to Reward

Additional Responses Panic (rage)

Passive Avoidance

Empathy/AffiliationSocial Approach—Cooperation(reverse = hostile, indifferent)

Frustrative/Angry ApproachSocial Approach/DominanceActive Avoidance

Note. Adapted from Nigg, J. T. 2006. Temperament and developmental psychopathology. Journal of Child Psychology and Psychiatry 47 (3–4): 395–422; adapted from Beauchaine, T. P. 2001. Vagal tone, 

development, and Gray’s motivational theory: Toward an integrated model of autonomic nervous system functioning in psychopathology. Development and Psychopathology 13 (2): 183–214; Calkins, S. D., 

and N. A. Fox. 2002. Self-regulatory processes in early personality development: A multilevel approach to the study of childhood social withdrawal and aggression. Development and Psychopathology 14 (3): 

477–98; Markon, K. E., R. F. Krueger, and D. Watson. 2005. Delineating the structure of normal and abnormal personality: An integrative hierarchical approach. Journal of Personality and Social Psychology88 (1): 139–57; Shiner, R., and A. Caspi. 2003. Personality differences in childhood and adolescence: Measurement, development, and consequences. Journal of Child Psychology and Psychiatry 44 (1): 2–32. Openness, associated with E, is omitted for simplicity. N = neuroticism, negative affectivity, withdrawal responding; E = extraversion, approach responding; C = constraint; A = affi liation/agreeableness; CNS = central nervous system; EEG = electroencephalographic; L = left; PFC = prefrontal cortex; R = right; PNS = peripheral nervous system; RSA = respiratory sinus arrhythmia.
346

8. Endophenotypes for Nicotine Dependence Risk

endophenotypes, particularly those that 

may be nonspecifi c before exposure, and an 

analysis of previously studied risk factors 

at lower neurobiological levels, thus 

suggesting additional endophenotypes 

for consideration. Figure 8.2 provides an 

illustration of the potential links between 

genes, neurotransmitter activity and 

receptor function, endophenotypes for 

nicotine-dependence risk at or before initial 

nicotine exposure, and subsequent nicotine 

dependence.

Smoking Initiation 

and Progression 

Risk: Examination 

of Key Candidate 

Psychological 

Domains

A large literature base has linked adolescent 

smoking initiation and progression to 

several pre-occurring social, psychological, 

and behavioral factors. The smoking risk 

variables that are reviewed below are not 

exhaustive but refl ect those most likely to be 

linked to potential genetic endophenotypes. 

For example, although peer smoking has 

consistently been shown to infl uence the 

likelihood of adolescent smoking initiation 

and progression,57–59 the underpinnings of 

peer behavior infl uence may more likely 

be environmental rather than genetic. 

Of course, parental smoking is a signifi cant 

predictor of smoking initiation and 

progression.58,60–63 Clearly, the effects of 

parental smoking on adolescent smoking 

may be genetic and environmental, or may 

refl ect gene-environment correlations, 

in that an adolescent both (1) inherits 

genotypes conferring smoking risk and 

(2) is in an environment in which smoking 

is modeled. Thus, no attempt is made to 

address all vulnerability to smoking and 

subsequent nicotine dependence; rather, 

the focus is on potential markers of the 

genetic component of vulnerability. 

In reading the sections on smoking risk 

variables, the reader should keep in mind 

that these factors themselves are complex 

phenotypes. They are framed here through 

the lens of a neurobiological temperament 

model that allows a multilevel analysis of 

these surface-level endophenotypes, perhaps 

bringing them closer to gene action. Each 

section that follows, therefore, begins at a 

“high,” or abstract, level of analysis with 

behavioral traits. It then proceeds to what 

is known about lower level, more molecular 

(i.e., either construct pure or single factor) 

laboratory measures. The laboratory 

measures can be viewed here as being 

genetically simpler and more promising as 

endophenotypes than are the trait measures. 

However, whether this is always true about 

these measures remains an empirical 

question in nearly every case. The purpose 

here is to show the linkages across these 

levels of analysis to assist the fi eld in 

conceptualizing endophenotypes as target 

measures. This is illustrated by analyzing 

the most-well-studied molecular measures 

and by suggesting logical, additional 

measures of the same systems that are 

essentially unstudied in relation to smoking 

vulnerability.

Approach-Related Risk 

Variables: High-Level 

Psychological Traits

The neural incentive system, labeled here as 

“approach,” is associated with psychological 

processes, such as willingness to approach 

possible incentive or reward/reinforcement, 

and with speed of reinforcement learning. 

It is related also to the personality traits 

of impulsivity and novelty seeking as 

well as extraversion64,65—all of which are 

among the surface traits that have been 

linked to smoking risk. Extraversion, the 

most abstract of these traits, includes 
347

Monograph 20. Phenotypes and Endophenotypes

Figure 8.2 Example of How Potential Endophenotypes Can Link Genes to NicotineDependence Risk at or before Initial Nicotine Exposure

CHRNA4, CHRNA7, CHRNB2, ?

5-HTTLPR

Nicotine

Metabolism nAChRs Dopamine Serotonin

Nicotine-Dependence Risk

Smoking

Progression

Reward

Response Arousal AnxietyResponse

Attention

Capture

Cognitive

Control

Tobacco Use

General

Vulnerability

Initial Nicotine

Sensitivity

Specific

Vulnerability

CYP2A6 DRD4 TH DRD2

Note. Endophenotype areas are presented in gray squares. Specifi c and general vulnerability paths are recognized. Selected 

examples of genes (bottom row) that contribute to neurotransmitter activity and receptor function (dark blue bar) related to these 

endophenotype areas can be identifi ed. This fi gure is illustrative only and does not refl ect a consensus on the factors responsible 

for neurotransmitter function or for the endophenotype areas. nAChRs = nicotinic acetylcholine receptors.
348

8. Endophenotypes for Nicotine Dependence Risk

several lower order constituent traits such 

as positive emotionality, sociability, and 

activity level.49 An extensive literature 

documents both the reliability of individual 

differences in children and adolescents on 

these dimensions and the fact that they 

cohere in a superordinate factor at least by 

early childhood (for reviews see Calkins and 

Fox;45 Putnam and colleagues;66 Rothbart 

and Bates;48 and Shiner and Caspi49), 

although some developmental change may 

emerge with regard to the lower order traits 

contributing to extraversion.52 Disagreement 

remains as to the neurobiological core 

element of this supertrait (see Depue and 

Collins67 and accompanying commentaries). 

However, to facilitate neurobiological 

and cross-species analysis of smoking 

risk endophenotypes, extraversion is 

conceptualized here as related at the level 

of the CNS to the appetitive, dopaminergic 

systems, including the nucleus accumbens 

and ascending frontal-limbic dopaminergic 

networks.42,64,65 At the level of the PNS, 

extraversion is related to sympathetic 

activation, with one index being heart rate 

acceleration following the application of 

effort or the appearance of incentive.44,68

These CNS and PNS measures then become 

operational candidate endophenotypes 

that may be closer to gene action than 

are surface traits such as extraversion or 

novelty seeking.

Impulsivity

Impulsivity is used here to mean the 

tendency to act without adequate 

preparation or thought or to act 

hastily in contexts that call for a slow, 

careful response. One common way to 

operationalize impulsivity is via delay 

discounting. “Delay discounting,” a concept 

found in behavior economic theory, among 

other literatures, describes the process in 

which the value of a reward is discounted 

as a function of delay to its delivery.69

Like other impulsive subjects, smokers 

tend to discount the value of future 

reinforcers more than do nonsmokers.70,71

Thus, impulsivity, seen as a tendency to 

choose reward immediacy over reward 

magnitude,70,72 is a risk factor for smoking. 

Delay discounting rates have been shown 

to correlate with impulsivity, age at fi rst 

substance use, and substance use.72–75 Delay 

discounting affects the type of reinforcers 

that adolescents choose over time76 and 

appears to involve two separate neural 

systems.77 However, a key component of 

neural support for delay weighting involves 

ascending midbrain dopamine circuits. 

Thus, genes and measures tapping these 

circuits are likely to be of interest.

Novelty-Seeking Personality

Novelty seeking is characterized by a 

tendency to seek out new and exciting 

stimuli; engage in sensation-seeking, 

impulsive, and risk-taking behavior; and to 

be sensitive to reward.78–80 This personality 

dimension predicts tobacco use during 

adolescence81,82 and early onset of smoking 

in adolescent boys.83 Indeed, a study of 

longitudinal smoking patterns from ages 

14 to 18 years found that adolescents high 

in novelty seeking were about 15%–20% 

more likely to be members of a trajectory 

involving regular smoking than of a neversmoking trajectory.84

Adolescents high in novelty seeking also 

tend to be more receptive to tobacco 

advertising, which, in turn, has been linked 

to smoking progression.85,86 The heightened 

receptivity to tobacco advertising among 

youth high in novelty seeking may be 

attributable to their greater need for 

stimulation and rewarding experiences. 

Structural equation models suggest that 

novelty seeking indirectly affects substance 

use through other variables that are more 

proximal to use.82,87 This might especially be 

the case for cigarette smoking.88 Evidence 

also suggests that exposure to novelty 

activates the same neural structures that 

mediate the rewarding effects of substances 
349

Monograph 20. Phenotypes and Endophenotypes

of abuse.89 Thus, like impulsivity, individual 

variability in novelty-seeking and drugseeking behaviors may be related to 

individual differences in the dopamine 

reward pathway.90,91

Extraversion

Extraversion is characterized as an outgoing, 

sociable, energetic disposition. Data suggest 

that extraversion is associated with smoking 

initiation among adolescents92 as well as 

current smoking status.93–95 A later study 

found that higher levels of extraversion 

increased the odds of initiating smoking by 

about 40%.92 Extraversion appears to have 

direct and indirect effects on adolescent 

smoking progression.96 Extraversion is a 

multidimensional trait that has several 

alternative formulations. However, one 

major psychobiological formulation is that 

it pertains to the approach system—that is, 

the same ascending dopamine circuitry 

involved in motivation and reinforcement 

response noted above.

Approach: Neural Analysis 

and Laboratory-Based 

Endophenotype Measures

The appetitive, or approach, system, 

involving the midbrain or mesolimbic 

dopamine circuitry (including the nucleus 

accumbens) is central here. Experimental 

probes typically involve examining 

differential response to (1) anticipated 

and (2) actual reward versus control or 

baseline responding. (“Reward” here refers 

to the reinforcing substance, or object 

of the goal-directed behavior, not to the 

hedonic response to smoking or nicotine 

discussed later in this chapter and in 

chapter 9.) Tasks of this nature can then 

be examined behaviorally (e.g., changes in 

reaction time), physiologically (in particular, 

changes in heart rate), and neurobiologically 

(in particular, changes in activation in 

nucleus accumbens via neuroimaging).97,98

Nearly all of these types of tasks have been 

experimentally designed in a nonstandard 

manner across different laboratories, 

so their reliability and heritability are poorly 

assessed. However, what is known about key 

candidate measures is highlighted here.

Reinforcement Response

Reinforcement response is related to 

cognitive control in that (1) the two 

processes are mutually modulating and 

(2) ascending dopaminergic circuits are 

also important in reinforcement response. 

Relevant brain structures again include 

prefrontal cortex, as well as limbic-striatal 

structures, perhaps most notably the 

nucleus accumbens (which activates for 

potential reward [a signal reinforcer] 

as well as actual reward). Here, several 

angles on the reinforcement response 

system are considered. First, this system 

is responsible for learning associations 

that are meaningful. This learning 

(e.g., correlational learning or associative 

learning) is poorly studied in youth who 

go on to smoke. Second, the system is 

responsible for learning associations with 

predictors for reward (operant learning), 

and similarly, for extinguishing response 

to operant predictors that are no longer 

linked to the reward or reinforcer. Third, 

one can ask about the weight put on a 

potential reward (as opposed to an actual 

reward; here the interest is in the signal 

stimulus). A highly active ascending 

dopamine circuitry in the approach circuit 

is expected to place high value on signal of 

potential reward.99 One can then ask about 

weighting of immediate, small reward versus 

later, larger reward, or delay discounting. 

Steep delay discounting is related to 

impulsive behavior and may be related to 

differences in this reinforcement system. 

This last perspective on reinforcement 

response is the only one that has been 

studied as of 2008 in relation to smoking 

onset, so it is focused on here via the 

following key tasks. 
350

8. Endophenotypes for Nicotine Dependence Risk

Reward Signaling and Discounting 

Tasks

Reward signaling in the brain involves 

several discrete elements99 that will be 

useful to decompose in future studies of 

reward and smoking risk. The properties of 

nearly all tasks are still being worked out. 

However, several promising probes that 

could serve as endophenotypes for future 

research have emerged, such as the Iowa 

gambling task.100 This task is the one most 

often used in substance-use research to 

assess reward weighting and is associated 

with alcohol and drug abuse.101 As of 2008, 

it has not yet been utilized to assess risk for 

smoking onset. In this task, the individual 

“plays” a series of cards from four decks. 

Each deck has a different reward-cost ratio. 

Impulsive individuals tend to choose big 

rewards even though they come with bigger 

losses (and a net loss in the end) instead 

of smaller rewards that lead to a net gain. 

The biological linkage to this task of brain 

regions for the ascending dopamine circuitry 

described previously is supported by 

lesion.100 Another related paradigm is reward 

signaling. In this task, the youth sees a cue 

indicating that a reward of varying size will 

soon be received. The cue appears to activate 

the nucleus accumbens.97 In one small 

study, failure of such activation was related 

to ADHD.98 Reward signaling has promise 

but has not been studied genetically. 

Reward-discounting tasks may be the most 

promising; these are used with either real or 

hypothetical rewards, with similar effects,102

and tasks using real rewards can be adapted 

for very young children.103 Most well studied 

is a hypothetical reward-discounting task, 

which can be useful beginning as early as 

middle childhood. In this task, the youth 

makes a series of hypothetical choices 

indicating a preference for a larger amount 

of money later (e.g., $100) and a smaller 

amount now (e.g., $10), with the amounts 

stochastically varied to fi nd that individual’s 

breakpoint of preferring to wait. This 

task has the advantage of being directly 

transferable to animal studies, a major 

advantage for an endophenotype. As a result, 

linkages to reward circuitry in ventral and 

orbitoprefrontal cortex and ventral striatum/

nucleus accumbens have been demonstrated 

in animal research104 and in human 

neuroimaging studies.105 Further, behavior 

on this task is related to ADHD,106 which is 

one behavioral risk factor for smoking. 

In fact, considering predictive validity, 

these types of measures are not well 

utilized with regard to risk of nicotine use 

initiation or, for that matter, much studied 

in relation to children. The majority of 

studies of delay discounting have involved 

adult populations or those who are already 

smoking (chapter 9), whereas most studies 

of reward cue response tasks have not 

looked at smoking outcomes in youth. 

However, current smokers tend to discount 

the value of future rewards compared to 

never smokers and those who do not smoke 

daily or regularly (e.g., chippers).102,107 It is, 

therefore, unclear if reward discounting 

refl ects propensity to become addicted once 

exposed to cigarettes or refl ects risk for 

onset.102 Further, the role of this variable in 

adolescent smoking has either been unclear 

or indirect.84,108 For example, one study 

found that delay discounting (based on a 

self-report measure) was indirectly related to 

smoking initiation and progression through 

variables more proximal to smoking.76 Data 

related to adolescent smoking cessation 

indicated that adolescents unable to achieve 

abstinence discounted monetary rewards on 

a computerized discounting task more than 

did those adolescents who were abstinent 

from smoking.109 Finally, laboratory studies 

of adult smokers (smoking ≥15 cigarettes 

daily) suggest that upon abstinence, 

regular smokers experience abstinenceassociated defi cits in incentive motivation.110

For example, compared to performance 

during an abstinence phase, smokers show 

increased responsiveness to monetary 

reward on the Card Arranging Reward 
351

Monograph 20. Phenotypes and Endophenotypes

Responsivity Objective Test during a 

nicotine phase.110

With regard to heritability, these tasks are 

not well studied. One small twin study 

suggested that heritability of delay aversion 

in young children is quite low, on the order 

of .2 to .3,111 suggesting that unless its 

genetic architecture is very simple, it will 

not be a useful endophenotype. However, 

it may be that either latent variables that 

resolve measurement unreliability will yield 

a stronger genetic signal in this domain 

or that delay discounting tasks will exhibit 

higher heritability. 

Physiological Measures of Reward 

Response

In addition, this system can be measured 

either peripherally by heart rate acceleration 

to a possible reward or centrally by 

functional magnetic resonance imaging 

(fMRI) measures of nucleus accumbens 

activation to potential reward.97 These 

measures have extensive validation 

literature, suggesting they tap the relevant 

reward circuitry,97,112 but virtually no 

heritability studies.

Avoidance-Related Risk 

Variables: High-Level 

Psychological Traits

Neuroticism

Neuroticism, a basic, higher-order 

personality trait, refl ects a generalized 

tendency to experience negative affect, 

to have diffi culty coping with stress, and 

to be nonresilient in the face of change. 

It has substantial heritability.113–115 High 

neuroticism has been shown to prospectively 

predict smoking behavior in adolescents 

and young adults.116–118 These studies of 

neuroticism and youth smoking acquisition 

appear to be consistent with a large body of 

adult research showing a positive association 

between neuroticism and smoking.113 Later 

fi ndings indicate a signifi cant association 

between platelet monoamine oxidase (MAO) 

activity and neuroticism,69 which are both 

associated with smoking behavior.119 About 

10% of the genetic variation in neuroticism 

appears to be due to genes that also act 

on MAO. MAO activity has been shown 

to increase as a result of smoking and 

to decrease during periods of smoking 

cessation.120,121 Thus, genes related to MAO 

activity and their biological markers may 

be useful targets for genetic research on 

smoking risk. 

Stress

Related, and often considered within the 

overall construct of neuroticism, are the 

subjective feelings of stress. There has been 

less research on the impact of subjective 

feelings of stress on adolescent smoking 

acquisition than on other psychological 

variables. The available research, however, 

suggests that stress is related to smoking 

initiation,116,122 smoking status,123–125 and 

a decreased likelihood of quitting126 in 

adolescents. Yet, an important and often 

overlooked aspect of this link between stress 

and smoking is that it appears to act in only 

one direction. Controlled studies in adults 

confi rm that acute stressful challenges, for 

example, reliably increase smoking behavior, 

but that an increase in smoking does not 

seem to subsequently relieve the subjective 

distress resulting from the challenges,127

although such smoking clearly relieves 

distress due to tobacco abstinence.128 It is 

not at all clear that stress relief explains the 

reliable increase in smoking due to all or 

even most stressors.

Depression

Depression is one of the most common 

psychiatric disorders in adolescence. 

It is characterized by depressed mood, 

anhedonia, vegetative symptoms, and 

impaired psychosocial functioning. 
352

8. Endophenotypes for Nicotine Dependence Risk

Subthreshold depression (depression that 

does not meet all criteria for the diagnoses 

of major depression) is also prevalent in 

youth; it is associated with psychosocial 

impairment and often precedes and 

follows a major depressive episode.129–135

Neuroticism is a major diathesis for 

depression.136 Depression predicts smoking 

initiation,137,138 current smoking,139,140 and 

nicotine dependence in adolescents.141 About 

32% of adolescent smokers have a lifetime 

history of major depression compared to 

17% of nonsmokers.142 Major depression 

is associated with a 19% increase in the 

average daily smoking rate (cigarette intake) 

and a 75% increase in the odds of being 

nicotine dependent from mid-adolescence to 

young adulthood (16–21 years old).143 Young 

adults (aged 21–30 years) with a history 

of major depression are three times more 

likely to progress to daily smoking compared 

to those without major depression144 and 

over two times more likely to progress to 

nicotine dependence.145

Some research suggests that the association 

between smoking and depression results 

from common factors (e.g., genetic or 

environmental factors) that are associated 

with both increased risks of depression and 

increased risks of smoking.146,147 Signifi cant 

comorbidity between smoking and major 

depressive disorder was found before, 

but not after, adjustment for presence of 

other psychiatric disorders.142,148 Other 

studies of adolescents and adults suggest 

that control for factors common to smoking 

and depression was not adequate to explain 

their association.143,145,149,150 Alternatively, 

the association between smoking and 

depression may refl ect a cause-and-effect 

relationship. The direction of the causal 

effect is controversial.58,138,140,142,144,151

Thus, highlighting concerns with 

heterogeneity in risk pathways to smoking, 

some fi ndings indicate subpopulations 

of adolescents who differ with respect 

to the relationship between smoking 

and depression (i.e., smoking increases 

depression symptoms in some and decreases 

depression in others). Specifi cally, the study 

empirically identifi ed three distinct 

depression trajectories from ages 14 to 

18 years. Smoking was not associated 

with being in the low symptoms trajectory 

but was associated with acceleration in 

depressive symptoms for adolescents in the 

moderate symptoms trajectory and with 

a deceleration of depressive symptoms in 

the high symptoms trajectory.152 Thus, 

a subgroup may exist (those with higher 

symptoms) who “self-medicate” depressive 

symptoms with nicotine. A later section 

considers whether this is a direct effect or 

an indirect effect mediated by the improved 

attention provided by the nicotine.153

Another study found that cigarette smoking 

had disproportionate reward value for 

depressed smokers.154 It is possible that the 

heightened reinforcing value of smoking 

may mediate the relationship between 

depression and smoking behavior. The 

mesocorticolimbic dopamine reward pathway 

appears to be dysfunctional in individuals 

with major depression, such that they are 

more responsive to substances that activate 

these reward systems.155,156 Within a tripartite 

neurobiological model,157,158 depression 

is viewed as refl ecting both an elevated 

neuroticism, which is a nonspecifi c marker 

of internalizing psychopathology, as well as a 

shortage of positive affect (underfunctioning 

of an approach system). Thus, a key question 

for smoking endophenotypes is whether 

smoking risk is associated with over- or 

underfunctioning of the incentive reward 

systems in the brain. Multiple genetic 

pathways are possible in this regard.

Anxiety

Like depression, anxiety disorders can 

range in degree from a full-scale disorder 

to subthreshold levels.159 Anxiety tends to 

be linked to neuroticism and to negative 

affect.157 The hallmark features of anxiety 
353

Monograph 20. Phenotypes and Endophenotypes

disorders include uncontrollable worry, 

physical symptoms such as sweating palms 

and increased heart rate, and secondary 

features such as restlessness and diffi culty 

concentrating.159 Research suggests an 

association between cigarette smoking and/or 

nicotine dependence and anxiety disorders 

in young adults and adolescents.149,160–162

However, it appears that smoking may 

precede the onset of anxiety disorders.160,163

In fact, adolescents who smoked more than 

20 cigarettes a day were 6.8 times, 5.5 times, 

and 15.9 times more likely to be diagnosed 

with agoraphobia, generalized anxiety 

disorder, and panic disorder, respectively.163

Anxiety disorders during adolescence were 

not associated with cigarette smoking during 

young adulthood. In contrast, another study 

found that anxiety symptoms predicted 

smoking initiation in youth.138 Chronic 

symptoms of anxiety during adolescence 

predicted progression to nicotine dependence 

during young adulthood.164 In addition, 

adolescents and young adults with social 

fears have an increased risk of nicotine 

dependence.165 Thus, the relationship 

between anxiety and smoking may depend 

on the degree of anxiety (clinical diagnosis 

versus subclinical symptomatology) as well 

as the type of anxiety disorder.

Alternatively, the relationship between 

smoking and specifi c anxiety disorders 

may not be best represented by a direct 

effect. Neuroticism predicts the cooccurrence of smoking and panic disorder166

and moderates the effects of maximum 

smoking rate on lifetime history of panic 

disorder.167 Indeed, it has been argued that 

mediator and moderator approaches that 

consider contextual factors may be more 

informative than direct-effect approaches 

for understanding the relationship between 

negative affective states, such as anxiety, 

and the smoking behavior developmental 

continuum.168

Neurobiologically, anxiety, and its emotional 

cousin, fear, are related to activation of 

the particular nuclei in the amygdala and 

associated neural structures that signal 

potential negative events.169,170 MAO plays a 

signifi cant role in serotonin metabolism and 

transmission,171,172 which has been implicated 

in anxiety disorders.173 Models that consider 

the links between MAO, serotonin, and 

smoking may advance understanding the 

relationship between anxiety and smoking 

behavior from a genetic perspective.

Avoidance: Neural Analysis 

and Laboratory-Based 

Endophenotype Measures

The avoidance dimension, as conceptualized 

here, is anchored by readiness of behavioral 

withdrawal-related behavior in potentially 

unrewarding or uncertain contexts, and 

with associated affective reactivity (i.e., fear, 

anxiety, and sadness). This dimension 

is related to emotions of anxiety and 

depression, as well as to the personality 

trait of neuroticism, which, as noted above, 

is another set of surface traits related to 

smoking risk. Neuroticism is the most 

abstract of these and has component factors 

such as negative affectivity and anxiety. 

As discussed in the previous section, 

neuroticism can be viewed at lower levels 

of analysis that may be closer to gene 

action. In this case, the reactivity of these 

avoidance responses is related at the level of 

the CNS to limbic-frontal neural circuitry 

and the amygdala. Depue and Lenzeweger174

describe fear as an immediate threat 

response involving short-term activation 

in the central amygdala nucleus, whereas 

anxiety is a long-term activation to lowgrade threat associated with activation in 

the bed nucleus of the stria terminalis in 

the extended amygdala. Thus, reactivity of 

a stress-response or danger-alarm system 

(hypothalamic-pituitary-adrenocortical 

[HPA]) axis and associated autonomic and 

hormonal effects, which at the CNS level 

includes the lateral hypothalamus, reticular 

formation, and other structures) is a key 
354

8. Endophenotypes for Nicotine Dependence Risk

feature. At the level of the PNS, reactivity of 

this set of response systems is hypothesized 

to emanate in sympathetic activation 

of autonomic systems, in particular 

electrodermal skin response to anticipated 

loss of reward.44,112 At the CNS level, as noted 

in table 8.1, electroencephalogram (EEG) 

measures appear to index relative degree of 

predisposition to approach and avoidance 

activation by characteristic lateralized 

asymmetries in EEG power.45,175–178 The fMRI 

data suggest that amygdala activation 

(associated with avoidance of potentially 

unpleasant events) and nucleus accumbens 

activation (related to approaching a 

potentially positive event) appear to 

be mutually inhibiting responses.179

Examination of these types of physiological 

measures as potential endophenotypes 

may bring data closer to gene action 

and help identify risk mechanisms for 

smoking beyond the broad surface trait of 

neuroticism or its constituent elements of 

anxiety or negative mood. However, only 

a handful of such measures have been 

examined, as noted in table 8.1.

Neuroendocrine Response 

to Stress/Cortisol

In particular, neuroendocrine response 

in relation to danger and stress response 

systems, as potential refl ection of avoidancerelated responding,174,181,182 includes two 

biological systems (conceptually related 

respectively to psychological fear and anxiety, 

as distinguished in the previous paragraph). 

First, the sympathetic adrenomedullary 

system is thought to be a fast-acting 

system (including providing adrenalin for 

“emergency” or alarm response) that in 

day-to-day regulation of behavior may index 

excitement, vigilance, or alertness; however, 

another interpretation is that it indexes 

the negative affectivity “fear” response.174

Second, the limbic HPA system is thought 

to be a slow-acting stress response system 

associated with arousability and negative 

emotions181—more specifi cally, anxiety.174,183

Its activity (primarily, corticotrophinreleasing hormone) is most often indexed 

by peripheral cortisol levels. Set points or 

reactivity in these systems may underlie the 

observed personality correlates of smoking 

risk (e.g., smoking to alleviate fear or 

anxiety, or attentional bias toward drugs of 

opportunity to relieve internal emotional 

discomfort). Therefore, cortisol reactivity 

is a candidate endophenotype that may 

capture predispositions to smoking, albeit 

nonspecifi cally, at a lower level of analysis 

neurobiologically. However, its promise is 

somewhat unclear. Associations of cortisol 

measures with behavioral measures are 

decidedly mixed,176 due in part to the need to 

interpret cortisol (and for that matter, other 

biological markers) in relation to behavioral 

context.44,182 Therefore, it may be useful to 

examine cortisol reactivity in relation to 

smoking cues or in relation to stressors that 

are contextually linked to smoking onset. 

Yet, the decline in cortisol soon after quitting 

is predictive of quitting success in chronic 

smokers, as noted in chapter 9, suggesting a 

very different process indexed by cortisol in 

that population.

Other Measures

Further measures could be considered. These 

include skin conductance response and heart 

rate to potential loss of reward, as well as 

other measures of avoidance learning.112

Control-Related Risk Variables: 

High-Level Psychological Traits

Attention Defi cit Hyperactivity 

Disorder

ADHD is a developmental disorder 

characterized by age-inappropriate levels 

of hyperactivity and impulsivity and an 

inability to sustain directed attention.184

Because of its very high heritability, early 

onset (generally much earlier than smoking 

initiation), and long-term stability of 
355

Monograph 20. Phenotypes and Endophenotypes

Table 8.1 Extant Data on Potential Endophenotypes and Their Measurement

Neural system/function Reliability Heritability Validity

Attentional capture

Orienting and alerting tasks

Flanker task

Posterior activation on fMRI

N1 ERP component

P2 ERP component

Unknown

Moderate

Unknown

Moderate

Moderate

Unknown

Low

Unknown

Moderate

Moderate

NA

NA

NA

NA

NA

Arousal

EEG slow-wave activity

Reaction time

Signal detection

High

High

Moderate

High

Moderate

Unknown

NA

NA

NA

Cognitive control/top-down attention

Stroop interference task

Working memory tasks

Digit span backwards

N-back

Spatial span back

Response inhibition

Stop-go task

Go/no-go task

Antisaccade task

Cardiac measures

Vagal tone/RSA

CNS measures

P300 amplitude

Moderate to low

Moderate to high

Unknown

Unknown

High

Moderate

Moderate

High

High

Poor

Unknown

Unknown

Unknown

Moderate

Moderate

Unknown

Unknown

Mod to high

NA

NA

NA

NA

NA

NA

NA

NA

1

Executive functioning/planning

Tower of London

Tower of Hanoi

Poor

Poor to mod

Unknown NA

NA

Trait measures

Personality constraint

Effortful control

High

High

Mod to high

Moderate

NA

NA

Approach-related and reward response markers

Iowa gambling task

Delay discounting task

Incentive response reaction time

Cardiac measures

Heart rate acceleration to possible reward

CNS

Nucleus accumbens activation

Trait measures

Extraversion

Positive affectivity

NA

High

NA

NA

NA

NA

High

High

NA

Poor

NA

NA

NA

NA

Mod to high

Moderate

NA

1

NA

NA

NA

NA

2

NA

Anxiety response and avoidance-related measures

Response cost measures

PNS

Skin conductance

Heart rate to loss of reward

CNS

Lateralized EEG profile

Trait measures

Neuroticism

Negative affectivity

Moderate

NA

NA

High

High

Poor180

NA

Mod to high

Mod to high

Mod to high

NA

NA

NA

2

NA

Note. For reliability, high = ≥.7, moderate =.5–.7, poor = ≤.5; for heritability, high =.5–.7, moderate =.3–.5, low = ≤.3. See text for 

biological plausibility and references. Predictive validity pertains only to smoking onset, not to other outcomes. In that respect, validity 

here is rated as follows: 1 = little supportive data; 2 = moderate amount of supportive data; 3 = well established. Heritability data are 

provided in the text; see corresponding sections in the text for review of literature and citations relevant to the conclusions stated in this 

table. NA = data are too sparse to enable any comment or studies are not available in this domain to insert a rating; fMRI = functional 

magnetic resonance imaging; ERP = event-related potential; EEG = electroencephalogram; mod = moderate; RSA = respiratory sinus 

arrhythmia; CNS = central nervous system; PNS = peripheral nervous system.
356

8. Endophenotypes for Nicotine Dependence Risk

symptom levels (though not of diagnostic 

type),185,186 it has some advantages over 

later-onset disorders (such as anxiety 

and depression) in potentially predicting 

smoking onset.

The Diagnostic and Statistical Manual of 

Mental Disorders, fourth edition (DSM-IV)159

identifi es three subtypes of ADHD: 

predominantly inattentive, predominantly 

hyperactive and impulsive, and combined, 

although the appropriate etiological 

subtyping of ADHD and characterization of 

its own cognitive endophenotypes remain 

an active area of investigation.187,188 ADHD 

has been associated with an increased 

risk of adolescent smoking initiation and 

progression.189–196 Youth diagnosed with 

ADHD and youth with higher ADHD 

symptoms (although not a diagnosis) tend 

to start smoking earlier than those without 

either.192,193,197,198 ADHD history also predicts 

inability to quit among dependent smokers, 

as discussed in chapter 9.

It is unclear whether inattention or 

hyperactivity/impulsivity are equally 

predictive of smoking or whether one set 

of symptoms is more strongly associated 

with smoking than the other. This is 

important because some models suggest 

that symptoms of inattention may yield 

partially distinct temperamental and neural 

correlates (primarily related to cognitive 

control) versus hyperactivity/impulsivity 

(primarily related to reward response).188

Adolescent and adult research supports 

an association between smoking and 

inattention, but not between smoking and 

hyperactivity.195,199 It has been speculated 

that those with ADHD may smoke to 

self-medicate their attentional defi cits.200

In support of this notion, Molina and 

Pelham193 found inattention rather than 

hyperactivity/impulsivity to be more 

predictive of subsequent smoking. However, 

retrospective reports of childhood ADHD 

symptoms among young adults suggests 

that hyperactivity/impulsivity is a stronger 

predictor of regular smoking than are ADHD 

inattention symptoms.197 Laboratory-based 

research has found that acute nicotine 

administration positively affects both 

cognitive and behavioral inhibition among 

nonsmoking adolescents with ADHD,201 but 

both of these may be related to cognitive 

control and inattention symptoms.187

However, many studies did not adequately 

control for conduct problems/conduct 

disorder. These antisocial behavior 

problems often overlap with ADHD and 

may identify the subgroup at greatest risk 

of smoking. The association between ADHD 

and adolescent substance use, including 

smoking, is often weakened or rendered 

insignifi cant when comorbid conduct 

disorder is considered,200,202–204 although 

not in all studies,153,203,205 especially when 

the independent effects of inattention are 

evaluated.203 Some data suggest that ADHD 

and conduct disorder may be associated with 

different substance-use characteristics, such 

as early onset and frequency of use.206

Neurobiologically, ADHD, and particularly 

the inattention component, is thought to be 

related to defi cits in cognitive control that 

are instantiated in the prefrontal cortex, 

striatum, and cerebellum. These frontalsubcortical circuits are involved in working 

memory, cognitive control, and planning 

and execution of complex behaviors. 

Laboratory measures of these abilities are 

well associated with ADHD207 and, therefore, 

may be potential laboratory-based candidate 

endophenotypes for smoking onset. ADHD 

has been reliably associated with a handful 

of specifi c genes, including the dopamine 

transporter gene, dopamine D4 receptor 

gene, and others,208 potentially providing 

further clues to the genetics of smoking 

initiation risk. An additional neurobiological 

aspect of ADHD is apparent association 

with low cortical arousal, as indicated by 

poor signal detection209 and excess slowwave EEG.210 Also consistent with arousal 

dysregulation as a risk phenotype, Wong and 
357

Monograph 20. Phenotypes and Endophenotypes

colleagues211 found that sleep problems of 

three- to fi ve-year-olds, as rated by mothers, 

predicted early drug-use onset, including 

smoking by 14 years of age, in adolescence. 

Whether smoking provides specifi c 

compensation and is uniquely related to 

the underaroused profi le described above is 

unclear, but as noted, this is one possible way 

of understanding smoking attraction in these 

youth. Endophenotypes that tap an arousal 

system, particularly ascending noradrenergic 

circuits, may therefore be of use.

Conduct Disorder and Aggression/

Hostility

Conduct disorder is defi ned as a persistent 

pattern of behavior in which age-appropriate 

societal norms are repeatedly violated.159

Typical behaviors include aggression, deceit, 

stealing, damage to others property, cruelty, 

and general rule violations. Adolescents 

with conduct disorder are almost 13 times 

more likely to be current smokers than are 

adolescents without conduct disorder.212

In fact, conduct disorder predicts earlier, 

regular (daily) adolescent cigarette smoking 

and has been shown to be a mechanism by 

which family risk factors affect adolescent 

smoking.189 Externalizing disorders, such as 

conduct disorder, tend to have the highest 

associations with progression to daily 

smoking and nicotine dependence compared 

with other psychiatric disorders.194 At the 

same time, they are clearly recognized as a 

general risk factor for drug use overall and 

are not specifi c to nicotine use.9,43,213

A later study found that physical aggression 

increased the odds of smoking before 

14 years of age by 16%. Thus, adolescents 

with earlier onset of smoking tend to be 

more physically aggressive than those who 

have not initiated smoking by this age.85

It is possible that adolescents who have 

diffi culty coping with anger and frustration 

use cigarettes as a coping method. Nicotine 

has been shown to have palliative effects 

on anger and to reduce the frequency of 

anger reports in smokers with high levels of 

hostility.214,215 In fact, research that evaluated 

the metabolic effects of nicotine in the brain 

found that nicotine triggered dramatic 

changes in regions of the brain important 

in behavioral control in individuals rated as 

more aggressive or easier to anger.216 This 

may be especially relevant for understanding 

adolescent smoking in that adolescents 

attempt to manage extremes in emotion 

before behavioral control centers in the 

brain have fi nished maturation.217 Animal 

models indicate that aggressiveness may 

be partially due to fetal nicotine exposure; 

for example, rodents exposed to nicotine 

in utero had higher levels of aggressive 

behavior compared to those with no in utero 

nicotine exposure.

However, the developmental progression 

requires further elucidation. Most 

youth with conduct disorder had earlier 

oppositional defi ant disorder,218 and in turn, 

youth with oppositional defi ant disorder 

tend to have irritable early temperament.219

Irritable temperament may refl ect perinatal 

risks, including prenatal exposure to 

nicotine,220 or genetic effects on regulation 

of negative affect/irritability. Likewise, 

ADHD is a risk factor for later development 

of conduct disorder.219 It may be that these 

represent related routes of vulnerability, 

with smoking onset as a later outcome of 

these early risks. Identifying endophenotypes 

related to conduct disorder will therefore 

overlap with endophenotypes related to 

ADHD. Indeed, studies suggest that conduct 

disorder, ADHD, and substance use may 

be explained by a highly heritable latent 

phenotype of behavioral disinhibition.221

Control: Neural Analysis 

and Laboratory-Based 

Endophenotype Measures

Previously, two superordinate dimensions 

were noted: (1) extraversion and 

(2) negative emotionality, or neuroticism. 
358

8. Endophenotypes for Nicotine Dependence Risk

The third superordinate dimension in 

personality structure is variously labeled 

as low constraint,222 unsocialized sensation 

seeking,51 and low effortful control.48,223

Higher levels of constraint and effortful 

control are inversely related to ADHD 

(see Nigg187 for a review) as well as to 

a lesser extent with aspects of conduct 

disorder and impulsive aggression. When 

dysfunctional, it is related to diffi culty in 

regulating attention and may be related to 

ease with which attention can be captured 

by incentives or potential incentives 

in the environment (e.g., possibility of 

trying drugs or cigarettes). “Effortful 

control in young children,” as defi ned by 

Rothbart and colleagues (e.g., see Putnam 

and colleagues66) includes elements of 

attentional control, low-intensity pleasure, 

and attentional shifting and focusing 

behaviors. Again, these surface behaviors 

can be analyzed at a neurobiological level 

that may suggest candidate endophenotypes.

The capacity for and tendency to exert 

effortful control is theorized to depend 

on anterior neural systems. These neural 

systems emphasize frontal-striatal 

neural loops that are dopaminergically 

modulated.224 This system can regulate the 

affective response systems. For example, 

human neuroimaging studies have now 

shown a role for top-down prefrontal 

modulation of subcortical regions.225,226

In other words, prefrontal activation is 

associated with reduced limbic activation. 

The importance of this is that it provides 

imaging evidence confi rming the direct 

neural regulation of affective response by 

top-down effortful control. If weakness in 

the top-down control system is associated 

with smoking risk, future smokers would 

be expected to show weaker prefrontal 

activation on the types of challenge tasks 

used in these studies.

Cognitive control is a more formal term 

for effortful control. It refers to the ability 

to manage competing information and 

deliberately direct attention in the service 

of task demands. It includes subsidiary 

abilities such as response suppression, 

working memory, and response selection. 

From this angle, numerous available 

laboratory measures can be identifi ed that 

may be viable endophenotypes. These are 

extensively validated by neuroimaging 

data as activating the neural circuits of 

interest (described in sections below). 

These measures tend to involve circuitry 

modulated by dopamine and noradrenergic 

activity. In turn, acetylcholine neurons likely 

modulate these circuits.227 They, therefore, 

are relevant to nicotine maintenance as 

well as onset. The endophenotypic criteria 

(reliability, heritability, and predictive 

validity) are described and considered below 

for selected measures of cognitive control, 

identifi ed by their having some data on 

association with smoking onset and/or 

maintenance. Again, if weak cognitive 

control is associated with vulnerability to 

smoking onset, adolescents who go on to 

smoke would be expected to have slower 

reaction times and more errors on executive 

function tasks than do adolescents who do 

not go on to smoke. Also, these cognitivecontrol abilities are important to academic 

success; if nicotine improves these abilities, 

then that improvement could add to 

nicotine’s reinforcing effects, as discussed 

in the next section. For example, it may be 

that nicotine acutely enhances cognitive 

control.227

Response Inhibition

Response inhibition is the ability to suppress 

a prepared response in a rapid-decision 

context. Several widely used tasks have been 

used to assess it. The go/no-go task is the 

most well known. The individual presses a 

key as rapidly as possible when the target 

appears (e.g., the letter “X” appears at 

variable intervals averaging once per second 

on the computer screen). On a minority 

of trials (25%), the “X” is colored red, 

or a different letter appears, meaning it is a 
359

Monograph 20. Phenotypes and Endophenotypes

“no-go” trial. Another task is the antisaccade 

task in which individuals are to refrain 

from moving their eyes toward a target that 

suddenly appears in the periphery of their 

vision; that is, they must suppress the refl ex 

to move toward that target and, instead, 

move their eyes in the opposite direction to 

get a correct response. Perhaps the mostwell-validated measure of the ability to 

suppress a prepared response is the stop-go 

task.228 The individual faces a computer 

screen and on a series of trials decides as 

quickly as possible whether the letter that 

appears is an “x” or an “o.” On 25% of the 

trials, however, a tone sounds indicating that 

the response should be interrupted (stop 

trials). The timing of the warning tone is 

varied to enable estimation of how much 

warning the individual needs, interpreted 

as speed or effi ciency of the stop process. 

Physiological data have demonstrated that 

response interruption on this task involves 

both central and peripheral mechanisms.228

Both lesion data and imaging data indicate 

that this ability involves a circuit in the brain 

that includes the right inferior frontal gyrus 

and the striatum.229,230 Brain recording data 

in primates indicate that specifi c neurons 

in these brain regions are active during 

response interruption.231 The computerized 

measure has excellent reliability.232

Heritability of these individual measures 

has not been well established, although 

forthcoming data appear to place heritability 

of stop-go task stop-signal reaction time 

(SSRT) at <.50, the antisaccade task at about 

.56 (E. Willcutt, personal communication, 

January 2007), and the go/no-go task at 

<.50.233 However, it is notable that when a 

latent variable is constructed from these 

response inhibition measures, it appears 

to have more robust heritability, although 

this latent variable heritability has 

varied widely in two studies from .48 to 

.99.234–236 The promise of this function as 

an endophenotype appears to rely upon 

utilizing latent variables (an approach not 

yet attempted to evaluate risk for smoking 

onset or persistence) or the hope that the 

less-heritable individual measures will be 

genetically simpler than the phenotypes to 

which they are indexed. No evidence on this 

last point has emerged. However, the brain 

circuitry involved is dopaminergically 

modulated and also appears to depend 

on acetylcholine receptors.227 Thus, 

examination of receptor genes for 

dopamine and acetylcholine may clarify the 

endophenotypic value of these measures.

Further, surprisingly few data are available 

regarding response suppression and risk 

for smoking onset. It is unclear whether 

stop-go task performance predicts smoking 

initiation or progression to regular smoking 

and nicotine dependence. One study found 

that the SSRT was signifi cantly improved 

following nicotine administered via 

transdermal nicotine patch in nonsmoking 

adolescents diagnosed with ADHD.201

Another study of healthy adult regular 

smokers did not fi nd acute effects of nicotine 

on this inhibition measure.237 With respect 

to the go/no-go task, smokers tend to show 

more impulsivity on these measures than 

do nonsmokers.238

P300 Event-Related Potential

Several EEG/ERP measures may be 

worthwhile as endophenotypes. However, 

the aspect most studied in relation to 

smoking risk is the P300 wave. The P300 is 

an ERP component thought to be related to 

working memory and stimulus evaluation. 

As such, it probably indexes cortical 

activity. It is typically assessed by having 

an individual complete a computerized 

attention task or go/no-go task with 

unexpected events included in a minority 

of trials (sometimes called “oddball” trials). 

The individual has to evaluate this event and 

update working memory; this is thought 

to be indexed by differences in the peak 

amplitude (strength) and speed (latency) of 

the ERP response at 300 milliseconds. Initial 

data in adults indicate that the reliability of 
360

8. Endophenotypes for Nicotine Dependence Risk

this index is excellent (>.80), and heritability 

of the amplitude is moderate to high, in the 

range of .6 to .7;239–241 heritability may be 

higher in males than in females,241 whereas 

heritability of latency was unclear.239 Hence, 

the focus here is on the P300 amplitude.

P300 amplitude appears to have important 

linkages at the phenotypic level with smoking 

and related risk behaviors. In a community 

sample of 17-year-old males, reduced P300 

amplitude was related to externalizing 

behavior (defi ned as the common factor 

underlying nicotine and other drug 

dependence, conduct disorder, and adult 

antisocial behavior).242 A series of studies 

from the Minnesota Twin Family Study 

has shown that reduced P300 amplitude at 

17 years of age predicted the subsequent 

development of substance-use disorders, 

including nicotine dependence.241,243

The P300 may be related to persistence as 

well as to onset. Studies of adults found 

lower P300 amplitude in current smokers 

compared to never smokers, whereas former 

smokers did not differ signifi cantly from 

never smokers.244 In addition, the amplitude 

has been shown to be reduced in nicotineabstinent adults compared to nonsmokers 

but, after smoking, was equivalent to that of 

nonsmokers.245 Further clarifi cation of the 

state or trait characteristics of this measure 

in relation to onset and persistence appears 

to be warranted.

Other Candidate Tasks

A wide range of other psychometrically 

reliable measures relevant to cognitive 

control are available and may warrant 

exploration. Their heritability data, however, 

varies. Key examples are as follows. First, 

measures of working memory tap cognitive 

control systems and are strongly related 

to risk for psychopathology.42,246 These 

include such measures as counting and 

sentence span (the child recalls and repeats 

ever larger lengths of items, sometimes 

backwards or while doing a competing 

task), and N-back tasks (the child updates 

working memory with a new total every 

N-items, e.g., every three items). These 

tasks have excellent validity with regard to 

neural activation in dorsolateral prefrontal 

cortex,247 psychometric reliability, and 

theoretical coherence. Their individual 

heritability appears to be modest, in the 

range of .4.233–235,248 However, they may be 

infl uenced by a simpler genetic architecture 

involving the noradrenergic alpha-2A 

receptor gene249 and dopaminergic genes. 

These measures, however, have not 

been widely studied with regard to their 

phenotypic or genotypic association with 

smoking onset.

Second, measures of set shifting or task 

switching have become quite sophisticated 

in their ability to assess cognitive control.250

Simple neuropsychological measures such 

as the card sorting tasks, in which the 

individual must remember the working 

rule for sorting cards (e.g., by color, 

number, or shape) while problem solving 

errors, are of interest. These tasks have 

large validation literatures indicating that 

they entail activation of the dorsolateral 

prefrontal cortex.63 Although heritabilities 

based on single measures are modest 

(in the range of .50),234,251 a composite latent 

variable of set shifting on card sort measures 

has heritability approaching .80.234

Third, direct neuroimaging measures of 

brain morphometry have been utilized very 

little in assessing smoking risk. However, 

because brain imaging measures show 

moderate associations with other risk 

phenotypes (such as ADHD), they may be 

worth pursuing. Further, substantial data 

show that a range of relevant morphometric 

measures have heritability exceeding .8252,253

or are highly familial,254 and that some 

directly relevant functional activation 

patterns are also quite heritable, including 

relevant activation in the anterior cingulated 

cortex during stimulus appraisal.255
361

Monograph 20. Phenotypes and Endophenotypes

For PNS concomitants of regulatory 

control, more consideration is warranted 

of the utility of parasympathetic response 

measures. For example, an extensive 

physiological literature suggests that 

heart-rate variability, and cardiac 

vagal tone in particular, is a potential 

index of regulatory processes.256–259 The 

parasympathetically mediated cardiac 

response refl ected in vagal tone is 

operationalized as respiratory sinus 

arrhythmia (RSA) both at rest (high resting 

levels associated with greater response 

potential) or in response to an attentional 

challenge (stronger response to challenge 

associated with better regulation). 

RSA reactivity in this situation is viewed as 

a direct index of effortful control because 

reactivity of heart rate is directly suppressed 

by neocortical action during attention,45

which, in turn, inhibits sympathetic 

infl uences to keep heart rate low (although 

fi ndings vary somewhat with age). If weak 

regulatory control is associated with 

smoking risk, adolescents who go on to 

smoke would be expected to have weaker 

RSA response to attentional challenge 

compared with other adolescents.

Finally, important to note, though more 

elusive, is the concept of “executive 

functioning.” Its usage here refers to 

response suppression and working memory 

as elements of cognitive control; in this 

case, executive function means the complex, 

temporal organization of multiple steps 

(such as completing a recipe). It requires 

planning, which is assessed on tasks 

such as the Tower of London that require 

multistep operations. Planning involves 

working memory, but also reasoning 

and intelligence, as well as suppression 

of competing responses; thus, it is 

multicomponential. Although these types 

of planning tasks have been notoriously 

poor in reliability, some versions have 

become more reliable.260 However, they 

are for the most part unstudied with regard 

to heritability.

Attention and Alertness

Two related ideas are introduced here: 

attention and alertness. Attention is 

how people select information, from the 

nearly infi nite amount of input available, 

for further processing. It is infl uenced 

in turn by two types of mechanisms. 

One type of mechanism is bottom-up and 

relatively automatic (for example, capture 

of attention by a sudden movement or 

sound, or involuntary attraction of attention 

to a frightening possibility). A second type 

of mechanism is top-down, effortful, and 

goal directed (for example, ignoring others 

talking to fi nish an important memo for 

a deadline). It may be that bottom-up, 

motivated processes cause attention to be 

easily captured by the possibility of nicotine 

(or other drugs) or make one susceptible 

to societal images or opportunities to 

use cigarettes. For example, attraction 

to novelty, wish to escape from anxiety, 

or other motives may “bias” attention 

toward drug-related information in the 

environment, and thus, infl uence initial 

substance experimentation. Important 

neural systems are the posterior-anterior 

cortical loops as well as neural loops from 

the limbic system to the prefrontal cortex.

Alertness (related to the older concept 

of arousal) modulates cognitive control. 

Alertness reaches its nadir in sleep and its 

zenith during episodes of panic. In day-today adaptation, alertness enables one to 

notice and mobilize a response to important 

information (bringing the system into 

readiness) and to maintain attention on an 

important issue (maintaining readiness).

For alertness or arousal, relevant neural 

structures include a right-lateralized 

network of neural structures that include 

the noradrenergic system originating in 

the locus coeruleus, the cholinergic system 

of the basal forebrain, the intralaminar 

thalamic nuclei, the right prefrontal 

cortex,261 and possibly the ascending 
362

8. Endophenotypes for Nicotine Dependence Risk

reticular activating system (the latter is 

related to wakefulness). Probes of this 

system include simple reaction time on fastreact tasks, response variability, response 

time to unwarned left-visual fi eld targets, 

EEG slow-wave activity, excess vigilance 

decrement, and signal detection effi ciency.209

A continuous performance task (CPT) is one 

in which the individual must identify a rare 

target in a fi eld of events (similar to a radar 

operator watching for an occasional missile 

amid many birds and friendly planes). 

One hypothesis, for example, would be that 

excess resting slow-wave activity on an EEG 

is a liability marker for increased risk of 

smoking. As shown in table 8.1, EEG theta 

rhythm (slow-wave activity) is the most 

advanced of these measures with regard 

to reliability and heritability data and the 

most recommended endophenotype for 

liability studies from this group. Early ERP 

components, such as the N1 and the P2, also 

may have promise here, although initial data 

indicate they are less reliable and heritable 

than the slow-wave indices. See chapter 9 

for a discussion of research linking the EEG 

and the ERP, as well as CPT responses, to 

persistence of smoking. Relatedly, multiple 

measures of attentional control are available. 

Gardner and colleagues153 used a cueorienting task and found that attentional 

control was correlated with nicotine use.

Note that cholinergic (nicotinic) receptors 

are important in attentional function and 

modulation of dopaminergic activity. These 

receptors may be involved in smoking onset 

as preexisting vulnerabilities that contribute 

to attraction to nicotine via low arousal, 

energy, or attention. However, given a 

dearth of data on that point and the obvious 

relevance of cholinergic systems to response 

to initial exposure, those endophenotypes 

are discussed later in this chapter.

Affi liation and Hostility

As a fi nal note, many personality models 

include an affi liation dimension.42,49,51,66,262

This trait may be relevant in view of the 

data cited earlier on hostility and smoking 

onset. However, aside from direct trait 

measures of hostility, consideration of 

this trait does not introduce additional 

low-level experimental paradigms at the 

present time and is not considered in 

further detail here.

Smoking and NicotineDependence Risk: Summary 

and Future Directions

Table 8.1 lists the major measures discussed 

and what is known about their relevant 

characteristics. The higher-order traits 

can be conceived as part of a hierarchical 

model rooted at the most abstract level in 

reactivity of basic approach and withdrawal 

neural systems in early life but that 

differentiates into additional meaningful 

lower-order behavioral response systems 

during childhood. Differentiated at a fourfactor level, which is useful for a broad 

overview, these include (1) an approach

system related to responses to potential 

reward; (2) a frontal-limbic avoidance

system related to stress-response 

systems and sympathetic autonomic 

response; (3) a control system that is 

multicomponential and related to cognitive 

operations such as working memory and 

response inhibition; and (4) a closely related 

affi liation/empathy system, related to 

effortful control and also to the capacity for 

negative affect, leading to empathy and a 

desire for and tendency toward affi liation 

and cooperation (as opposed to social 

dominance or social interaction, which are 

refl ected in the reward-based socializing 

infl uenced by reward/approach systems). 

The affi liation/empathy system may not 

emerge distinctly throughout childhood, 

but it may be notable in adolescence. It may 

be better thought of as personality than as 

temperament. However, further examination 

of this system (or trait) in younger children 

remains of interest. (For more discussion 
363

Monograph 20. Phenotypes and Endophenotypes

of distinctions and similarities between 

temperament and personality, see Nigg.42)

The higher-order trait domains all have 

some promise in relation to smoking risk. 

It may be that there are multiple routes to 

risk or that smoking risk is overdetermined 

biologically. However, these traits are 

best understood in relation to lower level 

neural systems, which, in turn, points to 

more molecular cognitive or physiological 

measures that can be examined as 

endophenotypes. The traits themselves 

will continue to be subjected to genetic 

investigation, but they are unlikely to be 

genetically simpler than smoking itself.

As outlined here, a range of context-sensitive 

physiological measures are candidates to 

tap these systems at a lower level of analysis 

than personality. However, as table 8.1 

demonstrates, data on basic properties, 

such as heritability, familiality, performance 

in unaffected relatives, or even reliability, 

remain limited for many of these candidate 

measures. Such basic work will be needed 

before their promise can be fully evaluated. 

On the other hand, some measures already 

have promising preliminary characteristics 

and may warrant more aggressive 

examination in relation to smoking risk.

Initial Nicotine 

Exposure Response: 

Conceptual Framework 

and Candidate 

Endophenotypes

This chapter addresses a general approach 

to the study of factors that increase 

vulnerability to nicotine dependence 

in adolescents in an effort to identify 

endophenotypes that may index this 

vulnerability. As discussed up to this point, 

most of these factors are likely to be present 

and, for the most part, measurable before 

the onset of tobacco exposure. However, 

some factors predisposing to dependence in 

youth may be observable only in response 

to initial tobacco (or nicotine) exposure. 

Obviously, escalation to dependence is not 

possible in those who avoid ever being 

exposed to tobacco in the fi rst place, even 

if they otherwise are at great vulnerability 

for dependence. Among those ever exposed, 

escalation to dependence is actually less 

common than no escalation,263 suggesting 

great variability in the consequences 

of initial nicotine exposure. Factors 

accounting for variability in the short-term 

consequences of initial nicotine exposure 

warrant examination as potential predictors 

of nicotine dependence. There appear to 

be unique as well as common behavioral 

and genetic factors that predict the risk 

of smoking initiation, response to initial 

nicotine exposure, and subsequent smoking 

progression.21,23,264,265 This section focuses 

specifi cally on the effects of initial exposure 

to nicotine that may lead to progression in 

smoking behavior and nicotine dependence. 

It departs somewhat from a model of neural 

networks and moves to a model at a lower 

level of analysis involving synaptic reactivity 

to nicotine. This model is more appropriate 

to what is known about the physiology of 

nicotine response. Ideally, future research 

will examine initial responsiveness to 

nicotine within the comprehensive 

framework presented in the fi rst section 

of this chapter to build a more complete 

picture of vulnerability to nicotinedependence risk in children that includes 

both general and specifi c streams of risk 

infl uence at the genetic level.

This discussion, therefore, begins by 

considering the sensitivity model. This 

is a theoretical model of vulnerability to 

dependence that provides the starting 

point for considering endophenotypes of 

initial nicotine exposure. An alternative, 

the exposure model, is also noted. In brief, 

these models predict that greater or lesser 

initial sensitivity, respectively, to drug 
364

8. Endophenotypes for Nicotine Dependence Risk

effects increases vulnerability to onset of 

dependence. Because sensitivity to the 

same responses is relevant to either model, 

the same literature can be used to evaluate 

both models. However, as detailed below, 

the sensitivity model may have greater 

support and is used as the framework 

for identifying potential initial nicotine 

exposure endophenotypes. Nevertheless, 

variability in initial sensitivity to nicotine 

effects—either greater or lesser—may in 

fact have no consistent association with 

subsequent risk of dependence. This 

research is being examined in this chapter 

because of substantial plausibility for the 

role of sensitivity in dependence risk, despite 

a lack of clear empirical support that greater 

initial sensitivity prospectively predicts risk. 

The evidence for a potential endophenotype 

is considered within the methodological 

constraints of the existing literature. 

This section closes with a discussion of 

the research needed to fi ll the gaps in 

knowledge about initial nicotine exposure 

and promising endophenotypes.

Theoretical Support for 

“Innate” Sensitivity to Nicotine 

as an Index to Dependence 

Vulnerability

Vulnerability to dependence may be 

associated with the magnitude of an 

individual’s initial sensitivity—upon fi rst 

exposure—to the rewarding and reinforcing 

effects of smoking, and specifi cally, nicotine. 

Evaluating this potential mechanism of 

vulnerability requires assessment of acute 

responses to early exposures to smoking 

(or other methods of administering 

nicotine). For many reasons, including 

substantial practical and ethical issues, 

little research in humans has prospectively 

examined whether sensitivity to initial 

nicotine exposure is associated with greater 

risk of dependence. Yet, this notion has 

some theoretical support and is bolstered 

by animal research fi ndings.

Theoretical support for this notion comes 

from the sensitivity model of dependence 

vulnerability.266 This model essentially states 

that individuals who have higher “innate” 

sensitivity to nicotine will experience 

greater positive (i.e., pleasurable), but 

perhaps also aversive, effects from initial 

experience with nicotine. Such individuals 

will quickly become tolerant to the aversive 

effects, allowing the relative enhancement 

of positive effects. These changes result 

in greater reinforcement from smoking, 

promoting escalation of use and the onset 

of dependence. Those with lower innate 

sensitivity will be less likely to continue 

experimenting with tobacco because of a 

lack of positive effects. “Innate” sensitivity 

is sensitivity to nicotine upon fi rst 

exposure and is based on genetic and other 

constitutional factors. It can be assessed 

only during “early” experiences with 

nicotine. It cannot be directly measured 

after the escalation of smoking frequency 

beyond experimentation (e.g., daily 

smoking) because of the onset of chronic 

tolerance, which is reduced sensitivity to 

nicotine as a function of tobacco exposure 

history.267 Onset of chronic tolerance and 

other indices of adaptation to chronic 

nicotine may be rapid,268 leaving only 

a narrow window of tobacco exposure 

occurrences during which to assess “innate” 

sensitivity to nicotine. These methodological 

issues will be discussed further below.

The sensitivity model is derived largely from 

animal research,269,270 which shows that some 

rat strains are more sensitive than others 

to nicotine upon initial exposure, and these 

strains may show greater acquisition of 

nicotine reinforcement. Thus, greater 

initial sensitivity may directly promote 

processes of nicotine dependence in humans, 

especially adolescents, and individuals who 

are more sensitive to nicotine upon initial 

exposure may be at greater risk of smoking 

progression and subsequent nicotine 

dependence compared to those who are less 

sensitive to this initial exposure.
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Monograph 20. Phenotypes and Endophenotypes

Overview of Measures of Innate 

Sensitivity to Acute Effects 

of Nicotine

Selected animal studies and the limited 

human research exploring the notion that 

variation in innate, or “initial,” sensitivity to 

smoking or nicotine is associated with risk 

of nicotine dependence will be examined 

in this subsection. Endophenotypes that 

may tap initial sensitivity to nicotine will 

be considered, with substantial attention 

paid to the practical problems in conducting 

such research. Owing to a lack of research, 

one aspect of this model of variability 

in initial nicotine sensitivity will not be 

examined, specifi cally that these individuals 

rapidly become tolerant to nicotine’s 

aversive effects, although the potential 

utility of studying this phenomenon will 

be discussed in the section “Discussion of 

Future Directions.” Also, unlike chapter 9, 

nonpharmacological effects of smoking, 

The Exposure Model: An Alternative View of Initial Response to Nicotine

In contrast to the sensitivity model of initial nicotine exposure, the exposure model proposes 

that reduced, not enhanced, initial sensitivity predicts greater risk of nicotine dependence. 

The rationale for this idea is that experiencing few aversive effects from smoking makes 

subsequent experimentation more likely, such that other effects of nicotine can begin to produce 

changes that lead to dependence. Also, such individuals from the very outset may take in larger 

drug amounts to counter their attenuated sensitivity. This greater consumption can accelerate 

the consequences of heavy drug exposure, including dependence and physiological pathology. 

The exposure model is derived mostly from the alcohol research literature, especially studies 

of alcohol responses in offspring of alcoholics compared to controls.a,b Disparities between 

the sensitivity and exposure models may stem from the different substances involved, which 

may induce dependence either by unique and different processes, or by the different responses 

assessed.a,c Supporting the latter possibility were fi ndings from a study of women either with 

or without a paternal history of alcoholism who were given an acute dose of alcohol.d

 Those 

with a positive paternal history exhibited less impairment due to alcohol on one performance 

task—digit-symbol substitution—consistent with the exposure model. Yet, they showed greater 

reward responses to alcohol (e.g., “liking,” “good drug effect”), consistent with the sensitivity 

model, as well as more impairment on a second performance task—digit recall. Other research 

also has found greater, rather than lesser, sensitivity to the intoxicating effects of alcohol 

(as well as barbiturates) in men with a positive family history of alcoholism.e

 Thus, because the 

sensitivity model has somewhat more support in explaining the association of some responses 

to nicotine-dependence risk, potential endophenotypes are evaluated from the perspective of the 

sensitivity model.

a

Eng, M. Y., M. A. Schuckit, and T. L. Smith. 2005. The level of response to alcohol in daughters of alcoholics 

and controls. Drug and Alcohol Dependence 79 (1): 83–93.

b

Schuckit, M. A., and T. L. Smith. 1996. An 8-year follow-up of 450 sons of alcoholic and control subjects. 

Archives of General Psychiatry 53 (3): 202–10.

c

Pomerleau, C. S., O. F. Pomerleau, S. M. Snedecor, S. Gaulrapp, and S. L. Kardia. 2004. Heterogeneity in 

phenotypes based on smoking status in the Great Lakes Smoker Sibling Registry. Addictive Behaviors 29 (9): 

1851–55.

d

Evans, S. M., and F. R. Levin. 2003. Response to alcohol in females with a paternal history of alcoholism. 

Psychopharmacology (Berl) 169 (1): 10–20.

e

McCaul, M. E., J. S. Turkkan, D. S. Svikis, and G. E. Bigelow. 1991. Alcohol and secobarbital effects as a 

function of familial alcoholism: Extended intoxication and increased withdrawal effects. Alcoholism, Clinical 

and Experimental Research 15 (1): 94–101.
366

8. Endophenotypes for Nicotine Dependence Risk

such as conditioned responses to smoking 

cues (e.g., cue-induced craving), are not 

included here. The emergence of such 

conditioning requires extensive exposure 

to smoking, and the concern here is only 

with short-term or relatively immediate 

responses to “initial” (or early) exposure. 

Similarly, consequences of abstinence 

from smoking, notably onset of withdrawal 

symptoms, are not relevant here because 

these also arise only after extended exposure, 

as discussed elsewhere (chapters 3 and 9).

Measures of innate sensitivity to nicotine are 

subdivided here into two areas: (1) initial 

nicotine reinforcement and reward and 

(2) initial sensitivity to other effects of 

nicotine, mostly affective, behavioral, and 

cognitive performance measures that may 

help explain initial reinforcement and 

reward from nicotine use. Reinforcement is 

a central facet of the dependence process; 

the persistence of reinforcement from 

smoking is the hallmark of dependence once 

it is established. Reinforcement is necessary 

for smoking’s motivational effects to develop 

in a regular smoker and, thus, is proximal 

to processes of dependence. “Reward” is 

meant here to refer to the hedonic value 

(e.g., “liking”) of the drug as reported by the 

user and may refl ect subjective responses to 

drug use that encourage the onset of drug 

reinforcement. Yet, why nicotine acquires 

motivational effects of being reinforcing 

and rewarding may also be important and 

may vary between individuals, perhaps 

because of genetic or constitutional factors. 

Other nicotine responses may help explain 

its reinforcing and rewarding infl uences 

and are therefore viewed as more distal to 

dependence processes. These responses 

include affective (mood) and physiological 

effects; behavioral effects related to attention 

(inattention, disinhibition), which may, in 

turn, help to regulate mood; and cognitive 

processing performance (e.g., alertness), 

which may have indirect effects on a 

sense of well-being. Note that this same 

organizational framework, involving two 

broad areas of motivational effects and 

other smoking effects, is used in chapter 9 

to evaluate potential endophenotypes of 

dependence in chronic smokers.

For the measures of nicotine reinforcement, 

reward, and mood effects, the information 

is suffi cient to address, if not draw 

conclusions on, some or all of the criteria 

of a putative endophenotype for nicotine 

dependence (e.g., biological plausibility, 

predictive validity, heritability or a 

suffi ciently broad distribution of responses 

to the measure in the population, and 

reliable measurement). These criteria are 

relevant to the utility of these measures in 

research on the genetic determinants of 

nicotine-dependence risk, and all need to 

be demonstrated to verify that the measure 

is a likely endophenotype. For example, 

some measures may have a strong rationale 

for relevance to dependence, and some 

evidence linking them to dependence, 

but no evidence on heritability or reliability. 

For others, heritability and reliability may 

be strong, but their link to dependence 

risk may be unknown. In either case, the 

missing information seriously limits the 

utility of the measure in genetic research 

on vulnerability to nicotine dependence. 

A subsequent discussion will point out the 

additional research needed to fi ll in these 

gaps and fully evaluate these measures as 

endophenotypes for vulnerability to nicotine 

dependence.

General Methodological 

Concerns with Innate Sensitivity 

Research

Several concerns that limit the 

interpretation of results of research in 

this area need to be kept in mind. First, 

what constitutes “initial” exposure is not 

necessarily clear. Ideally, “initial” should 

be only that exposure to tobacco occurring 

before the onset of chronic changes in 

sensitivity to nicotine due to extended 
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Monograph 20. Phenotypes and Endophenotypes

tobacco use. The most common changes 

are chronic tolerance, or reduction in 

sensitivity, and the onset of withdrawal 

in the absence of nicotine, which also 

can infl uence responses to nicotine, as 

discussed to a greater extent in chapter 9. 

How much exposure is needed to precipitate 

these changes is not known, but it may 

be very modest.268,271 It is probably fewer 

than 100 cigarettes, which is the standard 

cutoff of exposure that differentiates 

never smokers from ever smokers in 

epidemiological research.272 How many 

fewer is uncertain. Much of the research on 

adolescents does not specify the amount of 

tobacco exposure that individuals have had. 

However, some research on initial sensitivity 

in young adults has limited such exposure 

to fewer than about a dozen lifetime uses 

of tobacco products.273

Second, the most rigorous method of 

assessing initial sensitivity is prospectively, 

such as by administering nicotine to 

naive subjects, ideally young adolescents, 

to simulate “initial exposure.” This 

is problematic, however, for obvious 

ethical reasons, so most of the research 

on adolescent responses to smoking is 

retrospective self-report. In some studies, 

the self-report of adolescent responses 

is assessed when these individuals have 

become adults, years after the initial 

smoking exposure, increasing the potential 

for poor or biased recall. Asking adolescents 

who recently initiated smoking to recall 

their responses to initial smoking just 

one year later does not appear to reduce 

the problem of decay in recall accuracy.274

Adolescents are also inconsistent in recall 

of a fact that should be much easier to 

remember, the age at which they initiated 

smoking,275 causing further concern 

about the reliability of retrospective data 

on smoking. Similarly, participants may 

recall responses to a particularly salient 

adolescent smoking experience but not 

“initial” exposure. A later study examining 

prospective nicotine effects as a function of 

retrospective self-report of early smoking 

experiences in young adult nonsmokers 

suggests some validity for self-report of two 

similar effects—dizzy and buzzed—but less 

so for other effects.276

Third, differences in sensitivity to initial 

smoking exposure cannot be easily 

interpreted without control over the 

amount of nicotine exposure, or “dose.” 

However, the “dose” of this exposure is 

not controlled: some adolescents will selfadminister signifi cant amounts of nicotine 

from initial smoking, and others may not 

inhale suffi ciently to obtain much nicotine 

upon fi rst exposure. Variation in responses 

to nicotine due to variation in self-dosing

has far different biological implications 

than does variation in responses to the same 

nicotine dose due to variation in tissue 

sensitivity to nicotine. Retrospective reports 

cannot distinguish between these potential 

causes of variability in apparent sensitivity. 

A similar concern is lack of control over 

the context of initial smoking exposure. 

Responses, and thus sensitivity, may vary as 

a function of situational factors (e.g., other 

drug use, social factors, mood), which are 

uncontrolled in initial smoking exposure 

of adolescents.

Fourth, a strategy used to get around the 

problems inherent in retrospective selfreport could be to administer nicotine via 

novel methods (i.e., other than smoking, 

such as by nicotine gum, patch, or spray) 

to young adults with little or no prior 

tobacco exposure. This approach allows 

for controlled exposure to nicotine in 

young individuals who have not become 

tolerant, and would truly refl ect initial 

sensitivity, without the abuse liability of 

smoking. One concern with this approach 

is whether responses to novel nicotine 

generalize to responses to initial tobacco 

smoking. A second concern is whether 

differences among individuals in nicotine 

sensitivity “track,” or persist unaltered, 

from youth to adulthood. If not, genetic 
368

8. Endophenotypes for Nicotine Dependence Risk

factors responsible for variability in initial 

sensitivity among adults may not relate to 

sensitivity among youth.

Finally, assessing initial sensitivity 

requires participants who are willing 

to be exposed to nicotine through selfselected experimentation with tobacco or 

self-selected exposure through research. 

It is not clear if results would generalize 

to individuals who choose to avoid any 

exposure to nicotine, even for research 

purposes. Thus, individual variability 

in sensitivity to nicotine responses may 

not generalize to all naive individuals at 

risk. (Note that “initial” exposure is not 

considered here to include in utero exposure 

to smoking or nicotine, and this infl uence 

on risk of nicotine dependence will not be 

examined.277)

Initial Sensitivity to Smoking 

or Nicotine: Reinforcement

Reinforcement

A drug is reinforcing if it is self-administered 

more than an inert comparison substance 

(e.g., placebo). Drug reinforcement is 

the sine qua non of dependence in that 

dependence on a substance cannot occur 

if the substance is not reinforcing. Thus, 

the magnitude of the reinforcing effects 

of nicotine upon initial exposure likely 

contributes to a greater probability and 

faster speed of becoming dependent. 

As discussed in more detail in chapter 9, 

reinforcement is believed to comprise 

several related concepts (e.g., drug 

seeking or drug-motivated behavior, drug 

preference, inability to abstain from drug 

use or persistence of use) that are assessed 

with different procedures. The amount and 

persistence of smoking self-administration 

are critical indices of nicotine dependence 

among those who have become established 

smokers, after chronic exposure to smoking. 

With initial exposure to nicotine, however, 

these measures are not as applicable 

because intake is very limited in frequency, 

by defi nition. Most of these procedures are 

not included here because they are less 

relevant during initial exposure. (Similarly, 

the infl uence of nicotine on enhancing 

reinforcement from other reinforcers may 

not be very apparent with initial exposure 

to the drug and is also not discussed here, 

although it is addressed in chapter 9.) 

Possible measures of initial reinforcement 

outside the laboratory are shorter intervals 

between smoking exposures and the amount 

of cigarette consumption (e.g., nicotine 

or smoke intake) per exposure. However, 

objective measurement of these variables 

is diffi cult, necessitating self-report. 

An alternative laboratory-based procedure, 

nicotine choice, may be able to objectively 

index initial reinforcement from nicotine 

per se and will receive the most specifi c 

attention because of its promise as 

an endophenotype. Other potential 

endophenotype measures will also be noted.

Biological Plausibility 

of Reinforcement Measures

A number of species acquire robust nicotine 

self-administration that persists in the face 

of increased response requirements, and 

abstinence from nicotine in such animals 

leads to a syndrome of withdrawal signs.278

Although nicotine self-administration in 

nonhuman animals may not be completely 

homologous with tobacco, or even nicotine, 

self-administration in humans, the similarity 

of factors that infl uence this behavior in 

both groups is notable.278 In regard to 

initial sensitivity to nicotine reinforcement, 

Donny and colleagues279 found in rodents 

that more rapid acquisition of nicotine 

self-administration across days predicted 

a greater subsequent intensity of nicotinemotivated behavior (higher breakpoint on 

the progressive ratio test), a component 

of reinforcement related to dependence. 

The difference in self-administration 

was very small at the start of acquisition 
369

Monograph 20. Phenotypes and Endophenotypes

(i.e., “initial exposure”) but grew over time. 

In examining neurobiological differences 

between the animals who rapidly, as 

compared to slowly, acquired nicotine selfadministration, Donny and colleagues279

found that the former tended to be those 

with less density of nicotine receptors 

in the brain by the end of acquisition. 

Thus, certainly in animals and probably in 

humans,161 onset of nicotine reinforcement 

can occur very early after fi rst exposure, 

and the subsequent escalation of use varies 

signifi cantly. However, the fi ndings by Donny 

and colleagues279 question whether the 

former directly causes the latter; that is, that 

differences upon initial exposure are robustly 

predictive of the rate of onset of dependence.

Other factors associated with the acquisition 

of nicotine self-administration in animals 

are also being examined. Differences in 

nicotine reinforcement between rodent 

strains are discussed extensively in 

chapter 9. In addition, rats bred for high 

alcohol consumption tend to show greater 

acquisition and persistence of nicotine selfadministration, suggesting overlap in the 

factors producing vulnerability to alcohol and 

nicotine dependence.280 Greater locomotor 

response to novelty has been studied as 

an indicator of greater predisposition to 

self-administer stimulant drugs;281 several 

studies have found an association between 

this response and greater acquisition of 

nicotine self-administration in rats282 as well 

as in mice.283

Nicotine Choice

Description and Rationale of Measure.

The amount of smoking frequency upon 

initial exposure has high face validity as 

a measure of reinforcement in that the 

measure involves tobacco smoking behavior. 

However, this measure does not differentiate 

whether the frequency is due to the effects 

of nicotine per se or to effects of nonnicotine 

aspects of smoking. Although conditioned 

responses to smoking are essentially absent 

at initial exposure, as noted, various other 

nonnicotine aspects of smoking can promote 

acute smoking frequency, such as social 

facilitation (e.g., peer approval). Dependence 

is driven mostly by the effects of nicotine, 

and genetic infl uences on smoking are 

believed to act primarily through these 

effects. Consequently, when it comes 

to endophenotypes of initial sensitivity, 

the reinforcing effects of nicotine per se 

may be more relevant than the reinforcing 

effects of tobacco smoking in general, 

although kinetics of the method of nicotine 

administration (particularly speed of uptake) 

could be critical.284

One objective measure of initial sensitivity 

to the reinforcing effects of nicotine in 

prospective laboratory-based research is a 

choice procedure, involving choice between 

substances containing either active nicotine 

or a placebo.285,286 Subjects are instructed 

to select a specifi c number of total “uses” 

(e.g., puffs or, with naive individuals, 

units of a novel nicotine-delivery method 

such as nasal spray or piece of gum) from 

between the two available substances. 

The greater the choice of active drug versus 

placebo, presumably the more the drug 

is reinforcing. A discussion of the pros 

and cons of this procedure can be found 

in Perkins.287 Thus, the choice procedure 

indexes the relative reinforcing effects of 

nicotine and not necessarily the absolute 

reinforcing effects. (The latter is shown only 

when nicotine is chosen more often than 

placebo, which is not common in nicotinenaive subjects.) So, if nicotine choice is 

greater in some subjects or under some 

conditions rather than others, the relative 

reinforcing effects of nicotine are greater in 

those subjects or conditions. Variations in 

the choice procedure, including those more 

appropriate for use in chronic smokers, are 

described in chapter 9.

Association with Nicotine Dependence.

Most research on nicotine choice has focused 

on smokers rather than nonsmokers, 

but observations of smokers suggest a link 
370

8. Endophenotypes for Nicotine Dependence Risk

between choice behavior and dependence. 

For example, among smokers, acute 

nicotine choice behavior in the laboratory is 

correlated with self-reported cigarettes per 

day285 and with diffi culty quitting smoking,288

suggesting that choice has concurrent 

validity in indexing several aspects of 

tobacco dependence (chapter 9). Studies of 

initial sensitivity to nicotine reinforcement 

in young adult nonsmokers indicate that 

nicotine choice is not greater than placebo 

choice, whether administered by nasal 

spray285 or gum.289 However, of greater 

interest here is the fact that nonsmokers 

differ very widely in the degree to which 

they choose nicotine, and a minority of 

nonsmokers do choose nicotine more 

than a placebo. Greater choice of nicotine 

in nonsmokers (and, to a lesser extent, 

in smokers and former smokers) is associated 

with greater pleasurable responses (pleasant 

effects, vigor, arousal) and attenuated 

aversive responses (e.g., tension, fatigue, 

confusion) to nicotine.285 On the other hand, 

several individual-difference characteristics, 

including personality measures of 

impulsivity (response disinhibition, delay 

discounting), are not related to nicotine 

choice (via nasal spray) in nonsmokers, 

while other measures (novelty seeking, 

extraversion) may be inversely related to 

choice, particularly in women.290 These 

fi ndings, which contrast with the discussion 

of predisposing factors in the fi rst section 

of the chapter, may be specifi c to nicotine 

choice via nasal spray and require replication 

with tobacco smoking, if practical and 

ethical to do with naive subjects. However, 

associations of sensation seeking and other 

impulsivity measures with nicotine “reward” 

and with certain subjective mood responses 

to nicotine have been observed, as discussed 

below. In sum, while nicotine choice has 

been investigated in nonsmokers, and can 

provide an objective index of sensitivity 

to initial reinforcement, no research has 

prospectively determined that greater 

nicotine choice predicts greater vulnerability 

to dependence.

Heritability; Distribution of Responses in 

the Population. The full range of possible 

nicotine choice responses has been observed, 

from zero to 100%, in nonsmokers, when 

nasal spray is the delivery method. Dose is a 

key infl uence on this distribution, as choice 

of nicotine in nonsmokers is greater with 

lower doses, which produce less toxicity in 

naive individuals. When choice is between 

sprays delivering the equivalent of nicotine 

from about one-half puff on a cigarette, 

nicotine is chosen on about 25%–35% of 

all opportunities, and 15%–25% of adult 

nonsmokers choose nicotine over onehalf the time.290 That even a minority of 

nonsmokers fi nd nicotine via nasal spray 

reinforcing in an absolute sense is consistent 

with the notion of innate predisposition 

to dependence vulnerability. It is also 

consistent with other data showing that 

only a minority, about one-third, of those 

who ever try tobacco go on to become 

dependent.263 This one-third likely includes 

many of the naive individuals who fi nd 

nicotine reinforcing at fi rst exposure. Dose 

may also be critical for identifying individual 

differences in initial sensitivity to nicotine 

reinforcement in that nicotine choice is 

greater in men than in women when higher 

doses (2.5 micrograms per kilogram [lg/kg]) 

of nicotine spray are used,291 but not when 

lower doses (1.25 lg/kg) are used.292 Only 

one study has examined genetic infl uences 

on nicotine choice among nonsmokers, 

fi nding that those with an absence of the 

DRD4*7-repeat allele chose nicotine by 

nasal spray more than those with presence 

of the *7-repeat allele; gene variants 

for DRD2*TAQ1A, DRD2*C957T single 

nucleotide polymorphism (SNP) SLC6A3,

serotonin transporter (SLC6A4), and mu 

opioid receptor (OPRM1) were not related to 

nicotine choice.293

Other Potential Endophenotypes of Initial 

Reinforcement

Smoking/Nicotine Use Frequency. Little 

research has examined smoking frequency 

upon initial exposure, although some 
371

Monograph 20. Phenotypes and Endophenotypes

evidence suggests that greater frequency 

may predict vulnerability to dependence. 

One prospective follow-up study examined 

the risk of current smoking in high school 

as a function of amount of smoking 

exposure reported when participants 

were aged 8–10 years. Greater number of 

cigarettes smoked by that age was linearly 

associated with greater risk of current daily 

smoking.294 Yet, this effect may simply be 

due to younger age of fi rst exposure in 

that those who smoked more cigarettes 

by 10 years of age likely smoked their fi rst 

one earlier than did children who smoked 

fewer. In terms of potential endophenotypic 

measures of smoking frequency, a laboratory 

procedure that may refl ect reinforcement as 

indexed by smoking frequency is simple ad 

lib use of either nicotine or placebo products 

in a controlled setting.289 The utility of this 

ad lib nicotine reinforcement measure as 

an endophenotype is limited: there are no 

known data on reliability or heritability 

in naive individuals, and some research 

suggests that ad lib use of nicotine via novel 

means is very limited in such individuals.289

Latency to Subsequent Nicotine Exposure.

Rather than greater frequency of selfadministration upon initial exposure being 

important, it may be that faster escalation 

of smoking after initial exposure is a more 

relevant index of nicotine-dependence 

vulnerability,295 as suggested by the 

animal work by Donny and colleagues.279

For example, Hirschman and colleagues296

found that latency between the fi rst and 

second cigarette was an important indicator 

for adolescents who rapidly progressed to 

subsequent smoking. In fact, early smoking 

experiences accounted for signifi cant 

variance in the model for rapid acceleration, 

but not for adolescents who progressed 

slowly to a second cigarette. Other studies 

indicate that a shorter interval between the 

fi rst and second cigarette is associated with a 

greater likelihood of daily smoking.297 Shorter 

transition times from initiation to regular 

use are thought to refl ect drug reinforcement 

and risk for dependence, including tobacco.298

In fact, Audrain-McGovern and colleagues21

found that adolescents who had a CYP2A6

genotype associated with faster nicotine 

metabolism smoked a greater number 

of cigarettes and progressed to nicotine 

dependence at a faster rate (controlling for 

age of fi rst smoking exposure) compared 

to adolescents who had a CYP2A6 genotype 

associated with slower nicotine metabolism. 

Development of endophenotype measures 

of latency between self-administration 

experiences is challenging because of a 

variety of practical and ethical concerns. 

Latency between cigarettes may be very long 

in experimenting adolescents, so modeling 

this latency in laboratory procedures would 

seem impractical.

Age of Onset. As discussed previously, 

the younger the age of smoking initiation, 

the greater the probability of eventual 

nicotine dependence. Age of initial smoking 

exposure appears to increase subsequent 

dependence risk even if no further exposure 

occurs for several years,299 suggesting either 

an “incubation” effect of that initial exposure 

or that early exposure is a marker for other 

factors responsible for vulnerability. Basic 

animal research demonstrates that rodents 

are more sensitive to nicotine effects during 

adolescence than in adulthood, consistent 

with this notion.300 Thus, the earlier the 

initial exposure to nicotine, the greater 

the likely sensitivity to the drug, which 

may account for the increased risk of 

dependence. At fi rst glance, this association 

would not seem to offer directions for 

developing an endophenotypic measure 

because it is based solely on the age of 

self-selection to smoking initiation. It is 

diffi cult to see how this could be captured 

in controlled research involving nicotine 

administration in a laboratory setting, 

but it may serve as a marker in prospective 

research predicting smoking progression 

and nicotine dependence. However, genetic 

infl uences may differ by age,4 and eventually, 

age of onset may be a clue to genetic effects. 
372

8. Endophenotypes for Nicotine Dependence Risk

To take advantage of this, investigations 

would have to disentangle the infl uence of 

age of onset on greater smoking frequency294

and on faster escalation of smoking.298

Research also would have to control for 

psychiatric comorbidity that partially may 

account for early onset.143

Initial Sensitivity to Smoking 

or Nicotine: Reward

Description and Rationale of Reward 

Measures

Although reinforcement is in many respects 

the essence of dependence, other acute 

effects of smoking or nicotine may index 

processes relevant to the development of 

dependence and vulnerability to dependence. 

Drug reward is one such effect. Reward

does not have as specifi c a defi nition as 

reinforcement but is often viewed as the 

hedonic value of a substance. In this context, 

hedonic means the subjective evaluation of 

the substance’s incentive-motivating effects 

(see Everitt and Robbins301 for a discussion 

of the distinctions among subjective 

responses, reward, and reinforcement). 

Rewarding effects of drugs are often seen 

as a primary cause for the initiation and 

maintenance of drug self-administration 

(reinforcement), although some theories 

question their importance after the onset 

of dependence.302 Reward is different from 

subjective measures of mood, discussed later, 

which are commonly obtained in studies of 

drug effects. Mood measures are (typically) 

self-report ratings of the subjective mood 

state of the person. By contrast, reward 

is a subjective rating of the hedonic 

characteristics of the substance itself, 

albeit from the user’s perspective, obtained 

immediately after using the substance. 

Thus, while mood effects of substance use 

may infl uence reward (and reinforcement), 

they are certainly not the same thing. 

As with reinforcement, reward can only be 

measured concomitant with actual substance 

use, while the subjective mood state of the 

user can be assessed at any time, even in 

the absence of the subject ever using the 

substance. Typical measures relevant to 

reward in humans are ratings of “liking,” 

“good effects,” or “bad effects” of the 

substance completed on 7-point Likert or 

visual analog scales. The extreme-response 

options for each item may be anchored by 

“not at all” to “extremely.” Little research 

has documented the reliability of such 

responses to initial nicotine intake, although 

research in adult smokers suggests good 

reliability, as noted in chapter 9.

Biological Plausibility of Reward 

Measures

Neurobiological changes associated with 

“liking” and other reward measures in 

humans have not been extensively studied, 

and there does not appear to be any such 

research in naive subjects (i.e., initial 

exposure). However, research assessing 

reward via retrospective self-report suggests 

that greater initial smoking reward is 

associated with greater risk of dependence. 

One study of several thousand adults found 

that 94% of those who reported having liked 

their early exposures to smoking progressed 

to smoking at least 100 cigarettes in their 

lifetime (the standard epidemiological 

defi nition of a lifetime smoker) compared 

to only 57% of those who reported no liking 

of their early exposures to smoking.303

Because animals cannot provide selfreport ratings, there may be no directly 

homologous measure of reward in animals. 

However, two measures that may be used 

to model reward are the conditioned place 

preference (CPP) procedure and intracranial 

self-stimulation (ICSS) procedure. In the 

CPP procedure, animals are placed in 

distinctive environmental contexts 

(e.g., different sides of a partitioned box) 

after receiving injection of either drug 

or saline, with each paired to one of the 

contexts. After several pairings of each, 
373

Monograph 20. Phenotypes and Endophenotypes

the animal is then tested for preference for 

one or the other context by the amount 

of time it spends in each when allowed to 

move freely between them. Greater time 

spent in the drug-paired side is believed to 

index preference for the drug (versus saline), 

while less time spent in the drug-paired 

side is believed to index aversion to the 

drug. The ICSS measures the intensity of 

electrical stimulation in the brain required 

to maintain behavior, similar to drug selfadministration paradigms in animals. 

Drugs or other conditions that increase 

the intensity of stimulation necessary to 

maintain behavior appear to be aversive, 

whereas drugs or conditions that decrease 

this intensity appear to be pleasurable. Most 

drugs that produce dependence in humans, 

including nicotine, decrease the intensity 

of stimulation required to maintain 

responding. The CPP and the ICSS are 

discussed more extensively in chapter 9.

Association of Reward Measures 

with Nicotine Dependence

Very little research has examined factors 

associated with greater nicotine or smoking 

reward in humans upon initial exposure. 

However, greater pleasurable responses to 

initial nicotine spray (such as vigor and 

pleasant effects) were found to predict 

greater subsequent nicotine choice in 

nonsmokers.285 Also, smokers report 

greater “liking” in response to nicotine 

nasal spray, compared to nonsmokers, 

showing concurrent validity of reward with 

dependence.273 It is unclear if other research 

exists relating rewarding effects of initial 

nicotine or smoking exposure to dependence 

vulnerability.

Heritability; Distribution 

in the Population

The limited research on initial sensitivity 

to nicotine reward precludes much 

information on variability in this response. 

However, in a study of individual differences 

in nicotine sensitivity in 131 young adult 

(aged 21–39 years) nonsmokers administered 

nicotine via nasal spray, reward ratings (want 

more, satisfying) were higher in men, but not 

in women, as a function of novelty seeking.293

Genetic variants related to dopamine 

function were largely unrelated to reward in 

nonsmokers, although DRD2*C957T SNP 

(TT or CT versus CC genotype) and DRD4

(presence of *7-repeat allele versus absence) 

were associated with stronger perception 

of nicotine effects from the spray.293 Other 

analyses showed greater responses on some 

reward ratings in those with two rather 

than one or no parents who were smokers 

and as a function of earlier experience with 

marijuana.292 Existing levels of caffeine 

or alcohol use were unrelated to nicotine 

reward. These fi ndings should be interpreted 

with caution; they were conducted with 

young adults who had self-selected to 

nonsmoking status, and results with a more 

heterogeneous sample including those at 

greater risk could show different results.

Initial Sensitivity to Other 

Responses to Nicotine

Other responses to initial nicotine exposure 

may also provide information about valid 

endophenotypes related to dependence risk, 

especially effects that relate to affective 

regulation. Other effects that could be 

relevant but have generally not been studied 

in naive users will be very briefl y noted. 

These include behavioral effects related 

to attention and impulsivity as well as 

cognitive-processing performance after 

initial nicotine exposure. The same concerns 

about the limitations of research on initial 

exposure presented earlier apply to studies 

of sensitivity to these responses.

Affective/Mood Responses

Most of the research in this area of 

other responses to nicotine as potential 

endophenotypes focuses on self-reported 
374

8. Endophenotypes for Nicotine Dependence Risk

mood (affective) responses to smoking or 

nicotine. Yet, as discussed in chapter 9, 

mood is believed to comprise effects 

measurable across several response domains, 

including physiological, behavioral, and 

cognitive. Studies limited to self-report 

likely fail to adequately characterize mood 

and the endophenotypes of initial sensitivity 

to nicotine’s mood effects.

Biological Plausibility

Some self-reported mood effects of smoking 

(“euphoria” and “elation,” which may be 

similar to “head rush or buzzed”) in smokers 

have been related to dopamine release in 

the striatum.304 Increases in dopamine 

in the striatum and ventral tegmentum 

are believed to be critical to nicotine 

reinforcement.305 This is consistent with 

effects in the approach system described 

earlier and would be expected to make 

rewards more salient and satisfying. This 

would also enhance attentional focus, 

leading to a potential cascade of reinforcing 

effects. It was also noted in the fi rst part of 

this chapter that mood-related factors may 

bias attention toward drug-related relief 

and infl uence dependence onset. However, 

these effects may be even more powerful 

in maintaining smoking after the onset of 

dependence (chapter 9).

Plausibility also comes from clinical or 

retrospective reports of mood effects from 

initial smoking. In several studies, adults 

who were current smokers retrospectively 

reported having had greater pleasant 

sensations and “head rush” or “buzz” the 

fi rst time they ever smoked compared to 

adults who were currently nonsmokers 

but had some smoking exposure.306 Adult 

smokers also tend to report having had 

equal or fewer unpleasant responses to 

their fi rst cigarette, suggesting that greater 

pleasant effects are important and lesser 

(or greater) unpleasant effects are not as 

important. In one study, Pomerleau and 

colleagues307 reversed the direction of the 

comparison and examined current smoking 

amounts in adults as a function of whether 

they reported retrospectively that they did 

or did not experience a “pleasurable rush 

or buzz” during their fi rst cigarette. Those 

who said “yes” (i.e., they did experience 

rush or buzz during their fi rst cigarette 

as an adolescent) currently smoked more 

cigarettes per day than those who said 

“no.” Interestingly, those who said “yes” 

also reported greater “pleasurable buzz” 

and “euphoric sensations” prospectively 

in response to acute administration with 

nicotine nasal spray, suggesting a continued 

greater sensitivity to one effect of nicotine 

even after the onset of tolerance due to 

chronic smoking.

Note that the association between 

pleasurable responses to early use and 

subsequent dependence may not be 

specifi c to smoking. Greater positive 

mood responses, but few or no negative 

responses, to early use of cannabis308 and 

cocaine309 have been associated with greater 

indices of dependence to these drugs. Thus, 

greater mood effects of early drug use may 

be broadly linked to vulnerability to drug 

dependence. This research and the studies 

of smoking responses should be viewed 

cautiously, given the biases inherent in 

retrospective recall of drug-use experiences.

Examined next will be research on 

sensitivity to initial mood effects of smoking 

assessed by retrospective clinical reports in 

adolescents varying in amount of current 

smoking. We will also discuss the few 

prospective laboratory-based studies of 

responses to smoking or nicotine in those 

believed to vary in risk of dependence.

Acute Self-Reported Mood Effects

Description of Self-Reported Mood 

Measures. Self-reported mood is assessed 

with a number of validated measures. 

Common mood measures assessed 

in acute smoking or nicotine studies 

include the Positive and Negative Affect 

Schedule,310 the Mood Form of Diener 
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Monograph 20. Phenotypes and Endophenotypes

and Emmons,311 and the Profi le of Mood 

States.312 The measures and results from 

studies of acute mood effects of nicotine or 

smoking administration are presented in 

Kalman.313 The reliability of these responses 

to nicotine is very high in both smokers 

and nonsmokers via nasal spray314 and 

probably via other controlled methods of 

administration. Assessment of acute mood 

effects of smoking and nicotine in the 

laboratory, including the use of measures 

other than self-report, is discussed more 

extensively in chapter 9.

As noted above in outlining plausibility, some 

studies have related self-reported mood effects 

of initial smoking to risk of dependence 

by studying adult smokers recalling their 

experience upon smoking their fi rst cigarette. 

Most of these studies were done by Pomerleau 

and colleagues with their self-report 

measure, the Early Smoking Experiences 

(ESE) scale.315 The responses to smoking 

include nicotine-related effects of “pleasant 

sensations,” “unpleasant sensations,” 

“nausea,” “relaxation,” “dizziness,” and 

“pleasurable rush or buzz,” and two effects 

specifi c to smoke inhalation, “coughing,” 

and “diffi culty inhaling.” Each is rated on a 

4-point Likert scale from “none” to “intense,” 

with a fi fth option of “don’t remember.” 

Unfortunately, test-retest reliability of recall 

of initial smoking experiences assessed 

in adolescents a year apart is quite low,274

despite the relative recency of those 

experiences. Research in adults suggests 

that the reliability of responses two years 

apart may be satisfactory if response options 

are dichotomized (i.e., yes/no, rather than 

a 4-point scale316). However, the ESE may 

have limited validity, as just two (dizziness, 

pleasurable rush or buzz) of the six items 

retrospectively assessing pharmacological 

effects of smoking predicted prospectively 

assessed nicotine nasal spray effects on those 

same items in young adult nonsmokers,276

although comparing nicotine administration 

via the same method would provide a 

stronger test of validity.

Association with Dependence Risk. There 

appears to be no prospective research 

relating acute mood responses to nicotine 

in nonsmokers to indices of dependence 

risk. However, a number of studies of 

recall of recent smoking experiences in 

adolescents or young adults demonstrate 

some association between sensitivity 

to these mood effects and dependence. 

The potential advantage here over the 

retrospective studies in adults noted 

previously is that the recall of experience 

with initial smoking may be more reliable 

since less time has passed. In two studies 

of adolescents, recall of being “relaxed” 

in response to their fi rst cigarette was 

strongly associated with subsequent onset 

of dependence, as defi ned by smoking at 

least monthly161 or weekly.274

In perhaps the most rigorous study of this 

kind in smoking, Hu and colleagues317

reinterviewed 15,000 young adults (mean 

age of 22 years) who had been included in 

earlier national surveys of adolescent health. 

Retrospective reports of greater pleasant 

effects and dizziness (related to “head rush”) 

but lesser unpleasant effects from initial 

smoking increased the risk of progressing 

to daily smoking in those who had ever been 

exposed. Pleasant effects, but not dizziness 

or other unpleasant effects, were also 

associated with greater risk of transition to 

dependence among those who ever smoked 

daily. However, persistence of smoking 

(i.e., failure to quit) among those who were 

ever dependent was weakly related to lesser, 

not greater, pleasant and unpleasant effects 

from initial smoking. Thus, greater initial 

sensitivity to pleasant effects of smoking 

may infl uence the early progression to daily 

smoking and onset of dependence but is 

less important in explaining persistence of 

smoking once dependence is established. 

This observation perhaps further exemplifi es 

the difference in factors promoting onset 

versus persistence of dependence, as 

represented in this chapter and chapter 9, 

respectively.
376

8. Endophenotypes for Nicotine Dependence Risk

Several older cross-sectional studies 

compared reports from early smoking in 

adolescents with minimal lifetime exposure 

(just a few cigarettes) compared to those 

with greater exposure. Those adolescents 

who were currently smoking to a greater 

degree reported having experienced greater 

pleasurable effects (e.g., “feeling high”) and 

fewer aversive effects (e.g., “feeling sick”) 

at their initial exposure to smoking than 

did adolescents with little current smoking 

(see review in Eissenberg and Balster).318

Similarly, among adolescents who smoked, 

reports of greater “relaxed,” “high,” and 

“dizziness” (similar to “rush or buzz”) 

and lower “cough” from fi rst cigarette 

were associated with faster escalation of 

smoking, while other aversive effects had 

no association.296,319 Interestingly, similar 

fi ndings were reported in a study of Chinese 

10th graders, demonstrating cross-cultural 

consistency in the relationship between 

pleasurable responses to initial smoking and 

subsequent smoking escalation.320

One study tested whether pleasant or 

unpleasant initial smoking experience 

mediated the relationship between the 

CYP2A6 genotype (genetic variation in 

nicotine metabolic inactivation) and 

nicotine dependence. CYP2A6 did not have 

a signifi cant effect on either pleasant or 

unpleasant initial smoking experience, 

negating the possibility of mediation.21

These initial smoking experiences may 

not account for the relationship between 

CYP2A6 genetic variation and emergence 

of nicotine dependence, or the mediated 

relationship is more complex than modeled. 

Likewise, adolescents might not view the 

initial experience as positive or negative, 

and/or the initial experience may be modifi ed 

by the presence of other smokers and other 

substances, such as alcohol or marijuana.321

Consistent with these fi ndings, O’Loughlin 

and colleagues22 did not fi nd that initial 

smoking experiences mediated the effect 

between CYP2A6 and the odds of becoming 

nicotine dependent. The role of initial 

positive and negative smoking experiences 

in subsequent smoking warrants further 

attention. Methodological issues surrounding 

prospectively measuring initial reactions to 

nicotine and a lack of attention to the impact 

of contextual factors may be disguising 

important relationships. In addition, 

heterogeneity in the initial responses to 

nicotine may be hidden by evaluating the 

average response of the sample rather than 

accounting for interindividual variation. 

Some responses (e.g., “head rush,” “buzz”) 

may be more discriminatory than others 

(e.g., “dizziness”).322

Finally, some research has examined 

concurrent association between mood 

responses to nicotine and self-administration 

by using young adult nonsmokers to 

simulate adolescents experimenting with 

smoking. This approach ensures that 

responses to nicotine in subjects are “initial.” 

Nicotine is administered via novel means, 

such as nasal spray, patch, or gum. Using 

this approach, associations were found 

between pleasurable mood responses to 

nicotine via nasal spray and subsequent 

choice of nicotine in nonsmokers as well as 

in smokers,285 suggesting that these mood 

responses are related to nicotine’s reinforcing 

effects and, perhaps, risk of dependence.

Heritability; Population Distribution 

of Acute Self-Report Mood Response 

to Initial Smoking

A prospective study of young adult 

nonsmokers found greater aversive mood 

responses to nicotine via nasal spray, such 

as decreases in vigor and positive affect 

(but greater buzz) among those with the 

DRD4*7-repeat allele compared to those 

without the *7-repeat allele.293 Other genes 

(DRD2*TAQ1A, DRD2*C957T, serotonin 

transporter, dopamine transporter, OPRM1) 

were not clearly related to acute mood 

responses to nicotine. The neuronal 

nicotinic acetylcholine receptors 
377

Monograph 20. Phenotypes and Endophenotypes

CHRNA2,323 CHRNA3324 and CHRNA534 have 

been related to several retrospective ratings 

of initial smoking responses in young adults 

(dizziness, buzz or rush, relaxed). Regarding 

impulsivity factors, aspects of the sensationseeking personality, which is associated with 

risk of nicotine and other drug dependence 

(see earlier sections of this chapter), 

have been found to be related to greater 

sensitivity to subjective mood responses to 

nicotine via nasal spray in young adult never 

smokers.325 Specifi cally, Sensation Seeking 

Scale subscales of experience-seeking and 

disinhibition were associated with mostly 

pleasurable effects of nicotine (pleasant 

effects, head rush, vigor, and arousal), 

but also some aversive responses (tension, 

confusion). However, a subsequent, larger 

study related impulsivity and other factors 

associated with dependence risk to nicotine 

sensitivity in young adult nonsmokers290

and found only modest associations 

between one impulsivity factor—response 

disinhibition—and acute mood responses 

to nicotine (greater increases in anger and 

stimulated, blunted decrease in relaxation). 

History of other drug use and parental 

smoking history are unrelated to mood 

effects of nicotine via nasal spray.292

Mood effects of nicotine patch on 

nonsmoking adults appear to also vary 

as a function of “trait hostility,” another 

personality factor associated with greater 

risk of nicotine dependence in addition to its 

potential effects on onset, as noted earlier. 

Jamner and colleagues215 found that nicotine, 

compared to placebo patch, prospectively 

decreased self-reported anger more among 

those high versus low in trait hostility. 

Notably, the same results were observed in 

smokers, suggesting that this association 

of trait hostility with anger reduction from 

nicotine does not moderate with chronic 

smoking exposure. High trait hostility was 

associated with high frequency of anger 

during placebo, suggesting that nicotine’s 

effects may be more pronounced in those 

with extreme baseline levels of response, 

as has been found with other research on 

mood and behavioral responses to nicotine.326

Similar to this observation, animal research 

shows that nicotine attenuates startle 

response, a physiological measure associated 

with mood, to a greater degree in those with 

larger baseline startle magnitude.327

Physiological Indices of Affect

Description of Physiological Measures 

of Affect. Mood is most commonly 

assessed via self-report measures, but some 

physiological responses related to affect 

include cardiovascular effects and startle 

response; several of these were outlined 

earlier and are listed in table 8.1. These 

same markers can be used to evaluate 

response to smoking as well as vulnerability 

to onset. Cardiac measures in this context 

(e.g., reward responsivity) are complicated 

by the fact that nicotine increases 

cardiovascular responses. However, it does 

not appear to modulate the effects of other 

infl uences on cardiovascular responses, 

such as acute environmental challenges.328

An alternative approach is to examine 

physiological startle—that is, the intensity 

of the eyeblink response to a sudden 

stimulus such as a sharp loud noise or 

electrical stimulation. The neurobiological 

signifi cance of startle is discussed in 

chapter 9. Briefl y, the magnitude of startle 

response is associated with the degree of 

negative affect reported by the person, 

and so, may index the negative affective 

limbic circuitry outlined earlier. Smoking 

and nicotine do not clearly alter startle 

response in dependent smokers or in 

nonsmokers,329 although some evidence 

indicates that nicotine attenuates startle 

in animals,327 as measured by whole-body 

startle response to the stimulus. However, 

nicotine infl uences the related measure 

of prepulse inhibition of startle, which is 

considered a measure of sensory processing 

rather than affect, and is discussed later. 

Regarding other physiological indices 

of affect, it has been noted earlier that 

electrodermal (skin conductance) and 
378

8. Endophenotypes for Nicotine Dependence Risk

electromyographic (muscle tension) 

measures are commonly obtained in studies 

of affective regulation, but few studies have 

examined responses to smoking or nicotine, 

and none (as far as is known) in nonsmokers 

administered nicotine.

Association with Dependence Risk.

There appears to be no evidence relating 

physiological indices of mood responses 

to nicotine to subsequent risk of nicotine 

dependence.

Heritability; Distribution of Responses in 

the Population. Research shows that startle 

response to low dose, but not moderate dose, 

nasal spray nicotine (i.e., curvilinear) was 

greater in those with the presence (versus 

absence) of the DRD4*7-repeat allele and 

in those with the DRD2/ANKK1*CC allele 

(versus *TC or *TT allele), but only among 

men and not women.293

Other Responses to Nicotine

No prospective studies have related onset 

of dependence to sensitivity to other 

responses to nicotine, likely for the same 

ethical and practical reasons noted above. 

Thus, this area will be only briefl y noted. 

Effects of nicotine on these responses in 

chronic smokers are discussed in chapter 9. 

In addition, several of the variables listed 

below as important in the initial response 

to nicotine may also be considered factors 

that place an adolescent at risk for smoking 

initiation and subsequent progression. 

The description and rationale are outlined 

below for considering measures from 

various response domains as potential 

endophenotypes for risk of dependence.

Attention and Arousal. Smoking or nicotine 

typically helps prevent the deterioration 

in cognitive task performance over time 

in smokers, particularly when abstinent, 

but nonsmokers (i.e., testing of initial 

sensitivity to nicotine) have rarely been 

tested. However, in one interesting study,330

young adult nonsmokers were divided 

into high- and low-baseline attention 

subgroups based on ADHD scales and 

given either a nicotine (7 milligrams [mg]) 

or placebo patch. Nicotine reduced errors 

of commission on the Conners’ CPT in the 

low-baseline attention group, but impaired 

performance on another attention task, the 

Wisconsin Card Sorting Test, in the highbaseline attention subgroup. Thus, nicotine 

enhanced functioning only in those with 

weaker attentional control (and likely, lower 

arousal). As has been noted already, this 

characteristic is a risk factor for smoking 

onset; these data suggest it may be a risk 

factor that moderates a potential source of 

reinforcement from nicotine. This infl uence 

of nicotine as a function of baseline level of 

attention is consistent with results found 

in a few studies of mood, noted previously, 

and discussed elsewhere in greater detail.326

Electrophysiological Responses. As noted 

previously, startle response to a brief, 

loud tone assesses processes associated 

with affect. The degree to which startle is 

attenuated by a milder acoustic stimulus 

immediately preceding the tone is called 

prepulse inhibition (PPI) and indexes 

attention to sensory stimuli. Background 

on this measure is provided in detail in 

chapter 9. In one study of young adult 

nonsmokers administered low and 

moderate doses of nicotine via nasal 

spray,293 PPI tended to worsen (i.e., reduced 

inhibition of startle) in those with the 

DRD2*C957T CT (versus TT or CC) genotype 

(at the low dose only), with the absence 

of the SLC6A3*9-repeat allele, and with 

the DRD2/ANKK1 CC (versus TT or CT) 

genotype (at the moderate dose only). Other 

individual difference characteristics, such as 

other drug use history or parental smoking 

history, are unrelated to PPI response to 

nicotine spray in nonsmokers.292

Impulsivity Via Cognitive Control and 

Approach Measures. Acute effects of 

nicotine on impulsive behavior can be 

assessed via variations on the stop/go 
379

Monograph 20. Phenotypes and Endophenotypes

task, which as noted earlier, is an index 

of the frontal-striatal output control 

circuit, or by delay discounting, which is 

likely an index of the approach circuitry 

described earlier. Research examining 

initial sensitivity to nicotine’s effects on 

each (i.e., in nonsmokers) is limited, 

but a few studies support the notion that 

these effects may promote dependence. 

In one study, nonsmoking adolescents with 

ADHD had improved stop/go responding 

(less disinhibition) following transdermal 

nicotine (7 mg), relative to placebo.201

Methylphenidate, the standard medication 

to treat ADHD symptoms, also improved 

stop/go responding.

Cognitive Control and Executive 

Functioning. Several studies have looked 

at nicotine effects on measures conceived 

as tapping the cognitive-control circuitry 

described earlier. Cognitive function 

measures used in nicotine research are 

described in more detail in chapter 9. 

In terms of nicotine’s effects on cognitive 

functioning upon initial exposure 

(i.e., initial sensitivity), little research shows 

clear improvement in such functioning. 

Exceptions include improvements in simple 

psychomotor tasks such as fi nger-tapping 

speed, perhaps refl ecting gains in cognitive 

control, alertness, or arousal.331 Research 

is mixed in terms of the performance of 

nonsmokers on more complex tasks, such 

as choice-reaction time speed.331 However, 

short-term memory recall has been shown 

to be improved in nonsmokers by 2 mg 

of nicotine gum,332 a 5-mg patch,333 or a 

1-mg injection.334 Yet, memory recognition 

and delayed recall are impaired by 4 mg of 

gum,335 perhaps suggesting a nonlinear dose 

effect of nicotine on memory in nonsmokers.

The Stroop interference task is a measure of 

rapid information processing and cognitive 

control in that a rapidly activated dominant 

response must be suppressed in preference 

to a slower-activated nondominant response, 

producing a “confl ict.”336 It involves 

activation of the anterior cingulated cortex, 

which is involved in the cognitive-control 

loop.337 Nicotine (7-mg patch), but not 

methylphenidate, improved performance on 

the Stroop task by reducing this interference 

in adolescents with ADHD, who had poor 

baseline performance.201 However, smoking 

did not affect Stroop interference in lightsmoking adolescents without ADHD,338

suggesting that nicotine’s effects may be 

more apparent as baseline performance 

worsens. This is consistent with fi ndings 

noted previously with regard to the 

infl uence of baseline on the observed effects 

of nicotine.

Finally, the Sternberg memory task is 

another rapid information processing task 

that requires subjects to briefl y memorize 

one or a string of fi ve target letters and 

then respond as quickly as possible to a new 

series of letter pairs in a way that indicates 

whether the given letter pair did (“hits”) 

or did not (“correct rejections”) contain a 

target letter. The difference in reaction time 

in milliseconds between the one- and fi veletter trials (“D-prime”) on items requiring 

correct rejection (involving processing of 

all target letters) is the primary measure 

of memory scanning speed (information 

processing).339 Although some studies show 

no clear effects of nicotine via nasal spray on 

performance of this task in nonsmokers,290,340 

complex dose-related associations between 

DRD4 genotypes and performance have been 

reported in nonsmokers.293

Nicotine Responses Assessed by 

Neuroimaging. Perhaps the most 

intriguing potential endophenotypes for 

initial nicotine sensitivity are effects of 

the drug on neurobiological changes, 

such as those revealed in neuroimaging 

measures (e.g., brain metabolic changes 

via positron emission tomography [PET]; 

blood fl ow changes in brain regions via 

fMRI). As emphasized above, CNS probes 

of the major neural circuits involved in 

behavioral risk markers may be promising 
380

8. Endophenotypes for Nicotine Dependence Risk

as endophenotypes for risk of onset as 

well as for the reinforcing effects of 

nicotine exposure. These tactics may 

prove to be particularly important for 

evaluating nicotine’s effects on the brain 

in adolescence.

As is increasingly recognized, adolescence 

is a period of dramatic and ongoing neural 

development, particularly in circuits 

involved in cognitive control (the topdown control circuits described earlier). 

These circuits mature via myelination and 

pruning into early adulthood, probably in 

experience-dependent fashion. Further, 

such maturation is certainly moderated 

by sex hormones that are a major factor in 

adolescent development. Thus, one crucial 

direction for endophenotypes of exposure 

response may entail looking at alterations 

in the trajectory of brain maturation in 

response to nicotine exposure. Other 

research on initial nicotine effects 

(i.e., in nonsmokers) suggests that greater 

brain metabolic responses via 2-fl uoro-2-

deoxy-D-glucose (or FDG) PET are seen 

as a function of the personality factor of 

hostility.216 As in the naturalistic research by 

Jamner and colleagues,215 noted previously, 

the infl uence of trait hostility on brain 

metabolism due to nicotine was observed in 

smokers as well as in nonsmokers.

Initial Nicotine Sensitivity 

Endophenotypes: Summary 

and Future Directions

Nicotine Reinforcement and Reward

Nicotine reinforcement is the key process 

involved in the onset of dependence. 

Assessment of reinforcement at initial 

nicotine exposure is diffi cult, owing 

to ethical and practical problems with 

exposing naive individuals, especially youth, 

to nicotine. Moreover, there is not much 

evidence that variability in the reinforcing 

effects of initial nicotine exposure predicts 

vulnerability to dependence. One objective 

measure of initial nicotine reinforcement—

choice of nicotine or placebo—shows 

some concurrent validity with dependence, 

but choice in naive subjects has not 

been related to dependence vulnerability. 

However, animal research suggests that 

differential nicotine reinforcement emerges 

relatively quickly across early exposures. 

Thus, the trajectory of escalation in 

reinforcement across early exposures, rather 

than reinforcement at initial exposure, 

may hold promise as an index of dependence 

vulnerability (chapter 5). Age of initial 

smoking exposure is a strong predictor of 

smoking escalation and persistence but may 

have limited utility as an endophenotype. 

Nicotine reward is readily measurable 

in naive subjects via self-report and has 

been related to some genetic factors and 

other individual difference characteristics, 

including novelty seeking. Retrospective 

research suggests that liking of initial 

smoking is associated with greater 

subsequent dependence. Further support 

for this link is needed. Animal research 

on CPP and ICSS provides some potential 

avenues for development of more objective 

measures related to nicotine reward in 

humans.

Mood Effects and Other Responses 

to Smoking

Retrospective studies show with some 

consistency that greater pleasurable 

responses to initial smoking experiences, 

especially feeling “relaxed,” are associated 

with greater subsequent risk of nicotine 

dependence, largely supporting the 

sensitivity model of dependence 

vulnerability. Aversive responses to 

initial smoking appear to be unrelated to 

dependence vulnerability. On the other 

hand, the few prospective studies of acute 

nicotine administration in nonsmokers do 

not show robust mood effects, particularly 

pleasurable effects. This inconsistency in 

fi ndings could be due to either biases in 
381

Monograph 20. Phenotypes and Endophenotypes

retrospective self-reports or to the different 

populations studied. The retrospective 

studies included those who became 

dependent smokers as well as those with 

less smoking history, while the prospective 

studies of initial sensitivity involve only 

those who remained nonsmokers. A third 

possibility is that the retrospective studies 

examine responses to smoking, while the 

prospective studies examine responses to 

novel methods of nicotine administration, 

which are often more aversive than 

is smoking.341 These methodological 

diffi culties need to be controlled to verify 

that initial sensitivity to mood effects of 

nicotine predicts dependence vulnerability. 

Regarding other measures of mood and 

other responses to nicotine, research is too 

limited to determine whether any of these 

may be related to dependence risk.

Assessment of all of these responses in 

adolescents and relating these prospectively 

to risk of nicotine dependence would 

appear to minimize most sources of bias. 

However, this approach raises considerable 

ethical concerns, especially in youth who 

have never previously been exposed to 

nicotine. An alternative that may be ethical 

is to prospectively assess responses to 

acute smoking or nicotine in adolescents 

who have already begun to smoke, 

although such exposure almost certainly 

would be well after their initial exposure. 

Longitudinal research surveying adolescents 

regarding their self-report responses to 

smoking in general is being conducted.317

Intermittent assessment of such responses 

prospectively, in laboratory-controlled 

studies of acute smoking exposure, could 

reveal more reliable and objective changes 

in acute responses that predict subsequent 

escalation of smoking to dependence.342

Yet, as previously noted, the progression 

to daily smoking is not necessarily gradual 

and can occur quickly,343 leaving only a very 

brief window of opportunity for assessment 

of these responses to early smoking 

exposure.

Discussion of Future 

Directions

Numerous potential measures have been 

discussed in this chapter that may relate to 

nicotine dependence risk at or before initial 

exposure to nicotine. This fi nal section 

will review some of the key conceptual and 

methodological issues that may need to be 

considered in future work examining such 

measures to establish endophenotypes that 

may inform genetic research on nicotine 

dependence.

Conceptual Issues

This chapter began by outlining a 

multilevel-analysis perspective on key neural 

systems related to smoking initiation and 

progression risk and attempted to identify 

low-level experimental measures, which are 

presumably closer to gene action, that may 

serve as endophenotypes for future studies. 

Data on the criteria for determining the 

validity of a putative endophenotype, such as 

heritability, reliability, and predictive validity, 

are limited for many of these candidate 

measures. This groundwork will need to be 

laid before these endophenotypic measures 

can be fully evaluated. A few measures have 

promising preliminary characteristics and 

may warrant more aggressive examination 

in relation to smoking initiation and 

progression risk. For example, cognitive 

control is a biologically well-studied ability 

anchored in the striatum and orbital and 

dorsal regions of the prefrontal cortex. It can 

be indexed via component cognitive measures 

as well as ERP measures. The predictive 

validity for specifi c measures is promising 

(i.e., P300 amplitude). Its heritability needs 

more study, but particular confi gurations 

(e.g., latent variable measures of response 

inhibition or set shifting) and measures 

(e.g., the P300) have strong heritability and 

deserve particularly close attention. A key 

gap is the extent of the understanding of 

the phenotypic and bivariate genotypic 
382

8. Endophenotypes for Nicotine Dependence Risk

associations of these measures with smoking 

onset risk.

Somewhat surprisingly, important measures 

of approach-related processes, such as delay 

discounting, reward cue detection, and 

other indicators of functioning of neural 

systems in the nucleus accumbens, orbital 

prefrontal cortex, and related ascending 

mesolimbic dopaminergic systems are little 

investigated with regard to precursive risk 

for later cigarette-use onset. Although many 

of these measures are related to smoking, 

it remains unclear whether alterations 

in these functions are risk factors for 

smoking, risk factors for persistence, or the 

consequences of smoking. This is important 

because some potential factors are likely 

to be present and measurable before the 

onset of tobacco exposure, and other factors 

predisposing to dependence in youth may 

only be observable in response to initial 

nicotine exposure. Therefore, in the second 

part of this chapter, the discussion moved 

to a more molecular level of analysis and 

considered a pharmacological perspective. 

In doing so, the available literature was 

reviewed on the processes that occur in 

the early stages of nicotine exposure that 

may increase the likelihood of further 

exposure to nicotine and the progression 

to nicotine dependence. There is not 

much evidence to support the notion that 

the reinforcing effect of initial nicotine 

exposure predicts vulnerability to nicotine 

dependence. However, nicotine-choice 

paradigms and trajectory of escalation 

in reinforcement across early exposures 

both hold promise as an index of nicotine 

dependence vulnerability. In addition, initial 

fi ndings on the link between nicotine reward 

and subsequent development of nicotine 

dependence suggests that more research of 

reward is warranted.

As this chapter focused on potential 

endophenotypes for (1) smoking initiation 

and progression to nicotine-dependence 

risk and (2) the response to that initial 

nicotine exposure, it was assumed that 

genetic infl uences on these points are at 

least partially distinct. Although initiation is 

an obvious prerequisite for progression and 

then dependence to emerge, it is likely that 

risks for initiation substantially involve both 

the general substance-use pathway and a 

specifi c pathway involving nicotine, whereas 

drug-specifi c factors may predominate 

in the initial response to nicotine. It is 

important to note that numerous factors 

place an individual at risk for smoking 

initiation, progression to regular smoking, 

and to nicotine dependence.41 Thus, 

smoking occurs in a psychosocial context, 

of which nicotine availability is a necessary 

but not suffi cient condition. In focusing 

on endophenotypes for genetic risk for 

nicotine dependence, it is acknowledged 

that environment plays a large role in who 

exposes themselves to nicotine via cigarette 

smoking and who continues irrespective 

of their initial smoking experience or their 

genetic susceptibility.

It is suggested here that both general and 

specifi c genetic risk factors have to be 

targeted via endophenotype studies. These 

genetic risk pathways are probably not 

completely independent of one another. 

Indeed, similar and unique neural circuitry 

may be involved in smoking initiation risk 

and in response to initial nicotine exposure. 

For example, reduction in reward may 

be particularly important as a reinforcer 

for initial nicotine exposure response, 

whereas breakdowns in cognitive control 

and attentional regulation may be crucial 

to smoking initiation and progression. 

Conversely, similar operations may be 

involved in both smoking initiation risk 

and initial nicotine exposure response. 

For instance, approach systems may 

contribute to an adolescent exploring 

nicotine use, and reactivity of that same 

system may contribute to reinforcing 

properties of nicotine upon exposure. 

As such, the discrete treatment of these 

nicotine-dependence phenotypes in this 
383

Monograph 20. Phenotypes and Endophenotypes

chapter is heuristic. However, such analytic 

treatment may be necessary to identify 

mechanisms, and potential, unique genetic 

infl uences, at each infl ection point.

It is important to note that the smoking 

initiation and progression risk variables 

have been framed through a neurobiological 

temperament model that permitted a 

multilevel analysis beginning with surface 

behavioral traits, and proceeding to lowerlevel laboratory measures as candidate 

endophenotypes, perhaps getting closer 

to gene action. Of course, whether or not 

something is “closer to the gene” is an 

empirical question. Some seemingly simple 

markers may in fact be genetically complex 

(e.g., it is not clear that “attention” as 

measured on a cognitive task is genetically 

simpler than nicotine dependence as 

measured in a structured interview). Here 

there is no claim to genetic simplicity for 

any of these candidates: each will require 

evaluation with respect to the criteria 

set forth to support the likelihood that a 

measure is an endophenotype. Likewise, 

temperament and personality variables, 

such as novelty seeking, may indeed be 

endophenotypes (mediators) that are 

genetically more complex than nicotinedependence phenotypes. These trait 

variables also may serve as moderators as 

well as diathesis variables; their specifi c role 

will depend on the specifi ed conceptual and 

statistical model. These types of conceptual 

issues are highlighted in chapter 3.

Under the premise of multiple pathways, 

a given endophenotype should capture 

a subset of the population (just as will a 

given genotype). Thus, a relative with little 

exposure to nicotine may appear similar to 

a nicotine dependent smoker on a putative 

endophenotype. That is, not all adolescents 

who have initiated smoking and progressed 

to nicotine dependence will have a particular 

endophenotype, and not all adolescents 

with an endophenotype will have initiated 

smoking and progressed to nicotine 

dependence. Likewise, the evaluation 

of endophenotype-by-endophenotype 

interactions may (1) help to identify 

genetic signals across multiple pathways, 

which at a more surface level may refl ect 

interactions of the biological and traitlike 

systems, or (2) aid in understanding why 

endophenotypes are present in adolescents 

who initiate smoking experimentation 

but do not progress to nicotine dependence.

The search for endophenotypes for nicotinedependence risk at or before initial nicotine 

exposure will likely raise important issues 

with respect to smoking phenotypes, 

endophenotypes, and their distinction. 

If a smoking phenotype is weak, this 

may negatively affect statistical models 

designed to link genes to endophenotypes 

to phenotypes. An important question is 

whether phenotypic defi nition is improved 

by clarifying candidate endophenotypes. 

Effective endophenotypes may inform 

phenotype defi nition (e.g., smokers who 

have strong PET response to nicotine 

compared to those who do not) in the 

future. Likewise, the conceptual distinction 

between an endophenotype and a “refi ned 

phenotype” is murky. For example, rate of 

smoking escalation could be considered 

a phenotype as well as an index of an 

endophenotype (reinforcement) for nicotine 

exposure.

Methodological Issues

Methodological problems in identifying 

and measuring liability in those who have 

not yet initiated smoking are not trivial.39,40

Yet, measuring endophenotypes for nicotinedependence risk in the context of prospective 

designs are crucial to establishing 

predictive validity as well as the utility of 

this approach. For example, prospective 

observational cohorts usually rely on a 

suffi ciently large number of youth (general 

population or those at risk) measured 

repeatedly across time. A majority of the 

endophenotype measures are laboratory 
384

8. Endophenotypes for Nicotine Dependence Risk

based and possibly time variant. Thus, 

recruiting and retaining youth in studies 

involving the completion of laboratory-based 

tasks (endophenotype measures) on several 

occasions across time can be challenging. 

In addition, as noted in the second part 

of this chapter, there are methodological 

concerns with innate sensitivity research. 

For the retrospective studies, these 

include the defi nition of “initial” exposure, 

the reliance on retrospective reports of 

smoking experiences, and the unknown 

role differential nicotine dosing during 

initial exposure may play in determining 

differences in self-reported sensitivity to that 

exposure. For prospective studies, concerns 

include ethical dilemmas surrounding 

administering nicotine to naïve adolescents, 

generalizability of novel nicotine delivery 

methods to smoking, whether nicotine 

sensitivity is consistent from adolescence to 

young adulthood, and self-selection biases 

associated with the willingness to be exposed 

to nicotine through research.

Despite these methodological challenges, 

such studies could potentially offer 

comprehensive directional models 

that include surface characteristics, 

endophenotypes, and genes, which 

would provide support for one or more 

endophenotypes as mediator of the genetic 

effects on a nicotine-dependence phenotype. 

The endophenotype(s) should mediate the 

association between the candidate gene and 

the phenotype, indicating that the effects 

of a particular gene are expressed, fully or 

partially, through the endophenotype(s).13

These types of models have been proposed in 

studies investigating endophenotypes for the 

genes that underlie psychiatric disorders. 

As far as is known, only a few studies have 

evaluated these types of models with respect 

to nicotine-dependence risk phenotypes.21,24

More complex relationships may also be 

possible. For example, a gene may have a 

delayed effect on a phenotype, which is not 

evident until a particular developmental 

period (e.g., mid to late adolescence, 

late adolescence to young adulthood). 

In addition, a suppressor effect may be 

present (e.g., genotype is positively related 

to the endophenotype and the phenotype, 

but the endophenotype is negatively 

related to the phenotype). In this situation, 

a simple assessment of the indirect effects 

to total effects may lead to the erroneous 

conclusion that the endophenotype does not 

account for the relationship between the 

genotype and the phenotype.344,345 In addition 

to mediation, the endophenotype should 

moderate association between the candidate 

gene and the phenotype, indicating that the 

effects of a particular gene are stronger in 

individuals with a phenotype who also show 

the endophenotype.13

With respect to analytic approaches, 

one must also consider the utility of using 

a latent variable as an endophenotype 

measure; that is, endophenotypes are latent 

(factors), rather than observed, and are 

comprised of several indicators (more 

than one endophenotype measure). This 

has been found to strengthen heritability 

coeffi cients,234,235 an important criterion 

for validating endophenotypes. However, 

it is unclear whether a composite measure 

renders the endophenotype more complex 

than the phenotype it is indexed to and 

is, therefore, less genetically simple. This 

approach has not yet been attempted to 

evaluate endophenotypes for smoking 

initiation, progression, and the initial 

response to nicotine.

Summary

This chapter has described potential 

endophenotypes for nicotine-dependence 

risk at or before initial nicotine exposure. 

The available literature points to several 

promising endophenotypes and highlights 

the limited research on the validity of 

putative endophenotypes. This research 

foundation will need to be built before the 

utility of the endophenotype approach can be 
385

Monograph 20. Phenotypes and Endophenotypes

evaluated. While an endophenotype approach 

may help close the explanatory gap between 

candidate genes and the onset of nicotine 

dependence, it relies on conceptually and 

methodologically well-grounded research.346

A conceptual framework has, therefore, been 

emphasized that could guide future studies, 

including the selection of endophenotypes, 

and enable the integration of research 

on specifi c endophenotypes for nicotinedependence risk and general substanceabuse endophenotypes.

Conclusions

1. Several higher-order psychological 

constructs can consolidate many 

smoking initiation and progression risk 

variables. These constructs, as well as 

sensitivity to initial nicotine exposure, 

can be related to observable neural, 

physiological, and behavioral measures 

that may, in turn, serve as potential 

candidate endophenotypes for genetic 

research on nicotine dependence.

2. Several laboratory measures exist that 

could be associated with the risk for 

smoking initiation and progression 

and subsequent nicotine dependence, 

but these associations have yet to be 

investigated. Findings are mixed for 

the reliability and heritability of these 

measures, and minimal evidence exists 

for their validity, representing an area 

for further study.

3. Measurement of sensitivity to initial 

nicotine exposure is subject to 

numerous methodological limitations, 

including ethical diffi culties with 

empirical measurement in naive 

(e.g., previously unexposed to nicotine) 

subjects, a lack of consideration 

of smoking dose and context from 

retrospective self-reports, recall 

bias, and self-selection to early 

smoking experience. At the same 

time, preliminary fi ndings indicate 

that measures of reward and mood 

effects surrounding initial exposure to 

smoking show promise as a potential 

basis for endophenotypes of a genetic 

predisposition to nicotine dependence.

4. The available evidence points to the 

plausibility of endophenotypes that 

link factors at or before initial nicotine 

exposure with the potential for nicotine 

dependence. These endophenotypes 

refl ect approach, avoidance, and controlrelated traits as well as initial sensitivity 

and exposure measures in response to 

nicotine intake. Further research is 

needed to help identify endophenotypes 

that connect risk variables for nicotine 

dependence to genetic infl uences.
386

8. Endophenotypes for Nicotine Dependence Risk

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292. Perkins, K. A., S. B. Coddington, 

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294. Jackson, C., and D. Dickinson. 2004. 

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877–84. 

315. Pomerleau, O. F., C. S. Pomerleau, and 

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163–80.
403

A key area in understanding the biology of smoking behavior is the search for measures 

of smoking persistence, which, in turn, may help predict the likelihood of successful 

cessation among long-term users of tobacco. This chapter explores the existing evidence 

base for purported endophenotypes for nicotine dependence in chronic smokers and 

discusses measures in the following key areas:

■ Motivational measures, including reinforcement, as measured by selfadministration of nicotine, and reward (i.e., the subjective evaluation of 

the hedonic effects of smoking)

■ Sensory measures, including resting electroencephalogram (EEG) activity, 

event-related potentials (ERPs), and the prepulse inhibition (PPI) of startle 

response

■ Measures of cognitive function, including attention and vigilance as well as 

working memory

■ Measures of abstinence-induced and cue-induced craving

■ Affective regulation and impulse control

Each of these measures is examined from a standpoint of biological plausibility, objective 

measurement criteria, genetic infl uences, and association with nicotine dependence. 

Available research shows a relationship between motivational measures and dependence, 

as well as evidence of heritability and genetic associations for many sensory, cognitive, 

affective, and behavioral measures. Further research is indicated to establish the potential 

viability of measures such as these as endophenotypes for nicotine dependence.

9

Nicotine-Dependence Endophenotypes 

in Chronic Smokers

Caryn Lerman, Kenneth A. Perkins, and Thomas J. Gould

The analyses described herein were supported in part by National Institutes of Health grants AA015515, CA/DA084718, 

DA017489, DA05807, and DA19478. The authors would like to thank Dr. Riju Ray for his assistance with the literature 

review.
404

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

Introduction

This chapter examines purported 

endophenotypes relevant to smoking 

persistence—that is, phenotypes that can 

be measured objectively in chronic smokers 

and that predict continued smoking versus 

cessation. Nicotine dependence requires 

chronic nicotine exposure, which produces 

neuroadaptive changes that promote 

continued smoking. First, a brief overview 

is provided of the evidence for specifi c 

genetic infl uences on nicotine dependence, 

a prerequisite in the search for valid 

endophenotypes. Then, two overarching areas 

of potential endophenotypes are covered: 

(1) measures of smoking’s “motivational 

effects” that directly refl ect smoking 

persistence: smoking reinforcement (i.e., selfadministration) and reward; and (2) measures 

of smoking’s other effects, and of responses 

to abstinence, on sensory processing, 

cognitive, affective, and behavioral (especially 

impulsivity) functions that may help explain 

smoking’s motivational infl uences. This latter 

section includes acute craving, or urge to 

smoke, because craving measures encompass 

each of these response dimensions. Also 

discussed are the potential endophenotype 

measures of smoking (and nicotine) effects 

in nondeprived smokers, abstinence-induced 

effects in nicotine-deprived smokers, and 

smoking’s reversal of these abstinence effects. 

As noted in chapter 8, endophenotypes can 

be conceptualized as one of three “subtypes”: 

(1) component phenotype, (2) intermediate 

phenotype, and (3) covariate.1

 While chapter 8 

focused on intermediate phenotypes, 

or mechanisms believed to be part of the 

causal chain in the disorder, this chapter will 

emphasize component phenotypes, which 

capture one aspect of the multidimensional 

disorder phenotype but are not necessarily 

part of the causal chain. The focus here 

differs from that of the previous chapter; 

the population of interest in this chapter 

comprises those already dependent on 

nicotine (i.e., already “affected”). Therefore, 

the potential endophenotypes to be discussed 

will be responses to nicotine or smoking, 

and other measures, that are believed to 

refl ect the critical dimensions of the nicotinedependence phenotype.

It is assumed that the motivational effects of 

smoking, the fi rst area, are more proximal 

to persistence of smoking behavior, 

or dependence, virtually by defi nition, as they 

are usually indexed by measures capturing 

smoking- or nicotine-seeking behavior or 

its direct hedonic effects (reward). It is also 

assumed that the other effects of smoking 

and abstinence, the second area, are more 

distal to smoking persistence, again virtually 

by defi nition, as they are not indexed by 

measures of smoking-seeking behavior 

or direct hedonic effects but rather by 

responses on other dimensions that may or 

may not relate to smoking behavior. Thus, 

endophenotypes related to dependence may 

be identifi ed as proximal or distal to smoking 

persistence. While this organization does 

not assume one area is more important 

than the other, it does presume that all 

factors promoting dependence in chronic 

smokers act by increasing smoking’s 

motivational effects. Consequently, this view 

also assumes a general pathway to smoking 

persistence; that is, various acute effects 

of smoking and abstinence serve to foster 

greater smoking reinforcement and reward, 

directly promoting smoking persistence 

(dependence). 

For the fi rst area, drug-motivated 

behavior is the centerpiece of any drug 

dependence. The existing criteria for 

diagnosing drug dependence in psychiatry 

emphasize persistence of drug use 

(i.e., self-administration) despite adverse 

consequences for the user.2

 Meeting all 

criteria that refl ect persistence of smoking 

behavior is suffi cient for a diagnosis of 

dependence, while meeting all criteria 

other than those refl ecting persistence of 

smoking (namely, withdrawal) would not. 

Identifi cation of potential endophenotypes 
405

Monograph 20. Phenotypes and Endophenotypes

refl ecting reinforcement may be relatively 

straightforward, as reinforcement is usually 

indexed by drug self-administration, 

a discrete behavior that can be measured 

acutely in the laboratory. Thus, individual 

differences in smoking or nicotine 

reinforcement in chronic smokers can 

be assessed objectively in a number 

of ways. These include measures of 

cigarette consumption or brief laboratory 

evaluations of self-administration of 

nicotine administered by novel means 

(e.g., gum, nasal spray). Within the area 

of motivational mechanisms, smoking or 

nicotine’s “rewarding” (or hedonic) effects 

are included; these are direct evaluations 

of the smoking experience that may help 

explain reinforcement. Identifi cation of 

endophenotypes of smoking reward may 

be more complicated because reward in 

humans is typically measured with selfreport questionnaires. However, basic 

research with nonhuman animals suggests 

the possibility of more objective measures 

that refl ect drug reward. This research 

will be discussed in terms of its potential 

applicability to identifying endophenotypes 

of smoking or nicotine reward in humans. 

For the second broad area—that is, nicotine 

or abstinence effects—acute and chronic 

nicotine exposure produces physiological, 

cognitive, affective, and behavioral responses 

in both animals and humans. Chronic 

exposure, the focus of this chapter, can 

lead to defi cits in these functions following 

smoking abstinence, refl ecting the onset 

of withdrawal. Nicotine delivered acutely, 

via smoking or other delivery systems, 

may enhance function and often reverses 

abstinence-induced defi cits. These effects 

of nicotine may, in turn, prompt smoking 

or nicotine-seeking behavior to enhance 

function and/or to ameliorate withdrawal 

symptoms. Thus, nicotine or abstinence 

effects can help explain smoking’s 

motivational effects. Craving is included 

in this section because it purportedly 

contains elements of each response domain 

covered here, including physiological, 

cognitive, affective, and behavioral aspects. 

Note that craving is separated here into 

two types: abstinence induced and cue 

induced. Although craving, especially 

abstinence induced, is typically measured 

via self-report, it may also be captured by 

objective measures being explored in human 

studies of cue-induced craving. In terms 

of endophenotype measures, relatively few 

of the objective measures of defi cits or 

enhancements due to nicotine have been 

clearly related to dependence. This chapter 

will review existing measures, identify 

gaps in knowledge related to the viability 

of these measures as endophenotypes, 

and discuss future directions for identifying 

and validating nicotine-dependence 

endophenotypes in chronic smokers. 

Finally, several formal self-report dependence 

measures have been developed to capture 

putative dimensions of dependence, and 

some have been related to ability to quit 

smoking, or smoking persistence, with 

varying predictive validity. These measures, 

such as the Fagerström Test for Nicotine 

Dependence (FTND),3

 the Wisconsin 

Inventory of Smoking Dependence Motives,4

and the Nicotine Dependence Syndrome 

Scale,5 generally assess smoking patterns, 

smoking effects, and the consequences 

of abstinence as part of clinical research 

aimed at predicting quitting success. 

Such responses could refl ect facets of the 

measures of interest here, specifi cally 

smoking persistence and reinforcement or 

reward (i.e., motivational effects), as well 

as effects experienced during smoking 

abstinence and sensitivity to acute nicotine 

effects on various responses (i.e., nicotine 

or abstinence effects). However, these selfreport dependence measures will not be 

examined in this chapter. Instead, the goal of 

this chapter is to identify objective laboratory 

procedures that may reliably capture facets of 

smoking reinforcement or reward, effects of 

abstinence, and acute responses to smoking 

that relate to dependence. The description 
406

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

of these self-report dependence measures 

and their relationship to dependence are 

comprehensively discussed in chapter 3 

and described elsewhere.5

Rationale 

for Investigating 

Endophenotypes 

of Chronic Nicotine 

Exposure

Genetic Infl uences on Nicotine 

Dependence

Nicotine dependence, which underlies 

persistent smoking, is a complex trait, 

infl uenced by genetic and environmental 

factors. Twin studies indicate that 

approximately 60%–70% of the variance 

in nicotine dependence and smoking 

persistence is due to genetic infl uences.6,7

Further, at least 50% of the variance in 

successful quitting, given a quit attempt, 

is due to heritable factors.8 Nicotine 

dependence has a strong genetic association 

with alcohol dependence,9 and linkage 

studies have pointed to loci common 

to alcohol and nicotine-dependence 

susceptibility.10 Common genetic infl uences 

are also thought to contribute to nicotine 

dependence, personality traits, and 

psychiatric conditions, such as attention 

defi cit hyperactivity disorder (ADHD),11

depression,12 and schizophrenia;13 however, 

interactions of biological and environmental 

factors clearly play a role.14

Given consistent evidence for the 

heritability of nicotine dependence, 

attention has shifted to investigations 

of specifi c genetic infl uences. Genetic 

variation in enzymes (e.g., CYP2A6) that 

metabolize nicotine to its inactive forms 

(cotinine and 3-hydroxycotinine) infl uence 

peripheral levels of nicotine and smoking 

behaviors.15 Smokers who are genetically 

faster metabolizers of nicotine smoke more 

cigarettes per day, are more dependent on 

nicotine, and are more likely to relapse 

following transdermal nicotine replacement 

therapy (NRT) than are smokers who are 

slower metabolizers (e.g., carriers of *2, *4, 

*9A, and *12A alleles).16,17 Thus, measures 

of nicotine metabolism are important 

endophenotype measures.

Candidate genes in neurobiological 

pathways mediating drug reward have been 

extensively studied for associations with 

nicotine dependence. Nicotine binds to 

neuronal nicotinic acetylcholine receptors 

(nAChRs) expressed on dopamine and 

c-aminobutyric acid (GABA) neurons in the 

ventral tegmental area (VTA), resulting in 

Smoking Persistence Versus Smoking Onset: An Area for Endophenotype Research

Many of the processes involved in the onset of smoking are likely to be different from those 

involved in smoking persistence. Chapter 8 explores potential phenotypes and endophenotypes 

for nicotine dependence at or before nicotine exposure. The areas investigated include some 

measures similar to those in this chapter, such as nicotine reinforcement (self-administration) 

and reward, as well as other potential endophenotype areas such as latency and age of onset. 

This chapter focuses on purported endophenotypes relevant to smoking persistence—that is, 

phenotypes that can be measured objectively in chronic smokers and that predict continued 

smoking versus cessation, with inability to quit being the primary index of dependence in chronic 

smokers. These, in turn, have the potential to help understand the biology of tobacco use among 

a population at greatest risk for tobacco-related health problems.
407

Monograph 20. Phenotypes and Endophenotypes

increased dopamine release in the nucleus 

accumbens.14,18 Despite the importance of 

nAChRs in nicotine dependence, particularly 

the a4b2 subtypes,19 data on the functional 

relevance of genetic polymorphisms are 

limited, with the possible exception of data 

on two functional variants in CHRNA4.20

A few SNPs in CHRNB2 have been examined 

for associations with nicotine dependence, 

but fi ndings were not signifi cant.21 However, 

a more comprehensive analysis of CHRNB2

suggests that variation in CHRNB2 is 

associated with smoking cessation.22

In addition, there is growing evidence for 

association of CHRNA4 haplotypes with 

nicotine dependence.23,24 Other work shows 

that haplotypes at the CHRNA5-A3-B4 locus 

are associated with nicotine-dependence 

severity as indexed by the FTND among 

smokers who began smoking daily by 

16 years of age, but not among those who 

began smoking after 16 years of age.25 The 

age dependence of these fi ndings highlight 

the notion, discussed in chapters 5–8, that 

infl uences on dependence susceptibility, 

including genetics, can vary by age.

Given the central role of dopamine signaling 

in the reinforcing and rewarding effects 

of nicotine, alcohol, and other addictive 

drugs,26–28 many initial studies focused on 

the common *TAQ1A polymorphism in a 

neighboring gene, ANKK1.29 With respect to 

smoking behavior, some association studies 

have reported a higher prevalence of the 

low-activity DRD2*TAQ1 A1 allele among 

smokers compared to nonsmokers,30,31

while other fi ndings have been negative.32

Mixed results have also been reported for 

associations of a variable number tandem 

repeat (VNTR) polymorphism in the 3 end 

of the dopamine transporter (SLC6A3) gene 

with smoking behavior.33–35

More robust fi ndings have been observed for 

polymorphisms in DRD2 with documented 

functional effects—for example, variants 

that alter transcription or translation. 

For instance, the promoter variant 

DRD2-141C INS/DEL, associated with 

transcriptional effi ciency, has been associated 

with response to pharmacotherapy for 

smoking cessation.36 The reduced activity 

*7-repeat allele of the DRD4 gene VNTR has 

been associated with smoking persistence.37,38

The high-activity (*VAL) allele of the COMT

gene, associated with more rapid degradation 

of dopamine, has been associated with 

smoking persistence in a retrospective casecontrol study and in a prospective smoking 

cessation study.39

Nicotine also increases levels of endogeneous 

opioids that bind to mu opioid receptors on 

GABA interneurons in the VTA.28 Consistent 

with neurobiological evidence, the mu opioid 

receptor (OPRM1) ASN40ASP functional 

variant has been associated with response to 

NRT; however, the direction of association 

in different populations has not been 

consistent.40,41 A study comparing smokers 

with high versus low levels of nicotine 

dependence did not fi nd associations with 

this OPRM1 variant; however, haplotype 

analysis suggests that other variants, 

which may be in linkage disequilibrium 

with the ASN40ASP polymorphism, are 

linked with this smoking phenotype.42

Finally, despite effects of nicotine on 

serotonin neurotransmission, there is no 

strong evidence linking smoking behavior 

or smoking cessation with genes in the 

serotonin pathway.43,44 Thus, it has proven 

diffi cult to identify candidate genes with 

robust, replicable associations with nicotine 

dependence and smoking persistence. 

In addition to the candidate gene approach 

used in the studies above, specifi c genetic 

infl uences on nicotine dependence are being 

identifi ed through linkage analysis and 

genome-wide association studies.45–48 Similar 

analyses have been performed to predict 

successful smoking cessation.49 In contrast 

to the hypothesis-driven approach based 

on neurobiology described above, genomewide studies have the potential to identify 

novel susceptibility loci that may not be 
408

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

considered as a priori candidate genes. 

As with the candidate gene studies, fi ndings 

from these approaches require independent 

validation. In addition, pharmacological 

challenge studies (e.g., dopamine depletion, 

agonist or antagonist compounds) may help 

to elucidate novel neurobiological pathways 

that infl uence endophenotypes of relevance 

to nicotine dependence.

The Case for Endophenotypes

As described in the introduction to 

chapter 8, one promising approach to 

elucidate the genetic basis of nicotine 

dependence is to study the underlying 

motivational, affective, and neurocognitive 

processes that underlie this complex 

phenotype.50,51 These intermediate 

measures of nicotine dependence, referred 

to as “endophenotypes,” are thought to 

be more proximal biologically to their 

genetic antecedents than are the complex 

behavioral phenotypes described above and, 

therefore, may provide a stronger genetic 

signal;51 however, this point is the subject 

of some debate.52 In the context of nicotine 

dependence, the optimal endophenotype 

measures would have a biologically 

plausible link to nicotine dependence and 

would be reliable, heritable, and valid 

(i.e., predictive of nicotine dependence, 

such as predicting smoking persistence 

versus abstinence after a quit attempt). 

Although many candidate endophenotype 

measures have potential utility in genetic 

studies of nicotine dependence, few meet 

all of these criteria. 

Figure 9.1 illustrates the potential links 

between genes, neurochemical processes, 

and behavioral and physiological responses. 

Selected examples of genes coding for 

proteins involved in the biosynthesis, 

transport (e.g., 5-HTTLPR), and metabolism 

(e.g., COMT) of neurotransmitters 

(e.g., serotonin, dopamine), and those coding 

for receptors (e.g., DRD2, OPRM1) are 

depicted in the shaded area. Also illustrated 

are genes that code for nicotine-metabolizing 

enzymes (e.g., CYP2A6) and nAChRs that 

have been implicated in nicotine dependence 

(e.g., CHRNA4, CHRNA7, CHRNB2). These 

neurochemical processes, in turn, infl uence 

the specifi c nicotine effects (e.g., effects on 

affect and cognition) or abstinence effects in 

chronic smokers that help explain smoking’s 

motivational effects. These processes may 

also directly affect those motivational effects. 

Measures of smoking’s various motivational 

effects (fi rst area) and nicotine or abstinence 

effects (second area) are viewed here as 

potential endophenotypes of dependence. 

They differ primarily in their proximity to 

dependence, with the motivational effects 

more proximal, and nicotine or abstinence 

effects less proximal, to dependence.

In the sections below, evidence is described 

regarding the potential utility of different 

endophenotype measures associated with 

chronic exposure to nicotine and refl ecting 

either of the two broad areas: smoking’s 

motivational effects or the acute effects of 

nicotine or abstinence that may promote 

those motivational effects. For each 

endophenotype construct, evidence is 

reviewed pertinent to the previously noted 

four criteria in the evaluation of the utility 

of each as endophenotypes: (1) biological 

plausibility—that is clinical evidence 

(typically involving self-report) linking 

a response area to dependence, as well 

as the neurobiological basis for specifi c 

nicotine effects, including fi ndings from 

animal models, and preclinical evidence 

suggesting genetic infl uences on those 

effects; (2) reliability—standardized, 

objective measurement of the construct 

in humans; (3) heritability—evidence 

for genetic infl uences in humans from 

heritability, linkage, and candidate gene 

studies; and (4) predictive validity—evidence 

supporting a relationship of the measure 

to nicotine dependence in chronic smokers 

(i.e., smoking persistence). As will become 

readily apparent, evidence for the association 

of most endophenotype measures to nicotine 
409

Monograph 20. Phenotypes and Endophenotypes

Figure 9.1 Example of How Potential Endophenotypes Can Link Genes to NicotineDependence Risk 

CHRNA4, CHRNA7, CHRNB2

CYP2A6 OPRM1 5-HTTLPR DRD2 COMT

Nicotine

Metabolism

nAChRs

Opioid

Serotonin Dopamine

Sensory Behavior Cognitive Affective Craving

Self-Administration Reward

Nicotine Dependence

Motivational

Mechanisms

Acute Smoking or

Abstinence Effects

Note. Endophenotype areas are presented in gray squares, divided into motivational mechanisms and acute smoking or abstinence 

effects, the two broad areas outlined in the chapter. Selected examples of genes (bottom row) that contribute to neurotransmitter 

activity and receptor function (dark blue bar) related to these endophenotype areas can be identifi ed. This fi gure is illustrative only 

and does not refl ect a consensus on the factors responsible for neurotransmitter function or for the endophenotype areas.
410

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

dependence, the fourth criterion, is limited. 

Therefore, studies are included that are 

suggestive of an association, such as those 

documenting differences between smokers 

and nonsmokers, in addition to studies 

relating the endophenotype to validated 

dependence measures and to abstinence 

outcomes. For each endophenotype 

measure, the limitations as well as gaps in 

knowledge that may be addressed in future 

research are discussed. Also, since most 

research has focused on cigarette smoking, 

rather than on other forms of tobacco use, 

the focus will be on this aspect of nicotine 

dependence.

Motivational 

Mechanisms

Reinforcement

The concept of drug reinforcement is defi ned 

by the degree to which the drug is selfadministered, or in other words, the degree 

to which it increases the probability of a 

behavior that leads to administration of that 

drug (such as pressing a lever or inhaling 

on a lit cigarette).53 Such behavior is readily 

assessed in animal models, as well as in 

humans.

Biological Plausibility

Preclinical Research

Although diverse factors infl uence drug 

reinforcement, one common factor across 

all drugs of abuse is that they activate the 

mesolimbic dopaminergic system.54–56

Dopaminergic neurons in the VTA of 

the midbrain send efferent projections 

to areas involved in drug-motivated 

behavior (and reward) such as the nucleus 

accumbens, amygdala, and the prefrontal 

cortex.57–59 Nicotine stimulates the release 

of dopamine in the nucleus accumbens via 

effects at nAChRs in the VTA.60–63 However, 

it is not only activation of the nucleus 

accumbens that mediates reinforcement 

but also the pattern of activation. Drugrelated stimuli shift the fi ring of dopamine 

neurons from tonic or single-spike activity 

to a phasic pattern of activation.64 Nicotine, 

via desensitization of a4b2 nAChRs, shifts 

dopamine release in the nucleus accumbens 

to a phasic pattern of release.65,66 Thus, 

the reinforcing properties of nicotine 

may be related to the ability of nicotine to 

increase the phasic pattern of VTA activation 

and dopamine release.

Two routes of administration commonly 

used in animal research to assess the 

reinforcing effects of nicotine are 

intravenous (IV) nicotine self-administration 

and oral nicotine self-administration. 

The behavioral and pharmacological 

features of each approach are briefl y 

reviewed next, with an emphasis on genetic 

analyses of reinforcement fi ndings in 

preclinical models. 

Intravenous Self-Administration 

Multiple studies have demonstrated that 

rodents will self-administer IV nicotine.67–76

The most common procedure, developed 

by Corrigall and Coen,68 was adapted from 

earlier methods of self-administration 

of other drugs in rodent models. In this 

IV self-administration procedure, rodents 

are presented with two levers in a test 

apparatus. Animals in the experimental 

group access an active lever, which 

results in jugular vein administration 

of nicotine, and an “inactive” lever that 

has no programmed consequences. The 

inactive lever is a control condition used 

to determine whether study procedures 

nonspecifi cally increase behavior 

(i.e., increase both active and inactive lever 

pressing via changes in general locomotor 

behavior) or specifi cally increase nicotine 

reinforcement behavior (i.e., active lever 

pressing only). For animals in the control 

group, the active lever delivers saline, while 

the inactive lever has no consequences; 

as expected, control animals emit low 
411

Monograph 20. Phenotypes and Endophenotypes

levels of pressing on either lever. The rate 

of lever pressing is then compared between 

groups; a higher rate of pressing the active 

lever that delivers nicotine indicates that 

nicotine is reinforcing lever pressing.68,69,77

Importantly, responding for nicotine can be 

greatly enhanced by the stimuli associated 

with nicotine infusions, such that these 

stimuli (commonly called “cues”) become 

secondary reinforcers and able to support 

(i.e., reinforce) responding independent 

of nicotine availability.78 Examples of such 

cues include tones and brief onset or offset 

of animal chamber lights. Other variations 

on this self-administration procedure have 

been used, such as by varying the particular 

behavior contingent on drug administration 

(e.g., a nose-poke response instead of lever 

pressing), but the basic study designs are 

essentially the same.

Evidence suggests that the reinforcing 

properties of IV nicotine self-administration 

result from nicotine-mediated activation 

of the mesolimbic dopamine system. First, 

rats will self-administer nicotine into the 

posterior VTA;79 this demonstrates that 

nicotine effects in the VTA are suffi cient 

to support nicotine self-administration. 

Second, disruption of dopaminergic 

processes in the VTA decreases IV 

nicotine self-administration. For example, 

the D1 antagonist SCH23390 and the 

D2 antagonist spiperone both decrease 

nicotine self-administration.80 Furthermore, 

6-hydroxydopamine (6-OHDA) lesions of the 

nucleus accumbens reduce dopamine levels 

in the nucleus accumbens by 92.9% and 

disrupt IV nicotine self-administration.81

Many different nAChRs exist in the human,19

but only a few appear to be potentially 

important for understanding nicotine 

dependence. In particular, the a4b2 nAChRs 

are thought to mediate the reinforcing 

properties of nicotine. Dihydro-betaerythroidine (DHbE), an nAChR antagonist 

with high affi nity for the a4b2 nAChRs, 

decreases nicotine self-administration,82

and direct VTA infusion of DHbE decreases 

IV nicotine self-administration.83 Selfadministration of nicotine, but not cocaine 

(showing selectivity), is decreased in 

b2 knockout mice compared to wild-type 

mice.84,85 These studies not only suggest the 

involvement of VTA dopamine processes 

in nicotine dependence but also suggest 

involvement of the a4b2 nAChRs.

Comparison of IV nicotine selfadministration across strains of rats suggests 

that natural genetic variance may infl uence 

IV nicotine self-administration. In a study 

comparing choice of IV nicotine selfadministration across Sprague-Dawley rats, 

Long-Evans rats, Fischer 344 (F344) rats, 

and Lewis rats that were either preexposed 

to nicotine or saline for seven days before 

self-administration, Sprague-Dawley rats 

showed high levels of IV nicotine selfadministration for all three doses tested 

(0.015, 0.03, and 0.06 milligrams per 

kilogram [mg/kg]/infusion) regardless of 

preexposure condition.86 Long-Evans rats 

also self-administered nicotine; however, this 

was limited to rats in the saline preexposure 

condition and to higher doses of nicotine. 

Neither F344 nor Lewis rats reliably selfadministered nicotine. Clearly, genetic 

differences in nicotine self-administration 

(and thus, reinforcement) exist. 

Rats selectively bred for high versus low 

alcohol preference demonstrate a genetic 

infl uence on nicotine self-administration 

as well, suggesting a common genetic 

infl uence. Alcohol-preferring rats have twice 

the intake of IV nicotine as nonpreferring 

rats.87 Mice bred for increased sensitivity 

for the sedative effects of alcohol are 

more sensitive to the effects of nicotine 

on thermoregulation and locomotor 

activity.88,89 In addition, mice bred for high 

sensitivity to the sedative effects of alcohol 

develop greater tolerance to nicotine than 

do mice bred for low sensitivity.90 These 

fi ndings in rodent models are consistent 

with the notion of individual differences 
412

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

in vulnerability to comorbid alcohol and 

nicotine dependence in humans.91

Oral Self-Administration

In addition to IV self-administration, 

rodents will also self-administer nicotine 

orally.92–96 IV administration, used in rat 

models, is diffi cult to achieve with mice for 

a variety of practical reasons. Thus, oral 

self-administration is the common method 

for studying drug reinforcement in mice, 

although it is also used with rats. As a 

result, differences in results between IV and 

oral methods may often be due to species 

differences, although the kinetics of nicotine 

intake between these methods (rapid with 

IV, slow with oral) can also account for 

different results.97

Multiple methods have been used 

successfully for oral self-administration. 

One is a 24-hour, free-access approach in 

which animals are individually housed 

in cages with two bottles—one bottle 

containing water and the other nicotine—

and consumption is compared between 

bottles.93 The restricted access method is 

a variant of the two-bottle-choice method. 

Animals are maintained on water restriction 

except for a given period (e.g., 2 hours/day) 

during which they have access to two tubes, 

one fi lled with water and the other fi lled 

with a nicotine solution.92 Another approach 

is to use an operant oral self-administration 

procedure; animals are water restricted 

except in the operant trials during which 

a response on one lever delivers a nicotine 

solution and a response on the other lever 

delivers water.94 Finally, some studies have 

combined a sucrose solution with both the 

vehicle and nicotine in an effort to increase 

palatability, with the difference between 

nicotine and vehicle solutions indexing the 

reinforcing effects of nicotine.96

Although methods may vary, oral selfadministration of nicotine has been used 

to demonstrate genetic infl uences on 

nicotine intake in mice. Strain surveys 

of inbred mice demonstrate that genetic 

variance contributes to differences in oral 

nicotine self-administration. C57BL/6 

mice show a higher preference for oral 

nicotine than do DBA/2 mice in a twobottle-choice paradigm;98 the C57BL/6 mice 

also show greater preference for ethanol 

and amphetamine, and the DBA/2 show 

greater preference for aspartame. In an 

extensive strain survey of oral nicotine 

consumption using the two-bottle-choice 

test, the C57BL/6 strain consumed the most 

nicotine, followed in order of descending 

consumption by DBA/2 > BUB > A ≥ C3H 

≥ ST/b mice.99 Another strain survey 

compared oral nicotine self-administration 

in the following strains of mice: A/JxNMRI 

cross, C57BL/6, C3H/J, DBA/2, NMRI, 

ST/bJ; as in a study by Robinson and 

colleagues,99 the C57BL/6 mice consumed 

the most nicotine and the ST/bJ mice 

consumed the least.100 Strain survey results 

provide important information to guide 

the appropriate selection of experimental 

subjects on the basis of the research 

question and also provide important 

information for future genetic analysis.

Work from Collins’s laboratory identifi ed 

a single nucleotide polymorphism in 

the gene that codes for the a4 nAChR 

subunit, CHRNA4, that results in either 

alanine or threonine at position 529 on the 

a4 protein.101 This polymorphism alters 

a4 nAChR function and sensitivity to the 

behavioral effects of nicotine.101–103 To test 

whether the CHRNA4 polymorphism alters 

nicotine preference, choice of nicotine 

consumption was compared across 

14 strains of mice that differed in expression 

of the A529 versus T529 variant.104 Strains 

with the A529 variant of CHRNA4 had 

signifi cantly lower levels of nicotine 

consumption. Consistent with human 

data,20,23,24 these results demonstrate that 

an altered sequence of CHRNA4 infl uences 

nicotine intake and, thus, could infl uence 

development and persistence of nicotine 

dependence.
413

Monograph 20. Phenotypes and Endophenotypes

Using data indicating that C57BL/6 

mice show high levels of nicotine 

consumption and ST/b mice show low 

levels of nicotine consumption, a study 

investigated if alterations in expression of 

Cyp2a5—the homologue of the human gene 

CYP2A6, which codes for an enzyme involved 

in the metabolism of nicotine—were related 

to oral nicotine self-administration in 

mice.105 F2 mice from a C57BL/6 and 

ST/b cross were segregated into high- and 

low-nicotine consumers, and levels of 

Cyp2a5 protein were analyzed.105 In male 

F2 mice, the high nicotine consumption 

was associated with higher levels of Cyp2a5 

protein and faster nicotine metabolism. 

This corresponds well with what is seen in 

smokers: smokers with a null CYP2A6 allele 

smoke less and smokers with a duplicate 

copy of CYP2A6 smoke more than do 

homozygous wild-type smokers.106

The preclinical studies described above 

provide strong evidence for a biological 

basis of nicotine reinforcement—one key 

criterion for an endophenotype. In addition, 

evidence for strain differences in nicotine 

reinforcement paradigms supports the search 

for specifi c candidate genes and pathways 

that may underlie nicotine reinforcement 

measures in humans. Studies documenting 

effects of genetic and pharmacological 

manipulation on nicotine reinforcement in 

animal models point to specifi c candidate 

genes that can be tested for association in 

human studies. 

Reinforcement-Enhancing Effects of Nicotine

Before proceeding to the overview of human 

research on nicotine reinforcement, it is 

important to note that nicotine may have 

a second reinforcing function, aside from 

the direct (primary) reinforcing effects 

noted above. As noted previously, stimuli 

accompanying nicotine infusions can 

become secondary reinforcers through 

their association with nicotine (i.e., cues). 

However, animal studies show that nicotine 

can enhance the reinforcing value of other 

reinforcers not associated with nicotine 

intake. In this work, primarily conducted 

by Caggiula and colleagues (e.g., Chaudhri, 

et al. 2006107), nicotine has been shown 

to enhance responding for reinforcement 

from stimuli, such as a light offset (darker 

environments are preferred by rodents), 

that are available independent of the 

responses for nicotine. In other words, 

in addition to the stimuli associated with 

nicotine infusion becoming secondary 

reinforcers that enhance responding for 

nicotine,78 nicotine can enhance responding 

for other reinforcing stimuli, showing 

a dual reinforcing function. Nicotine’s 

“reinforcement-enhancing” effects differ 

from the secondary reinforcing effects of 

cues in that the latter develop through 

associative processes requiring a contingency 

between the cues and nicotine administered 

in rapid fashion, while the former are 

nonassociative and can occur regardless 

of nicotine delivery speed.107 Later work 

suggests that the reinforcing-enhancement 

effects of nicotine may occur in humans;108

inadequate study of this phenomenon 

in humans, however, does not allow for 

extensive discussion of the potential for 

measures of the reinforcement-enhancing 

effects of nicotine as endophenotypes. 

However, this infl uence warrants greater 

attention in the broader fi eld to help explain 

why smoking appears to acutely increase 

consumption of other reinforcers, such as 

alcohol.109 It also may contribute crucially to 

understanding why smoking is so diffi cult 

to quit. Quitting smoking would remove 

not only the direct reinforcing effects from 

smoking, as is commonly the sole focus, 

but also these reinforcement-enhancing 

effects. This would lead to a lessening of 

reinforcement from many other reinforcers, 

causing greater deprivation than might 

be expected based on the observed direct 

reinforcing effects of nicotine. 

Human Clinical Research 

Additional evidence for biological plausibility 

of reinforcement measures as potential 
414

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

endophenotypes comes from research linking 

clinical (self-report) measures of the amount 

and persistence of smoking reinforcement 

with the outcome of a subsequent quit 

attempt. Poorer outcome of a quit attempt 

is typically determined by faster time to 

relapse (i.e., shorter duration of abstinence), 

and secondarily, by more severe withdrawal. 

These results support the notion that 

objective (i.e. non-self-report) measures of 

smoking amount and persistence may be 

candidate endophenotypes.

The amount, or frequency, of cigarette 

consumption typically is assessed simply 

by self-report of number of cigarettes per 

day during “maintenance,” or when not 

attempting to cut down or quit. Greater 

number of cigarettes per day has been 

related to poorer outcome of a quit attempt 

(i.e., greater dependence) in that amount of 

smoking is often related to greater severity 

of withdrawal and to shorter time to relapse 

after a quit attempt.110 Measures of smoking 

persistence are also relevant, such as time 

to fi rst cigarette of the day after waking; 

longer times are related to lower levels 

of dependence. While smoking frequency 

and persistence are not interchangeable 

(i.e., measure the same thing), they are also 

not independent in that greater amount 

of smoking is associated with faster time 

to fi rst cigarette and shorter duration of 

prior quit attempts.111 In any case, across 

various types of clinical trials or among 

self-quitters, a greater number of cigarettes 

per day (frequency) and faster time to fi rst 

cigarette of the day (greater persistence) 

before quitting are associated with poorer 

cessation outcome—notably, shorter 

duration of abstinence and greater severity 

of withdrawal symptoms.112–114 Note that selfreported number of cigarettes per day and 

time to fi rst cigarette are two items from the 

FTND self-report dependence measure3 that 

are most predictive of cessation outcome; 

together, they are sometimes used as the 

Heaviness of Smoking Index dependence 

measure.3 Those high on this index are less 

able to quit, even for 24 hours, compared to 

those low on this index.115

After starting a quit attempt, any smoking 

at all (a lapse) strongly predicts eventual 

relapse, further illustrating the importance 

of smoking persistence (inability to refrain 

from smoking) as an index of dependence. 

This effect is very pronounced if the 

smoking occurs on the quit day itself (very 

strong smoking persistence),116 but remains 

strong even if it occurs after weeks of 

maintaining abstinence, whether with 

or without cessation medication.113,117,118

Smoking persistence appears to be a stable 

characteristic in that the faster a smoker 

resumes smoking (relapses) during a prior 

quit attempt, the greater the chances 

of relapsing during a subsequent quit 

attempt.113 Those who have never tried to 

quit at all (no prior demonstration of ability 

to refrain from smoking) also are typically 

less successful when they try to quit. 

As suggested, smoking frequency can predict 

persistence during a given quit attempt in 

that those who smoked more cigarettes per 

day before quitting are more likely to lapse 

on the quit day or soon after quitting.117

Some studies have shown that after having 

quit, the amount (i.e., frequency) of 

smoking during the fi rst lapse predicts faster 

occurrence of the second lapse and perhaps 

risk of full-blown relapse.119 In sum, whether 

before or after the quit attempt, self-report 

measures of frequency and persistence of 

smoking predict poorer outcome of a quit 

attempt, a key index of dependence. 

Description of Potential 

Endophenotype Measures of Nicotine 

or Smoking Reinforcement

“Reinforcement” is a broad concept that is 

characterized by several dimensions and 

cannot be captured by a single measure,53,120

as evidenced by the separate consideration 

above of smoking frequency versus 
415

Monograph 20. Phenotypes and Endophenotypes

persistence. Various short-term objective 

measures of reinforcement, and what they 

purport to assess, are outlined here. Some 

capture smoking frequency (e.g., ad lib selfadministration), while others may refl ect 

smoking persistence (e.g., progressive 

ratio). One approach, behavioral economics, 

may be able to model both. “Drug choice” 

is a separate concept that is not generally 

captured by dependence criteria of smoking 

frequency or persistence but that has 

been shown to relate to dependence in 

laboratory studies. Drug choice (i.e., nicotine 

preference) is the degree to which drugcontaining substances are preferred over 

otherwise equivalent nondrug substances 

(e.g., placebo cigarettes). All of these 

procedures are derived from research on 

nicotine and other drugs of dependence with 

nonhuman animals. The biggest limitation 

of these measures of reinforcement is 

uncertain generalizability to smoking 

behavior in the natural environment. 

Ad Libitum (ad lib) Drug Self-Administration 

In the natural environment, nicotine delivery 

is usually accomplished with a fairly simple 

response—that is, puffi ng on a cigarette once 

it is lit (although more extensive behavior 

may be required to obtain the cigarettes). 

Thus, observation of smoking behavior, or ad 

lib self-administration, over a specifi c period 

of time may have the strongest face validity 

as an objective measure of reinforcement. 

A variation, adopted from animal research 

(described above), involves requiring the 

subject to make one response (e.g., pressing 

a computer key) that is reinforced by one 

unit of drug (e.g., a puff). This procedure 

assesses smoking intensity, amount (or rate) 

of consumption, or simple drug-taking 

behavior,53 similar to the self-reported 

number of cigarettes per day.

Smoking consumption in the laboratory can 

be assessed by simply counting the number 

of cigarettes or individual puffs (usually from 

videotapes of the subjects). Consumption can 

also be measured indirectly by biochemical 

indices of recent smoking exposure, such as 

blood nicotine level or expired-air carbon 

monoxide boost from before to after the 

session.121 The reliability of behavioral 

observation of smoke puffs is very high 

because it is a rather discrete behavior.122

The test-retest reliability of measures 

of ad lib smoking also tends to be high. 

In unpublished analyses, the authors of this 

chapter examined the correlation of puffs 

taken during a brief ad lib smoking period 

on each of two days in 54 smokers who had 

abstained overnight. The number of puffs 

correlated 0.67 (p < .001) between sessions, 

although latency to fi rst puff, a measure of 

persistence, was not signifi cantly correlated 

between sessions (0.18). This difference in 

reliability suggests that smoking persistence 

(as measured by latency to fi rst puff) may 

be less reliable, and also, that persistence 

and frequency (as measured by total puffs) 

may capture different aspects of smoking 

reinforcement. Use of smoking topography 

devices, particularly the Clinical Research 

Support System,123 can also provide an 

objective assessment of intake by quantifying 

puff volume, puff duration, interpuff 

interval, and puff velocity.124 Amount of 

consumption of nicotine per se can also be 

assessed by providing smokers with novel 

nicotine delivery methods, such as nicotine 

spray or intravenous infusion, and perhaps 

gum.122,125,126

A variation on this procedure is to require 

more than one response per drug unit 

received, such as providing one drug unit for 

every 5 or 10 responses, as commonly done in 

animal research (e.g., fi xed ratio or variable 

ratio, reinforcement schedules; see also 

subsections on “Behavioral Economics” 

and “Progressive Ratio Measures” below). 

The greater the response requirement, 

the more drug-motivated behavior generated, 

up to a point. (Another variation that requires 

large amounts of responding and that exploits 

the important motivating effects of drug 

cues is the second-order schedule, which 

is not discussed here in detail, given its 
416

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

rarity in human studies.) Beyond a certain 

point, the amount of responding required 

for a unit of drug becomes so great that 

self-administration is discouraged; this is 

essentially a means to assess persistence. 

This “breakpoint” is the key measure in the 

progressive ratio procedure, as discussed 

later, and is an important index of the 

reinforcing value of the drug.

While amount of ad lib smoking in a 

laboratory session appears to have face 

validity, relatively little effort has been 

made to show that those who smoke more 

under such conditions also smoke more 

outside the laboratory on a day-to-day basis. 

A more specifi c limitation of this approach 

is that drug satiation can occur quickly 

in even a brief laboratory session, thereby 

resulting in little subsequent drug-motivated 

behavior. Low rates of such behavior 

complicate the interpretation of comparisons 

between individuals in intensity of selfadministration. This problem is reduced by 

use of schedules requiring multiple responses 

per reinforcement, noted previously. A third 

limitation is that because cigarettes contain 

much more than just nicotine (most 

substance abuse involves more than one 

component), self-administration of cigarettes 

does not necessarily index the reinforcing 

effects of nicotine per se (see “Nicotine 

Choice” below). Use of these procedures 

with novel nicotine delivery methods can 

determine nicotine reinforcement.

Nicotine Choice 

The nicotine choice procedure more 

specifi cally addresses the degree to which 

the drug nicotine is reinforcing.127 This 

procedure compares self-administration 

of one substance that contains a drug with 

another that is identical except for containing 

no drug (i.e., a placebo) and is analogous to 

use of active versus inactive lever pressing in 

animal studies, described previously. Here, 

the absolute levels of self-administration, 

or intensity, are not of primary interest, but 

rather, the difference in self-administration 

between drug and placebo. Greater selfadministration of the former versus the latter 

indicates that the drug itself is reinforcing. 

This procedure essentially assesses preference 

between two alternatives, one containing a 

drug and the other not, and it controls for 

virtually all nonpharmacological aspects of 

substance use (e.g., responses to conditioned 

cues), isolating the pharmacological effects. 

When choice is compared between conditions 

or groups, differences refl ect the relative 

reinforcing value of nicotine (relative means 

greater choice in one condition or group 

versus the other, even if neither or both 

choose nicotine more than placebo). When 

the two substances are made available ad 

lib, this is often called a concurrent choice 

procedure.122

Because subjects may vary markedly in 

overall drug self-administration frequency, 

comparisons of drug choice may be diffi cult 

between subjects. One common way to 

reduce this problem is to standardize the 

procedure by requiring a fi xed number of 

choices (forced choice), spaced apart to avoid 

satiation, and determine whether the drug 

is chosen more often than the placebo.127,128

This approach is described in chapter 8; 

it is perhaps the only self-administration 

procedure that can be used with naive 

individuals with no prior experience with 

the drug. Other variations on choice can 

involve different drug doses (e.g., highversus low-nicotine cigarette) or substances 

differing on other characteristics of interest 

(e.g., nicotine by nasal spray versus gum). 

A limitation of this measure is that 

interpretation of results can be unclear. 

Choice of drug versus placebo is a function 

of the specifi c procedures—namely, the dose 

per drug use and the number of choices 

provided. Because drugs often have toxic 

or satiating effects, drug choice will be less 

as dose per administration and number 

of choices increase. Thus, whether or not 

the drug is chosen more than the placebo 

is specifi c to the procedures used, and 
417

Monograph 20. Phenotypes and Endophenotypes

choosing the drug less than the placebo 

does not necessarily mean that the drug is 

not reinforcing. It is the relative difference 

in choice between conditions or individuals 

that is the important measure.

Behavioral Economics 

Drug use in the natural environment can 

require more than a single simple response 

and may require engaging in extensive 

behavior (e.g., having to go outside to smoke 

at work, walking through snow to a store 

that sells cigarettes). Behavioral economics 

is one standard approach to determining 

how different response requirements 

for a drug affect intake. See Bickel and 

colleagues129,130 and Perkins and colleagues131

for a more thorough discussion of this 

approach. Typically, the number of 

responses (price) required per drug unit 

is manipulated, so that consumption of 

drug (demand) across increasing prices can 

be determined, forming a price-demand 

curve. Across low to moderate prices, 

consumption is usually maximal and 

unchanged, producing a curve that is fl at 

in that responding can easily increase to 

meet the increasing behavioral prices of 

smoking. Demand is said to be inelastic, 

or unchanging, with respect to price, and 

responses here may refl ect the individual’s 

typical smoking frequency. At higher 

prices, however, responding continues to 

increase up to a point to meet increasing 

price, but responding eventually slows 

and consumption decreases (i.e., becomes 

elastic), indicating a limit to the price that a 

given individual will pay for the drug. 

The higher this maximum behavioral price 

a subject will pay, the higher the maximum 

reinforcing value of the substance (similar 

to the breakpoint in the progressive 

ratio procedure), which may also refl ect 

smoking persistence. Such an approach can 

comprehensively characterize differences 

in drug reinforcement due to various acute 

(e.g., medication) or chronic (e.g., individual 

differences in dependence) factors.131

For example, the price-demand curve may 

decrease across all prices (i.e., in parallel 

fashion), indicating an overall reduction 

in frequency of drug use and a drop in the 

reinforcing value of the drug. Alternatively, 

the price-demand curve may shift to the 

left, indicating that the demand for the drug 

is unaffected at most prices, but decreases 

only at high prices, such that the maximum 

price is smaller. This shift would suggest a 

selective drop in the maximum reinforcing 

value of the drug, but not an overall drop 

in the drug’s reinforcing value, since 

responding does not change at lower prices. 

This outcome also indicates a decrease in 

the persistence of drug use. An important 

variation of this procedure involves 

examining changes in the price-demand 

curve as a function of the availability of 

alternative reinforcers, since drug use often 

involves choosing between the drug versus 

some alternative, such as money (e.g., buying 

cigarettes is a choice of cigarettes over 

money). Comparing the infl uence of various 

alternative reinforcers is a central focus of 

the behavioral economics approach because 

the price-demand curve can shift to the left if 

an attractive alternative is present.

This approach has some limitations. 

Obtaining the data to construct pricedemand curves can be time-consuming in 

that a separate session may be needed to 

determine consumption at each given price, 

unless actual consumption is kept low to 

prevent satiation. (Otherwise, reduction 

in consumption caused by satiation will 

confound assessment of the reinforcing 

effects of smoking under conditions 

presented later in the session). Whether 

greater responding of the type required 

in laboratory sessions (e.g., pressing a 

computer key) corresponds to greater 

responses to obtain the drug in the natural 

environment is not known. 

Progressive Ratio Measures 

A key aspect of dependence is the persistence 

of drug use despite its costs. As described 
418

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

above, persistence is commonly and directly 

assessed by determining the maximum 

amount of responses the individual will 

engage in for one unit of the drug. Formal 

behavioral economics approaches can have 

practical limitations, as noted, such as 

requiring multiple sessions to determine 

price-demand curves, with a single price, 

or reinforcement schedule, per session. The 

progressive ratio (PR) procedure provides a 

way to assess maximum price, or persistence, 

more effi ciently, although it cannot 

also assess frequency, as can behavioral 

economics. In the PR procedure, the number 

of responses required per reinforcement 

(e.g., one puff) increases within the session, 

after each earned reinforcer, until the point 

at which responding for the drug is not 

maintained. The response requirement 

at that point, termed the “breakpoint,” 

is believed to index persistence of drug use, 

or incentive motivation.53 The increase 

in response requirement is usually 

rapid (e.g., 30%–50% higher than prior 

requirement) to limit actual drug intake so 

that drug satiation does not interfere with 

assessment of maximum price (persistence). 

The breakpoint for smoke puffs is 

signifi cantly associated with the maximum 

price paid for smoking (number of responses 

for one drug unit) in a behavioral economics 

paradigm,132 consistent with the notion that 

both refl ect smoking persistence. Moreover, 

PR breakpoint is not related to choice 

measures, consistent with the notion that 

they tap different facets of reinforcement.132

PR procedures have some limitations. 

First, the breakpoint, or highest completed 

reinforcement schedule, is a nonparametric 

measure, rather than a continuous 

measure, since only specifi c schedules of 

reinforcement are set. Thus, the number of 

these schedules completed, or reinforcers 

earned, is the dependent measure of 

interest. Because satiation must be avoided 

to allow a true measure of the breakpoint, 

the number of earned reinforcers must be 

small. One consequence of this procedure 

is that statistical power can be limited 

in comparisons of breakpoints between 

individuals or conditions. Secondly, 

as in behavioral economics, whether 

greater responding of the type required in 

laboratory sessions corresponds to greater 

drug-seeking responses in the natural 

environment is not known. 

Genetic Infl uences on Measures 

of Smoking Reinforcement in Humans

Although there is ample evidence supporting 

the heritability of nicotine dependence as 

assessed by self-report measures (chapter 2), 

no studies were found that parse out the 

relative contribution of genetic infl uences 

to laboratory-based measures of nicotine 

or smoking reinforcement. Despite this 

critical gap in the literature, evidence for 

rodent strain differences in nicotine selfadministration and results of transgenic 

mouse studies (described above) provide a 

strong biological rationale for investigating 

the role of specifi c genetic factors in 

objective behavioral measures of smoking 

reinforcement. As far as known, only two 

studies have examined this question in 

humans.

As discussed above, individuals with low- or 

null-activity genetic variants of the nicotinemetabolizing enzyme CYP2A6 tend to smoke 

fewer cigarettes per day by both self-report 

and biochemical measures.17 To extend this 

assessment to objective laboratory-based 

measures of consumption, Strasser and 

colleagues133 compared smoking topography 

indices in normal versus genetically slow 

nicotine metabolizers on the basis of 

the CYP2A6 genotype. Smokers carrying 

reduced- or null-activity CYP2A6 alleles 

(slow metabolizers) had signifi cantly lower 

puff velocities than did normal metabolizers, 

controlling for gender and cigarette 

nicotine level. However, as discussed below, 

the relationship of smoking topography 

to nicotine dependence has not been 

thoroughly investigated.
419

Monograph 20. Phenotypes and Endophenotypes

A second study examined genetic 

associations with the relative reinforcing 

value of nicotine, as measured by a nicotine 

cigarette choice paradigm.134 This analysis 

focused on the role of the functional 

OPRM1 gene A118G variant and is based 

on preclinical evidence that nicotine 

reward is, in part, mediated by mu opioid 

receptors135 and on clinical data supporting 

an association of this variant with smoking 

cessation.40 In this double-blind, cross-over 

study, 60 smokers (30 with the OPRM1

wild-type AA genotype and 30 with at least 

one reduced-activity *G allele) participated 

in the nicotine cigarette choice paradigm 

following either four days of the mu opioid 

antagonist naltrexone or a placebo (order 

of study medication counterbalanced with 

a fi ve- to seven-day washout period). This 

paradigm provided a choice between puffs 

of a denicotinized cigarette and a 0.6-mg 

nicotine cigarette over a three-hour period. 

The results revealed a signifi cant OPRM1

genotype by gender interaction. Among 

females, those with a reduced activity 

OPRM1*G allele self-administered only 

50% of puffs from the nicotine cigarette 

(and the other 50% from the denicotinized 

cigarette), compared to smokers with the 

AA genotype who took about 75% of puffs 

from the nicotine cigarette. Males, regardless 

of genotype, took about 75% of puffs from 

the nicotine cigarette. Secondary exploratory 

analyses from this study suggest that effects 

of OPRM1 may be modifi ed by genetic 

variation in the intracellular-signaling 

cyclic adenosine monophosphate–response 

element binding protein CREB1,136 an effect 

consistent with preclinical research.135

A later study examined genetic factors 

associated with the increase in ad lib 

smoking due to negative versus positive 

mood, as well as moderating infl uences 

of actual or expected nicotine content of 

cigarettes.137 The increase in ad lib smoking 

amount due to negative mood was associated 

with DRD2*C957T (CC > TT or CT), 

SLC6A3 (presence of *9-repeat > absence 

of *9-repeat), and among those given a 

nicotine cigarette, DRD4 (presence of 

*7-repeat > absence of *7-repeat) and 

DRD2/ANKK1 *TAQ1A (TT or CT > CC). 

Although no genetic studies were found 

using behavioral economics measures in 

smokers, there are data to support the role 

of specifi c polymorphisms in the relative 

reinforcing value of alcohol.138

Relation of Smoking Reinforcement 

Measures to Dependence

Ad Lib Smoking

The relationship of ad lib smoking 

measures to nicotine dependence has been 

explored in very few studies. One way of 

approaching this is to determine whether 

ad lib smoking is sensitive to nicotine 

deprivation. For example, Perkins and 

colleagues139 administered a placebo or 15 or 

30 micrograms (lg)/kg nicotine by nasal 

spray every 30 minutes for 2.5 hours in 

smokers who had abstained overnight and 

found a dose-dependent decrease in ad lib 

puffs, cigarettes, and carbon monoxide boost 

from baseline. However, ad lib smoking may 

not be sensitive to slower methods of nicotine 

delivery, such as the nicotine patch.140

Other procedural factors can moderate the 

sensitivity of this measure to pretreatment 

manipulations and medication.141 In a more 

direct association with dependence, one study 

found that pretreatment assessment of a 

very specifi c smoking topography measure 

of ad lib smoking—that is, typical size of 

puffs—predicts smoking cessation outcome 

in an NRT trial.124 It is likely that the shorter 

the ad lib smoking period in a laboratory 

session, the weaker the expected link between 

smoking during that period and indices of 

dependence, given the restricted duration of 

smoking being sampled.

Nicotine Choice

Choice procedures have been used in a 

growing number of studies relating nicotine 

choice to some indices of dependence or 

to other manipulations of interest. Greater 
420

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

choice of nicotine over placebo spray has 

been found in dependent smokers versus 

nonsmokers or former smokers, whether 

by nasal spray128 or gum.142 However, 

questions about the relation of choice to 

dependence remain, as dependent and 

nondependent smokers did not differ in 

nicotine choice in one study.128 Increasing 

the dose of the nicotine choice or extending 

the duration of the session may make this 

procedure more sensitive to differences 

in reinforcement between dependent and 

nondependent smokers. Nicotine choice 

(via gum) is greater in alcoholic smokers 

than in nonalcoholic smokers, who also 

differ in other indices of dependence, such 

as diffi culty in quitting.143 In addition, 

those who self-administer more nicotine 

than a placebo spray in a concurrent choice 

procedure also self-report smoking more 

cigarettes per day and tend to take more 

puffs from their preferred brand during a 

laboratory assessment of ad lib smoking.122

These results suggest that greater nicotine 

versus placebo spray choice in this 

laboratory procedure is associated with a 

generally greater frequency of smoking 

intake in the natural environment. 

Overnight abstinence from smoking in 

dependent smokers increases choice of 

nicotine over placebo, whether by cigarette 

or nasal spray.127 This infl uence of brief 

abstinence has also been shown when 

subjects were freely able to adjust nicotine 

intake independent of puff number via a 

smoke mixing device drawing smoke from 

nicotine versus denicotinized cigarettes.144

Most important, choice of nicotine over 

placebo nasal spray in a forced choice 

procedure assessed before a quit attempt 

predicts greater severity of withdrawal in 

the week after quitting and faster time to 

relapse.145 Thus, greater choice of nicotine 

versus placebo substances in this laboratory 

procedure has been associated with various 

indices of dependence and may serve as a 

promising endophenotype of dependence 

after chronic smoking exposure.

Behavioral Economics

Overnight abstinence increases responding 

for smoking versus the alternative of 

money,122,146,147 indicating greater frequency 

of smoking due to abstinence. Availability 

of nicotine gum may modestly attenuate 

responding for smoking,130 further showing 

that nicotine deprivation can increase 

measures of smoking reinforcement via 

behavioral economics procedures. However, 

as far as known, neither the overall demand 

for smoking (frequency) nor the maximum 

price smokers will pay for smoking 

(persistence) before a quit attempt has been 

related to the outcome of that subsequent 

quit attempt.

PR Measures

As with prior procedures, overnight 

abstinence increases responding on a PR for 

smoke puffs.148 The amount of responding on 

a PR for smoke puffs is greater for nicotine 

versus placebo cigarettes under some but 

not all conditions and in most but not all 

smokers.148 For example, PR responses were 

greater for nicotine versus denicotinized 

cigarettes when the two were concurrently 

available, but much less so when they 

were available independently in different 

sessions, particularly in women.149,150

Yet, pretreatment with nicotine spray or 

patch only slightly and nonsignifi cantly 

reduces the breakpoint of responding for 

smoke puffs in smokers not trying to quit.151

Failure of nicotine pretreatment to alter 

the breakpoint for smoking is not unique 

to PR assessment; it has been seen with 

ad lib smoking and other reinforcement 

measures.139,141

Smoking Reward

The defi nition of reward and its distinction 

from reinforcement and mood effects of 

smoking are discussed in the last part of 

chapter 8 (“Initial Nicotine Sensitivity 

Endophenotypes: Summary and Future 

Directions” section). In humans, reward 

refl ects the hedonic value of a substance, 
421

Monograph 20. Phenotypes and Endophenotypes

or the subjective evaluation of the 

substance’s incentive motivating effects.53

Its measurement, as with reinforcement, 

requires consumption of a substance, while 

most other self-report measures obtained in 

drug research (e.g., mood, craving) do not. 

Below, preclinical evidence is discussed 

supporting the biological plausibility of a 

genetic basis for nicotine reward, followed 

by discussion of measurement, genetic 

infl uences, and relation to dependence in 

human populations. Because there does not 

appear to be an “objective,” non-self-report 

measure of smoking reward in humans, 

the utility of self-reported reward in 

predicting dependence will be examined.

Biological Plausibility

Preclinical Research

In contrast to the self-report measures of 

reward in humans, measures thought to 

refl ect drug reward in animals are behavioral 

and, thus, potentially “objective,” by 

necessity. The most widely used assessment 

of drug reward in animals is the conditioned 

place preference (CPP) paradigm. In CPP, 

drug administration is paired with a novel 

environment, and vehicle administration is 

paired with a second novel environment in a 

place-conditioning chamber. The time spent 

in the environment previously paired with a 

drug is used as a measure of the rewarding 

properties of the drug (i.e., it is assumed 

that spending more time in the environment 

associated with experiencing the effects of 

the drug indicates that the environment 

has acquired positive effects through its 

association with the drug). Rodents develop 

CPP for nicotine.135,152–157 

Another measure believed to index 

reward is intracranial self-stimulation 

(ICSS). Rats can be readily trained to selfadminister electrical stimulation of the 

lateral hypothalamus or medial forebrain 

bundle. It is believed that this stimulation 

is self-administered because it activates 

the underlying neural circuit involved 

in reward.158 Drugs that are reinforcing 

generally lower the current suffi cient to 

sustain ICSS (i.e., the threshold current), 

indicating that lower thresholds refl ect 

pleasure states. By contrast, withdrawal 

from these drugs after chronic exposure 

often raises the ICSS threshold, which is 

believed to refl ect aversive states.158 In rats 

trained to ICSS, systemic administration of 

nicotine decreased the threshold current 

for ICSS by approximately 20%, indicating 

that nicotine results in pleasure states and 

so is rewarding,159 while abstinence from 

nicotine has been shown to raise the ICSS 

threshold.160

A review of studies using CPP and ICSS 

suggests that ICSS and CPP may have 

different neurobiological substrates. Work 

by Panagis and colleagues159 and Kenny 

and Markou160 suggest that the effects 

of nicotine on ICSS involve both lowaffi nity (e.g., a7 nAChRs) and high-affi nity 

(e.g., a4b2) nAChR subunits. In contrast, 

Walters and colleagues161 found that lowaffi nity a7 nAChRs were not necessary for 

nicotine CPP, but b2 subunits were critically 

involved in this behavior. It is possible, 

however, that a7 nAChRs may modulate 

the effects of b2-containing nAChRs on 

ICSS. In support, Mameli-Engvall and 

colleagues162 found that b2-containing 

nAChRs in the VTA mediated changes in 

the resting state of VTA dopaminergic cells 

from inactive to active, suggesting a critical 

role of b2-containing nAChRs in dopamine 

release. The a7 nAChRs also modulated 

the active state of the dopamine neurons, 

but the a7 nAChRs were only effective after 

activation of b2-containing nAChRs. In other 

words, the rewarding effects of nicotine could 

be expressed independently of a7 nAChRs 

but, depending upon conditions, a7 nAChRs 

could also be involved in changing nicotinestimulated dopamine release related to 

reward. Thus, CPP and ICSS may measure 

different aspects of the rewarding effects of 

nicotine because different nAChR subtypes 

appear to be involved.
422

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

Genetic approaches such as strain surveys 

have been useful in identifying (1) natural 

genetic variance that contributes to 

differences in CPP, (2) receptors involved 

in CPP, and (3) how polymorphisms in 

genes encoding those receptors could 

alter nicotine intake. In a study comparing 

nicotine CPP in C57BL/6 versus DBA/2 mice, 

C57BL/6 mice but not DBA/2 mice developed 

CPP.154 These results match those of oral 

nicotine self-administration that reported 

higher levels of self-administration in the 

C57BL/6 mice compared to DBA/2 mice.98–100

Knockout models provide further evidence 

for genetic infl uences on nicotine reward. In 

one study, infusion cannula were implanted 

into the VTA of wild-type and b2 nAChR 

subunit knockout mice before training on a 

task that combined place conditioning and 

self-administration.163 Mice were trained 

to associate one arm of a Y-maze with VTA 

infusion of nicotine. The wild-type but not 

b2 knockout mice showed greater preference 

for nicotine as measured by an increase in 

time spent in the arm of the maze associated 

with nicotine infusions. In addition, systemic 

administration of nicotine increased 

dopamine levels in the nucleus accumbens 

in wild-type but not b2 knockout mice, 

providing a neurochemical mechanism for 

these genetic effects on reward. Further 

support for a4b2 nAChR involvement in 

nicotine reward comes from a study that 

investigated CPP in mice with a single 

point mutation that results in an increase 

in sensitivity of a4-containing nAChRs. 

The a4 mutant mice showed dramatically 

increased sensitivity for the development 

of CPP; wild-type mice developed CPP with 

a dose of 500 lg/kg, while the a4 mutant 

mice developed CPP with a 10 lg/kg dose of 

nicotine.164 Not only does this study strongly 

suggest that a4b2 nAChRs are involved in 

the rewarding properties of nicotine but also 

demonstrates how a polymorphism of the 

gene coding for the a4 nAChR subunit could 

alter the threshold for developing nicotine 

dependence.

Polymorphisms in genes related to other 

neurotransmitter systems may also 

contribute to nicotine intake. For example, 

adenosine2A (A2A) knockout mice did not 

develop nicotine CPP nor have increased 

extracellular levels of dopamine in 

the nucleus accumbens after nicotine 

treatment. In addition, cannabinoid (CB1) 

receptor knockout mice did not develop 

CPP for nicotine.165 However, in another 

study, CB1 knockout mice and wild-type 

mice showed similar levels of IV nicotine 

self-administration,166 suggesting that CPP 

(i.e., reward) and IV self-administration 

(i.e., reinforcement) may be mediated by 

different cellular and genetic processes. 

As suggested in the introduction to this 

chapter, these data support the notion that 

reward and reinforcement are different 

constructs from a biological and behavioral 

perspective. 

Opioid receptors may also mediate the 

rewarding properties of nicotine. In 

preproenkephalin knockout mice, no CPP 

was seen compared to wild-type mice.167

In addition, mu opioid knockout mice have 

defi cits in nicotine CPP.168 Furthermore, 

nicotine CPP is absent in CREBaD mice, 

consistent with new human data associating 

CREB1 and the human OPRM1 receptor 

gene with individual variation in nicotine 

reward.134,136

Genetic vulnerability to the effects of nicotine 

on locomotion may also predict genetic 

susceptibility to the rewarding properties of 

nicotine. Two lines of mice were generated 

from a heterogeneous stock: in one line, 

nicotine depressed locomotor activity; in the 

other, nicotine increased locomotor activity. 

The two lines were tested for nicotine 

CPP; the line in which nicotine depressed 

locomotor activity showed less CPP than the 

line in which nicotine increased locomotor 

activity.169 These results suggest that genetic 

infl uences that mediate the psychostimulant 

properties of nicotine also mediate the 

rewarding properties of nicotine. 
423

Monograph 20. Phenotypes and Endophenotypes

Studies in rats also demonstrate the 

involvement of genetics in the rewarding 

properties of nicotine as measured by CPP. 

F344 rats and Lewis rats were compared 

for development of CPP for nicotine.170

After fi ve trials, the Lewis rats showed CPP, 

but the F344 rats did not. When training 

was extended to 10 trials, Lewis rats still 

showed CPP for nicotine, but the F344 rats 

showed conditioned place aversion. Similar 

results were found in another study: Lewis 

rats showed CPP, but F344 rats did not.171

It is interesting to note that for IV nicotine 

self-administration, neither F344 rats nor 

Lewis rats self-administered nicotine.86

The difference in results could be due 

to methodological differences or further 

demonstration that reward, as measured by 

CPP, and reinforcement, as measured by IV 

self-administration, are largely independent 

facets of nicotine’s motivational effects and 

are mediated by different genetic substrates. 

Description of the Measurement 

of Nicotine Reward in Humans

Objective measures of reward in 

humans that could potentially serve as 

endophenotypes similar to CPP and ICSS 

in animals have not been identifi ed, leaving 

only self-report measures for evaluation. 

Typical measures relevant to reward in 

humans are self-report ratings of “liking,” 

“satisfying,” “good effects,” or “bad effects” 

that are completed following consumption 

of the substance. Items refl ecting reward 

are included in several self-report measures, 

including the Cigarette Evaluation Scale of 

Rose and colleagues,172 which perhaps is the 

most widely used measure of hedonic and 

sensory effects of smoking. Items include 

asking how much did the subjects “like” the 

puffs they just took and how “satisfying” 

they were, along with other questions not 

pertaining to reward, such as how high in 

nicotine or similar to their own brand the 

puffs were (items that do not directly refl ect 

the cigarette’s hedonic value). Because of 

limited data on reliability of these measures, 

the authors of this chapter examined testretest consistency of ratings of “liking” and 

“satisfying” of puffs in a study of 54 smokers 

who smoked the same brand of cigarettes 

on two days, each following overnight 

abstinence (unpublished data). Subjects 

took four puffs in controlled fashion before 

one set of ratings and then smoked the 

cigarette ad lib for 14 minutes before a 

second set of ratings. The ratings of “liking” 

and “satisfying” of the four controlled puffs 

correlated .58 and .59, respectively (both 

p < .001), between sessions, while the same 

ratings of the cigarettes after ad lib smoking 

correlated .55 and .50, respectively (both 

p < .001), between sessions. Thus, these 

reward ratings are highly reliable. 

Genetic Infl uences on Measures 

of Nicotine Reward in Humans

No investigations were found of the 

heritability of the self-reported rewarding 

or other hedonic effects of smoking, 

and only limited data on specifi c genetic 

associations with this outcome were 

identifi ed. For example, in the study 

of nicotine choice by OPRM1 genotype 

described above,134 participants also 

completed the Cigarette Evaluation Scale 

and Sensory Questionnaire172 following 

initial exposure to the two research 

cigarettes: 0.05-mg (denicotinized cigarette) 

and .6-mg nicotine cigarette. The difference 

in ratings of the two cigarettes served 

as a measure of the rewarding effects of 

nicotine per se, and not simply of smoking, 

and was examined by the OPRM1 genotype. 

Consistent with the fi nding of a reduced 

nicotine choice among smokers carrying 

the OPRM1 low-activity *G allele, noted 

earlier, these smokers reported signifi cantly 

smaller differences in ratings of satisfaction 

(and “strength”) between the nicotine 

and denicotinized cigarettes. In a study 

described earlier,137 which examined 

increased smoking behavior and reward 

due to negative mood, the increase in 

smoking reward (“liking”) was associated 
424

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

with DRD2/ANKK1*TAQ1A (TT or CT > CC) 

and OPRM1 (AA > AG or GG).

Association of Nicotine Reward 

Measures with Dependence

Smoking reward in humans is a focus of 

acute laboratory-based manipulations, such 

as medication pretreatment, but generally 

has not been studied prospectively in 

cessation trials. An exception is a study 

by Shiffman and colleagues119 in which 

greater hedonic rating (“pleasantness” of 

cigarette and “satisfying” averaged together) 

of the cigarette smoked during the fi rst 

lapse after quitting predicted greater speed 

of a second lapse and eventual relapse. 

Yet, nicotine versus placebo patch did not 

reduce the hedonic rating of the lapse 

cigarettes, even though the nicotine patch 

slowed progression from fi rst to second 

lapse. Another study suggested that higher 

ratings of the positive effects of nicotine 

nasal spray at pretreatment predicted 

subsequent abstinence in a nasal spray open 

label trial.173 However, in a cross-sectional 

comparison, ratings of nicotine spray 

reward did not differentiate dependent and 

nondependent smokers.174 Thus, some data 

support the association of smoking reward 

before quitting with success of a subsequent 

quit attempt (i.e., dependence).

Acute Smoking 

or Abstinence Effects 

on Cognitive, Affective, 

and Physiological 

Function

Although research clearly shows that 

smoking in general, and nicotine in 

particular, is reinforcing, and that this 

reinforcing effect is key to dependence, why

smoking is reinforcing remains uncertain. 

A variety of the effects of smoking or its 

abstinence may contribute to the motivation 

to self-administer nicotine in chronic 

smokers. For example, smoking may be 

motivated either by the desire to enhance 

cognitive functioning and performance 

or to relieve negative mood. Examples of 

this include nicotine’s effects on sensory 

processing, cognitive function (i.e., attention 

and working memory), affective regulation, 

and impulse control. However, as a 

consequence of chronic smoking and neural 

adaptations, abstinence from nicotine 

can also produce decrements in these 

domains. Subsequently, smoking relieves 

these symptoms very reliably, resulting in 

negative reinforcement of smoking behavior 

(i.e., smoking-elicited relief from aversive 

effects of nicotine abstinence increases 

the probability of future smoking when 

experiencing abstinence effects). Thus, these 

responses in chronic smokers are not simply 

the acute effects of smoking or nicotine but 

rather their effects in reversing the defi cits in 

function resulting from smoking abstinence. 

(For simplicity, “smoking” and “nicotine” are 

used largely interchangeably here unless a 

study specifi cally examined only one.)

Therefore, an important issue in 

interpreting all research on nicotine’s 

effects on functioning in chronic smokers 

is to determine whether the effects refl ect 

a reversal of abstinence-induced defi cits in 

function or whether direct pharmacological 

changes are unrelated to the abstinence 

state of the subject (i.e., do not depend on 

abstinence-induced defi cits in function). 

Practically speaking, this issue depends 

on whether the prenicotine baseline 

condition for a chronic smoker is (1) brief 

abstinence from smoking (e.g., overnight), 

or (2) no abstinence.175 Requiring brief 

abstinence from smoking prevents the 

infl uence of acute tolerance to nicotine from 

distorting responses to subsequent smoking 

or nicotine administration.176 However, such 

abstinence can also lead to mild withdrawal 

symptoms, including the defi cits in 

function noted above. In this case, measures 
425

Monograph 20. Phenotypes and Endophenotypes

following acute nicotine administration may 

refl ect a reversal of these withdrawal-related 

defi cits rather than direct effects of nicotine. 

This interpretation is supported by the 

general absence of many effects of nicotine 

in drug-naive individuals who do not 

experience withdrawal (i.e., nonsmokers) 

and the attenuated effect of nicotine in 

smokers who are not abstinent at baseline 

(and not in withdrawal).177

Neither procedure—brief abstinence 

or no abstinence from smoking before 

the administration of nicotine or 

smoking—is necessarily superior to the 

other; the choice of procedure depends 

on the goal of the research. However, 

the baseline state of the smoker must be 

considered in interpreting results of nicotine 

effects.178 Related to this issue is research 

on nicotine’s effects in smokers who exhibit 

defi cits in function from causes other 

than tobacco abstinence. As will be noted, 

nicotine can reverse many of these defi cits 

as well as those due to ADHD symptoms, 

fatigue, or disease such as Alzheimer’s, even 

when no effects are seen in smokers without 

these conditions. Therefore, the subject 

sample and session procedures need to be 

taken into consideration when interpreting 

nicotine’s effects on function.

The following types of potential 

endophenotype measures will be 

considered in this section, both from the 

perspective of measuring nicotine effects 

(in a nondeprived state) as well as effects of 

nicotine deprivation: (1) sensory processing, 

(2) cognitive function, (3) craving, 

(4) affective regulation, and (5) behavioral 

regulation (impulse control). As explained, 

craving is included as a separate subarea 

because it is believed to comprise several of 

these functions, particularly cognitive and 

affective regulation, and has historically 

been a key concept in understanding 

dependence.179 As in the above sections, 

a review is given for each measure of 

what is known concerning the biological 

plausibility, measurement, evidence for 

heritability and specifi c genetic associations, 

and relationship to nicotine dependence.

Electrophysiological Measures

Resting EEG Activity

Electrical brain waves (EEG signals) can 

be measured to monitor changes in the 

brain’s activity by using electrodes placed 

on multiple scalp locations. The spectrum 

of EEG activity is summarized in terms of 

the peak amplitude or power (area under 

the curve) or frequency (rate of oscillation), 

and is categorized into four broad frequency 

bands. From fastest to slowest, these include 

beta (13–25 hertz [Hz]), alpha (8–12.5 Hz), 

theta (4–7.5), and delta (1.5–3.5). The power 

and frequency of these EEG oscillations 

refl ect generalized neural activity in the 

cerebral cortex. This activity, in turn, refl ects 

overall level of arousal and information 

processing. The arousal-enhancing effects of 

psychostimulant drugs, including nicotine, 

are believed to be important to explaining 

their abuse liability. Therefore, nicotine’s 

effects on EEG activation provide a potential 

endophenotype for dependence; however, 

the links of such measures to dependence 

are not known.

Biological Plausibility

Preclinical Research. The effects of acute 

and chronic nicotine treatment on cortical 

EEG activity have been assessed in Wistar 

rats.180 Acute doses of 0.3, 0.9, and 2.7 mg/kg 

nicotine tartrate decreased high-voltage 

spindles. The effect was blocked by the 

nAChR antagonist mecamylamine, and 

when administered alone, mecamylamine 

increased high-voltage spindles. To test 

if tolerance would develop for the effects 

of nicotine on EEG activity, rats were 

chronically treated with three daily 

injections of 0.9 mg/kg nicotine tartrate 

for 10 days. No tolerance was seen for the 

effects of 0.9 or 2.7 mg/kg nicotine tartrate 

on EEG activity. In nucleus-basalis-lesioned 
426

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

rats, nicotine did not alter EEG activity. 

The authors conclude that both acute and 

chronic nicotine treatment desynchronizes 

EEG activity. Thus, the effects of nicotine 

on EEG activity appear to be dependent on 

nucleus basalis function.

Human Clinical Research. In humans, 

nicotine causes EEG activation, as evidenced 

by increases in alpha and beta frequency 

and decreases in theta and delta power, 

providing a neural correlate of nicotine’s 

arousing effects.181 Abstinence from 

nicotine in chronic smokers produces 

decreases in alpha and beta frequency and 

increases slow wave activity; however, there 

is signifi cant variability in the pattern 

and time course of such effects.182–185

The slowing of EEG activation during 

nicotine abstinence in chronic smokers is 

associated with decrements in performance 

on neurocognitive tasks.183

The effects of tobacco abstinence on 

resting EEG can be prevented by nicotine 

replacement with nicotine gum or 

transdermal nicotine.183,184 Smoking a 

cigarette after a brief abstinence period 

can reverse the decremental effects of 

nicotine abstinence on resting EEG182 as 

does nicotine administration.186 Further, 

nicotine abstinence effects on resting 

EEG can be mimicked by mecamylamine, 

an antagonist of brain nicotine receptors.187

Mecamylamine pretreatment also blocks 

EEG effects of nicotine, suggesting that 

EEG neural correlates of nicotine abstinence 

effects are mediated by nicotinic cholinergic 

receptors.187 The central role of nicotine, 

rather than tobacco more generally, 

is supported by the failure of denicotinized 

cigarettes to produce the same changes 

in EEG activity as nicotine cigarettes; 

however, nonnicotine factors may also 

alter EEGs.188

Description of Measurement of Resting EEG 

EEGs are measured by using electrodes 

placed on multiple scalp locations. The 

assessment, analysis, and interpretation of 

EEG data are quite complex and beyond the 

scope of this chapter. Readers interested in 

designing EEG experiments are referred to 

an excellent introduction to EEG methods 

and measurement by Luck.189

Genetic Infl uences on Resting EEG in Humans 

Measures of resting EEG are highly stable 

over long periods of time,190,191 suggesting 

that this trait is heritable. For absolute 

EEG power (across the EEG spectrum), 

heritability estimates from twin studies 

range from 55% to 90% in child twin pairs192

and from 70% to 90% in adults.193 Among 

a sample of 760 young adults from the 

Dutch twin registry, heritability estimates 

for the different EEG power bands were 

beta (.79), alpha (.90), theta (.85) and delta 

(.62); among middle-aged adults, estimates 

were similar: beta (.75), alpha (.85), theta 

(.75) and delta (.53).194 In a review of 

10 twin studies measuring alpha power, 

the average heritability was reported to be 

79%.195 The relatively lower estimates for 

heritability of delta wave activity suggest 

that environmental infl uences may play a 

more important role. 

Although data from twin studies support 

the premise that resting, or background, 

EEG measures have a strong genetic basis, 

no studies were found of the heritability 

of chronic nicotine effects on EEG 

measures. Despite the strong evidence for 

the heritability of resting EEG measures, 

the literature on candidate gene associations 

is also scant. Only one genetic study was 

found of resting EEG components in 

smokers. Gilbert and colleagues followed 

67 female smokers during 31 days of 

abstinence.196 Individuals carrying the 

minor (*A1) allele for the DRD2*TAQ1A

polymorphism, associated with decreased 

D2 receptor availability,197 showed 

signifi cantly greater EEG slowing during a 

high-stress task. Similar effects were found 

among subjects with higher levels of nicotine 

dependence. This study provides the fi rst 
427

Monograph 20. Phenotypes and Endophenotypes

evidence for a genetic association with EEG 

measures and also suggests a link of this 

endophenotype with nicotine dependence. 

A series of studies has been conducted in a 

large sample of members of families with 

dense histories of alcoholism.198 These 

studies may be relevant, given the high 

rate of comorbidity between alcohol and 

nicotine dependence.91 Alcohol-dependent 

males had signifi cantly higher beta and 

theta EEG power compared to controls.199,200

Genetic marker alleles across the genome 

were examined in these subjects, and 

evidence for linkage for the beta power 

endophenotype was found on a region on 

chromosome 4 that harbors the GABAA

receptor gene.198 Another investigation found 

a genetic association of resting EEG with a 

substitution polymorphism in exon 7 of the 

GABAB receptor gene, but only in normal 

subjects, and not in alcoholics.201

Association of Resting EEG with Dependence 

No published studies were found that relate 

resting EEG measures to quitting success. 

The study described above by Gilbert and 

colleagues196 reported a correlation between 

Fagerström tolerance scores and EEG 

slowing at day three of nicotine abstinence; 

however, the relationship of these changes to 

quitting success is unknown. To determine 

the potential utility of resting EEG as 

an endophenotype, this critical gap in 

knowledge must be addressed.

Event-Related Potentials

General Description of ERP 

and Measurement 

ERPs are positive and negative EEG 

voltage defl ections in response to specifi c 

stimuli, including visual, auditory, 

or somatosensory.189 These positive- and 

negative-voltage fl uctuations in the 

amplitude of electrical activity are labeled 

according to their direction (P for positive, 

N for negative) and time (or latency) 

following presentation of a discrete 

stimulus. ERPs are also categorized as either 

exogenous or endogenous. Exogenous ERPs 

are early defl ections linked to the features 

of the stimulus, such as intensity of the 

visual or auditory stimulus. For example, 

the P50 ERP is an exogenous ERP observed 

as a positive increase in amplitude 

occurring at about 50 milliseconds (ms) 

following stimulus presentation. Exogenous 

ERPs, such as P50, are thought to refl ect 

initial sensory registration. By contrast, 

endogenous ERPs have a longer latency, 

following stimulus onset, and refl ect 

stimulus processing and evaluation. 

For example, the P300 ERP occurs in 

response to an infrequent presentation of 

an irrelevant stimulus, typically measured 

during a target detection task.

Common ERP measures include P50, 

N100, N200, and P300 as well as contingent 

negative variation and mismatch negativity. 

P50 and P300 have been studied most 

frequently in tobacco research and will be 

the focus here (for an in-depth review on 

nicotine effects on ERPs, see Pritchard and 

colleagues202).

P50 ERP

Biological Plausibility

Preclinical Research. In the mouse model, 

the P50 ERP is measured with a paired click 

paradigm but has a shorter latency (20 ms). 

In rodents, it is therefore referred to as the 

P20-N40 wave. DBA/2 mice have a defi cit 

in auditory gating of the P20-N40 wave, 

and nicotine reverses this defi cit.203,204 Acute 

nicotine also increases the amplitude of 

the P20 wave and decreases the amplitude 

of the N40 wave in C57BL/6J mice and 

DBA/2Hsd mice.205

There is evidence from rodent models for 

a7 nAChR involvement in P20-N40 amplitude 

and P20-N20 gating.206 Nine strains of inbred 

mice were analyzed for a-bungarotoxin 

binding (a ligand for a7 nAChR) and P20-N40 

gating. A signifi cant correlation was observed 
428

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

between hippocampal a-bungarotoxin 

binding and the P20-N40 response to the fi rst 

auditory stimulus and the ratio of response to 

the fi rst and second stimulus (i.e., the gating 

response). Nicotine has been shown to 

increase P20 and reduce N40 amplitude. 

These effects are sensitive to manipulation 

of dopamine.207 Mecamylamine attenuates 

nicotine effects on P20, but not on N40, 

suggesting a different role for nAChRs in 

these response waves.208

Human Clinical Research. Much of what 

is known about the P50 has come from 

research in the area of schizophrenia 

that focuses on a common P50 sensory 

gating defi cit. Some studies suggest that 

schizophrenic patients exhibit a reduced 

ability to inhibit, or complete failure to 

inhibit, a brain response to the second 

of two auditory stimuli (see below).209,210

Smoking prevalence rates are as high as 

80% among individuals with schizophrenia, 

signifi cantly higher than in the general 

population.211 It has been posited that these 

elevated smoking rates are partly due to a 

normalizing effect of nicotine on the P50 

response.212,213 Therefore, the literature on 

genetic associations with the P50 response in 

schizophrenia, discussed below, may help to 

elucidate the possible use of this measure as 

an endophenotype of nicotine dependence.

Description of Measurement of P50 ERP 

in Humans 

As mentioned above, the P50 ERP is a 

positive EEG voltage defl ection that occurs 

about 50 ms after presentation of an auditory 

or visual stimulus, and it refl ects initial 

sensory registration. Much of this research 

in humans focuses on the P50 sensory gating 

defi cit. This is typically measured in a pairedstimulus paradigm in which two stimuli 

(usually a sound or a “click”) are presented 

about 5 ms apart. The ratio of response to 

the second stimulus versus the fi rst stimulus 

is averaged over a large number of trials in 

this paradigm. In normal subjects, there 

is an average reduction in the response to 

the second stimulus, refl ecting an adaptive 

sensory gating or fi ltering mechanism.

Genetic Infl uences on the P50 ERP in Humans 

Existing evidence suggests that the P50 

ERP has a substantial genetic component. 

In healthy twins, heritability estimates for 

the P50 sensory gating response range from 

.44 to .68 for this measure.195,214,215 Given 

the evidence for genetic infl uences, it is 

not surprising that the measure is fairly 

stable over time; interclass correlations 

of .66–.77 have been reported for P50 

suppression, when measured on two 

separate occasions.216 Interestingly, there is 

not strong evidence for signifi cant shared 

genetic infl uences with the P300, suggesting 

different neurobiological mechanisms for 

P50 and P300.

Work on the specifi c genetic basis of 

the P50 ERP has focused on the P50 

suppression defi cit seen in schizophrenics. 

Consistent with evidence for the central 

role of the a7 nAChR cited above, 

a genome-wide analysis found evidence 

for signifi cant linkage of the P50 auditory 

to a region in chromosome 15 that 

includes the a7 nicotinic receptor gene 

CHRNA7.217 Subsequently, Leonard and 

colleagues identifi ed polymorphisms in 

the promoter region of CHRNA7 with 

reduced transcriptional activity in reporter 

gene assay.218 In this study, schizophrenic 

patients exhibited less P50 inhibition than 

did controls, and a functional CHRNA7

polymorphism was associated with this 

measure.218 Although schizophrenia has 

been linked to this region and associated 

with CHRNA7,218 another group was unable 

to replicate the associations of the promoter 

variants with the P50 gating defi cit.219

Association of P50 ERP with Nicotine 

Dependence 

No published studies were found of 

the relationship of the P50 ERP or 

P50 suppression with level of nicotine 

dependence or quitting success.
429

Monograph 20. Phenotypes and Endophenotypes

P300 ERP

Biological Plausibility 

There has been little attention to effects 

of nicotine on the P300 in animal models. 

In one study, prenatal nicotine exposure in 

rats predicted a reduced auditory P300 ERP 

in the adult offspring relative to controls.220

In humans, differences between smokers 

and nonsmokers in the P300 ERP have 

been documented in a few studies.221,222

Both current and former smokers show 

reduced P300 amplitude that correlates with 

hypoactivation in the anterior cingulate and 

frontal cortical regions.223 The presence of 

the defi cit in former smokers suggests that 

this may be a predisposing factor rather 

than a consequence of nicotine exposure. 

However, it is also possible that both current 

and former smokers have neuroadaptive 

changes due to chronic nicotine exposure 

that are not reversed following long-term 

cessation. 

Of greater relevance to withdrawal-related 

phenotypes are studies examining effects 

of tobacco abstinence on the P300 ERP. 

Brief abstinence from tobacco increases 

P300 latency and decreases P300 amplitude, 

effects that are reversed by smoking.181

In one study of smokers abstaining for nine 

hours, smoking two cigarettes reduced 

P300 amplitude.224 However, another study 

found that 12-hour abstinence had no 

effects on P300 amplitude but did increase 

P300 latency.225 Although the results of 

investigations of effects of nicotine and 

of tobacco abstinence on P300 are not 

entirely consistent, there is some evidence 

suggesting that P300 defi cits may predispose 

to smoking, are intensifi ed by abstinence 

in chronic smokers, and are reversed by 

smoking following brief abstinence.

Description of Measurement of P300 ERPs 

in Humans 

As mentioned above, the P300 is an 

endogenous, positive EEG defl ection at 

about 300 ms following a stimulus. Unlike 

the P50 ERP, which is a purely sensory 

response, the P300 is sensitive to differences 

in stimulus parameters. It is typically 

measured in a visual or auditory oddball 

paradigm in response to an infrequent 

(i.e., “oddball”) stimulus occurring in the 

context of common target and nontarget 

stimuli in a target-detection task. 

Generally speaking, the more unexpected 

and infrequent the oddball stimulus, the 

stronger is the ERP response. The P300 is 

measured across a large number of trials 

and reported in terms of both average peak 

amplitude and average latency from the 

stimulus, with the former refl ecting the 

amount of cognitive resources required for 

stimulus processing and evaluation and the 

latter refl ecting the time required for such 

processing.202

Genetic Infl uences on the P300 

In general, P300 amplitude and latency 

appear to be stable and heritable traits. Testretest correlations of .66–.67 are reported 

for assessments performed on two separate 

occasions.214 In adolescents, test-retest 

correlations are also high.226 The strongest 

evidence for the heritability of the P300 ERP 

is presented in a meta-analysis of fi ve twin 

studies, reporting a “meta-heritability” of 

60% (95% confi dence interval [CI], 54%–

65%) for P300 amplitude and 51% (95% CI, 

43%–58%) for P300 latency.195 In individual 

twin studies, heritability estimates for P300 

amplitude and latency range from .41 to 

.78.214,227,228 Although P300 amplitude and 

latency share genetic variance (i.e., onehalf of the variance in these measures 

is due to common genetic infl uences), 

there is no evidence for signifi cant shared 

genetic infl uences for the P300 and P50 

ERP, suggesting different neurobiological 

mechanisms.216 No studies were identifi ed of 

genetic infl uences on effects of nicotine or 

tobacco abstinence on the P300; however, 

given consistent evidence that the trait itself 

is heritable, genetic variation in nicotine 

effects would be expected.
430

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

Genetic association studies of P300 focusing 

on effects of nicotine or smoking are rare. 

However, there is growing evidence for 

specifi c genetic infl uences on the P300 in 

the general population and in populations in 

which smoking rates are high. For example, 

using genome-wide linkage analysis in 

the Collaborative Study on the Genetics of 

Alcoholism, Porjesz and colleagues198 found 

evidence for linkage of P300 (measured in 

a visual task) to regions on chromosomes 2, 

5, 6, and 3. 

Evidence for genetic linkage supports the 

pursuit of specifi c genes that may underlie 

defi cits in P300 that may have relevance to 

nicotine dependence. Given the importance 

of dopamine signaling in schizophrenia, 

polymorphisms in genes in the dopamine 

pathway have been examined for associations 

with P300, although with mixed results. 

The DRD2*TAQ1 A1 variant, associated with 

smoking risk in some studies, has also been 

linked with prolonged P3 latency in the 

sons of active and recovering alcoholics.229

A nonsynonymous (SER9GLY) variant in 

the dopamine receptor D3 gene (DRD3)

previously associated with schizophrenia has 

been related to reduced P300 amplitudes in 

the left parietal area.230 The reduced activity 

*7-repeat allele of a common dopamine 

receptor D4 VNTR polymorphism has been 

linked with P300 response to novel stimuli; 

the results, however, were modifi ed by a 

measure of dopaminergic tone (i.e., the 

eyeblink response).231 Although these studies 

have not focused specifi cally on nicotine 

effects, both the DRD2*TAQ1 A1 and the 

DRD4*7-repeat allele have been associated 

with smoking status in some studies,31,38

and DRD3 activity mediates, in part, nicotine 

self-administration in rodent models.232

As discussed further below with respect 

to neurocognitive defi cits, the COMT

gene is an excellent candidate gene for 

measures involving sensory processing and 

neurocognitive function. The COMT enzyme 

inactivates dopamine, with important effects 

in the prefrontal cortex where dopamine 

transporter (reuptake) levels are low.233

Among schizophrenics, carriers of the lowactivity *MET allele (increased dopamine) 

show smaller frontal P300 amplitudes, an 

effect interpreted as refl ecting less “noise” 

in the prefrontal cortex.234 During a task 

of behavioral inhibition mediated by the 

frontal cortex (i.e., go/no-go) COMT*MET

allele carriers show an anteriorization of 

the P300 response during the no-go target, 

which the authors suggest may alter ability 

to inhibit responses.235 However, other 

studies have found no association of COMT

genetic variation with P300 amplitude or 

latency.236 No studies were found examining 

the role of genetic factors on nicotine effects 

on the P300.

Association of P300 ERP with Nicotine 

Dependence 

As with the P50 ERP, no published studies 

were found of the relationship of the P300 

ERP with level of nicotine dependence or 

quitting success.

The PPI of Startle Response

The PPI of the acoustic startle refl ex is 

another task thought to measure the 

ability to fi lter sensory information or 

sensory gate.237,238 Although the basic 

construct involving inhibition or “gating” 

of response to a second stimulus is similar 

to the P50 ERP, this measure is based on 

an eyeblink refl exive response, rather than 

on electrophysiological measurement 

(discussed below).

Biological Plausibility

The effects of acute nicotine on PPI 

across strains of mice and rats are highly 

variable, supporting genetic infl uences. 

In one study, acute nicotine administration 

enhanced PPI in C57BL/6 mice.239 In a 

strain survey of the effects of nicotine 

on PPI in 129S6, BALB/cByJ, C57BL/6J, 

DBA/2, and NMR1 mice, nicotine enhanced 

PPI only in NMR1 mice.240 Another study 
431

Monograph 20. Phenotypes and Endophenotypes

found no enhancement of PPI with 

nicotine in DBA/2J, C3H/HeJ, C57BL/6, 

or 129T2/SvEmsJ mice.241 The different 

effects of nicotine on PPI may be due to 

different doses of nicotine used and to strain 

differences. The study by Spielewoy and 

Markou241 found genetic differences in the 

ability of nicotine to reverse phencyclidine 

(PCP) disruption of PPI; nicotine reversed 

PCP-associated defi cits in PPI in DBA/2J 

and C3H/HeJ mice but not in C57BL/6 or 

129T2/SvEmsJ mice. In Sprague-Dawley 

rats,242,243 nicotine enhanced PPI, but in 

Wistar rats, nicotine had no effect on 

PPI.244 In a study that compared the effects 

of nicotine on PPI between SpragueDawley rats and BALB/c mice, nicotine 

disrupted PPI in the Sprague-Dawley rats 

but enhanced PPI in the BALB/c mice.245

In a7 nAChR subunit knockout mice, 

no defi cits in PPI were found.246 However, 

PPI was disrupted in b3 nAChR knockout 

mice, suggesting that b3-containing nAChRs 

are involved in PPI.247

With respect to preclinical studies of nicotine 

withdrawal effects on PPI, DBA/2 mice 

withdrawn from nicotine showed decreased 

PPI for the 8-decibel (dB) and 12-dB 

prepulses but not for the 4-dB prepulse.248

A follow-up study from the same laboratory 

compared the effects of nicotine withdrawal 

on PPI in DBA/2 mice and C57BL/6 mice 

and found no withdrawal-associated PPI 

defi cits.249 The different results across studies 

could be related to the different doses used 

or could suggest that the effects of nicotine 

withdrawal on PPI are mild. In support of the 

latter, no nicotine withdrawal defi cits were 

seen in PPI in Long-Evans rats, SpragueDawley rats, and Wistar rats.237,250

In humans, acute smoking of a cigarette 

has been shown to increase PPI (i.e., reverse 

the attenuation due to abstinence) very 

acutely within minutes after smoking.251,252

Demonstration that a subcutaneous 

injection of nicotine (6 or 12 lg/kg) also 

increased PPI confi rmed that nicotine 

per se increases PPI.253 In contrast, PPI is 

attenuated (i.e., less inhibition of startle or 

sensory gating) by overnight abstinence in 

dependent smokers.252 Thus, while there has 

been less attention to nicotine’s effects on 

PPI, as compared with EEG measures, these 

data suggest that PPI could be a plausible 

endophenotype.

Description of PPI Measurement in Humans 

PPI is typically measured within a 

classic startle paradigm that assesses 

refl exive muscle contractions by using 

electromyographic, or EMG, recording of 

the orbicularis oculi muscles (eyeblink 

response) following presentation of a sudden 

intense stimulus (visual, auditory, or tactile). 

The startle refl ex itself is thought to relate 

to mood or affect and is discussed later in 

this chapter as a potential endophenotype of 

affect regulation. PPI of the startle response 

refl ects the extent to which a preceding 

weaker stimulus suppresses or attenuates 

the sensorimotor refl ex response to the 

subsequent intense stimulus. This response 

occurs in animals and humans, although 

there is substantial individual variability.254

Various adaptations of this paradigm have 

used pictorial representations of smoking 

cues or affective stimuli; however, using 

smoking cues as the prestimulus does not 

appear to modulate the acoustic startle 

response.255

Genetic Infl uences on Prepulse Inhibition 

of Startle 

Although there is evidence for high retest 

reliability for PPI measures, suggesting 

that this is a stable trait measure,256,257

only one study has examined the heritability 

of PPI. In this study of 170 female twins 

aged 18–28 years, it was estimated that 

roughly 50% of the genetic variability in 

PPI is due to genetic infl uences, some of 

which are shared with absolute startle 

response.258 However, a follow-up study of 

affective modulation of startle provided 

no evidence for signifi cant heritability.259

At the writing of this chapter, no studies 
432

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

were identifi ed relating specifi c genetic 

variants to PPI.

Association of PPI with Nicotine Dependence 

Of these EEG measures, only PPI has been 

studied in relation to cessation outcome 

or other indices of nicotine dependence. 

In the study by Kumari and colleagues252

described above, PPI was attenuated 

(i.e., less inhibition of startle or sensory 

gating) by overnight abstinence to a 

greater degree in more dependent smokers, 

based on their score on the Fagerström 

Tolerance Questionnaire. This suggests 

that attenuation in PPI due to overnight 

abstinence relates cross-sectionally to 

degree of current dependence on one selfreport measure of dependence.

Cognitive Function

Attention and Vigilance

Biological Plausibility

Nicotine’s effects on attention have been the 

focus of several studies in rodents. The fi vechoice serial reaction time task (5CSRTT) 

is one of the best studied of these models. 

In the 5CSRTT, rodents must attend to an 

array of fi ve apertures for presentation of 

a brief light stimulus and respond with a 

nose poke in the illuminated aperture for 

food reinforcement. The 5CSRTT allows for 

assessment of multiple behavioral measures 

that include the percentage of correct 

responses (i.e., accuracy), percentage of 

omissions (i.e., the failure to respond to 

the stimulus), response latency, latency to 

collect the reinforcement, and premature 

responding (i.e., nose pokes during the 

intertrial interval); for a review, see Kumari 

and colleagues.260

Several studies have used the 5CSRTT 

to study nicotine effects on attentional 

processes. Acute nicotine enhances 

attention in the 5CSRTT, increases reaction 

time on correct responses, and increases 

accuracy.261–263 Surprisingly few animal 

studies have examined the effects of nicotine 

withdrawal on attentional processes. In one 

study, hooded Lister rats were tested for the 

effects of withdrawal from 3.16 mg/kg/day 

of nicotine on the 5CSRTT.264 Increased 

omissions were seen after both spontaneous 

withdrawal and precipitated withdrawal with 

the high-affi nity nAChR antagonist DHbE; 

the a7 nAChR antagonist methyllycaconitine 

did not precipitate withdrawal. Thus, 

nicotine withdrawal was associated with an 

increased failure to respond to the stimuli. 

This defi cit in attention to the stimuli 

involves high-affi nity nAChRs such as the 

a4b2 nAChR. 

An alternate paradigm for assessing 

nicotine’s cognitive effects in rodents 

is fear conditioning (in which a neutral 

stimulus is paired with an aversive stimulus, 

and then freezing to the neutral stimulus is 

measured). In one study,265 C57BL/6 mice 

were treated with nicotine for 12 days and 

then withdrawn from nicotine; 24 hours 

later, mice were conditioned. Nicotine 

withdrawal disrupted contextual fear 

conditioning, a hippocampus-dependent 

version of fear conditioning,266,267 but not 

cued fear conditioning, a hippocampusindependent version of fear conditioning.266,267

The selectivity of the withdrawal defi cits 

suggests that nicotine withdrawal affects 

specifi c types of learning and does not 

affect processes common to both types of 

learning. It is possible that relapse occurs 

in smokers after withdrawal from nicotine 

as an attempt to ameliorate learningrelated defi cits. In support, the withdrawal 

defi cit in contextual fear conditioning 

in mice was reversed by treatment with 

acute nicotine.265

Animal studies examining the genetic 

basis of nicotine effects on attention are 

limited, but the effects of nicotine on fi vechoice serial reaction time (5CSRT) have 

been shown to be strain dependent in 

rats. Nicotine improved choice accuracy 

in Sprague-Dawley rats but not in hooded 
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Monograph 20. Phenotypes and Endophenotypes

Lister rats.268 Another study demonstrated 

that nicotine enhanced 5CSRT in C57BL/6 

mice.269 In this study, drug-naive a7 nAChR 

subunit knockout mice showed defi cits 

in 5CSRT, compared to wild-type mice. 

Thus, a7 nAChR may be involved in some 

attention processes. 

In humans, several converging lines 

of evidence have linked self-reported 

inattention symptoms to smoking behavior. 

Individuals with a clinical diagnosis of ADHD 

have higher rates of smoking initiation 

and persistence. Further, smokers with a 

history of ADHD (current or childhood) 

are more likely than those without a history 

of ADHD to experience nicotine withdrawal 

symptoms, including irritability and 

problems with concentration.270 Inattention 

symptoms are also associated with selfreported reasons for smoking (e.g., smoking 

for stimulation) and nicotine dependence 

in the general population of smokers.271

Impulsivity symptoms are also associated 

with smoking prevalence in young adults.272

Most critically, smokers without a diagnosis 

of ADHD who reported increases in 

subclinical ADHD symptoms during the 

fi rst week of abstinence were signifi cantly 

more likely to relapse than were smokers 

who did not report increases in inattention 

symptoms.273 Improvements in attention and 

performance due to nicotine have also been 

reported in studies of nonsmokers without 

ADHD274 and smokers and nonsmokers 

with ADHD.275

Description of Measures of Attention 

and Vigilance in Humans 

Measures of attention tap the ability to focus 

and sustain attention on relevant stimuli. 

The most commonly used measure of visual 

attention is the continuous performance 

task (CPT). In this computerized task, 

participants are presented with a visual 

target for 50 ms (e.g., an “X”) and nontarget 

stimuli (e.g., an “O”) in rapid succession. 

They are instructed to make a rapid response 

(e.g., press a button) only when a target 

stimulus is presented. A variation on the 

basic CPT (CPT-identical pairs [IP]) is to 

instruct participants to make a response 

when they see an identical pair of targets 

(e.g., two digits or letters) presented in 

succession.276 CPT-IP has been advocated 

for use in adults, as the basic CPT may not 

be sensitive enough to capture inattention 

symptoms in the general population.277,278

The CPT has been shown to discriminate 

between those with and without ADHD 

among children and adults279–281 and 

to be sensitive to the effects of ADHD 

medications.282 As described above, the 

CPT is sensitive to the effects of nicotine 

abstinence282 and nicotine administration.275

Other measures of visual attention that 

are sensitive to nicotine effects include 

the Rapid Visual Information Processing 

(RVIP) task283 and the letter cancellation 

task.284 Regular smokers observed over 

24 hours of abstinence performed more 

poorly on this cancellation task, with 

reduced rates of target detection and 

increased response times as duration 

of abstinence increased, demonstrating 

withdrawal-induced defi cits. Finally, 

auditory attention can be measured with 

the Digit Span test of the Wechsler Adult 

Intelligence Test-Revised, which is sensitive 

to medication effects,285 but not well 

studied with respect to nicotine effects. 

In general, most studies show that smoking, 

or nicotine delivery by other methods in 

abstinent smokers, produces only modest 

improvements in simple reaction time 

performance, fi nger-tapping speed over 

short periods (e.g., less than one minute), 

or other simple psychomotor tasks.177

Genetic Infl uences on Attention and Vigilance 

Although no studies were found that 

examined the heritability of nicotinerelated effects on measures of attention 

and vigilance, existing data support the 

heritability of baseline task performance.286

For the CPT, heritability estimates of 

39% and 49% have been reported for verbal 
434

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

and spatial attention, respectively.276 For the 

digit symbol substitution test, heritability 

estimates of 67% have been reported.287

By using a simple reaction time task in 

a sample of 213 twins, the heritability 

of attentional/motor performance was 

estimated to be 64%. Other studies have 

focused on the heritability of performance 

within families with schizophrenia. 

For example, using a registry of families 

with schizophrenia in Finland, TuulioHenriksson and colleagues,288 reported 

heritabilities of .09 and .20 for visual and 

auditory attention, respectively. Thus, while 

there is general support for the heritability 

of performance of tasks assessing attention, 

the genetic contributions appear to vary by 

both measure and population.

Associations of candidate genes in the 

dopamine pathway with attention-vigilance 

measures have also been reported; however, 

the results have not been consistent. 

In the single study of genetic associations 

with nicotine effects on attention, Gilbert 

and colleagues found that smokers 

carrying the “high-risk” *A1 allele of the 

*TAQ1 polymorphism in the DRD2 gene 

exhibited greater improvements in RVIP 

task performance following nicotine 

administration.289 Several studies have 

examined the VAL/MET polymorphism in 

the COMT gene described above. Consistent 

with the premise that dopamine levels 

in prefrontal cortex facilitate attention, 

the low-activity *MET allele has been 

associated with better performance on the 

CPT;290,291 however, another study found 

no association between CPT performance 

and the COMT genotype.292 Performance 

on the CPT has also been associated 

with a common repeat polymorphism in 

the dopamine transporter gene among 

children with ADHD; however, the direction 

of association is inconsistent across 

studies.293,294 One study provides evidence 

for an association of a repeat polymorphism 

in the dopamine receptor D5 gene DRD5

with CPT performance in children with 

ADHD and their parents.295 Visuospatial 

attention has also been associated with a 

polymorphism in the a4 nicotinic receptor 

gene CHRNA4, providing further support 

for attention-related endophenotypes of 

nicotine dependence.296 Thus, although 

only one study examined the role of 

specifi c genetic factors in nicotine effects 

on attention,289 the genetic associations 

identifi ed for task performance (independent 

of nicotine) are consistent with those found 

for smoking status and smoking cessation.

Relation of Attention and Vigilance Measures 

to Dependence 

Several of the measures described above are 

sensitive to effects of nicotine deprivation 

in dependent smokers.297,298 In addition, 

smokers with higher scores on the FTND 

exhibit increased neural activation in 

regions related to visuospatial attention 

(e.g., anterior cingulate cortex) while 

viewing smoking and neutral pictures,299

suggesting that nicotine dependence may 

moderate attentional task performance.

Two small studies assessed relationships 

of CPT to quitting success. In one study 

of adolescent smokers, commission errors 

on the CPT predicted relapse;300 however, 

commission errors may be more refl ective 

of impulse control defi cits than attentionvigilance (see section below on “Impulse 

Control”). In a study of schizophrenic 

smokers, baseline CPT performance did not 

predict quitting in a smoking treatment 

program.301

Working Memory

Biological Plausibility 

Nicotine’s effects on learning are a plausible 

mechanism for its positive and negative 

reinforcing effects.302 For example, learned 

associations of nicotine delivery with 

smoking-related stimuli may promote drug 

craving. Likewise, the ability of nicotine to 

reverse cognitive defi cits could contribute 

to relapse if abstinent smokers attempt 
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Monograph 20. Phenotypes and Endophenotypes

to ameliorate the withdrawal defi cits by 

resuming smoking.

This premise has received substantial 

support in rodent models of nicotine’s effects 

on learning. Specifi cally, nicotine enhances 

hippocampus-dependent contextual fear 

conditioning;265,303–306 it does not enhance 

the hippocampus-independent association 

between the auditory conditioned stimulus 

(CS) and the foot shock unconditioned 

stimulus (US),305,306 even when the diffi culty 

of the task is increased.307 Acute nicotine 

has also repeatedly been shown to enhance 

working memory, as measured in the 8-arm 

radial maze (for a review see Levin and 

Simon308) and as measured in trace fear 

conditioning.307,309 Nicotine also improves 

learning in paradigms such as passive 

avoidance,310–312 active avoidance,313,314

the Morris water maze,315,316 and a visual 

discrimination task.317

With respect to the genetic underpinnings 

of nicotine’s effects on learning, the earliest 

studies focused on strain surveys of inbred 

mice. In work by Bovet and colleagues,318

nicotine produced the most active avoidance 

in C3H/He mice followed by CBA mice, 

C57BL/6 mice, AHe mice, Swiss mice, 

BALB/c mice, and then DBA/2 mice. In the 

remaining strains, nicotine disrupted 

learning, with the greatest defi cit seen 

in C57BR/cd mice followed by C57BL/10 

mice, and then A/J mice. In a visual 

discrimination task in which mice learned 

to exit a chamber through the correct door 

to avoid a shock, nicotine enhancement 

of learning varied across inbred strains of 

mice.319 In A/J, C3H/He, and DBA/2J mice, 

nicotine enhanced visual discrimination 

(C3H/He ≥ DBA/2J ≥ A/J), but in the BALB/c 

strain, nicotine disrupted performance. 

In a comparison of the effects of nicotine 

on consolidation of a Y-water-maze task, 

nicotine improved consolidation in C57BL/6 

mice but disrupted consolidation in DBA/2 

mice.320 Thus, comparisons across inbred 

strains of mice show clear infl uences of 

genetics on the acute effects of nicotine on 

learning and also suggest that these effects 

may be task specifi c. 

Targeted mutations and selective breeding 

studies also support the infl uence of 

genetic factors in the effects of nicotine 

on cognition. No defi cits in either passive 

avoidance or fear conditioning were seen 

in b2 nAChR subunit knockout mice;321,322

nicotine, however, failed to enhance passive 

avoidance and contextual fear conditioning 

in the b2 knockout mice.322,323 In contrast to 

the b2 knockout mice, nicotine enhanced 

contextual fear conditioning in a7, b3, 

and b4 nAChR subunit knockout mice.323

Studies with nAChR knockout mice also 

suggest that the b2 nAChR subunit is 

involved in the effects of nicotine on 

working memory. Working memory 

is defi ned as the processes by which 

information is maintained for access 

while performing complex cognitive tasks. 

One measure of working memory is trace 

fear conditioning in which the CS and 

the US are separated by a trace period 

during which no stimulus is presented; 

therefore, a representation of the CS must 

be maintained during the trace period for 

a CS-US association to be learned.324–326

Both b2 and a7 nAChR subunit mice 

develop trace fear conditioning, but in 

the b2 knockout mice, nicotine does not 

enhance conditioning.309 Together, these 

results suggest that genetic alterations 

of the b2 subunit gene alter the effects of 

nicotine on multiple types of learning.

The human data on nicotine’s effects on 

working memory are less clear. There is 

evidence for enhancement of working 

memory following acute nicotine delivery 

in nonsmokers.260 However, nicotine 

gum does not improve working memory 

in nonsmokers.327 The nicotine patch 

(six hours) enhances working memory only 

in a subgroup of individuals characterized 

as “highly attentive.”328 In chronic smokers, 
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9. Nicotine-Dependence Endophenotypes in Chronic Smokers

acute nicotine delivered via nasal spray 

appears to have no effect on verbal working 

memory, but may have small effects on 

spatial working memory.329

Nicotine deprivation in chronic smokers 

appears to produce decrements on working 

memory tests. Adolescent smokers deprived 

of nicotine for 24 hours exhibit signifi cant 

decrements in performance on an auditory 

working memory task, compared to 

performance in a nondeprived state.330

More than 13 hours of nicotine deprivation 

also results in longer response latency and 

poorer performance on an N-back task in 

adult smokers, compared to performance 

when nondeprived.331 Similarly, Foulds 

and colleagues332 found that subcutaneous 

nicotine injections (0.3 and 0.6 mg) in 

abstinent smokers produce faster reaction 

time on some working memory tasks 

(e.g., the RVIP), but decreased accuracy 

on others (e.g., digit recall), compared to 

saline injections. Abstinent smokers tended 

to show stronger improvements in RVIP 

performance due to nicotine than did a 

comparison group of nonsmokers, again 

supporting the notion that much of the 

performance-improving effects of nicotine 

may refl ect reversal of defi cits due to 

withdrawal.

In using the Sternberg memory task, 

one study found that nasal spray nicotine 

improves performance of smokers but only 

under conditions of auditory distraction, 

which caused decrements in performance 

at baseline, and not under normal 

nondistraction conditions.333 Thus, effects 

of nicotine were seen only when the ability 

to perform the task was impaired because of 

an environmental condition (distraction), 

similar to other fi ndings showing nicotine 

effects when performance is impaired by 

withdrawal. Nicotine had no effect on 

performance in nonsmokers, showing that 

chronic smoking exposure is necessary for 

nicotine to have any apparent benefi cial 

effect. 

Description of Working Memory Tasks 

in Humans 

Some of these tasks are described in detail 

in chapter 8. Others used primarily with 

chronic smokers are described here.

N-Back Task. The N-back task, a measure 

of working memory, is being applied 

increasingly in human work on nicotine 

dependence.331,334 In this task, participants 

are asked to look at fl ashing letters 

(or geometric fi gures) on a computer 

screen, one at a time, and to press the space 

bar according to four principles or rules: 

0-back, 1-back, 2-back, and 3-back. During 

0-back, the participant must press the space 

bar whenever the target stimulus (e.g., letter 

“X”) appears on the screen. During 1-back, 

the participant must press the space bar 

whenever the target stimulus is the same 

as the previous stimulus (i.e., the stimulus 

1-back). A similar rule is followed for 

2-back and 3-back, with increasing memory 

load from 1-back to 3-back. The primary 

outcomes include the percentage correct 

and reaction time to correct responses.

Wisconsin Card Sorting Test. The 

Wisconsin Card Sorting Test (WCST) is a 

widely used measure of prefrontal cognitive 

function that is sensitive to a subject’s 

ability to generate hypotheses, establish 

response sets, and fl uently shift sets.335

Subjects are required to sort stimulus cards 

on the basis of perceptual attributes (color, 

form, number). The only feedback provided 

by the administrator is whether each 

response is correct or incorrect. The sorting 

rule is changed after 10 consecutive correct 

responses. Testing is discontinued when 

the subject has learned two iterations of 

the three sorting rules or has reached 

128 trials. The primary outcomes include 

number of categories achieved, number of 

trials, number of errors, and percentage 

and number of perseveration errors.

Sternberg Memory Task. Although not 

as widely used in nicotine research as 
437

Monograph 20. Phenotypes and Endophenotypes

the N-back and the WCST, the Sternberg 

memory task is a test of verbal memory 

that requires subjects to memorize a string 

of letters during a brief (e.g., 10 seconds) 

period and then to recognize these letters 

as they are presented individually (in a set 

that includes letters not part of the original 

set). Transdermal nicotine has been shown 

to reverse defi cits on this task produced by 

haloperidol administration.336 Yet, at least 

two studies show no clear effects of nicotine 

via nasal spray on performance of this task 

in nonsmokers,333,337 suggesting that effects 

of nicotine on such performance may 

depend on prenicotine level of impairment 

in performance.

Genetic Infl uences on Working Memory 

Genetic contributions to components of 

working memory have been explored in 

a couple of twin studies. For example, 

in a study of 236 healthy twin pairs, 

the heritability of working memory was 

found to range from 43% to 49% for verbal 

and spatial memory storage (with minimal 

difference for verbal vs. spatial).338 Among 

healthy twins, working memory task 

performance ranged from 35% to 50%.339

The degree of heritability of nicotine or 

abstinence effects on working memory is 

unknown.

The most widely studied genetic variant 

in studies of working memory is the 

COMT VAL/MET polymorphism, related 

to dopamine levels in the frontal cortex, 

a critical brain region for executive function. 

In studies of healthy children, adults, 

patients with schizophrenia, and their 

relatives, the high-activity *VAL allele of 

COMT (lower brain dopamine levels) has 

been associated with poorer performance 

on working memory tasks (e.g., WCST, 

N-back).290,292,340,341 Interestingly, a few of 

these studies assessed working memory 

concurrent with functional magnetic 

resonance imaging (fMRI). In addition to 

poorer task performance, several studies 

show increased activation in regions 

of interest (e.g., dorsolateral prefrontal 

cortex, anterior cingulate), suggesting 

“less effi cient processing” capacity in the 

*VAL allele carriers.233,290,340,342,343 Studies 

have explored associations of the functional 

brain-derived neurotrophic factor (BDNF) 

VAL66MET polymorphism with working 

memory performance. One study reported 

no association in healthy adolescents,344

two studies reported a positive association 

between the *VAL allele and performance 

in psychiatric patients,345,346 and another 

study reported abnormal neural activation 

in the hippocampus during N-back task 

performance in healthy adults with the 

BDNF *MET allele.347

Only one study was found that examined 

the relationship of a specifi c genetic 

polymorphism with nicotine effects on 

working memory performance in smokers. 

In this study,348 36 adults (22 smokers) 

completed the N-back task during 

two fMRI sessions (one with nicotine 

patch, the other with placebo patch). 

Individuals with the *T allele for the 

functional DRD2 C957T polymorphism 

had worse performance following nicotine 

administration than those with the *C allele, 

a fi nding attributed by the authors to excess 

dopaminergic stimulation by nicotine 

in *T allele carriers.348 Consistent with 

other evidence described above, nicotine 

enhancement of performance may be more 

diffi cult to demonstrate; therefore, genetic 

studies of nicotine abstinence effects on 

working memory performance may be 

more informative. Another study found 

complex, dose-related associations between 

DRD4 genotypes and acute nicotine effects 

on performance of the Sternberg memory 

task among nonsmokers,337 as noted in 

chapter 8.

Relation of Working Memory Measures 

with Dependence 

Only one study was identifi ed relating 

performance on a working memory task 

to nicotine-dependence measures or 
438

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

quitting success. In this small study of 

schizophrenics, defi cits in a visuospatial 

working memory task predicted a greater 

likelihood of relapse.301

Craving

Craving has long been viewed as a key 

element of drug dependence in general,179

and craving for cigarettes is a hallmark of 

nicotine withdrawal, along with negative 

mood.2 Craving to smoke is thought 

to be sensitive to at least two broad 

infl uences: (1) recent abstinence from 

smoking (withdrawal) and (2) the presence 

of discriminative stimuli for smoking 

(cues). Both types of craving tap the urge 

to smoke, but the specifi c underlying 

mechanisms are undoubtedly different. 

Notably, the evidence linking each of these 

types of craving to dependence differs 

substantially. Thus, these different types 

of craving appear to refl ect very different 

processes, justifying their clear distinction. 

The following sections distinguish between 

these types of craving in evaluating their 

potential as endophenotypes. However, 

the description of craving measures 

other than self-report will emphasize 

cue-induced craving; these measures are 

less common in studies of abstinenceinduced craving.

Biological Plausibility

Abstinence-Induced Craving 

Dependence is marked in part by persistent 

drug use despite the adverse consequences, 

sometimes indicated by an inability to 

abstain. Craving, or a desire to use the drug, 

that emerges as a result of abstinence is 

one index of diffi culty remaining abstinent, 

as greater craving is often viewed as a 

precipitant of relapse (failure to abstain).114

Craving is very reliably increased by 

duration of smoking abstinence, up to 

a few days when it tends to peak, and 

nicotine treatment reliably decreases this 

craving.349 Although drug use is not always 

directly predicted by self-reported craving, 

particularly in smokers not trying to quit,350

to some extent the biological plausibility 

for abstinence-induced craving being an 

endophenotype rests on its high face validity 

or the reasoning that greater self-reported 

desire to use the drug refl ects the intention 

to do so.

Cue-Induced Craving 

Because abstinence-induced craving has 

high face validity and has been shown to 

predict success of a quit attempt (see below), 

research has investigated the notion that 

very acute increases in craving elicited by 

smoking cues may also have predictive 

validity or, at least, are otherwise important 

to understanding nicotine dependence. 

The notion that much of smoking 

behavior is conditioned to environmental 

stimuli—that is, cues—has strong support 

in the literature. Environmental stimuli 

clearly become conditioned to nicotine 

and other drug intake in animal models, 

such that drug self-administration can 

come under the control of drug-associated 

cues, regardless of the presence of the drug 

itself.78 Because smokers strongly respond 

to smoking cues with increases in selfreported craving, and those with very little 

smoking exposure history (e.g., nonsmokers) 

do not,351 chronic exposure to smoking 

must condition these craving responses to 

cues. As further evidence of cue-induced 

craving, smokers have greater craving 

responses to environments generally 

associated with smoking, such as bars, 

but not to environments where smoking 

is discouraged, such as churches or 

theaters. Moreover, smokers respond 

with even more craving to environments 

in which they personally tend to smoke 

(e.g., the interior of their car or in their 

favorite bar) compared to environments 

unfamiliar to them but where other 

smokers tend to smoke (e.g., someone 

else’s car or an unfamiliar bar352). 

No other explanation is plausible other 

than that these environments have come 
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Monograph 20. Phenotypes and Endophenotypes

to elicit craving because of their past 

association with smoking behavior by the 

smoker—that is, cue-induced craving. 

Thus, it would seem very plausible that 

those who report greater desire to smoke 

in response to smoking-associated stimuli, 

or representations of those stimuli 

(e.g., pictures), should be less likely to 

refrain from smoking when they confront 

those stimuli in their environments after 

quitting (and thus are more nicotine 

dependent).

Evidence supporting a biological basis for 

cue-induced craving is found in human 

neuroimaging research.353 Experiments 

using fMRI and positron emission 

tomography (PET) have explored differences 

in regional brain activation during 

presentation of smoking and neutral cues, 

presented in pictorial or video format. 

Brain regions most commonly activated 

during smoking cue presentation include 

those important in incentive motivation, 

reward signal processing, and goal-directed 

behavior (e.g., orbitofrontal cortex, 

dorsolateral prefrontal cortex, anterior 

cingulate).354–357 Subjective craving during 

cue exposure correlates with a subset of 

these regions, although results are not 

consistent across all studies. Discrepancy 

in fi ndings across neuroimaging studies 

of cue-induced smoking craving may be 

attributable to individual and contextual 

factors that moderate these responses.358

For example, increased activation is reported 

when individuals are told they can smoke 

immediately following the session.359

Differences in racial background may also 

be important.357 Of importance for the 

endophenotype criteria used in this chapter, 

brain activation in response to smoking 

cues has also been associated signifi cantly 

with scores on the FTND,299 as well as 

with specifi c genetic polymorphisms in 

the dopamine reward pathway.360 These 

factors should be considered in laboratory 

assessment of cue-induced craving as an 

endophenotype.

Description of Craving Measures 

and Procedures

Measures of Craving 

Craving is typically viewed as the desire 

or urge to smoke,361 although others have 

argued that craving should be reserved for 

extreme urges to use a drug.362 Craving is 

often synonymous with the self-reported 

desire to smoke, but craving as a clinical 

phenomenon is believed to have affective, 

cognitive, and behavioral dimensions,363

which can be assessed “objectively,” 

thus offering the potential for being 

endophenotypes of dependence. Although 

abstinence-induced craving has been 

assessed primarily with self-report, measures 

aiming to capture these other dimensions 

have been used in studies of abstinenceinduced and cue-induced craving and will 

be discussed below.

Self-Report Measures. Craving, whether 

due to abstinence or cue exposure, is 

typically assessed via a number of self-report 

measures, ranging from single items asking 

how strong is the desire or urge to smoke 

(e.g., on Likert or 0–100 visual-analog 

scales ranging from none to extremely139

to multi-item validated scales, the most 

popular of which is the Questionnaire on 

Smoking Urges (QSU).361 Notably, the QSU 

has two factors: the fi rst taps anticipation 

of pleasurable effects (thought to refl ect 

positive reinforcement from smoking), 

and the second taps anticipation of relief 

from aversive mood effects of abstinence 

(refl ecting negative reinforcement). 

The factors have high reliability (>.90). 

The QSU has briefer 10-item364 and 4-item363

versions, although the 4-item version 

generates a single score. The authors of this 

chapter assessed the test-retest reliability of 

this 4-item version of the QSU in 54 smokers 

who abstained overnight on each of two 

days; the correlation between days was 

0.76, (p < .001), showing strong reliability 

(unpublished data). Moreover, the decrease 

in this measure of craving following 
440

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

ad lib smoking was also signifi cantly 

correlated between days, r = .48, p < .001. 

Other measures of craving include the 

craving subscale of the Shiffman-Jarvik 

Withdrawal Scale365 and the Tobacco Craving 

Questionnaire.366 Self-reported craving 

in response to auditory vignettes about 

smoking (i.e., imagery) appears to be stable 

and reliable.367 To determine abstinenceinduced craving, craving is typically 

measured during ad lib smoking, prior 

to quitting, and then intermittently over 

hours or days after abstaining. Similarly, 

cue-induced craving is usually assessed 

during a neutral baseline condition and 

then intermittently over seconds, or at most 

minutes, following presentation of cues.

Measures of craving other than self-report 

have been commonly used to assess cueinduced craving, although they should 

be equally applicable to assessing 

abstinence-induced craving. Such 

“objective” measures of craving may 

have promise as endophenotypes and 

include psychophysiological, cognitive, 

and behavioral responses, described below. 

Psychophysiological Measures of 

Craving. Since both tobacco abstinence 

and drug-related cues involve attentional, 

affective, and motivational processes, 

psychophysiological measures refl ecting 

these processes may be potential 

endophenotypes for craving. These measures 

include heart rate (HR), electrodermal 

activity (sweat gland activity in the skin), 

and skin temperature. HR has been 

examined as both phasic decreases (rapid 

changes over a few seconds), which tend 

to refl ect acute attentional processes, 

and tonic increases (changes over a few 

minutes), which tend to refl ect motivational 

or affective processes. In a meta-analysis of 

cue reactivity craving studies, Carter and 

Tiffany363 calculated the following effect 

sizes for these responses to smoking cues: 

0.21 for tonic HR, 0.44 for electrodermal 

activity, and –0.07 for skin temperature, 

with the fi rst two being clearly signifi cant. 

By contrast, these authors reported a very 

large effect size of 1.18 for self-reported 

craving in response to cues. (Comparable 

effect-size values were found for responses 

to cues for other drugs, except HR response 

to opiate cues, which was not signifi cant.) 

Thus, the sensitivity of psychophysiological 

measures to cues remains a question for 

research on individual differences in these 

responses to cues.

Cognitive Measures (attentional bias).

A subsequent approach examined the 

magnitude of attentional bias toward 

smoking-related stimuli (e.g., words) in a 

variation on the Stroop interference task. 

In this procedure, which has been used 

with other drugs of abuse,368 subjects 

are shown words related to smoking 

(e.g., tobacco, smoking, ashtray, puff, urge) 

or not related (i.e., control condition), 

with each word presented in a different 

color. The task is to respond quickly with 

the color of the word (i.e., informationprocessing reaction time). Reaction time 

slows when smoking-related words are 

presented, indicating increased allocation 

of attention to those words.

Procedures to Elicit Cue-Induced Craving

The procedures used to elicit craving in 

response to cues are almost as diverse as the 

dependent measures of craving. The most 

common approaches include presentation of

1. In vivo smoking cues, such as a lit 

cigarette (and including having the 

subject lighting and holding it) or the 

sight of the subject’s preferred brand.351

2. Photos of smoking-related stimuli, such 

as people smoking or a lit cigarette in an 

ashtray.352

3. Imagery-evoking thoughts of smoking, 

such as by auditory presentation of 

vignettes describing a common situation 

in which a strong desire to smoke occurs 

(e.g., work stress).179
441

Monograph 20. Phenotypes and Endophenotypes

Even newer approaches include use of 

virtual reality techniques to present visual 

smoking cues.369 A variation on these 

approaches is to personalize them, such as 

by using photos of pictorial stimuli from 

the smoker’s actual environment that 

are associated with his or her smoking, 

rather than the typical use of generic 

smoking-related photos. Research has 

demonstrated that pictorial stimuli of 

environments where smoking often occurs, 

but without any explicit smoking-related 

stimuli (e.g., a bar, but with no ashtrays 

or cigarettes), can increase self-reported 

craving.352

Each of these approaches elicits reliable 

increases in self-reported craving, although 

in vivo cues may be most robust. Yet, simply 

the presence of cues is insuffi cient to elicit 

craving. For example, cigarette availability 

is a clear moderator of craving response 

to cues, as craving increases much less in 

response to cues when subjects know that 

smoking is not possible compared to when 

they are told that smoking is possible.370

Expecting to be able to smoke also infl uences 

the magnitude of fMRI responses in the 

prefrontal cortex to smoking cues.358 Thus, 

the prospect of being able to act on cravings 

to smoke may be necessary for cues to 

induce motivational effects.

Genetic Infl uences on Craving

There are no published heritability or 

family-based studies that elucidate the 

overall contribution of genetic inheritance 

to abstinence-induced or cue-induced 

craving. However, three studies examined 

associations of genes in the dopamine 

reward pathway with different measures 

of cue-elicited cravings. Consistent with 

neuroimaging evidence for increased 

activation in the dopamine reward pathway, 

Hutchison and colleagues371 reported that 

smokers carrying the *7-repeat allele for the 

DRD4 gene reported increased craving in 

response to in vivo smoking cues compared 

to those homozygous for the shorter-repeat 

alleles. Similar results were seen in a 

neuroimaging study by McClernon and 

colleagues372 in that those with the *7-repeat 

allele showed greater activation of right 

superior frontal gyrus and right insula in 

response to pictorial smoking cues versus 

control cues, relative to those without the 

*7-repeat allele. The DRD2 gene *A1 allele 

and dopamine transporter (SLC6A3)

*9-repeat allele have also been associated 

with stronger smoking cue-induced craving 

in a laboratory paradigm.373 In a PET study, 

smokers carrying the DRD4 *7-repeat 

allele and SLC6A3 *9-repeat allele showed 

increased dopamine binding following 

cigarette smoking; however, smokingrelated cues were not explicitly manipulated 

in this experiment.360 Finally, the serotonin 

transporter gene 5-HTT has also been 

associated with craving as measured by 

the Stroop task measure of attentional 

bias among smokers, but not among 

nonsmokers.374 While preliminary, these 

data suggest that genes in the dopamine 

reward pathway, and possibly the serotonin 

affective regulation pathway, may be 

important in cue-induced craving.

Association of Craving 

with Dependence

Abstinence-Induced Craving 

Abstinence-induced craving assessed in 

the days after quitting often, though not 

always, predicts the outcome of that quit 

attempt.375,376 Abstinence-induced craving is 

also attenuated by most forms of NRT,349,377

bupropion,378 and varenicline,379 although 

it is not clear that this is the primary 

mechanism for the effi cacy of these FDAapproved cessation medications.

Cue-Induced Craving 

Despite some plausibility, available evidence 

shows no clear demonstration that greater 

self-reported craving response to smoking 

cues relates to dependence, as determined 

by persistence of smoking in a clinical 
442

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

trial,380 although dependent smokers have 

greater craving responses to cues than 

do nondependent smokers (i.e., chippers) 

in cross-sectional studies.381 Moreover, 

despite the observation that NRT alleviates 

abstinence-induced craving, self-reported 

cue-induced craving has not been clearly 

shown to be infl uenced by NRT382,383 or 

any other effective cessation medication, 

including varenicline.384 One study found 

attenuated craving during a cue reactivity 

procedure due to active nicotine versus 

placebo gum, but only in a subset of subjects 

who were particularly responsive to the cue. 

All subjects had abstained from smoking for 

several days, and the effect of the gum was 

not observed until more than 15 minutes 

after exposure to the cue, suggesting that 

nicotine gum attenuated abstinence-induced 

craving and not cue-induced craving.385

Yet, a rapid rise in urge to smoke during 

abstinence often precedes a lapse episode 

(i.e., smoking of at least one cigarette), even 

weeks after quitting.114 Because this rapid 

rise cannot be attributable solely to the 

time course of abstinence, it is conceivable 

that acute increases in craving in response 

to other types of environmental challenges 

(e.g., alcohol or work stress) may predict 

clinical outcome, even if responses to 

smoking cues per se do not.

In sum, no prospective study has clearly 

shown that the magnitude of self-reported 

craving response to cues prior to quitting 

predicts outcome of a subsequent quit 

attempt.380 The only possible exception, 

out of fi ve, is a study in which reactivity to 

holding an unlit cigarette prior to quitting 

predicted time to fi rst lapse and 1-week 

abstinence in smokers who subsequently 

quit while using the nicotine patch.386

However, reactivity did not predict lapse or 

relapse in smokers who quit while using 

placebo patch and did not predict outcome 

in the sample as a whole. The fact that cue 

reactivity did not predict outcome in those 

treated with placebo or in the entire sample 

supports null results in the other studies 

attempting to link self-reported cue-induced 

craving to dependence.380 A few studies 

have related psychophysiological responses 

to cues and clinical outcome, but these 

fi ndings are not robust and generally have 

not been replicated. Regarding heart rate, 

studies have shown that later relapse risk is 

related to larger increase in HR response to 

cues,351 larger decrease in HR response,387

or is unrelated to HR response to cues.380

Several studies examining electrodermal 

response to cues failed to show any 

relationship to relapse risk.380

However, a study published in 2007 reported 

that neural activation during viewing of 

smoking cues versus control cues was 

attenuated in the amygdala following 

an extinction-based smoking treatment; 

yet, reduction of cue-induced activation of 

the thalamus predicted smoking cessation 

success.388 Therefore, cessation may be 

predicted by greater attenuation of neural 

activation to smoking cues over the early 

course of treatment. Similar research on 

cocaine supports the potential validity of 

this approach, as will be discussed in the 

“Conclusions” section. Additional studies of 

this type are clearly needed to confi rm the 

reliability of these fi ndings. 

The predictive validity of a cognitively based 

cue reactivity measure—that is, attentional 

bias—may be more promising in that 

several studies by Waters and colleagues389

have related the magnitude of this response 

slowing (or attentional bias) to dependence. 

Notably, the authors389 showed that greater 

attentional bias predicts greater risk of 

lapsing in the fi rst week after quitting, 

and that a high-dose (35 mg) NRT patch 

reduces such bias. Waters and Feyerabend390

also showed that attentional bias is greater 

after overnight abstinence versus no 

abstinence and predicts shorter time to 

fi rst cigarette in the morning, a measure 

strongly related to cessation outcome, 

as noted earlier. However, attentional bias 

was unrelated cross-sectionally to other 
443

Monograph 20. Phenotypes and Endophenotypes

measures of dependence or smoking 

intensity, including FTND, cigarettes per 

day, and cotinine levels.389

In terms of behavioral responses to cues, 

as far as known, no research has examined 

the degree to which greater smoking 

behavior in response to cues prospectively 

predicts outcome of a quit attempt. Likewise, 

no cross-sectional comparison was found 

of cue-elicited smoking response between 

groups varying in level of dependence.

Affective Regulation

In addition to the reinforcing effects of 

nicotine and the ability of nicotine to 

alter cognitive processes and craving, 

the effects of nicotine on emotional states 

may also contribute to nicotine dependence. 

It has been proposed that in some cases, 

drug abuse may refl ect attempts at selfmedication for mental illness.14 Evidence 

for this includes the higher prevalence 

of smoking among those with major 

depression or schizophrenia; these are 

conditions with symptoms known to be 

ameliorated in part by nicotine. However, 

because the majority of smokers do not 

suffer from these disorders, a more relevant 

area of research for understanding basic 

processes in nicotine dependence is the link 

between negative affect after abstinence 

and subsequent smoking. A hallmark of 

the tobacco withdrawal that usually occurs 

in most smokers in the fi rst few weeks 

after quitting is negative affect—that is 

aversive mood symptoms such as dysphoria, 

fatigue, sadness, or anxiety.175,391,392 Relapse 

during the fi rst few weeks of abstinence 

is often seen as a means to relieve these 

symptoms by resuming smoking, which 

very reliably eliminates negative affect 

due to withdrawal. Some clinical research 

indicates that negative affect in the days or 

weeks after quitting not only is predictive 

of cessation outcome (i.e., one measure of 

dependence severity) but also essentially 

accounts for all the clinically predictive 

value of total withdrawal severity itself. 

In other words, when the negative affect 

symptoms of withdrawal are removed from 

consideration, the severity of the remaining 

symptoms of withdrawal generally do 

not predict cessation outcome.4

 Negative 

affect after quitting may predict cessation 

outcome better than do common measures 

of current smoking intensity, such as 

cigarettes per day.393,394

Discussed below is the general biological 

plausibility of measures of affective 

regulation as candidates for endophenotypes 

of nicotine dependence, including the 

substantial evidence in animal models 

for genetic control of affective regulation 

measures. Subsequently, various objective 

affect responses are described that have 

been examined in smoking and nicotine 

research as well as the limited data on 

the heritability of measures of affective 

regulation. A large number of measures 

have been used to assess affective regulation. 

For ease of reading, their description will 

be accompanied by the evidence linking 

responses on the particular measure to 

dependence rather than presenting that 

text in a separate subsection. 

Biological Plausibility

Preclinical Research 

Animal studies provide a means for further 

understanding the complex relationships 

between nicotine exposure and affect. 

Nicotine has both anxiolytic and anxiogenic 

effects, effects that are dependent on 

many factors including the test of anxiety 

used,395 dietary intake,396 and the dose 

of nicotine.397–399 For example, in the 

black/white box test of anxiety, BKW mice 

treated with nicotine spend more time 

in the brightly illuminated white side of 

the box, thus refl ecting reduced levels of 

anxiety-related behavior.400 In CD-1 mice, 

nicotine increases time spent in the open 

arms of the elevated plus maze, which is 

another indicator of decreased anxiety.401
444

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

Nicotine also increases the acoustic startle 

refl ex, a measure of affective reactivity.242,402

In addition, nicotine infused directly into 

the raphe nucleus decreases anxiety, as 

measured by increased social interaction, 

in hooded Lister rats.403 However, opposite 

(i.e., anxiogenic) effects of nicotine have 

also been observed in rats and mice.404–406

In addition, systemic administration 

of nicotine decreases time spent in the 

open arms of the elevated plus maze, but 

direct infusion of nicotine into the dorsal 

hippocampus increased time spent in the 

open arms in hooded Lister rats, suggesting 

that the hippocampus may not mediate the 

anxiogenic properties of nicotine but may 

be involved in the anxiolytic properties of 

nicotine in hooded Lister rats.407 Finally, 

in both mice and rats, nicotinic agonists 

have been shown to have properties similar 

to antidepressants.408–411

Genetic variability also contributes to 

the effects of nicotine on affect in rodent 

models. For example, nicotine can increase, 

decrease, or produce no effect on startle 

depending on the strain of mouse used.412,413

In a strain survey of open-fi eld activity in 

BALB, C57, C3H, and DBA mice, nicotine 

decreased open-fi eld activity in the BALB, 

C57, and DBA strains but increased activity 

in the C3H strain.412

Genetically modifi ed mice have been 

extremely useful for understanding both 

the genetic factors that infl uence the 

effects of nicotine on anxiety and for 

understanding nAChR subtype involvement 

in the effects of nicotine on anxiety. 

For example, a4 knockout mice spend 

signifi cantly less time in the open arms of 

the elevated plus maze (refl ecting increased 

anxiety) compared to wild-type controls.414

Interestingly, mice with a leucine to serine 

point mutation that results in hypersensitive 

a4 nAChRs also show decreased time 

in the open arms of the elevated plus 

maze.415 The results of these two studies 

suggest that a4-containing nAChRs may 

mediate anxiety such that overactivation 

or underactivation of these receptors may 

increase anxiety. Furthermore, in mice 

with a single point mutation that results 

in increased sensitivity of a4-containing 

nAChRs, the dose-response curve for 

nicotine disruption of the startle refl ex 

was shifted to the left.416 In b2 knockout 

mice, nicotine had no effect on startle, but 

nicotine did disrupt startle in wild-type 

littermates. These results suggest that 

genetic factors contribute to the effects of 

nicotine on startle and that the a4b2 nAChR 

may mediate these effects. In a5 nAChR 

subunit knockout mice, no change in openfi eld activity is seen, compared to wild-type 

mice.417 b3 knockout mice show more 

activity in the illuminated open-fi eld arena 

compared to wild-type mice.247 Because high 

illumination is anxiogenic in mice, this 

increase in activity could be due to changes 

in locomotor activity, changes in anxiety, 

or an interaction between the effects of 

nicotine on anxiety and locomotor activity 

in the b3 knockout mice. These genetic 

studies illustrate the complexity of both the 

genetic underpinnings and the phenotype 

assessments.

Human Clinical Research 

Clinical research demonstrates that selfreported negative affect is associated with 

persistence of smoking, supporting the 

notion that objective measures of negative 

affect and its change due to abstinence and 

smoking in chronic smokers may be relevant 

endophenotypes of dependence. As noted, 

smoking is much more common among 

those with a history of major depression. 

Much other research shows an association 

between negative affect and smoking. 

For example, in a study of 202 smokers, 

nearly one-half of the sample scored in the 

depressed range and were more likely to 

report smoking motivated by a desire to 

reduce negative affect.418 Similarly, greater 

depressed mood and anger after quitting 

smoking predicts risk of relapse.376,419

However, the momentary level of negative 
445

Monograph 20. Phenotypes and Endophenotypes

affect after quitting may be less important 

than the pattern or trajectory of negative 

affect over time.391 A rapid rise in negative 

affect in a period of hours predicts greater 

risk of lapsing, but a gradual increase in 

negative affect in a period of days does not.420

Moreover, although any lapse increases risk 

of relapse, as noted in the prior section of 

this chapter, lapses triggered by negative 

affect due to “stressful” events are more 

strongly predictive of relapse than are lapses 

triggered by activities such as eating or 

drinking alcohol.114 Thus, the predictive 

value of lapses is not uniform but depends 

on their context, and the presence of 

negative affect in the lapse context can be 

more interruptive of efforts to maintain 

abstinence.

Less evidence links smoking to anxiety, 

one component of negative affect. In a study 

that examined the relationship between 

anxiety sensitivity and drug use, smoking 

was positively correlated with scores on an 

index of anxiety sensitivity.421 In the National 

Comorbidity Study, current smoking rates 

were signifi cantly higher among individuals 

reporting anxiety-related disorders in 

the past month, including social phobia, 

agoraphobia, panic disorder, or generalized 

anxiety disorder, compared to respondents 

with no mental illness.422 Of greater 

relevance to the focus of this chapter, rates 

of self-reported quitting success (i.e., being 

a former smoker) was also signifi cantly 

lower in most of these groups, relative to 

the general population.422

A few studies have examined the affective 

responses to nicotine or smoking in 

abstinent smokers, hypothesizing that 

greater acute self-reported mood response to 

drug intake would characterize withdrawal 

relief and could relate to dependence. 

In two studies, greater reinforcement 

(i.e., self-administration) from nicotine 

spray, either in ad lib or choice procedures 

(see subsection above on “Reinforcement”), 

was predicted by greater pleasurable mood 

effects (refl ecting stimulation) from nicotine 

among briefl y abstinent smokers not 

currently trying to quit permanently.122,127

Somewhat similarly, Rose and colleagues423

found that self-report of smoking for 

stimulation reasons predicted relapse 

in young adult smokers, as did greater 

cigarettes per day. Although this self-report 

of smoking “motives” does not prospectively 

assess acute mood responses to smoking, 

it does presumably refl ect the general 

mood response of smokers to smoking, 

although biases with such self-report 

measures are considerable.424 Nonetheless, 

these data provide a plausible rationale 

for investigating objective measures of 

anxiety as potential endophenotypes for 

nicotine dependence.

Genetic Infl uences on Measures 

of Affect in Humans

Although twin studies have documented 

the role of genetic factors and geneenvironment interactions in mood 

disorders (e.g., anxiety disorder, major 

depression),425–427 as well as in anxietyrelated personality traits,428,429 the 

heritability of nicotine or smoking effects 

on anxiety symptoms is not known. 

However, a study of a large cohort of twins 

documented heritability estimates of 

about 25%–50% for self-reported nicotine 

withdrawal symptoms that are affective 

in nature.430 In addition, candidate genes 

in the dopamine pathway,431,432 serotonin 

pathway,433,434 and opioid pathway40 have 

been associated with withdrawal-related 

affect or moderation of the effects of selfreported affect on smoking behavior. This 

evidence from self-report measures suggests 

that laboratory-based measures of affect 

in chronic smokers may provide useful 

endophenotypes in future research.

One such laboratory measure, the acoustic 

startle response, has been shown to exhibit 

high test-retest reliability in schizophrenic 

patients and controls, suggesting a trait 
446

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

component.257 In a study of 170 female twins 

aged 18–28 years, the heritability of acoustic 

startle was estimated to be roughly 70%.258

There is also evidence for shared genetic 

variance with the PPI measure of sensory 

gating described above.258 No papers were 

found examining specifi c genetic variants in 

relation to the startle response in humans 

or genetic studies of nicotine effects on the 

startle response or other objective measures 

of affect.

Genetic analyses incorporated into 

functional neuroimaging investigations 

of affective responses have generated 

interesting results, however. For example, 

two studies using fMRI to assess neural 

responses during presentation of emotional 

images found increased activation in 

the amygdala of subjects who carry the 

short allele of the functional serotonin 

transporter promoter polymorphism 

(5-HTTLPR) compared with those with the 

long allele.435,436 These effects were equally 

signifi cant in males and females;435 however, 

effects of smoking status were not examined. 

Neural activation in response to unpleasant 

visual stimuli has also been related to the 

presence of the low-activity *MET allele 

for COMT (in contrast to the protective 

effects of the *MET allele for neurocognitive 

performance).437 Similarly, individuals 

who are homozygous for the COMT *MET

allele exhibit increased activation in the 

ventrolateral prefrontal cortex during 

presentation of faces expressing negative 

emotions.438 Thus, genes in both the 

dopamine and serotonin pathways may 

contribute to neural activity and emotional 

reactivity, but the role of nicotine in these 

effects is unknown. 

Description of Measures of Affectivity 

and Association with Dependence

Self-Reported Affect 

As with craving, affect (or mood) is usually 

assessed with self-report measures. Several 

valid multi-item scales are available, 

such as the Positive and Negative Affect 

Schedule,439 the Mood Scale of Diener and 

Emmons,440 or the Profi le of Mood States.441

These measures were not designed for 

studies of smoking or even acute drug 

use but have been shown to be somewhat 

sensitive to brief drug exposure. Some 

studies use more specifi c single items to 

assess particular moods (e.g., visual-analog 

scales, from 0 to 100, corresponding to 

“not at all” to “extremely,” for “stimulated” 

or “head rush/buzzed”174). For example, 

the most widely used self-report withdrawal 

scale, the Minnesota Nicotine Withdrawal 

Scale,442 is a series of individual symptom 

items that can be scored individually or 

combined into a single withdrawal score. 

Abstinence-induced increases in negative 

affect are very reliable and peak within 

a few days after quitting, although some 

quitters can experience prolonged and/or 

episodic increases in negative affect.391,392

In terms of smoking’s acute effects on 

mood, smoking, and nicotine in particular, 

dose-dependently increase responses on 

measures of arousal, vigor, and head rush, 

which are typically viewed as pleasurable, 

but also increase tension and jitteriness, 

which are usually considered aversive.443,444

However, as noted in the introduction to 

this section, these effects are seen primarily 

in abstinent smokers and are minimal in 

nonabstinent (i.e., nondeprived) smokers,443

suggesting that these effects may in fact 

refl ect withdrawal relief rather than the 

direct pharmacological effects of nicotine. 

Moreover, few pleasurable effects of nicotine 

are seen in nonsmokers, although they do 

report aversive effects of nicotine, such 

as increases in fatigue, along with those 

adverse effects seen in smokers. In fact, 

while “head rush” response is associated 

with greater nicotine reinforcement 

(i.e., self-administration) in smokers, that 

same response is inversely associated with 

nicotine reinforcement in nonsmokers.128

Thus, the same mood response may be 

pleasurable in smokers but aversive in 

nonsmokers.
447

Monograph 20. Phenotypes and Endophenotypes

Persistent smoking is strongly related 

to the degree to which negative affect 

(as assessed by several of these measures) 

increases after quitting, and sometimes in 

anticipation of quitting (i.e., in the days 

leading up to the quit day), as each increases 

the likelihood of relapse and speeds its 

occurrence.4,391,445 Thus, abstinence-induced 

increases in self-reported negative affect 

are very clearly related to dependence 

level in chronic smokers. As far as known, 

no research shows that the magnitude 

of acute changes in these measures in 

response to smoking or nicotine predicts 

persistence of smoking, and some research 

shows no association. In one prospective 

study, sensitivity to nicotine’s effects on 

about 12 mood measures or items before 

a quit attempt were examined for ability 

to predict withdrawal severity and time 

to relapse after quitting, and none was 

signifi cant.145 A measure of nicotine choice, 

however, predicted both, showing that the 

failure of mood responses to predict clinical 

outcome was not due to inadequate power. 

In any case, while the degree of self-reported 

negative affect in the days after quitting 

strongly predicts smoking persistence, 

no available research has shown any 

association between acute mood responses 

to nicotine or smoking before quitting and 

subsequent outcome of a quit attempt.

Also, as with craving, negative affect has 

physiological, cognitive, and behavioral 

dimensions that cannot be captured by 

self-report measures, and measures in 

each of these domains may be potential 

candidates for endophenotypes of negative 

affect during tobacco withdrawal. This 

section integrates a description of the 

measure with the available data on the 

relationship to nicotine dependence.

Physiological Responses to Abstinence 

Responses to abstinence include 

physiological changes including decreases 

in heart rate and in cortisol, a “stress” 

neurohormone that rises in a period of 

minutes following an affective challenge. 

The magnitude of decline in heart rate 

is not clearly associated with cessation 

outcome, but a few studies have related 

the decline in cortisol to outcome. Al’Absi 

and colleagues419,446 found that the larger 

the drop in cortisol in the fi rst day or two 

after quitting, the faster will be the time to 

relapse. Similarly, Ussher and colleagues447

found that decline in cortisol on the fi rst 

day of quitting was marginally related to 

relapse at six weeks in smokers treated 

with a 15-mg nicotine patch. However, 

they also showed that the smaller the 

absolute level of cortisol on the day after 

quitting, the higher is the self-reported 

craving and withdrawal, suggesting a link 

between low cortisol and the aversive 

symptoms of abstinence. This association 

was signifi cant even after controlling for 

number of cigarettes per day before quitting. 

Moreover, a drop in the ratio of another 

steroid hormone, dehydroepiandrosterone 

(DHEA), to cortisol during the fi rst week of 

quitting predicted relapse by the end of the 

second week.448 A decrease in this DHEA to 

cortisol ratio also predicted the increase in 

withdrawal and the symptom of depression 

among women, but not in men. Both DHEA 

and cortisol are released in response to 

activation of the hypothalamic-pituitaryadrenal axis, which is often associated with 

stress or negative affect. Thus, greater 

decline in cortisol or in the DHEA to cortisol 

ratio in the fi rst days after quitting appears 

to be a reliable predictor of quitting success 

and warrants further study. Few other 

neurohormones have been examined as 

indices of dependence, but cross-sectional 

comparisons have been made between 

hormone levels and current dependence. 

For example, allopregnanolone and 

pregnenolone levels were directly correlated 

with cotinine levels, an index of amount of 

recent smoking.449

Startle Response 

A psychophysiological response that is 

related to affectivity is the startle response. 
448

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

This response is the magnitude of eyeblink 

response to a sharp stimulus, usually a brief 

loud noise, but also can be an electrical 

pulse; it is thought to refl ect a defensive 

response to threat that may be mediated by 

the brain’s limbic system (i.e., amygdala).450

Recall that the PPI of the startle response 

was discussed previously as a measure of 

sensory processing. However, the startle 

refl ex itself is related to affect. Research in 

emotion has shown clearly that negative 

mood induction increases, and positive 

mood induction decreases, the magnitude 

of the startle response.451 Thus, greater 

affectivity should be evidenced by larger 

startle responses. However, a few studies 

have found no difference in startle 

magnitude between nondeprived smokers, 

briefl y deprived smokers, and nonsmokers, 

either during baseline (i.e., in the absence 

of mood induction)452 or in response to 

negative mood induction.453 Moreover, 

in within-subjects comparisons, neither 

overnight abstinence454 nor acute smoking, 

has consistent effects on acoustic startle 

response.455 Notably, however, one study 

found that smokers who were able to quit 

for 24 hours had larger startle response 

before quitting, and quitting decreased 

startle response 24 hours later.456 Startle 

magnitude was not signifi cantly correlated 

with scores on the Fagerström Tolerance 

Questionnaire. This result is contrary to the 

notion that greater affectivity as indexed by 

startle response is associated with greater 

dependence, in that greater startle response 

before quitting should predict lower, not 

greater, ability to quit. Similarly, the decline 

in startle response 24 hours after quitting 

is the opposite of what would be expected in 

light of the commonly observed increase in 

negative affect after quitting.

Distress Tolerance

Individual differences in ability to tolerate 

distress or to persist with frustrating 

tasks may put smokers at greater risk for 

relapse during a quit attempt. For example, 

Brandon and colleagues457 have shown 

that lack of persistence with a challenging 

psychomotor task—that is, mirror tracing 

(tracing a pattern when seeing its reverse 

image in a mirror)—before quitting 

prospectively predicts greater risk of relapse 

12 months after quitting. In a similar 

line of research by Brown and colleagues, 

responses on a self-report measure of 

distress tolerance were found to predict 

early smoking relapse.458 The authors made 

the point that how one reacts to distress, 

rather than severity of withdrawal per se, 

may be key to relating withdrawal to risk 

of relapse. Thus, rather than absolute severity 

of negative affect during withdrawal being 

the only important factor, it may be the 

smoker’s cognitive appraisal of that negative 

affect that interacts to predict relapse.

Psychophysiological Response to Acute 

Stressors 

Another approach to studying affective 

regulation during tobacco abstinence is to 

test psychophysiological responses to acute 

stressors (i.e., negative affect in response to 

contrived challenges rather than to smoking 

abstinence). Acute stress increases smoking 

behavior in smokers and increases relapse 

after a quit attempt.459 One notion is that 

abstinence removes an important method of 

coping with acute stress—that is, cigarette 

smoking—which may aid ability to cope 

via behavioral (e.g., perceived control) or 

pharmacological (direct actions of nicotine) 

mechanisms.4,459 Acute stress can mimic 

some of the symptoms of withdrawal, 

particularly negative affect. Thus, loss of 

ability to cope with stress may also refl ect 

loss of ability to cope with the symptoms of 

withdrawal and could relate to the distresstolerance characteristic noted above.

Psychophysiological responses to stressors 

may also relate to level of distress, given 

that systolic blood pressure response to 

the stressful tasks of mental arithmetic 

and speech preparation (having to quickly 

prepare a public speech) predicted faster 

relapse in women smokers. Male smokers 
449

Monograph 20. Phenotypes and Endophenotypes

did not show this association, although 

greater postural hypotension (drop in 

systolic blood pressure after standing) 

predicted faster relapse in men.460

Complicating this picture further, another 

study found that attenuated, not larger, 

adrenocorticotrophic hormones, cortisol, 

and diastolic blood pressure responses to 

mental arithmetic and speech preparation 

predict faster relapse at four weeks.419 Thus, 

psychophysiological responses to stressors 

are not consistently related to cessation 

outcome. Yet, as would be expected from 

the prior discussion of self-report measures 

of affect, those who relapsed had greater 

self-reported negative mood and withdrawal 

at baseline, as well as greater self-reported 

craving response to the stressor. Notably, 

these predictors of relapse remained 

signifi cant after controlling for smoking 

history characteristics. 

In summary, an increase in self-reported 

negative affect, and perhaps drops in cortisol 

and DHEA to cortisol ratio responses to 

abstinence, have been shown to predict 

faster relapse to smoking after quitting 

(i.e., smoking persistence). Few other 

measures of affective regulation have been 

shown to have consistent associations with 

cessation.

Impulse Control

Behavioral impulsivity is an important 

potential area of endophenotype measures 

for at least two reasons: (1) personality 

characteristics associated with impulsivity 

increase risk of becoming dependent on 

a number of drugs, including tobacco 

(as discussed in detail in chapter 8), and 

(2) diffi culty concentrating, which can be 

related to impulsivity, is a reliable symptom 

of tobacco withdrawal that is clearly 

relieved by both smoking and nicotine 

alone (i.e., NRT). In the smoking literature, 

substantial research has been conducted 

in both of these areas, but relatively little 

of it has focused on relating outcome of a 

cessation attempt to individual differences in 

the personality characteristic of impulsivity 

or in withdrawal symptoms related to 

impulsivity. 

Biological Plausibility

Preclinical Research 

Much less research has examined effects of 

chronic nicotine and nicotine withdrawal 

on impulse control in rodent models. 

In one study by Dallery and Locey,461 rats 

were trained on a delayed reinforcement 

paradigm in which they could choose either 

an immediate reward of a single food pellet 

or a larger reinforcement of three pellets 

that had a variable delay. The choice of the 

smaller, immediate reward over the larger, 

delayed reward is considered an impulsive 

choice (parallel to the delay discounting 

measure discussed below). Chronic nicotine, 

but not acute nicotine, increased impulsive 

choice in this study, with persisting effects 

for 30 days after termination of chronic 

nicotine treatment. These fi ndings suggest 

that chronic nicotine alters neural function, 

resulting in a long-lasting increase in 

impulsivity.

Although less is known about strain 

differences in nicotine effects on impulsive 

behavior in rodents, a few studies suggest 

that nicotine’s effects on impulsivity may 

be mediated by nAChRs. For example, 

spontaneously hypertensive rats, a rat 

strain often used as a model for ADHD, 

have decreased nAChRs in cortical and 

subcortical brain regions compared to 

Wistar-Kyoto rats; however, chronic 

nicotine produces nAChR upregulation 

only in the Wistar-Kyoto rats.462 Agonist 

compounds selective for a4b2 nAChRs 

reduce spontaneous alteration behaviors 

in a Y-maze task in SHRs,463 and DHbE, 

a competitive a4b2 antagonist, blocks 

nicotine’s effects on impulsive responding.464

Much less is known about genetic 

modulation of nicotine effect on impulsivity 

than on other endophenotypes examined 
450

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

in this chapter. However, the Dallery and 

Locey461 study described above suggests that 

effects of chronic nicotine in smokers could 

maintain dependence and facilitate relapse 

during abstinence attempts as smokers may 

favor the immediate gratifi cation of the 

cigarette over the delayed goals associated 

with remaining abstinent. 

Human Clinical Research 

The role of impulsivity in the onset of 

smoking is discussed at length in chapter 8. 

Here, the focus is on a smaller set of 

studies on the role of impulsivity in chronic 

smokers. As described in the “Attention 

and Vigilance” subsection above, adult 

smokers with current or childhood ADHD 

have more severe nicotine withdrawal 

after quitting, compared to smokers 

without any ADHD history.270 In the 

general population of smokers, the greater 

the increase in hyperactive/impulsivity 

symptoms after quitting, the greater the 

probability of relapse.273 Moreover, among 

smokers with a history of major depression, 

those with higher scores on the Barratt 

Impulsivity Scale, a common self-report 

measure, relapse more quickly.465 Among 

smokers not trying to quit, those higher in 

impulsivity on the Barratt Impulsivity Scale 

report greater relief of negative affect from 

a nicotine versus denicotinized cigarette 

during a laboratory mood-induction 

procedure.466 Such smokers also anticipate 

greater expectations for positive and negative 

reinforcing effects of smoking.467 This fi nding 

is in contrast to fi ndings from an earlier 

study of treatment-seeking smokers in which 

self-reported hyperactivity symptoms did not 

correlate with smoking motives.271

Description of Impulsivity Measures

In addition to the common self-report 

measures of impulsive personality 

characteristics, several “objective” measures 

of impulsivity and behavioral inhibition 

may serve as potential endophenotypes of 

dependence.

Delay Discounting 

Delay discounting measures the tendency 

to choose smaller, immediate rewards over 

larger, delayed rewards and is believed 

to refl ect impatience and a desire for 

immediate gratifi cation. Drug dependence 

is often viewed as choosing an immediate 

reward, drug use, over larger, delayed 

rewards—namely, the long-term gains 

in health outlook by abstaining from 

drug use. (Long-term gains in choosing 

abstinence among illicit drug users include 

increased employability, improved family 

relations, reduced legal problems). Delay 

discounting has been used in a variety of 

studies of drug dependence in both humans 

and nonhuman animals.468 In this task, 

participants are given repeated choice 

options between a large monetary option, 

to be made available to the participant after 

different durations of delay, such as in one 

day, one week, one month, six months, and 

a year, versus different amounts of lesser, 

immediate rewards.469 The smallest amount 

of immediate reward the participants select 

in preference to the larger, delayed reward 

at each duration of delay refl ects the degree 

to which they discount the value of the 

delayed reward. Plotting these choices leads 

to a temporal discounting function for 

each individual, which can be averaged for 

subgroups. The sharper the decrease in the 

function (i.e., the smaller the current reward 

chosen over the delayed reward), the greater 

the discounting and, presumably, the more 

impulsive the subject. Performing this task 

involves sorting (choosing) actual cards 

containing the different money and delay 

choices,469 but the task is easily presented 

by computer presentation of the choices 

and having subjects choose via computer 

key. This task is also commonly done with 

hypothetical choices, rather than actual 

choices. Some research suggests that 

fi ndings are similar regardless of whether 

actual or hypothetical choices are offered,470

but other studies suggest that actual 

choices may be more sensitive.471 A variation 

on this task involves probability discounting, 
451

Monograph 20. Phenotypes and Endophenotypes

or greater discounting of smaller, more 

certain rewards in favor of larger, less 

certain rewards.472

Go/No-Go Task 

The go/no-go task requires a subject to 

make a motor response according to a 

conditional rule (e.g., in response to a target 

stimulus) and to inhibit a motor response 

according to a similar rule.473–475 For example, 

a downward-pointing triangle may be used 

as a target stimulus and an upward-pointing 

triangle as the nontarget stimulus. Although 

this task is conceptually similar to the CPT 

described above, the rate and reaction time 

for commission errors (i.e., responses made 

to the nontarget or no-go stimulus) provides 

a measure of behavioral inhibition.

Stroop Task 

The Stroop task measures the ability to 

inhibit a prepotent response to a stimulus 

and, therefore, provides an objective 

measure of response inhibition of relevance 

to impulsivity traits. In this task, subjects 

view a series of words printed in color 

(e.g., either green or red) and are instructed 

to identify the color of the ink used. In some 

cases, the word color and the ink color 

match (e.g., the word red written in red 

ink; congruent word), and in other cases, 

the word and color are incongruent (e.g., the 

word green written in red ink). The classic 

Stroop effect is the difference in reaction 

time for naming colors for incongruent 

versus congruent words. This task has been 

adapted as a measure of attentional bias, 

as noted previously.368

Genetic Infl uences on Impulsivity 

Task Responses in Humans

A few studies have examined the heritability 

of laboratory-based measures of impulsivity. 

Using the standard Stroop task as a 

measure of resistance to interference in 

290 twins, Stins and colleagues476 reported 

a heritability of 50% for the Stroop effect 

(i.e., reaction-time difference). For the 

go/no-go task, among 400 twin pairs, 

heritability for mean reaction time (across 

fast and slow tasks) was 60%, and the 

heritability of commission error rates was 

18% for the slow condition and 38% for the 

fast condition.477 Groot and colleagues478

studied 237 healthy twin pairs and found 

differences in heritability estimates by 

gender. For example, the commission rate 

heritability on the go/no-go task was 36% 

among females and 53% among males. In a 

combined assessment of performance on 

the go/no-go task and ERPs, Anokhin and 

colleagues479 showed that about 60% of the 

variance in electrophysiological responses 

during the task was attributable to genetic 

infl uences. The go/no-go task also exhibits 

high heritability in extended pedigrees with 

schizophrenia.480

Associations of specifi c candidate gene 

variants with laboratory measures of 

impulsivity have been examined in a few 

studies. Cornish and colleagues481 studied 

58 boys scoring above the 90th percentile on 

ADHD diagnostic symptoms and 58 scoring 

below this cutoff. Children homozygous 

for the *10-repeat allele of the dopamine 

transporter gene had poorer performance 

on a response inhibition task, independent 

of ADHD symptoms. Three studies have 

examined genetic associations with 

performance on the go/no-go task. Among 

133 children with ADHD, those carrying 

the *7-repeat allele of the DRD4 gene had 

greater impulsivity, faster reaction time, 

and reduced accuracy, compared to those 

with the shorter-repeat variants.482 There 

is also evidence that delay discounting is 

associated with an interaction between the 

DRD2 TAQ1*A1 allele and the DRD4 VNTR 

(*7-repeat) allele.483

Two genes involved in the metabolism 

of dopamine have been associated with 

laboratory measures of impulsivity. 

In one study, a monoamine oxidase A gene 

polymorphism was linked with performance 

on the go/no-go task.484 In two other studies, 
452

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

the COMT VAL/MET genotype described 

above was associated with performance on 

the Stroop task,235,485 with an interaction 

between the DRD2 TAQ1A variant and COMT

variants in the earlier study.485

Emerging evidence also supports an 

association of genetic polymorphisms in 

the serotonin pathway with endophenotype 

measures of impulsivity. For example, 

a study in 2006 showed a relationship 

between the number of commission errors 

on the go/no-go task and the *A-1438A allele 

of the serotonin receptor 2A gene 5-HT2A.486

Of particular relevance to endophenotypes 

for nicotine dependence, one study suggests 

that a performance on a modifi ed “smoking 

stimuli” Stroop task in smokers is associated 

with the promoter polymorphism in the 

serotonin transporter gene;374 however, this 

modifi ed Stroop task may be measuring 

attentional bias to smoking cues rather 

than response inhibition per se. These data 

are preliminary but suggest that genetic 

variation in the dopamine and serotonin 

pathways may play a role in impulsive 

behavior as assessed by objective laboratory 

measures.

Association of Impulsivity Task 

Responses and Nicotine Dependence

Current smokers often, but not always, 

show greater delay discounting than never 

smokers or even former smokers,469,472,487,488

and cigarettes per day are correlated with 

degree of delay discounting,489 suggesting 

a linear relationship between amount of 

smoking intake and impulsivity. However, 

although greater delay discounting was 

associated with greater smoking frequency 

in one laboratory study, nicotine versus 

placebo patch did not infl uence delay 

discounting.490 The specifi c procedures used 

may moderate the fi ndings in laboratory 

studies of delay discounting. For example, 

in one study, brief abstinence increased 

delay discounting of both cigarettes and 

money when they were actually available, 

but no delay discounting was seen when the 

choices were hypothetical.471 Also, because 

lower education is associated with greater 

delay discounting,491 education needs to be 

controlled in comparisons between groups. 

Despite common fi ndings of greater delay 

discounting in smokers versus nonsmokers, 

there appears to be no reliable difference in 

probability discounting.472,489

Less is known about the relationship 

of objective laboratory measures of 

impulsivity with nicotine dependence or 

smoking cessation outcome. In one study 

of adolescent smokers, those with higher 

scores on the delay discounting measure 

were more likely to relapse.300 However, in a 

study of schizophrenics seeking treatment 

for smoking, the Stroop task did not predict 

smoking cessation success.301

Discussion and 

Recommendations 

for Future Research

This fi nal section reviews fi ndings on the 

potential for the measures discussed here as 

endophenotypes for dependence in chronic 

smokers and outlines future directions for 

this research. Each putative endophenotype 

will be addressed within its broad area in 

the following subsections on “Motivational 

Effect Endophenotypes” and “Acute Smoking 

or Abstinence Effect Endophenotypes.” 

The fi ndings are summarized in tables 9.1 

and 9.2, respectively.

Motivational Effect 

Endophenotypes

Measures of the motivational effects of 

nicotine would be expected to offer greater 

promise as endophenotypes early in this 

research effort, as they are more proximal 

to dependence, as indicated in fi gure 9.1. 

As noted, the frequency and persistence 

of drug reinforcement is a central feature 
453

Monograph 20. Phenotypes and Endophenotypes

Table 9.1 Putative Endophenotypes for Nicotine Dependence: Motivational Mechanisms 

and Nicotine or Abstinence Effects

Measure

Biological 

plausibility

Standard, 

objective, and 

reliable

Evidence 

of genetic 

influence

Linked to 

dependence

Reinforcement

Ad lib self-administered

Nicotine choice

Behavioral economics

Progressive ratio

++

+

+

+

+

+

+

+

±

+

0

0

+

+

0

0

Reward

Self-report of hedonic effects ++0±

Note. ++ = strong confi rmatory evidence; + = some confi rmatory evidence; ± = little or equivocal evidence; 0 = no available evidence.

Table 9.2 Putative Endophenotypes for Nicotine Dependence: Acute Smoking or Abstinence 

Effects

Measure

Biological 

plausibility

Standard, 

objective, and 

reliable

Evidence 

of genetic 

influence

Linked to 

dependence

Physiological

Resting EEG

ERP

PPI

±

±

+

+

+

+

+a

+a

+a

0

0

0

Cognitive function

Attention

Working memory

±

±

+

+

+a

+a

0

0

Craving

Abstinence-induced

Self-reported urge

Cue-induced

Self-reported urge

Psychophysiological

Cognitive/attentional bias

++

++

±

±

++

++

+

+

0

±

0

±

+

±

–

+

Affective regulationb

Abstinence-induced

Self-reported negative affect

Physiological

Startle

Distress tolerance

Stress/physiological

++

±

±

+

+

+

+

+

±

+

0

+a

+a

0

0

++

+

0

+

±

Impulse control

Delay discounting

Go/no go

+

±

+

+

0

+a

0

0

Note. EEG = electroencephalogram; ERP = event-related potentials; PPI = prepulse inhibition; ++ = strong confi rmatory evidence; 

+ = some confi rmatory evidence; ± = little or equivocal evidence; 0 = no available evidence; – = some contrary evidence.

a

Evidence regarding the measure in general, no evidence for effect of abstinence or acute smoking.

b

Virtually no evidence of acute effects of smoking on affective regulation was associated with dependence. Consequently, those 

measures are not included here; only measures during smoking abstinence are included.
454

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

of dependence and, therefore, acute 

laboratory measures of the frequency and 

persistence of reinforcement do not require 

extensive assumptions about the link 

between these measures and dependence. 

Measures of ad lib smoking or nicotine 

self-administration and nicotine choice 

procedures generally show some of the 

expected relationships between responses 

and simple manipulations of smoking 

abstinence. These measures are also fairly 

objective and reliable, and there is some 

evidence for associations with candidate 

genes; however, the heritability of nicotine 

self-administration measures is unknown. 

The behavioral economic and PR selfadministration procedures have received less 

scrutiny, particularly with regard to genetic 

infl uences. However, these are conceptually 

similar to self-administration measures 

and are comparably objective and reliable; 

thus, they may have similar strengths and 

characteristics, such as being heritable. 

However, aside from the choice procedure, 

few of these measures have been related 

prospectively to dependence by predicting 

outcomes of a quit attempt, ultimately the 

key clinical utility of this research. 

In terms of future directions for 

reinforcement measures, use of the PR is 

common in animal genetic models and for 

medication screening, and it warrants more 

attention in human studies. To enhance the 

sensitivity of this approach, methodological 

studies to determine the optimal duration 

of prior abstinence, timing of drug 

administration, rate of escalation of the 

PR schedule, and session length would be 

valuable. 

Another procedure that could be adapted to 

assess individual differences in some aspects 

of relapse proneness (i.e., dependence as 

indexed by smoking persistence) is the 

“programmed lapse” procedure.492 In this 

procedure, smokers are required to abstain 

for a few days and then instructed to either 

smoke a few cigarettes to simulate a lapse 

or to not smoke (control condition). All are 

instructed to then continue to maintain 

abstinence, and the measure of interest is 

duration of abstinence after the simulated 

“lapse” point. This procedure is sometimes 

viewed as comparable to the “reinstatement” 

procedure widely used in animal research as 

an analog to relapse,493 although there are 

substantial limitations of reinstatement as 

a model for human drug relapse.494 In any 

case, some aspects of the programmed lapse 

procedure could be used to assess each of 

the phases of smoking relapse in humans: 

(1) time to fi rst lapse could be examined 

by instructing subjects to abstain and 

then prospectively assessing the time to 

fi rst cigarette,495 (2) time interval between 

fi rst and second lapse is essentially what 

is already determined by the existing 

programmed lapse procedure,492 and (3) time 

to relapse would simply require more 

extended follow-up to determine when the 

criteria for relapse (e.g., seven consecutive 

days of any smoking)496 are met. For each 

of these measures, subjects who are able 

to abstain for longer periods presumably 

should be those able to quit for longer 

periods in an actual quit attempt, but this 

would need to be verifi ed. A more practical 

measure of persistence of abstinence may 

be to simply see if the smoker is able to 

quit for 24 hours, which differentiates 

high- and low-dependent smokers making 

an actual quit attempt115 or not trying 

to quit permanently.497 However, this 

approach results in a dichotomous measure 

(able versus unable to abstain), which may 

be insensitive for use in other research 

relating the measure to other factors. 

The other measure within this broad 

area, smoking or nicotine “reward,” has 

less evidence supporting its use as an 

endophenotype, as it is not yet measured 

in humans in an “objective” way. However, 

these measures, generally obtained in 

humans via self-report of “liking” or 

“satisfaction,” are easy to assess and are 
455

Monograph 20. Phenotypes and Endophenotypes

reliable. The magnitude of smoking or 

nicotine “reward” has strong biological 

plausibility, and there are objective 

measures in animals that are thought to 

refl ect reward (e.g., CPP and ICSS). Thus, 

these measures hold promise as potential 

endophenotypes for dependence in human 

smokers, if human equivalent measurement 

procedures can be found. Although there are 

obvious impediments to developing brain 

stimulation measures of reward threshold, 

it seems plausible that human models of 

CPP could be developed and validated. 

However, such complex measures may not 

add signifi cantly to the armamentarium of 

human laboratory models; perhaps more 

attention should be devoted to assessing 

genetic associations with self-report 

measures of nicotine reward within the 

context of other laboratory paradigms.

A key issue that pertains to research on all 

of the measures discussed in this chapter, 

not just self-administration and reward 

measures, is the failure of virtually all 

laboratory studies of these measures to 

assess them in smokers preparing to quit. 

The motivational effects of smoking and 

nicotine are clearly different in smokers 

preparing to quit than they are in smokers 

with no interest in quitting.141 There is 

reason to think that the effects of brief 

abstinence and the acute effects of smoking 

or nicotine on cognitive, affective, and 

other functioning may vary depending on 

whether the subjects are smokers preparing 

to quit or are not interested in quitting 

permanently. If so, use of non-treatmentseeking smokers (i.e., those not trying to 

quit) in this research may contribute to 

the failure of many of these measures to 

show sensitivity to dependence. Use of such 

smokers is not surprising; these procedures 

were adopted from animal research, which, 

perhaps necessarily, has focused only on 

the acquisition and maintenance of drug 

self-administration. Animal studies have not 

been used effectively to model “voluntary” 

abstinence from drug use, as in human 

quit smoking attempts,494 and none of 

the procedures directly assesses ability to 

maintain abstinence, a critical index of 

dependence. Thus, differences in quitting 

motivation between laboratory research 

participants and smokers in clinical 

studies may impede the development 

and validation of brief laboratory-based 

behavioral procedures that may serve as 

endophenotypes.141

Acute Smoking or Abstinence 

Effect Endophenotypes

The cognitive, affective, and behavioral 

impulsivity measures discussed in this 

section have at least some biological 

plausibility and preclinical data to support 

nicotine effects. Further, many of these 

constructs can be assessed in a very 

objective and reliable manner. Some, 

notably the measures of sensory processing, 

attention and vigilance, working memory, 

and impulsivity have clear evidence of 

heritability. However, this evidence pertains 

to responses on these measures in general 

rather than to acute responses on these 

measures to smoking or abstinence. 

Since acute smoking or abstinence effects on 

these measures are thought to be more distal 

to nicotine dependence (fi gure 9.1), these 

measures require a greater leap from the 

underlying mechanisms responsible for these 

effects and processes to nicotine dependence. 

Consistent with this assumption, virtually 

none of the measures in this broad area have 

been directly associated with dependence in 

chronic smokers, especially as predictors of 

smoking persistence during a quit attempt, 

the gold-standard index of dependence in 

smokers adopted in this chapter. Yet, virtually 

no evidence links any of these objective 

measures with persistent smoking in chronic 

smokers, with the exception of abstinenceinduced self-reported craving and negative 

affect and perhaps hormonal responses to 

abstinence (cortisol or related measures). 

It is important to note that lack of research 
456

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

attention, rather than disconfi rmatory 

fi ndings, characterize the research in this 

broad area.

Other objective measures with preliminary 

support for a relationship to dependence are 

the attentional bias measure of cue-induced 

craving389 and the affective regulation 

measure of “distress tolerance,”457,458 which 

are not strictly acute responses to smoking 

or to abstinence but are more traitlike. 

These measures deserve further attention 

with respect to heritability of smoking and 

abstinence effects as well as associations 

of candidate genes. Evidence that alcohol 

priming can alter attentional bias to 

smoking cues suggests a “state” component 

as well.498 Such cross-substance paradigms 

may provide interesting endophenotypes for 

genetic studies as well.

Cue-induced craving has substantial 

biological plausibility on the basis of 

preclinical and neuroimaging studies. 

However, thus far, cue-induced craving has 

virtually no validity as an index of dependence 

as determined by cessation outcome.380

Furthermore, NRT has no effect on cueinduced craving, whether in smokers wanting 

or not wanting to quit permanently.382,383,386

In contrast, NRT robustly reduces abstinenceinduced craving, even acutely in those not 

trying to quit,377 and reduces risk of relapse.349

Yet, olanzapine, an antipsychotic medication 

not known or proposed to be effi cacious for 

smoking cessation, nevertheless attenuates 

cue-elicited craving to smoke in healthy 

smokers.499 Thus, there appears to be no clear 

link between the magnitude of cue-induced 

craving or infl uences on this type of craving 

and indices of dependence in adult smokers. 

Some of the diffi culty with “reactivity” 

research could be lack of generalizability 

between the cues used (i.e., the independent 

variables) and the stimuli that elicits 

craving and lapses in the smoker’s natural 

environment. Research has demonstrated 

that photos of personalized contexts for 

smoking (e.g., one’s favorite bar) can elicit 

as robust an increase in self-reported 

craving as more typical cues, such as photos 

of lit cigarettes,352 and stronger craving 

than generic photos of the same contexts 

(e.g., a typical bar). Such research may also 

benefi t by using other types of stimuli that 

refl ect situations tied to smoking lapses 

but do not directly involve smoking, such 

as familiar stressors faced by the smoker. 

For example, in a study of cocaine abuse 

patients, Sinha and colleagues500 found that 

self-reported craving for cocaine in response 

to a personalized stress-related imagery 

script, but not to a personalized cocainerelated script, predicted faster relapse to 

cocaine use. Thus, greater generalizability in 

reactivity may result from use of personalized 

cue stimuli or stimuli that otherwise are 

more representative of the common relapse 

situations in a smoker’s environment, 

and reactivity to such cues may be more 

predictive of relapse after quitting. 

Alternatively, the problem with the lack 

of predictive validity of cue reactivity 

in the available studies may stem from 

the responses assessed—that is, the 

dependent variables—rather than, or in 

addition to, the independent variables 

used. The vast majority of studies assess 

self-reported craving, although some also 

assess psychophysiological responses.380

Perhaps broadening the reactivity responses 

may reveal some that are more strongly 

tied to relapse, as suggested in the small 

preliminary study by McClernon and 

colleagues388 noted previously. For example, 

in addition to showing that self-reported 

craving in response to stress imagery, 

but not to cocaine imagery, predicted 

cocaine relapse, the study noted above by 

Sinha and colleagues500 also found that 

greater corticotrophin and cortisol responses 

to the stress imagery predicted higher 

amounts of cocaine used per lapse occasion 

during the follow-up period, although these 

responses were not related to time to relapse. 

Somewhat similarly, it was found that fMRI 
457

Monograph 20. Phenotypes and Endophenotypes

measurement of brain activation in response 

to cocaine-related videotapes predicted 

subsequent relapse in cocaine patients, 

but self-reported craving in response to 

the videotapes did not.501 However, given 

that attenuated physiological responses, 

including cortisol, to acute lab-based 

stressors were shown to predict smoking 

relapse,419 it is not clear that heightened 

responding to stimuli should necessarily be 

of more interest than blunted response. 

Consistent with the notion that cue 

reactivity research may need to reconsider 

its dependent measures, such studies 

may benefi t from assessing smoking 

behavioral responses to such cues.380

Because prospective research relating any 

laboratory measure to cessation outcome 

can be diffi cult, one intermediate step 

may be to determine that cues robustly 

elicit increases in measures of smoking 

reinforcement, which may be more likely to 

relate to dependence than do other craving 

measures. Animal and human evidence 

shows that cues can have as much, and often 

more, infl uence on drug-taking behavior 

as the drug nicotine itself.78 Research from 

the cocaine fi eld indicates that human 

laboratory self-administration models are 

better predictors of the clinical effi cacy of 

medications than are results using selfreported craving as the primary dependent 

measure.502 Thus, variability in the degree 

to which smoking behavior, rather than 

self-reported craving, is altered by cues 

could provide a more fruitful direction for 

cue reactivity research aimed at identifying 

factors responsible for dependence. 

Assessing the infl uence of cues on 

reinforcement can be assessed with most 

of the acute procedures presented above in 

the “Motivational Effect Endophenotypes” 

section. For example, the presence of a 

lit cigarette cue increases responding for 

cigarette puffs under the highest response 

requirements (i.e., price) in a variation on 

the behavioral economics procedure.147 Also, 

rather than simply presenting a pictorial 

or in vivo cue for the smoker’s observation, 

as in standard cue reactivity research,363

research should increase the cue salience 

by providing virtually the entire smoking 

experience as a cue via denicotinized 

(placebo) cigarettes. Here, the smoker 

experiences not only the sight and smell 

of the cigarette, but also much of the taste 

and sensory effects of inhaling smoke, but 

with no nicotine intake. The availability of 

credible placebo cigarettes has resulted in 

an increase in their use in a number of areas 

of smoking research.137,149 The magnitude 

of responses to such smoking, which can be 

viewed as conditioned responses to smoking 

cues, may be related cross-sectionally to 

dependence, as suggested.503

Summary

This chapter describes a series of 

objective laboratory-based measures 

of motivational mechanisms and 

acute smoking or abstinence effects as 

potential endophenotypes for nicotine 

dependence. Although the motivational 

measures—in particular, ad libitum 

self-administration and nicotine choice—

have been related to dependence, data on 

heritability and genetic associations are 

lacking. The converse is true for measures 

of acute smoking or nicotine abstinence 

effects. Sensory, cognitive, affective, and 

behavioral measures in this area appear to be 

heritable, and specifi c genetic associations 

have been identifi ed; however, this research 

has not examined genetic infl uences in 

the context of nicotine effects, and no data 

are available to judge the relationship to 

nicotine dependence. As shown in tables 9.1 

and 9.2, there is great potential for research 

to provide evidence for or against the criteria 

important for endophenotype measures of 

nicotine dependence. Although the utility of 

endophenotypes in genetics research is still 

a topic of some debate,52 this debate can only 

be resolved through rigorous future research.
458

9. Nicotine-Dependence Endophenotypes in Chronic Smokers

Conclusions

1. Nicotine dependence in chronic smokers 

is characterized by persistent smoking 

behavior despite knowledge of its harm 

(e.g., an inability to sustain a quit 

attempt). Reinforcement measures such 

as nicotine choice have been related to 

nicotine dependence, although further 

research is needed on the relationship 

between dependence and ad libitum drug 

self-administration, behavioral economics, 

and progressive ratio measures. Genetic 

studies in reinforcement measures in 

mice indicate a potential for studying the 

heritability and genetic infl uence for these 

behaviors in humans.

2. Limited evidence exists regarding the 

relation between self-reported measures 

of reward and nicotine dependence in 

humans, while animal studies show 

a potential link between the rewardrelated measure of conditioned place 

preference and nicotine dependence.

3. Evidence of heritability and genetic 

infl uence has been established for 

measures of sensory processing, such as 

resting electroencephalogram activity, 

event-related potentials, and the 

prepulse inhibition of startle response, 

as well as cognitive measures such as 

attention and working memory. Further 

research is indicated to investigate 

the relationship of such measures to 

nicotine dependence in humans.

4. Self-report measures of abstinenceinduced craving have been related 

to the success of cessation efforts 

(i.e., dependence), while neither selfreport nor psychophysiological measures 

of cue-induced craving have been 

reliably shown to relate to nicotine 

dependence. The relationship of these 

measures with genetic factors remains 

an area for further investigation.

5. Self-reported levels of negative affect 

following smoking cessation have been 

strongly related to smoking persistence. 

Persistence has also been associated 

with abstinence-induced changes in 

physiological measures such as cortisol 

and the dehydroepiandrosterone to 

cortisol ratio. Other measures of affect 

have not been shown conclusively 

to relate to measures of nicotine 

dependence. 

6. Impulsivity and cognitive control 

measures such as delay discounting, 

the go/no-go task, and the Stroop 

interference task have not been shown 

conclusively to relate to nicotine 

dependence, while the go/no-go task 

has shown some evidence of heritability 

and relation to genetic factors.

7. Overall, the available evidence supports 

the possibility of endophenotypes 

for nicotine dependence in chronic 

smokers on the basis of motivational 

factors and, to a lesser extent, sensory, 

cognitive, affective, and behavioral 

measures. Further research is indicated 

to help establish a consistent pattern 

of heritability, genetic infl uence, and 

association with nicotine dependence 

for measures in each of these areas.
459

Monograph 20. Phenotypes and Endophenotypes

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485

The fi rst chapter of this part of the monograph explores the usefulness of epidemiologically 

defi ned phenotypes of tobacco use for genetic studies. Three analyses are presented that 

demonstrate the correlation between specifi c transition points along the smoking cessation 

trajectory with other self-reported measures of smoking and related behaviors. Subsequent 

chapters discuss a potential etiological architecture for genetic and environmental 

infl uences on smoking phenotypes as well as an ontological approach for hierarchical 

modeling of joint actions of genes.

Epidemiological and Methodological 

Considerations

Part

5

487

Traditional behavioral genetic studies based on phenotypes of observed smoking behavior 

often lack specifi city and are subject to classifi cation bias. This chapter explores the use 

of an epidemiological approach for modeling smoking phenotypes, based on transitions 

along the smoking trajectory and prior exposure, which may yield more accurate 

phenotype defi nitions for future genetic studies.

Three studies are presented that examine improved phenotypes in relation to numerous 

variables for smoking behavior and comorbid conditions:

■ An analysis of male Finnish smokers from the Alpha-Tocopherol, Beta-Carotene 

Cancer Prevention Study (ATBC), examining the behavior of sustained quitters, 

relapsers, and never quitters relative to several baseline variables involving 

smoking history, behavioral/psychological symptoms, and alcohol use

■ Cross-sectional data from the Tobacco Use Supplement to the U.S. Census 

Bureau’s Current Population Survey (TUS-CPS), comparing sustained quitters, 

relapsers, smokers with no quitting history, and sustained quitters on measures 

of smoking behavior and nicotine dependence

■ Data from the Third National Health and Nutrition Examination Survey 

(NHANES III) data set, comparing smoking subgroups on the basis of 

consumption levels and quit attempts with independent variables involving 

alcohol use, insomnia, and depression

The results of these analyses demonstrated substantial variations in measured variables 

between these more tightly controlled phenotypes. Results such as these lend support to 

the idea of creating more specifi c, epidemiologically based phenotypes of tobacco use, 

which, in turn, may correlate more closely with genetic variations.

10

Epidemiological Analysis of Variation 

in Phenotypic Defi nitions: A Proof 

of Concept Using an Example 

of a Cessation Phenotype

Kay Wanke and Erik Augustson
488

1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

Introduction

The overall goal of this chapter is to 

demonstrate the utility of epidemiological 

approaches for defi ning phenotypes by 

narrowing the inclusion criteria, and thereby, 

it is hoped, reducing misclassifi cation bias. 

Specifi cally, a series of analyses explore 

if changes in phenotype defi nition affect 

key indicators of the smoking behavior of 

interest. The analyses presented here use 

sustained cessation as an exemplar behavior. 

This chapter contributes to the discussion of 

various approaches to defi ning phenotypes, 

which is a crucial element of the questions 

this monograph addresses. The analyses 

in this chapter will provide an example of 

how epidemiology can play an active role in 

understanding smoking behavior and the 

impact of genetic factors.

A long-standing and solid literature 

supports the role of genetic factors in 

smoking behavior,1–4 and subsequent 

linkage studies continue to provide highly 

suggestive fi ndings.5–8 A review by Munafó 

and colleagues9 found that despite this 

foundation, overall behavioral genetic 

studies of smoking have produced only a 

limited body of consistent results. In part, 

this likely refl ects the multiple infl uences 

associated with smoking, including the 

potential additive effects of a large number 

of genes. However, this lack of solid fi ndings 

regarding the impact of specifi c genes is also 

indicative of a number of methodological 

limitations within the fi eld of behavioral 

genetic studies of tobacco use.9

 Among the 

various identifi ed obstacles to progress in 

the fi eld is the key observation that smoking 

phenotypes have typically been poorly 

defi ned with respect to important behaviors 

and exposures, leading to a high likelihood 

of classifi cation bias.9 Of particular concern 

is the inclusion of individuals who have 

been either only minimally exposed or 

unexposed to nicotine.10 The potential 

importance is highlighted by fi ndings from 

studies that have attempted to remediate 

this methodological problem by using 

more restrictive phenotypes.11–14 In doing 

so, it appears that convergent results are 

beginning to be demonstrated.

Although studies of genetic factors of 

smoking behavior are a prominent area of 

research in which phenotypic defi nitions 

are crucial, this same fundamental 

problem exists in purely behavioral and 

epidemiological studies as well. Some 

studies have used strategies similar to that 

presented here to address this issue.15–17

Given the historic limitations in the fi eld and 

the potential advances indicated by attempts 

to remediate the issues associated with 

defi ning phenotypes, a delineated method 

for doing so may be benefi cial to consider. 

To address the underlying methodological 

limitations of many approaches to defi ning 

phenotypes, a specifi c strategy is proposed. 

It is suggested that phenotypes be defi ned 

with two features: (1) behavior identifi ed by 

transitions along the smoking trajectory and 

(2) adequate exposure to a precursor of the 

behavior of interest. The rationale for using 

each of these is discussed below.

Transitions along the Smoking 

Trajectory

Smoking, like many behaviors, can be 

conceptualized as occurring along a 

trajectory, beginning with experimentation, 

and moving on to initiation, regular use, 

dependence, and then attempted cessation 

with success or relapse (fi gure 10.1). 

Common points of clinical concern 

(e.g., initiation, dependence) are labeled on 

the trajectory. Figure 10.1 shows examples 

of how various stages along the smoking 

trajectory represent choice points at which 

one could defi ne a phenotype based on 

smoking behavior (e.g., lifetime smoked 

<100 cigarettes versus ≥100 cigarettes). 

Hypothesized potential underlying genetic 

infl uences are also linked to areas along 
489

Monograph 20. Phenotypes and Endophenotypes

the trajectory. As shown in fi gure 10.1, 

a number of factors are likely to be more or 

less associated with behavior at a specifi c 

point on the trajectory and with transition 

to the next stage. Therefore, it is crucial 

that researchers identify the specifi c point 

they are studying and link this point to a 

specifi c behavior of interest. For example, 

given this trajectory of behavior, one might 

be interested in factors, including potential 

underlying genetic factors, specifi cally 

associated with the question of why some 

people progress beyond initiation to 

dependence and others do not. As such, 

multiple phenotypes of interest are defi ned 

by transitions along the trajectory within 

the broad category of smoking.

Considering specifi c points along 

the smoking trajectory suggests that 

inconsistent fi ndings within the tobacco 

behavioral genetic literature could be, 

in part, accounted for by differences in 

which behavior was studied. If the impact of 

a genetic trait on smoking varies depending 

on the specifi c behavioral point on the 

trajectory, then a given polymorphism 

associated with the genetic infl uence may 

Smoking Phenotypes: The Perils of Casting Too Broad a Net

Traditionally, phenotypes for many behavioral genetic studies have been based on broad 

classifi cations of behavior such as never versus ever smokers, current versus former smokers, 

or current smokers versus nonsmokers, which includes both former and never smokers. 

Although these types of phenotypes have strong face validity, there are a number of serious 

drawbacks, including the potential for biasing of results due to misclassifi cation. One of the most 

signifi cant problems with these phenotypes is that they are uninformative regarding exposure 

to the behavior of interest and, indeed, lack specifi city regarding what that behavior may be 

for the analysis.a

 Research by Saccone and colleaguesb found changes in their results when 

unexposed individuals were removed from analyses of a smoking phenotype based on sib-pairs. 

Although changes in sample size may have affected the fi ndings, the potential impact of including 

minimally and unexposed individuals in behavioral genetic studies of substance abuse represents 

a serious methodological issue in need of further investigation.a

 Given this, perhaps it should not 

come as a surprise that these traditional phenotypes have been problematic in behavioral genetic 

studies. As noted elsewhere in this chapter, the potential effect of poorly defi ned phenotypes is 

demonstrated by studies that have used phenotypes more specifi cally defi ned by a particular 

smoking behavior.c,d,e,f

a

Saccone, N. L., E. L. Goode, and A. W. Bergen. 2003. Genetic analysis workshop 13: Summary of analyses of 

alcohol and cigarette use phenotypes in the Framingham Heart Study. Genetic Epidemiology 25 Suppl. 1: 

S90–S97.

b

Saccone, N. L., R. J. Neuman, S. F. Saccone, and J. P. Rice. 2003. Genetic analysis of maximum cigarette-use 

phenotypes. BMC Genetics 4 Suppl. 1: S105.

c

Bierut, L. J., P. A. Madden, N. Breslau, E. O. Johnson, D. Hatsukami, O. F. Pomerleau, G. E. Swan, et al. 2007. 

Novel genes identifi ed in a high-density genome wide association study for nicotine dependence. Human 

Molecular Genetics 16 (1): 24–35.

d

Saccone, S. F., A. L. Hinrichs, N. L. Saccone, G. A. Chase, K. Konvicka, P. A. Madden, N. Breslau, et al. 

2007. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 

348 candidate genes with 3713 SNPs. Human Molecular Genetics 16 (1): 36–49.

e

Saccone, S. F., M. L. Pergadia, A. Loukola, U. Broms, G. W. Montgomery, J. C. Wang, A. Agrawal, et al. 2007. 

Genetic linkage to chromosome 22q12 for a heavy-smoking quantitative trait in two independent samples. 

American Journal of Human Genetics 80 (5): 856–66.

f

Uhl, G. R., Q. R. Liu, T. Drgon, C. Johnson, D. Walther, and J. E. Rose. 2007. Molecular genetics of nicotine 

dependence and abstinence: Whole genome association using 520,000 SNPs. BMC Genetics 8:10.
490

1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

or may not be associated with “smoking.” 

For example, if nicotine metabolism were 

associated with nicotine dependence, but not 

with risk of relapse, then polymorphisms 

associated with nicotine metabolism would 

be positively associated with smoking only 

in studies in which nicotine dependence 

was the basis of the phenotype.

Defi ning phenotypes based on choice points 

along the trajectory also suggests that by 

considering the mechanism by which a gene 

was believed to infl uence smoking behavior, 

one could hypothesize a priori its potential 

as a candidate gene for that specifi c behavior 

along the smoking behavior trajectory. 

For example, Lerman and colleagues18

reported the results of a trial investigating 

the role of the OPRM1*N40D variant on 

cessation. They found that smokers with the 

variant allele were signifi cantly more likely 

to be abstinent at the end of the trial and to 

report fewer symptoms of mood disturbance 

and less weight gain postcessation. These 

results suggest that at least some functions 

of the OPRM1 gene as it relates to smoking 

behavior are potentially implicated 

specifi cally in the maintenance of smoking 

abstinence.

Consideration of Adequate 

Exposure

The second feature used by this approach 

to defi ne phenotypes is to ensure that 

adequate exposure occurred. Lack of 

exposure has been identifi ed as a potentially 

signifi cant methodological challenge that 

may be particularly important in studies 

of smoking behavior.10 For the purposes of 

phenotype defi nition, it is suggested that 

adequate exposure in all individuals used in 

a study can be accounted for by including 

only those who have had exposure to a 

Figure 10.1 Phenotype Choice Points along the Smoking Trajectory

Impulsivity, 

risk-taking, 

extraversion

Functioning of brain 

reward pathways, 

negative affect, 

tolerance

Genetic 

liability

Disinhibition, nicotine 

metabolism, sensitization 

to rewarding effects of 

niccotine value, 

reinforcement value

Phenotype 

choice points

Never

smoker Experimenter Regularsmoker

Pack-a-day smoker

(or 2, 3,...)

Smoked

1/10/20 years Cessation

Never tried Ever tried

<100 cigarettes 

Never regular Ever regular

Not daily

< Pack a day

< 1 year

No quit attempt Quit attempt

1 week/

month/year(s)

Relapse

The smoking trajectory

Initiation

Progression Dependence

Intensity

Duration

Duration

Cessation Relapse

FTND, DSM, etc.

≥100 cigarettes 

Relapse

Daily smoking

≥ Pack a day

≥ 1 year

No relapse

Note. FTND = Fagerström Test for Nicotine Dependence; DSM = Diagnostic and Statistical Manual of Mental Disorders.
491

Monograph 20. Phenotypes and Endophenotypes

precursor of the behavior of interest along 

the smoking trajectory. Conversely, all 

individuals who have not passed through 

the previous stage on the trajectory must 

be excluded. A hypothetical example from 

pharmacology highlights the importance 

of appropriate exposure. If the goal of the 

study were to identify features of individuals 

at risk of adverse reactions to a medication, 

one would administer the medication to all 

of the participants and then compare the 

study measures among those who did versus 

those who did not have an adverse reaction. 

Any individual not taking the drug would be 

uninformative regarding possible adverse 

reactions (fi gure 10.2)

This point is relevant to behavioral 

genetic studies of smoking behavior. 

For example, in attempting to understand 

factors associated with progression 

from experimentation to daily smoking, 

individuals who had never experimented 

with cigarettes would be uninformative. 

That is, their risk to progress to regular use 

would be unknown because they had never 

been exposed.10 So the appropriate analysis 

would compare individuals who progressed 

to regular smoking versus those who did 

not, but only among those individuals who 

had experimented with cigarettes based 

on a specifi c “exposure”—for example, 

had smoked ≥100 cigarettes in their lives 

(fi gure 10.3). However, a common phenotype 

previously used in such studies is current 

smokers versus nonsmokers;9 the latter 

includes individuals who have experimented, 

but did not progress, and individuals who 

were never smokers. Similarly, in attempting 

to identify factors associated with relapse 

versus successful cessation, one would 

include only smokers who had made a quit 

Figure 10.2 Example of Phenotypic Comparison for Drug Response Using the Proposed 

Approach

No adverse reaction

Yes Adverse reaction

No

Takes drug

Exposure Comparison

Excluded

Note. Comparison is only among those who had adequate exposure, defi ned in this example as having taken the drug.

Figure 10.3 Example of Phenotypic Comparison for Progression to Daily Smoking (Behavior 

of Interest) Using the Proposed Approach

Never daily smoker

Yes Daily smoker

No

Smoked ≥100

cigarettes

Exposure Comparison

Excluded

Note. Comparison is only among those who had adequate exposure to precursor of the behavior of interest. Exposure is defi ned as 

having smoked ≥100 cigarettes in lifetime.
492

1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

attempt as only these individuals would 

be informative (fi gure 10.4). However, 

the classic comparison is often between 

former smokers and current smokers,9

which includes smokers who have never 

tried to quit.

The Proposed 

Approach

Strategy and Considerations

Given the above, the approach presented 

here proposes that phenotypes be 

formulated in the following way. First, 

a behavior of interest associated with a 

specifi c stage of the smoking continuum 

must be defi ned. Second, the comparison 

group must consist of individuals who have 

had adequate exposure to the behavior of 

the previous stage along the continuum. 

The remainder of this chapter focuses on an 

example using sustained smoking cessation 

as the phenotype of interest. Smoking 

cessation is defi ned by two separate 

behaviors: initiation of a quit attempt and 

sustaining that quit attempt.19 This then 

represents a choice point that can be used to 

defi ne inclusion in the phenotype analysis. 

The behavior of interest in the example 

used here is sustaining cessation. Adequate 

exposure to a precursor of the behavior of 

interest is defi ned by having made a quit 

attempt. Thus, this phenotype is based on 

a comparison of individuals who do and do 

not maintain cessation only among smokers 

who have made a quit attempt (fi gure 10.4).

As an additional consideration, given that 

epidemiology is fundamentally based on 

tracking behaviors associated with the 

distribution of public health outcomes, 

this approach focuses on observable 

behavior. (For additional examples and 

further discussion of behavioral taxonomies, 

see Gifford and Humphreys,20 Gifford and 

colleagues,21 Silva,22 and Follette;23,24 also 

see other articles in the December 1996 

special issue of the Journal of Consulting 

and Clinical Psychology.) In the case of 

behavioral genetics, phenotypes function as 

a means to defi ne categories of people who 

have certain features and behaviors. This is 

distinct from identifying why people have 

those features. The distinction is important 

in that it is inappropriate to include 

elements in the defi nition of a phenotype 

that address underlying causal differences 

between groups (see further discussion in 

Silva22 and Skinner25). The underlying causes 

of why groups differ on the outcome of 

interest—in this case, genetic infl uences—

are what the comparisons of phenotypes 

hope to elucidate. So, it becomes a circular 

argument to include them in the defi nition of 

the phenotype itself. Hypothesized underlying 

Figure 10.4 Example of Phenotypic Comparison for Sustained Smoking Cessation (Behavior 

of Interest) Using the Proposed Approach

Relapse

Yes Sustain cessation

No

Quit attempt

Exposure Comparison

Excluded

Note. Comparison is only among those who had adequate exposure to precursor of behavior of interest. Exposure is defi ned as 

having made a quit attempt.
493

Monograph 20. Phenotypes and Endophenotypes

causal mechanisms are appropriate for 

defi ning endophenotypes (see other chapters 

in this volume), which serve to bridge the 

phenotype-gene causal associations.

Testing the Approach

The fundamental assumption behind the 

argument for using more tightly defi ned 

phenotypes is that the detection of genetic 

infl uences on key smoking behaviors will 

be substantially improved; that is, reducing 

classifi cation bias improves the ability to 

detect associations. Although this logic 

seems sound, it is an empirical question, 

and analyses are needed to justify that this 

approach truly does improve the ability to 

fi nd replicable associations. Ultimately, the 

test of this question will need to be carried 

out using genotyping of various phenotypes 

within a single data set. However, this 

methodology is resource intensive, and the 

“proof of concept” analyses performed in 

this chapter are a necessary starting point.

Simply stated, the basic research question 

is, does it matter if “improved” phenotypes 

are used or not? For the purposes of this 

chapter, evidence of “improvement” is 

demonstrated when, as the phenotype 

changes, factors associated with the 

behavior of interest change in an a priori, 

potentially clinically meaningful direction. 

The rationale is that changes in factors 

associated with the phenotype are indicative 

of changes in the underlying endophenotype 

and genes associated with the behavior. 

In the examples presented in this chapter, 

“improved” defi nitions are detected 

by fi nding changes in characteristics 

predictive of smoking cessation that vary 

as the defi nition of the comparison groups 

changes. The characteristics chosen to 

serve as independent variables for the 

analyses presented in this chapter have been 

previously associated or may be associated 

with ability to sustain smoking cessation: 

(1) markers of nicotine dependence26–30 and 

(2) the presence of comorbid conditions 

including heavy alcohol use,31,32 anxiety/

depression,33–36 and insomnia.37,38

Epidemiology offers a potentially powerful 

set of methodological tools for addressing 

the importance of phenotypic defi nition. 

Various phenotypes can be defi ned on 

the basis of clearly observable behaviors, 

and analyses can be performed to assess how 

key indicators and outcomes change as the 

phenotypic defi nitions change. In addition, 

there are multiple, large-scale data sets based 

on a variety of samples that have measures 

of smoking across the behavioral continuum 

along with a wide array of potential 

covariates. This allows for considering a 

number of comparison phenotypes within 

a data set as well as potentially confi rming 

the fi ndings across data sets.

To test this approach, a series of analyses 

across multiple data sets are presented 

comparing different defi nitions of smoking 

cessation (table 10.1). Multiple data sets 

were analyzed for a variety of reasons. First, 

by applying the strategy to a number of 

different data sets, a basic replication of 

the validity of the approach was performed. 

Second, each of these data sets had varying 

methodological strengths and weaknesses; 

considering the impact of phenotypic 

variation across the data sets provided 

broader coverage across the methodologies of 

the surveys. Next, although all of these data 

sets assessed smoking behavior, no largescale data sets exist that were designed to 

focus on smoking behavior. As such, none of 

the smoking assessments are comprehensive. 

Analyzing multiple data sets allowed for 

covering some of the gaps present in any 

single survey. Each survey also assessed 

different aspects of sustained cessation and 

independent variables of potential interest. 

Again, performing an analysis across the data 

sets allowed for broader coverage of variables 

associated with nicotine dependence and 

comorbid conditions, as well as defi nitions of 

cessation. Lastly, because of methodological 

differences in each data set, it was possible 
494

1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

to vary the specifi c analyses that were 

performed to broaden the test of this general 

strategy for defi ning phenotypes.

Method

General Analytic Approach

The primary objective of these analyses 

was to perform a proof of concept of the 

approach to defi ning phenotypes laid 

out in this chapter. This was done by 

comparing the results of between-group 

analyses with smoking phenotype as the 

dependent variable and measures of nicotine 

dependence and comorbid conditions as the 

independent variable. Individuals who had 

successfully sustained cessation were the 

primary phenotype. With the use of classic 

contingency table analysis, comparisons 

were performed between those who had 

sustained cessation and either (1) phenotypes 

defi ned by more traditional approaches 

or (2) extreme examples of phenotypes 

that would not meet the inclusion criteria 

proposed here (i.e., individuals who had 

never made a quit attempt). The analyses 

were then repeated using the proposed 

strategy of “improved” phenotypic defi nition 

to assess how the results change. In general, 

results presented are based on v2

 and t-test 

univariate analyses by using data from 

the ATBC, the TUS-CPS, and NHANES III 

(table 10.1). Each of these data sets and 

analyses is discussed separately below.

Table 10.1 A Comparison of the Data Sets and Variables Used in the Analyses Presented 

in This Chapter

ATBC TUS-CPS NHANES III

Year collected 1985–1993 2003 1988–1994

Sample size per group ~1,380 1,500 Varies

Groups

Sustained quitters X X X

Current smokers X X

Relapsers X X X

Never quitters X X

Independent variables

Age at smoking onset X X X

Years smoked X X X

Cigarettes per day X X X

Pack-years X

Inhale when smoking X

Nicotine dependence X

Time to first cigarette X

Alcohol use/history X X

Anxiety X

Depression X X

Insomnia X X

Problems concentrating X

Note. ATBC = Alpha Tocopherol, Beta-Carotene Cancer Prevention Study; TUS-CPS = Tobacco Use Supplement to the U.S. Census 

Bureau’s Current Population Survey; NHANES III = Third National Health and Nutrition Examination Survey; Pack-years = the number 

of years of smoking multiplied by the number of packs of cigarettes smoked per day.
495

Monograph 20. Phenotypes and Endophenotypes

ATBC Analyses

Data Source

The fi rst set of analyses presented in this 

chapter was performed by using a sample 

drawn from the ATBC.39 Between 1985 

and 1993, this longitudinal, populationbased chemoprevention trial enrolled 

29,133 Finnish male current smokers 

between the ages of 50 and 69 years into 

a randomized primary prevention trial to 

assess whether alpha-tocopherol or betacarotene would reduce cancer incidence. 

At baseline, all individuals in the ATBC study 

smoked at least fi ve cigarettes per day and 

were generally in good health. Median age at 

entry into the study was 57 years, the median 

number of cigarettes smoked per day (CPD) 

was 20, and the reported median years of 

smoking was 36. Exclusion criteria for the 

ATBC included a history of cancer, signifi cant 

cardiac diagnoses, cirrhosis, chronic 

alcoholism, and signifi cant psychiatric 

diagnoses. Diagnosis of lung cancer during 

the trial was an additional exclusion for the 

analyses because of its impact on smoking 

cessation40,41 and high mortality.

Within the ATBC, extensive medical history 

data based on participant self-report and 

medical examination were collected at 

baseline, and participants were followed 

for fi ve to eight years with three scheduled 

follow-up visits per year (i.e., every four 

months). At each follow-up, participants 

were queried about health and smoking 

status since their last visit.

Phenotype Comparisons

All individuals in the ATBC were established 

heavy smokers, so the focus of analyses 

within this data set was on how the results 

might vary between phenotypic defi nitions 

that did and did not consider the issue 

of adequate exposure. Three groups were 

defi ned: sustained quitters, relapsers, 

and never quitters. Sustained quitters and 

relapsers were exposed to the key behavior 

of having made at least one quit attempt 

during the trial.15 Sustained quitters were 

defi ned as men who reported not smoking 

at all for at least 10 consecutive followup visits, equal to 40 months or more 

of abstinence. Relapsers were men who 

reported that they made a quit attempt 

but sustained it for no more than one 

consecutive interval and had a confi rmed 

relapse. Of the original ATBC sample, 

approximately 30% of the participants 

made a quit attempt during the trial. 

Of these, 1,379 men met the defi nition 

of a sustained quitter and 1,388 met the 

defi nition of a relapser. They were therefore 

included in this analysis. An additional 

group of 1,380 men who did not make a 

quit attempt during the trial were randomly 

selected to serve as the never quitter 

comparison group.

Comparison of the never quitters to 

sustained quitters served as the phenotype 

approach that did not address the issue 

of exposure to precursors of the behavior 

of interest, allowing for exploration of 

the potential impact of misclassifi cation 

of previous quit history. A more tightly 

defi ned phenotypic comparison was made 

of relapsers versus sustained quitters. 

In addition, relapsers were compared 

to never quitters to assess if differences 

among current smokers based on quit 

history were present.

Independent Variables

The analysis presented here is based 

solely on the relationship of baseline 

variables to sustained cessation. Variables 

included in this analysis were those 

associated with smoking history, behavioral/

psychological symptoms, and alcohol use. 

Smoking history included age of smoking 

onset, years smoked, cigarettes per day, 

pack-years,*

 and frequency of inhaling while 

smoking. Behavioral and psychological 
496

1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

symptoms reported as having occurred 

during the last four months included 

anxiety, depression, problems concentrating, 

and insomnia. Alcohol consumption was 

converted to mean grams per day on the 

basis of reported average number of drinks 

consumed daily during the last year.

Analysis

For each comparison, unadjusted analyses 

were performed on the basis of a classical 

contingency table analysis conducted using 

SAS 8.2.42 Reported p-values are based on 

either v2 for categorical variables or t-test 

for continuous variables.

ATBC Results

The results of the analyses of the ATBC 

comparisons are presented in table 10.2. 

Compared with sustained quitters, never 

quitters demonstrated heavier smoking 

histories, more current cigarettes per day, 

and were more likely to always inhale when 

smoking. Never quitters also reported more 

symptoms of coexisting comorbid conditions 

in that they drank more alcohol per day and 

were more likely to endorse experiencing 

depressed mood, problems concentrating, 

and insomnia during the last four months.

A similar general pattern was noted for 

sustained quitters compared to relapsers. 

Relapsers reported higher CPD, more 

pack-years, and being more likely to always 

inhale. Relapsers also reported drinking 

more alcohol per day and being more 

likely to endorse symptoms of anxiety, 

depression, and insomnia. However, closer 

examination of the data revealed that, 

although the general pattern of responding 

was similar, the differences between 

sustained quitters and the other two groups 

were more extreme for never quitters than 

for relapsers.

Further evidence of this pattern was 

demonstrated in the analysis of differences 

between relapsers and never quitters. 

Signifi cant differences between relapsers 

and never quitters were found for all 

markers of nicotine dependence, as well 

as mean alcohol use per day, with never 

quitters always being more extreme. 

No signifi cant difference between relapsers 

and never quitters was noted for any of the 

mood symptoms.

ATBC Discussion

These results suggest that sustained quitters, 

relapsers, and never quitters are distinct 

Table 10.2 Results from the Analysis of the ATBC Study Data

Never quitters

n = 1,380

Relapsers

n = 1,388

Sustained quitters

n = 1,379

Age at smoking onset (mean) 19.1†*** 20.0 20.3

Years smoking (mean) 36.7†*** 33.9 33.4

Cigarettes per day (mean) 21.7†*** 19.1*** 17.2

Pack-years (mean) 40.1†*** 33.0*** 29.2

Always inhale (% yes) 55.3%†*** 50.2%** 44.2%

Alcohol (mean grams/day) 19.6†* 17.0*** 12.5

Feelings of anxiety (% yes) 22.4% 23.7%* 19.9%

Feelings of depression (% yes) 15.1%* 15.0%* 11.2%

Problems concentrating (% yes) 12.5%* 11.4% 9.3%

Insomnia (% yes) 20.1%*** 19.7%*** 13.6%

Note. Comparison group vs. sustained quitters: *p ≤ 0.01; **p ≤ 0.001; ***p ≤ 0.0001. Relapsers vs. never quitters: †p ≤ 0.01.
497

Monograph 20. Phenotypes and Endophenotypes

phenotypic categories in that markers of 

nicotine and alcohol dependence varied 

across the groups. At fi rst, it was suspected 

that men from this sample with no quit 

history—that is, never quitters—had an 

“unknown” liability to relapse and, therefore, 

hypothetically would fall in the middle of 

the continuum of “relapse liability,” with 

relapsers and sustained quitters anchoring 

the ends of this continuum. This assumed 

that never quitters were ultimately made up 

of individuals who would become sustained 

quitters or relapsers should they make 

a quit attempt. That never quitters were 

more extreme than relapsers on markers of 

dependence suggests that they may in fact 

have greater liability to relapse and that not 

making a quit attempt during the eight years 

of the ATBC trial may be related to their 

dependence. Knowledge of quit history before 

entering the trial might help to further 

understand this possibility, but the ATBC 

did not collect this information, so prior 

experience with quitting is unknown.

Both relapsers and never quitters differed 

from sustained quitters on symptoms 

of mood disruption, but not from each 

other on this cluster of symptoms. 

Although there are a variety of potential 

interpretations, it is interesting to note 

that these symptoms are similar to those 

often experienced during withdrawal 

from nicotine.43 It may be that the higher 

frequency of the symptoms at baseline has 

important implications for whether an 

individual will attempt to quit smoking and 

the subsequent risk for relapse.

The ATBC data set has a number of features 

that contribute to its usefulness in this 

series of analysis. The trial has a large 

sample such that appropriate power was 

available for the analyses. The sample 

was followed for an extended period with 

excellent follow-up rates. This allowed for 

considering sustained cessation across a 

time frame much longer than is typically 

assessed. In addition, the ATBC was not 

a smoking cessation trial, so it affords 

an opportunity to observe self-initiated 

cessation attempts and sustaining those 

attempts. The study also assessed a variety 

of variables of potential interest, allowing 

for analyses of comorbid conditions.

The ATBC data set has limitations in regard 

to the question of appropriate strategies 

to defi ne phenotypes. Common across all 

data sets used in this study and cessation 

research in general, having made a quit 

attempt during the trial was based solely 

on self-report. Research suggests that 

individuals typically are truthful about 

smoking behavior in contexts in which no 

negative consequences are associated with 

smoking status. As the above-mentioned 

lack of data on prior cessation experience 

highlights, the ATBC was fundamentally 

a trial testing a nutritional cancer 

prevention intervention. As such, although 

all participants were established heavy 

smokers, available smoking variables are 

limited. The variable used to defi ne cessation 

attempts is somewhat imprecise, especially 

in terms of accurate length of cessation in 

fewer than four-month intervals. No data are 

available regarding what resources may have 

been used during a reported quit attempt. 

This could clearly affect cessation success, 

although nicotine replacement therapy and 

other pharmacological interventions known 

to affect cessation success were not widely 

available or used during the time frame of 

the ATBC study. In addition, because of the 

lack of data regarding quit attempts before 

entering the trial, misclassifi cation of quit 

history (the fundamental prior exposure 

variable) is still possible despite the long 

follow-up period during the trial. Given 

that all participants were heavy smokers, 

a limited range of smoking behaviors can be 

compared for the purposes of considering 

various phenotypes and the impact of varying 

defi nitions. Given some of these limitations, 

an effort was made to confi rm the usefulness 

of this approach in two additional data sets: 

TUS-CPS and NHANES III.
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1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

TUS-CPS Analyses

Data Source

In a fashion similar to that performed in the 

ATBC, cross-sectional data from the 2003 

TUS-CPS44 were used in a second analysis 

investigating how varying phenotypic 

defi nitions may affect the results. The CPS, 

administered by the U.S. Census Bureau, 

uses a multistage probability sample design 

to produce reliable national and state 

estimates on labor force characteristics 

among the U.S. civilian noninstitutionalized 

population aged 15 years and older. Every 

three years since the 1980s, the National 

Cancer Institute and the Centers for Disease 

Control and Prevention sponsor the TUS to 

be conducted in conjunction with the CPS. 

The TUS collects data on tobacco use and 

related attitudes and practices among those 

who completed the CPS. In 2003, the TUS 

collected detailed data on smoking, former 

smoking, and quitting behaviors from 

approximately 250,000 respondents in the 

months of February, June, and November. 

Details of the sampling methods for the 

TUS-CPS are reported elsewhere.45

Phenotype Comparisons

To mimic the above analyses using 

ATBC data and typical behavioral genetic 

studies focusing on specifi c homogeneous 

populations, a similar set of samples was 

selected, limited to white males. The sample 

size also was limited to 1,500 per group 

to approximate that used in the ATBC 

analysis and to refl ect the sample size of 

the parameters that might be seen in a 

behavioral genetics study. In this case, 

four groups were defi ned to be used in the 

comparison: all current smokers (current), 

established daily smokers with and without 

a history of making a quit attempt (relapsers 

and no quit, respectively), and sustained 

quitters. Relapsers, no quit, and sustained 

quitters all had histories of being daily 

smokers for at least fi ve years, and sustained 

quitters reported having sustained cessation 

for one to fi ve years. The current smoking 

group included respondents who indicated 

that they smoked “every day” or “some days.”

The analyses of current versus sustained 

quitters and no quit versus sustained 

quitters represented comparisons in which 

exposure to the precursor of the behavior of 

interest (having made a quit attempt) was 

not part of the phenotype defi nition. As in 

the analysis of the ATBC data, analysis of 

relapsers versus sustained quitters was the 

more tightly defi ned comparison. For each 

group, 1,500 white males were randomly 

selected, again to create a similar set of 

subsamples to that used for the ATBC 

analyses and behavioral genetic studies.

Independent Variables

The TUS-CPS includes questions associated 

with a variety of issues associated with 

smoking behavior and tobacco control. 

For this analysis, measures were used of 

number of CPD, age of onset of regular 

smoking, years smoked, time to fi rst 

cigarette in the morning, and items from the 

Shiffman Nicotine Dependence Syndrome 

Scale (NDSS). In addition to CPD and age 

of onset for regular smoking, time to fi rst 

cigarette was used as an indicator of nicotine 

dependence. Evidence from factor analysis 

strongly supports the use of this item as an 

overall marker for nicotine dependence,46–50

and it has previously been used in 

population-based surveys as an indicator 

of nicotine dependence.51 An abbreviated 

version of the NDSS52,53 is also available on 

the 2003 TUS-CPS and is asked of all current 

smokers and former smokers who have 

quit within fi ve years. The TUS includes 

NDSS questions about diffi culty delaying 

smoking, willingness to go out in a storm 

to get cigarettes, experiencing craving, and 

willingness to go outside to smoke even if it 

interferes with an activity. TUS scores these 

items on a two-point scale (1 = yes, 2 = no), 
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Monograph 20. Phenotypes and Endophenotypes

and the items were summed to provide 

a total with lower scores indicating less 

nicotine dependence.

Statistics

The analysis was conducted using SAS 8.242

and SUDAAN 8.0.1.54 SUDAAN was used to 

account for the complex sample design and 

the weights of the respondents. Standard 

contingency table analysis and univariate 

techniques were used in this analysis. 

All variables presented from this analysis 

are continuous, and reported p-values are 

based on t-tests by using corrected standard 

errors derived from the SUDAAN PROC 

DESCRIPT procedure.

TUS-CPS Results

Results from this analysis are presented in 

table 10.3. Results from the less restrictive 

phenotypes are presented fi rst. Compared 

to current smokers, sustained quitters 

demonstrated earlier age of onset, higher 

levels of CPD, longer smoking histories, 

and higher levels of nicotine dependence as 

assessed by the NDSS items. Time to fi rst 

cigarette was not signifi cantly different 

between current and sustained quitters. 

In contrast, sustained quitters had longer 

time to fi rst cigarette, lower level of CPD, 

and a later age of onset compared to no 

quit smokers. Sustained quitters continued 

to have a lower score on the NDSS items, 

indicating more frequent symptoms 

associated with nicotine dependence. 

Total years smoked was not signifi cantly 

different between the two groups.

Analysis of relapsers versus sustained 

quitters, the more restrictive phenotype 

comparison, showed no signifi cant 

difference between the groups on age of 

onset, CPD, and years smoked. Time to 

fi rst cigarette was signifi cantly different 

between relapsers and sustained quitters, 

with relapsers reporting a shorter mean 

interval to fi rst cigarette. As with the 

other comparisons, sustained quitters 

had signifi cantly lower scores on 

the NDSS items.

TUS-CPS Discussion

Findings from this analysis also point 

to the importance of tightly defi ned 

phenotypes in that the pattern of results 

varies depending on the comparison 

group. Use of the traditional comparison 

of all current smokers versus those who 

had sustained cessation for at least a year 

suggested possible higher levels of nicotine 

dependence as assessed by age of onset, 

CPD, years smoked, and total score on 

NDSS items. However, analysis of smokers 

who had progressed to established daily 

smoking but had not had exposure to the 

Table 10.3 Results from the Analysis of TUS-CPS Data

Current No quit Relapser Sustained quitters

Age of onset 17.4* 16.4** 16.9 17.1

Cigarettes per day 19.9*** 23.4** 20.9 21.2

Years smoked 23.2* 26.0 25.7 25.2

Minutes to first cigarettea 48.6 37.8** 43.5* 53.6

Nicotine dependenceb 5.7*** 5.6*** 5.5*** 5.1

Note. A randomly selected sample of 1,500 for each group was used in these analyses. All reported data are for mean response to 

variable. Comparison group versus sustained quitters: *p ≤ 0.01; **p ≤ 0.001; ***p ≤ 0.0001.

a

Time to fi rst cigarette in the morning in minutes.

b

Total score of abbreviated Nicotine Dependence Syndrome Scale items. Lower scale is indicative of higher level of nicotine 

dependence.
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1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

pre-behavior of interest (a quit attempt) 

suggested a reverse pattern, with later age 

of onset, fewer CPD, and longer interval to 

fi rst cigarette, for the sustained quitters. 

Although there appeared to be a trend 

of an increase of markers of nicotine 

dependence among smokers from sustained 

quitters versus relapsers versus no quit 

smokers, in this sample no substantial 

evidence was found of differences between 

relapsers and sustained quitters except 

in time to fi rst cigarette and score on the 

abbreviated NDSS.

The abbreviated NDSS is unique to the 

TUS among the data sets discussed in this 

chapter. Inconsistent with the general 

pattern of responses from the other variables 

used as markers for nicotine dependence, 

sustained quitters demonstrated more 

dependence than any of the other three 

groups. The reasons for this are unclear. 

One possibility is that the various groups 

of current smokers were asked about 

their current experience, while former 

smokers were asked to recall their previous 

experience of nicotine-dependence 

symptoms. Given that the sample of former 

smokers used in the analysis consisted of 

individuals who had quit at least one year 

ago, and up to fi ve years ago, it may be that 

recall bias played a role in these discrepant 

results. It may also be that this fi nding is 

unique to the samples selected and not 

refl ective of the overall national sample 

at large.

The TUS-CPS is useful for the proof of 

concept of the analytic approach being 

explored in this chapter in that it has a 

large sample size, includes a wide range 

of smokers in the sample, and contains 

a number of items specifi cally related to 

smoking behavior, including lifetime history 

of having made a quit attempt. However, 

given the cross-sectional nature of the data 

and the structure of the questions associated 

with smoking cessation, the survey is of 

limited use for tracking and understanding 

cessation in detail. For example, no data 

are available regarding the details of quit 

history, such as the time frame of having 

made an attempt, unless it occurred within 

the last year, nor is information available 

on what cessation methods have been 

used across the person’s quit history. Also, 

items from the NDSS are only asked of 

former smokers if they attempted to quit 

within the last fi ve years. As with all the 

data sets used in this chapter, smoking 

status and quit history were based solely 

on self-report. In addition, the absence of 

questions related to potentially important 

comorbid conditions such as alcohol use 

and psychiatric symptoms represents an 

important gap in the types of questions that 

can be addressed by this survey.

A fi nal analysis of this approach for defi ning 

phenotypes was performed by using the 

NHANES III data set. This nationally 

representative data set includes questions 

relevant to comorbid conditions and so 

can serve as a potential replication of some 

of the key fi ndings associated with these 

behaviors found in the ATBC and TUS-CPS 

analyses. As an additional “proof of concept” 

in this analysis, the impact of changing 

comparisons was explored within a more 

traditional psychological/epidemiological 

study in which the selected sample was 

not based on gender or race/ethnicity, 

but rather, on groups selected from the 

population-based, nationally representative 

sample as a whole.

NHANES III Analyses

Data Source

This sample was selected from NHANES III, 

conducted from 1988 to 1994 on a crosssectional representative sample of the 

U.S. civilian noninstitutionalized population 

aged two months and older living in 

households. Only individuals over the age 

of 18 years were included in the present 

analysis. Detailed descriptions of the 
501

Monograph 20. Phenotypes and Endophenotypes

sample design and operation of the survey 

have been published elsewhere.55 For this 

study, smoking information was obtained 

from the Household Adult Questionnaire 

administered in participants’ homes.

Phenotype Comparisons

Three phenotypes were defi ned for the 

purposes of these analyses on the basis of 

increasingly restrictive inclusion criteria 

to refl ect progression from regular use 

to cessation and relapse: all current daily 

smokers (daily, n = 4,990), current daily 

smokers who reported having made a quit 

attempt that lasted for at least one year 

(relapsers, n = 1,157), and former smokers 

who had been abstinent for longer than 

one year (sustained quitters, n = 4,463). 

The defi nitions were nonmutually 

exclusive, and daily smokers included all 

individuals who also qualifi ed as relapsers. 

Of importance, the NHANES III only asked 

about quit attempts that lasted at least one 

year. Therefore, former smokers had to have 

sustained cessation for one year or longer to 

be included in the analyses, and the sample 

of relapsers included only individuals who 

had sustained cessation for at least one year 

before returning to daily smoking. For this 

analysis, daily smokers versus sustained 

quitters served as the comparisons of less 

restrictive phenotypes, while relapsers 

versus sustained quitters represented the 

more tightly defi ned phenotype.

Independent Variables

Similar to the previous analyses, available 

variables were selected associated with 

nicotine dependence and comorbid 

conditions believed to affect sustained 

cessation. Smoking variables included in the 

analysis were age of smoking onset, years 

smoked, and cigarettes per day. NHANES III 

also provides information about a wide 

range of health and behavioral symptoms. 

For this analysis, current mean drinks per 

day, history of heavy drinking (5+ drinks 

per day), symptoms of insomnia, and 

lifetime diagnosis of major depression were 

included. Diagnosis of major depression 

was obtained from the Diagnostic Interview 

Schedule (DIS) administered as part of the 

NHANES III examination. Methodological 

constraints of the NHANES were such 

that only adults under the age of 40 years 

received the DIS, so reported results for 

lifetime history of major depression are 

limited to only the age range of 18–39 years.

Statistics

The analysis was conducted using SAS 8.242

and SUDAAN 8.0.1.54 SUDAAN was used to 

account for the complex sample design and 

the weights of the respondents. Standard 

contingency table analysis and univariate 

techniques were used in this analysis 

based on SUDAAN procedures. Preliminary 

analyses revealed large differences in current 

age among the three groups (data not 

shown). To address this, age adjustment 

was performed for all comparisons.

NHANES III Results

The results for the comparisons from 

NHANES III are presented in table 10.4. 

The general pattern was similar between 

daily smokers and relapsers when compared 

to sustained quitters with almost all 

comparisons being statistically signifi cant. 

In considering the pattern of responses, 

some differences were noted. Relapsers 

reported higher cigarette consumption and 

were more likely to have histories of major 

depression and sleep disturbance. Relapsers 

had a slightly earlier age of regular smoking 

onset. Daily smokers reported higher 

mean daily alcohol consumptions and were 

slightly more likely to report a history of 

drinking fi ve or more drinks per day.

NHANES III Discussion

Although the results of this analysis were 

not as clear, the potential usefulness of the 
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1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

more restrictive phenotype comparison 

appears generally supported. In general, 

similar patterns of statistical signifi cance 

were seen among the two comparisons. 

However, the levels of signifi cance 

are affected by the sizable sample size 

variations, and so it is diffi cult to interpret 

the meaningfulness of the statistical 

signifi cance. Consideration of variation in 

patterns of responses suggests that there 

were differences between the two phenotypic 

approaches.

Although fewer markers than desirable 

of nicotine dependence were available 

in this data set, NHANES does provide 

indications of potential alcohol abuse/

dependence and psychiatric comorbidity. 

Relapsers began smoking at an earlier age 

and smoked more cigarettes per day. They 

were more likely to be diagnosed with a 

history of major depression on the basis 

of report of symptoms and to report sleep 

disturbance. However, symptoms associated 

with alcohol abuse were more likely to be 

reported among the all daily smoker group. 

Thus, there appears to be an increase in 

markers of dependence and comorbidity as 

the comparison moves along the smoking 

trajectory and becomes increasingly 

restrictive, moving from all daily smokers 

to smokers who relapsed despite having 

maintained abstinence for a year or longer. 

This suggests that (1) the role of nicotine 

and alcohol dependence potentially increases 

as one moves along the smoking trajectory 

and (2) analyses based on phenotypic 

defi nitions that do not consider progression 

along the smoking continuum are more 

likely to experience misclassifi cation and 

thereby less likely to detect effects of 

potentially important genetic infl uences.

The distinctions between the phenotypes 

in the NHANES analysis may have been 

affected by a number of methodological 

issues. First, the analyses on this data set 

were not limited to only white men. It was 

not possible to explore any impact of this 

difference because of sample size limitations 

in the data set. A larger issue that likely 

had a signifi cant impact is the restricted 

nature of the available cessation question. 

In NHANES III, the cessation question only 

asks about quit attempts that lasted at least 

one year, so no data are available on the 

much more typical quit attempt followed by 

relapse within a few weeks.56 This suggests 

that a large number of individuals included 

in the daily smoker phenotype would have 

qualifi ed as relapsers had more refi ned data 

been available. In addition, as individuals 

Table 10.4 Results from the Analysis of NHANES III Data

Current smokers Relapsersa Sustained quittersb

Age onset (mean) 17.0*** 16.9* 17.2

Years smoked (mean) 21.0*** 20.0*** 20.7

CPD (mean) 24.1*** 27.7** 21.8

ETOH drinks per day (mean)c 4.4*** 3.7** 2.7

HX 5+ ETOH drinks per day (% yes) 22.0*** 20.6** 16.2

HX depression (% yes)d 11.6* 14.0* 7.6

HX insomnia (% yes)d 30.1 34.4* 25.2

Note. Data from all analyses are adjusted for age of participant. CPD = cigarettes smoked per day; ETOH = alcohol; HX = history. 

Comparison group versus sustained quitters: *

p ≤ 0.05; **p ≤ 0.005; ***p ≤ 0.0005.

a

Relapsers were defi ned as current daily smokers who reported having a history of being abstinent for at least one year. 

b

Sustained quitters were former smokers who had been abstinent for at least one year.

c

Only asked of individuals who reported alcohol use.

d

Age-restricted question.
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Monograph 20. Phenotypes and Endophenotypes

who have maintained cessation for a year 

or more may be more similar to sustained 

quitters than individuals who relapse more 

quickly, differences between individuals who 

relapse versus those who sustain cessation 

may have been masked in the analysis of 

this data set.

Strengths of this data set include that 

NHANES is a large-scale, nationally 

representative sample, and the smokers 

included in the sample are more diverse 

than those seen in the ATBC sample of older, 

heavy smoking, Finnish men. NHANES also 

collects data on a number of potentially 

important comorbid conditions and provides 

a clinical diagnosis of major depression, 

rather then mere endorsement of symptoms, 

giving NHANES some advantages over both 

the TUS-CPS and the ATBC. 

As noted above, a signifi cant limitation of 

the NHANES data set is that cessation is 

only assessed if it lasts a year or longer. 

In addition, individuals may be in the 

process of sustaining cessation but have 

not reached the one-year mark, and so 

are dropped from the analysis. Next, the 

constraints of the NHANES methodology 

are such that not all individuals are asked 

all questions; in particular, this affects the 

depression data included in this sample. This 

may represent a signifi cant issue in that the 

clinical structured interview for diagnosing 

major depression was not administered to 

individuals older than 39 years of age. Given 

that many individuals do not successfully 

quit smoking until they are in their 40s, 

the relation between depression and the 

behavior of interest (sustaining cessation) 

may be misrepresented. As with the 

ATBC, NHANES is not a smoking-specifi c 

survey, and the smoking questions are 

limited in scope. Lastly, the information 

provided in NHANES is self-reported and 

cross-sectional. However, despite these 

limitations, a number of fi ndings consistent 

with those seen from the ATBC and TUSCPS data sets emerged.

Summary

This chapter has presented an approach 

for refi ning phenotype defi nitions in the 

hopes of reducing “noise” associated with 

misclassifi cation that subsequently reduces 

researchers’ ability to detect small but 

important genetic infl uences on smoking 

behavior. The strategy employed by this 

approach is based on two features. First, 

an observable behavior of interest that 

identifi es a specifi c point along the smoking 

continuum must be chosen. Then, the 

comparison group must be defi ned such 

that it also has exposure to the precursor 

of the behavior of interest, excluding all 

individuals who have not progressed to 

that point on the smoking continuum. 

In the case of the example presented here, 

sustained smoking cessation was the 

behavior of interest, and the comparison 

group comprised smokers who had made 

a quit attempt but subsequently relapsed. 

In multiple data sets, the results of analyses 

of the improved phenotype were contrasted 

to classic, more broadly defi ned phenotypes 

and to phenotypes that lacked the key 

behavioral exposure.

This series of analyses has only considered 

a few of the possible comparisons that 

could have been used to validate the proof 

of concept for this approach. The goal of 

the current analyses was to demonstrate 

that even a small difference in defi nition 

could affect the subsequent results and 

the likelihood that associations between 

behavior and genes would be detected. 

As such, it was not the intent to fully explore 

this issue in each data set, although a 

number of interesting issues regarding the 

association of various markers of dependence 

and comorbidity were suggested.

Although it is argued that the phenotypes 

represent an improvement over those 

typically used in a number of studies, it may 

be that even more refi ned phenotypes are 
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1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

needed to truly advance the fi eld of genetics 

of smoking behavior. For example, some 

work has indicated that consideration of 

average CPD is insuffi cient to differentiate 

smokers.13,57–59 The results of a study by 

Saccone and colleagues13 on the genetic 

linkage of heavy smoking and chromosome 

22q12 suggest that even more precise 

phenotypes may be needed. They found 

that use of maximum cigarettes smoked 

in 24 hours was a more useful indicator of 

nicotine dependence and a possible important 

marker of genetic variation among smokers.

In considering the data from across the 

three data sets, there were consistent 

limitations that may infl uence the fi ndings. 

First, typical of epidemiological surveys, 

all data used were self-report. Although 

self-report of smoking status and quit 

history is consistently used in the literature 

and considered to be largely reliable in the 

context of most surveys, the fi ndings would 

be strengthened if confi rmation of smoking 

status was available.

Next, only limited information was available 

regarding the details of current and previous 

smoking behavior. This is a challenge 

across the available data sets that address 

smoking. Specifi c to this project on sustained 

cessation and relapse, no information was 

available on methods used to quit smoking. 

The techniques used to quit smoking could 

clearly have an impact on cessation success. 

If methods used varied signifi cantly between 

sustained quitters and relapsers, the potential 

infl uence of the factors on which these 

analyses focused—that is, markers of 

nicotine dependence and comorbidity—

would be misstated in the fi ndings. This 

would affect efforts to identify the role of 

specifi c underlying genetics polymorphisms. 

However, the impact of this lack of available 

data is likely mitigated by the observation 

that two of the data sets (ATBC, NHANES) 

were collected during time frames when 

pharmacological cessation interventions 

were not widely in use.

Lastly, although the ATBC data set was 

available for longitudinal analysis, the ATBC 

is not primarily a project of tobacco use and 

has a restricted range of smoking behaviors 

and ages of participants. Longitudinal 

data from a study that focuses on smoking 

behavior would be extremely valuable, 

providing detailed information on smoking 

as it varies across time.

Despite the limitations of each data set and 

the varying methodology across the data 

sets, it was consistently found that changing 

the comparison groups (e.g., phenotypic 

defi nition) affected the nature of the results 

when considering potential markers of 

nicotine dependence and factors known 

to affect cessation. This indicates both 

that use of classic phenotypes can lead 

to misclassifi cation errors and that the 

approach proposed based on trajectory and 

exposure has merit. Ultimately, the approach 

presented in this chapter needs to be 

tested within an analysis that includes 

genotyping to fully assess its value. Studies 

using more tightly defi ned phenotypes are 

demonstrating more convergent fi ndings 

in support of the role of specifi c genes and 

smoking behavior.11–14 A more complete 

test of the approach presented here would 

examine if varying the phenotypes affected 

the genetic fi ndings. However, the fi ndings 

presented here support this approach and 

lend credence to its adoption.

Conclusions

1. More tightly defi ned phenotypes of 

smoking behavior that are based on 

transitions along the smoking trajectory 

and adequate prior exposure have the 

potential to reduce the classifi cation 

bias and lack of specifi city inherent in 

broader existing phenotypes such as 

current smoking status. These improved 

phenotypes, in turn, may lead to closer 

correlations between smoking behavior 

and genetic variables in future studies.
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Monograph 20. Phenotypes and Endophenotypes

2. Studies involving both longitudinal and 

cross-sectional population data show 

measurable differences among improved 

phenotypes, including sustained 

quitters, relapsers, and never quitters, 

in key markers such as smoking history, 

other indices of nicotine dependence, 

and comorbid conditions such as 

psychological symptoms and alcohol use.

3. Refi ned nicotine-dependence phenotypes 

based on longitudinal characterizations 

of smoking patterns show promise 

for further testing in genetic studies 

in support of potential phenotypegene causal associations for nicotine 

dependence. Research indicates the 

potential need for further refi nement of 

such phenotypes.
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1 0 . E p i d e m i o l o g i c a l A n a l y s i s o f V a r i a t i o n i n P h e n o t y p i c D e f i n i t i o n s

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509

Tobacco use takes place within a social context that has been shown to interact with 

genetic factors to infl uence the defi nition and measurements of phenotypes and 

endophenotypes for nicotine dependence. This chapter examines available research 

and future trends related to social context factors that could inform subsequent genetic 

studies of smoking, including

■ Macrocontextual factors ranging from distal measures such as culture and 

socioregional factors to more proximal measures such as detailed data on 

socioeconomic status

■ Microcontextual factors such as smoking in specifi c interpersonal relationships, 

including fi ndings from the Nonshared Environment in Adolescent Development 

Project studying twins and siblings

■ Integrated proximal indicators of both macro- and microcontext such as 

ecological momentary assessment

Available evidence indicates that social context can have a clear impact on the 

heritability of smoking and many of its component traits. There is a growing case for 

such gene-environment interplay to become part of a broader matrix of etiological 

architectures employed in future genetic research on nicotine dependence.

11

Incorporating Social Context 

into Genetic Studies of 

Nicotine Dependence

Richard Rende, David V. Conti, Stephen E. Gilman, and Cheryl Slomkowski

The analyses described herein were supported in part by National Institutes of Health grants DA16795, CA084719, 

CA084735-06, and MH65563.
510

1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

Introduction

This chapter focuses on why the 

incorporation of social contextual infl uences 

could represent a core strategy for genetic 

studies of nicotine dependence—a complex 

phenotype that arises within socially defi ned 

(and in some cases socially controlled) 

contexts1—and how newer methodologies 

can be used to gain better traction on 

social contextual infl uences. The emphasis 

is on “social contextual” rather than 

“environmental” infl uences because the 

environment in the behavioral genetic 

paradigm includes any factor that is not, 

strictly speaking, heritable (and thus may 

include an extraordinary range of potential 

etiological contributors including biological 

infl uences). The working approach to social 

context taken in this chapter is simply to 

consider a small range of factors, typically 

thought of as part of the social environment, 

that represent putative infl uences on 

the developmental pathways to nicotine 

dependence and that have been, or could 

easily be, considered in either behavioral 

or molecular genetic research. The focus 

is on specifi c examples of social contextual 

factors that have received some attention in 

genetic designs to illustrate the conceptual 

grounding that guides such work, as well as 

provide examples of specifi c methodologies 

that are used to intensively measure these 

constructs. The broader point to be taken 

from these examples, however, is that social 

context matters, and that the full range of 

potent social infl uences should be taken 

seriously in genetic research on nicotine 

dependence by using the appropriate 

methodologies to bring these factors into 

genetically driven research.

As discussed in chapter 3, multiple levels 

of phenotypes contribute to and compose 

the construct of nicotine dependence. 

Similarly, a multitude of factors make up 

the social context, from the macro level 

(e.g., sociopolitical) down to the micro 

level (including psychosocial infl uences 

such as interpersonal relationships). This 

chapter focuses on selected examples of such 

macro and micro social factors of particular 

relevance to smoking phenotypes; the goal 

is to illustrate both concepts and methods 

rather than provide an exhaustive review.

Some behavioral genetic studies of 

tobacco use have used the phrase genetic 

architecture when describing the pattern of 

heritable infl uences that may be observed 

via genetically informative designs such 

as the twin paradigm.2

 This construct 

appeals because the expression of genetic 

systems is assumed to contribute to 

the structural foundations of complex 

phenotypes, such as the range of behaviors 

that involve use of tobacco. The descriptive 

statistic heritability typically is offered as 

a proxy for the overall strength of genetic 

contribution to a phenotype, and in this 

approach, is used to give some guidance to 

the phenotypes that most strongly refl ect 

underlying genetic effects.3 Thus, “genetic 

architecture” has been used to describe the 

underlying heritability of one (univariate) or 

multiple (multivariate) indices of smoking, 

such as age at initiation, amount smoked, 

and smoking cessation attempts.2

This chapter broadens the concept by 

focusing on “etiological architecture” 

to serve as a reminder of a number 

of principles that have long been 

acknowledged in behavioral genetics.3

The typical components of behavioral 

genetic models (heritability, common or 

shared environment, nonshared or unique 

or individual-specifi c environment) are 

estimates of the mix of etiological infl uences 

on phenotypic moving targets that refl ect 

a host of factors, including the population 

studied (embedding both geographical and 

temporal characteristics), the defi nition 

as well as measurement of the phenotype, 

and the extent to which environmental 

infl uences have been measured and 

modeled. In this sense, “etiological 
511

Monograph 20. Phenotypes and Endophenotypes

architecture” refers to the dynamic mix 

of genetic and nongenetic infl uences on 

phenotypes captured in particular periods 

and within specifi c social contexts. This 

point emphasizes that the understanding of 

genetic foundations of behavior undoubtedly 

can (and will) change as methods for 

measuring phenotypes, genotypes, and

nongenetic (or “environmental”) infl uences 

are refi ned. The “architecture” of smoking 

behaviors is not a fi rm foundation but rather 

a pliable blueprint of how to evaluate the 

role of genetic and nongenetic infl uences 

on particularly defi ned phenotypes—defi ned 

not just in terms of psychometrics but also 

as expressions of measurable behavior, 

in particular, historical, geographical, 

and social contexts. As a result, a primary 

goal of both quantitative and newer 

molecular methodologies is not only to 

get the phenotypic targets as well defi ned 

and measured as possible to move closer 

to “true” indicators and sequelae of gene 

expression but also to understand how 

gene expression operates in conjunction 

with, and in response to, a range of 

nongenetic infl uences.

The focus on social contextual infl uences 

in genetic studies certainly reverberates 

and builds upon the interest in geneenvironment interplay in behavioral 

science,4–6 and specifi cally as applied to 

drug use,1 which is the focus of chapter 3. 

The idea of bridges between genetic effects 

on phenotypes and environmental infl uences 

has been a theme in behavioral genetics 

for decades. This theme has taken on new 

momentum with the application of a number 

of novel methodologies and strategies, 

including an emphasis on both “measured 

genes” (molecular genetic markers such 

as candidate gene polymorphisms) and 

“measured environments” (inclusion 

of environmental variables in genetic 

analyses) as well as expansions of behavioral 

genetic paradigms, as shown in studies 

by Moffi tt and colleagues.4–6 These papers 

make very explicit the utility of directly 

incorporating environmental measures 

into genetic studies, the theoretical models 

that capture a variety of means by which 

genes and environment come together in 

producing clinically meaningful phenotypes, 

and the design strategies for achieving 

appropriate opportunities to examine the 

joint effects and interplay between genes 

and environment. Given this, the advances 

(as evidenced in the series of papers cited 

above, along with chapters 3 and 4) were 

chosen as a platform to consider how social 

contextual infl uences have been, and may 

be, incorporated into genetic studies of 

nicotine dependence. In particular, this 

chapter focuses on areas that have some 

empirical basis in terms of incorporating 

social context into genetically informative 

designs of tobacco use; it then considers 

newer methodologies that may provide even 

greater traction in future studies.

Why Incorporate 

the Social Context?

Most behavioral genetic studies have 

generated parameter estimates of genetic 

and environmental infl uences by using 

the fundamental quantitative genetic 

model, which does not incorporate actual 

(or “measured”) aspects of the environment.3

Why is it important to consider this? 

A fi rst key issue is that the “unmeasured” 

genetic and nongenetic effects generated 

in the traditional model are assumed to 

be additive in nature and are calculated 

as such. There is, thus, typically limited 

(or no) opportunity to detect statistical 

evidence for gene-environment interplay 

without utilizing alternative genetic 

designs (such as the Children-of-Twins 

design, or COT) in that the two primary 

forms of gene-environment interplay—

gene-environment interaction and geneenvironment correlation—are embedded 

within the additive genetic component 

and contribute to the overall estimation of 

heritability. Second, without the inclusion 
512

1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

The Classic Quantitative Genetic Model and Smoking Behaviors

In framing the potential utility of incorporating the social context in genetic studies of smoking, 

it is necessary to briefl y consider the core work in behavioral genetic studies. As discussed 

in chapter 6, a number of genetically informative studies have examined a range of smoking 

phenotypes. Indeed, since a landmark paper by Carmelli and colleaguesa

 provided evidence for the 

heritability of smoking by using the classic twin method, a number of twin studies have focused 

on varying levels of smoking intensity, including smoking initiation (e.g., ever puff versus never 

puff) and smoking frequency during adolescenceb,c,d,e,f,g,h,i,j,k,l as well as smoking persistence/regular 

smoking and nicotine dependence.m,n,o,p,q,r

As discussed in reviews by Sullivan and Kendlers

 and Li and colleagues,t the relative mix of 

genetic and environmental factors appears to be different for different levels of smoking intensity. 

Li and colleaguest

 have determined, using meta-analysis, that smoking initiation is infl uenced 

signifi cantly both by genetic factors (with heritability estimates of 0.37 ± 0.04 for males and 

0.55 ± 0.04 for females) and by shared environmental (nongenetic infl uences that operate to 

produce similarity in family members) factors (0.49 ± 0.04 for males and 0.24 ± 0.06 for females). 

Li and colleaguest

 also provide evidence of substantial heritability of smoking persistence 

(0.59 ± 0.02 for males and 0.46 ± 0.12 for females), with shared environmental infl uences being 

more prominent for females (0.28 ± 0.08) than for males (0.08 ± 0.04). Sullivan and Kendlers

reached somewhat similar conclusions, suggesting substantial heritability of smoking initiation 

(approximately 0.60), along with signifi cant shared environmental infl uences (approximately 

0.20), with genetic factors being primarily responsible (heritability of approximately 0.70) for 

the transition to nicotine dependence and with less impact observed from shared environmental 

infl uences. Both continuities and discontinuities in the genetic effects on smoking initiation 

and progression to higher levels of smoking intensity are being evaluated with quantitative 

approaches such as those discussed in chapter 6.

a

Carmelli, D., and G. E. Swan. 1995. Genetic and environmental infl uences on tobacco and alcohol 

consumption in World War II male veteran twins. In Alcohol and Tobacco: From Basic Science to Clinical 

Practice (NIAAA Research Monograph No. 30), ed. J. B. Fertig and J. P. Allen, 89–106. Bethesda, MD: 

U.S. Department of Health and Human Services.

b

Boomsma, D. I., J. R. Koopmans, L. J. Van Doornen, and J. F. Orlebeke. 1994. Genetic and social infl uences on 

starting to smoke: A study of Dutch adolescent twins and their parents. Addiction 89 (2): 219–26.

c

Han, C., M. K. McGue, and W. G. Iacono. 1999. Lifetime tobacco, alcohol and other substance use in 

adolescent Minnesota twins: Univariate and multivariate behavioral genetic analyses. Addiction 94 (7): 981–93. 

d

Koopmans, J., A. Heath, M. Neale, and D. Boomsma. 1997. The genetics of initiation and quantity of alcohol 

and tobacco use. In The genetics of health-related behavior, ed. J. R. Koopmans, 90–108. Amsterdam: Print 

Partners Ipskamp.

e

Koopmans, J. R., W. S. Slutske, A. C. Heath, M. C. Neale, and D. I. Boomsma. 1999. The genetics of smoking 

initiation and quantity smoked in Dutch adolescent and young adult twins. Behavioral Genetics 29 (6): 383–93.

f

Maes, H. H., M. C. Neale, N. G. Martin, A. C. Heath, and L. J. Eaves. 1999. Religious attendance and frequency 

of alcohol use: Same genes or same environments: A bivariate extended twin kinship model. Twin Research

2 (2): 169–79.

g

McGue, M., I. Elkins, and W. G. Iacono. 2000. Genetic and environmental infl uences on adolescent substance 

use and abuse. American Journal of Medical Genetics 96 (5): 671–77.

h

Rende, R., C. Slomkowski, J McCaffery, E. Lloyd-Richardson, and R. Niaura. 2005. A twin-sibling study of 

tobacco use in adolescence: Etiology of individual differences and extreme scores. Nicotine and Tobacco 

Research 7 (3): 413–19.

i

Rhee, S. H., J. K. Hewitt, S. E. Young, R. P. Corley, T. J. Crowley, and M. C. Stallings. 2003. Genetic and 

environmental infl uences on substance initiation, use, and problem use in adolescents. Archives of General 

Psychiatry 60 (12): 1256–64.
513

Monograph 20. Phenotypes and Endophenotypes

of specifi c nongenetic/environmental 

variables, no information is gleaned on how 

the effect sizes of the descriptive statistics 

generated in the quantitative genetic model 

(heritability, shared environment, nonshared 

environment) may change under varying 

environmental conditions.

These considerations are important for 

any clinical phenotype but take on added 

importance for substance-use behaviors, 

including smoking, which are defi ned in 

part by availability of and exposure to the 

substances in the environment. Merikangas 

and Avenevoli7 have described how the 

traditional genetic epidemiology triangle, 

which focuses on host susceptibility, 

environmental factors, and exposure to a 

disease-causing agent, is particularly well 

suited to the study of substance use. In this 

model, exposure to the source of nicotine 

(e.g., a cigarette) would be the primary agent 

that is a necessary condition for development 

of nicotine dependence, and both exposure 

to the agent and reaction to the agent would 

refl ect joint infl uences of host factors as well 

as environmental factors. In this regard, 

environmental factors including cultural 

forces (such as norms against women 

smoking), protobacco promotional activities, 

antitobacco activities (e.g., smoke-free 

environments), and proximal interpersonal 

infl uences (e.g., infl uences of parents, 

siblings, and friends) need to be integrated 

into the genetic epidemiology triangle to 

understand how they directly shape the 

expression of host/genetic susceptibility to 

nicotine dependence. These joint effects 

thus imply complex layers of potentially 

connected factors, as described by Eaves8

and quoted by Lessov and colleagues:

To the degree that drug-use behavior is 

heritable, inherited liability toward drug 

j

Slomkowski, C., R. Rende, S. Novak, E. Lloyd-Richardson, and R. Niaura. 2005. Sibling effects on smoking in 

adolescence: Evidence for social infl uence from a genetically informative design. Addiction 100 (4): 430–38. 

k

Stallings, M. C., J. K. Hewitt, T. Beresford, A. C. Heath, and L. J. Eaves. 1999. A twin study of drinking and 

smoking onset and latencies from fi rst use to regular use. Behavior Genetics 29 (6): 409-421.

l

White, V. M., J. L. Hopper, A. J. Wearing, and D. J. Hill. 2003. The role of genes in tobacco smoking during 

adolescence and young adulthood: A multivariate behaviour genetic investigation. Addiction 98 (8): 1087–1100.

mHeath, A. C., N. G. Martin, M. T. Lynskey, A. A. Todorov, and P. A. Madden. 2002. Estimating two-stage models 

for genetic infl uences on alcohol, tobacco or drug use initiation and dependence vulnerability in twin and 

family data. Twin Research 5 (2): 113–24.

n

Kendler, K. S., M. C. Neale, P. Sullivan, L. A. Corey, C. O. Gardner, and C. A. Prescott. 1999. A population-based 

twin study in women of smoking initiation and nicotine dependence. Psychological Medicine 29 (2): 299–308.

o

Madden, P. A., A. C. Heath, N. L. Pedersen, J. Kaprio, M. J. Koskenvuo, and N. G. Martin. 1999. The genetics of 

smoking persistence in men and women: A multicultural study. Behavior Genetics 29 (6): 423–31. 

p

Madden, P. A., N. L. Pedersen, J. Kaprio, M. J. Koskenvuo, and N. G. Martin. 2004. The epidemiology and 

genetics of smoking initiation and persistence: Crosscultural comparisons of twin study results. Twin 

Research 7 (1): 82–97.

q

Maes, H. H., P. F. Sullivan, C. M. Bulik, M. C. Neale, C. A. Prescott, L. J. Eaves, and K. S. Kendler. 2004. 

A twin study of genetic and environmental infl uences on tobacco initiation, regular tobacco use and nicotine 

dependence. Psychological Medicine 34 (7): 1251–61.

r

Vink, J. M., G. Willemsen, and D. I. Boomsma. 2005. Heritability of smoking initiation and nicotine dependence. 

Behavior Genetics 35 (4): 397–406.

s

Sullivan, P. F., and K. S. Kendler. 1999. The genetic epidemiology of smoking. Nicotine & Tobacco Research

1 Suppl. 2: S51–S57, S69–S70.

t

Li, M. D., R. Cheng, J. Z. Ma, and G. E. Swan. 2003. A meta-analysis of estimated genetic and environmental 

effects on smoking behavior in male and female adult twins. Addiction 98 (1): 23–31.
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use or misuse increases the risk for druguse behavior but it does not lead to or 

cause drug use. The expression of genetic 

liability (i.e., substance use and misuse) 

depends on environmental conditions. 

For example, exposure of the organism 

to a drug is necessary. Exposure, in turn 

depends on other environmental factors 

such as drug access and availability, 

which is related to neighborhood, 

home, and peer group environment, 

to name a few. People mistakenly think 

that “Everything is genetic,” ignoring 

that while an individual does not have 

control over their [sic] genetic makeup, 

an individual is in constant dynamic 

interaction with their [sic] environment; 

and it is that interaction that contains 

powerful information about the probability 

of drug use and misuse.1(p1519)

These perspectives from genetic 

epidemiology overtly posit that interaction 

between (potentially nested) levels of host, 

environment, and agent variables underlie 

substance use and, in particular, progression 

to problematic levels of use (such as nicotine 

dependence). Within this framework, both 

measurement of environmental factors and 

inclusion of these factors in the analytic 

models would be necessary for a complete 

understanding of the genetic effects on 

nicotine dependence. The traditional 

quantitative genetic model, with emphasis 

on additive genetic and environmental 

effects, along with no attention to measured 

sources of environmental infl uence, does 

not provide an optimal opportunity to 

delve into the purported interplay between 

agent, host, and environment. Rutter and 

colleagues6

 have provided a comprehensive 

review of the multiple models of geneenvironment interplay, and these models 

provide a platform for considering 

alternatives to the additive genetic model. 

Of particular relevance are major classes 

of gene-environment interplay, reviewed 

by Rutter and colleagues6 (see also 

Shanahan and Hoffer9), which include 

(1) variations in genetic infl uence according 

to environmental circumstances; (2) geneenvironment correlations; and (3) geneenvironment interactions. 

Behavioral Genetic 

Studies of Smoking 

That Incorporate 

Social Context

Although the behavioral genetic literature 

has primarily relied upon the application of 

biometrical models to data on cigarette use, 

examples from newer studies incorporate 

social contextual measures. These studies 

provide empirical foundations for the 

speculations offered above, and this section 

reviews pertinent studies. For heuristic 

purposes, both “macrocontextual” 

indicators of social context—constructs of 

infl uence that range from broad cultural 

expectations to more localized geographic 

effects—as well as “microcontextual” 

infl uences10 that reside closer to individuallevel factors, such as interpersonal 

relationships, will be used. In particular, 

specifi c social contextual variables that have 

been studied by using the behavioral genetic 

paradigm will be referenced. All the studies to 

be reviewed rely on modeling types of geneenvironment interaction by using extensions 

of the fundamental biometrical model of 

quantitative genetics via the inclusion of a 

specifi ed, measured environmental factor 

that can be tested as a moderator of the 

latent genetic effect (as well as the latent, 

shared environmental effect). The rationale 

for this approach, along with some of the 

methods that may be used, is discussed in 

Turkheimer and colleagues11 and Purcell and 

Koenen;12 see also Kendler and colleagues13

and Timberlake and colleagues14 for inclusion 

of moderators in biometrical models of 

smoking. The key propositions tested, 

using the nomenclature from Rutter and 

colleagues,6 are the following: 
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Monograph 20. Phenotypes and Endophenotypes

■ Are there environmental factors that 

reduce the impact of genetic infl uences?

■ Are there environmental contexts that, 

in contrast, especially accentuate genetic 

infl uences?

Macrocontextual Factors 

as Moderators of the Etiological 

Architecture of Smoking

It is well documented, and beyond the 

scope of this chapter, that population 

levels of cigarette use have been heavily 

infl uenced by many dynamic social factors 

that have changed over the decades, 

including tobacco control and prevention 

initiatives.15 The focus here is on the extent 

to which behavioral genetic studies have 

incorporated such macrocontextual factors 

into the biometrical modeling approach. 

Two points should be noted. First, these 

studies provide examples of the utility of 

incorporating macrocontextual factors into 

genetically informative studies of tobacco 

use and nicotine dependence. Second, these 

studies also highlight how limited this 

research has been, given the wide range 

of macrocontextual factors that could be 

incorporated into quantitative genetic 

paradigms. Thus, the studies reviewed below 

demonstrate how the expression of genetic 

liability to smoking may be shaped by the 

larger social culture (e.g., how the effects 

of genes change over time in concert with 

social changes or vary within populations 

that vary on macrosocial indices) and 

highlight approaches that can be used 

in future studies to integrate potent 

macrosocial infl uences.

Effects of Culture and Cohorts

One concrete and dramatic example comes 

from investigations into the etiological 

architecture of cigarette smoking in 

China. Lessov-Schlaggar and colleagues16

reported that within a sample of 1,010 adult 

Chinese twins, 58% of the male twins were 

smokers, but over 99% of female twins 

were nonsmokers. Whereas the etiological 

architecture of smoking in male twins was 

similar to that reported in studies from 

other cultures, there were no individual 

differences in female smoking to model. 

An examination of changes in heritability 

based on cohort effects from the populationbased Swedish Twin Registry expands 

this theme.17 Rates of regular tobacco 

use in women born before 1925 were low 

and found to be environmental in origin; 

in contrast, as smoking rates increased 

in women born after 1925, heritability 

estimates increased. This fi nding serves 

as a reminder that the macrocontext 

can have overwhelming impact on the 

choice or ability to use cigarettes that 

can fundamentally nullify or promote 

net genetic effects, and it reinforces the 

suggestion that the etiological architecture 

of smoking must be defi ned by reference to 

the social context in which it is observed.

Less dramatic, but nonetheless important, 

examples from behavioral genetic studies 

of smoking have attempted to account for 

differences across either cultures or birth 

cohorts. In 1993, Heath and colleagues18

demonstrated that the decline in smoking 

in more recent birth cohorts did not affect 

the estimates of genetic and environmental 

infl uence on smoking initiation. They 

did fi nd, however, differences in the 

heritability and shared environmental 

estimates in Australian versus U.S. samples. 

A subsequent study suggested that cultural 

background may infl uence the magnitude 

of shared environmental effects on lifetime 

smoking but that estimates of both genetic 

and environmental effects on smoking 

persistence were unaffected by culture.19

Socioregional Infl uences

The few twin studies mentioned above 

have provided tests of a particular type of 

gene-environment interaction by using 

distal environmental measures4,5 and latent 
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1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

genetic factors inferred by the comparison of 

monozygotic and dizygotic twins. Although 

some of the results refl ect profound 

social contextual effects (e.g., cultural 

discouragement of smoking in females in 

China), the studies examining cross-cultural 

differences introduce the possibility of 

examining more subtle differences that 

may exist within cultures that would not 

be detected without overt measurement of 

possible sources of environmental infl uence. 

Surprisingly, no behavioral genetic 

studies of smoking have used this strategy. 

The potential utility of this approach is 

illustrated by research on adolescent alcohol 

use in Finland. Rose and colleagues20 made 

the important observation that regional 

residency (urban versus rural) signifi cantly 

moderated genetic effects on drinking 

patterns, including longitudinal change 

in drinking observed over a 30-month 

period. Specifi cally, genetic effects were 

larger within the sample of adolescents 

who resided in urban areas, and shared 

environmental factors were larger for the 

subsample living in rural areas. 

Two important points are raised by the 

work of Rose and colleagues.20 First, 

it demonstrates that socioregional variations 

can be detected and modeled within the 

behavioral genetic paradigm and that such 

social contextual factors can have a large 

infl uence on the etiological architecture 

of substance use. Second, these authors 

took the important step of incorporating 

more specifi c measures of socioregional 

infl uences into their analyses—namely, 

the relative proportion of young adults in 

a regional area, the frequency of migration 

in and out of a region, and the relative 

amount of money spent on alcohol in an 

area.21 When these more specifi c social 

contextual measures were introduced into 

the biometrical models, clear evidence of 

gene-environment interaction was found. 

Both a higher proportion of young adults 

and higher migration levels were associated 

with stronger genetic effects on drinking 

patterns in adolescents, whereas lower 

levels of young adults and migration yielded 

greater shared environmental infl uences. 

As discussed by Dick and colleagues,21

variation in these social structures can 

either promote genetically infl uenced 

individual differences in drinking (via more 

opportunities with peers and less stable social 

structure) or mask genetic differences (as the 

strength of shared environment increases 

with more stability and less opportunity for 

peer infl uence). The more general point of 

this work, as noted by the authors, is that 

they moved from the more distal index of 

residential residence (urban versus rural) 

to potential proximal indicators of social 

context that may reside closer to actual 

mechanisms of infl uence. This important 

theme of translating distal environmental 

measures into more proximal indicators4,5

will be revisited in the following section on 

newer methodologies for examining social 

context—and certainly carries forward 

the theme of attending to multiple levels 

of assessment of both smoking-related 

phenotypes (chapter 3) and environments.

Microcontextual Factors 

as Moderators of the Etiological 

Architecture of Smoking 

As noted above, social contextual factors 

may be conceptualized as operating at 

multiple levels, with the fi nal important 

pathway being a proximal end point 

refl ected at the individual level. As these 

microcontextual features that are more 

individually based are considered, the 

primary focus is on interpersonal infl uences, 

which have received attention in some 

behavioral genetic papers. Interest in 

interpersonal dynamics came about, in part, 

because of concern that contact between 

twins could violate the equal environments 

assumption (EEA) (if the level of contact 

was greater for monozygotic as compared 

to dizygotic twins). That is, as the twin 
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Monograph 20. Phenotypes and Endophenotypes

method attributes greater similarity 

(or concordance) of monozygotic versus 

dizygotic pairs to differences in genetic 

relatedness (and hence heritability), 

uncontrolled nongenetic factors that 

promote differences based on zygosity may 

artifi cially infl ate heritability estimates. 

For example, Kendler and Gardner22 found 

that the heritability of smoking initiation 

was reduced by about 10% after controlling 

for the higher degree of social contact 

in monozygotic pairs as compared to 

dizygotic pairs. Kendler and colleagues23

also reported that their data were consistent 

with modest infl uences of social contact 

between twins (and the violation of the EEA) 

on nicotine dependence. Later evidence 

suggests that the socialization effects that 

differ between monozygotic and dizygotic 

twins may infl uence smoking initiation to 

a much larger degree than does smoking 

persistence.19,24 These effects may also differ 

based on gender. Hamilton and colleagues25

found strong moderation via social contact 

of both shared environment (which 

increased) and heritability (which decreased) 

in female, but not male, twin pairs. 

The implications of these studies go beyond 

the extent to which heritability estimates 

may, or may not, be biased by violations of 

the EEA. Of more substantive interest is 

the extent to which interpersonal dynamics 

may infl uence smoking behavior as a form 

of social infl uence, which may operate both 

as a main effect (i.e., independent of genetic 

relatedness) as well as in combination with 

genetic factors. As discussed earlier, the 

robust shared environmental effects found 

for smoking initiation suggest potential 

socialization effects that could derive in 

part from peers and siblings.26 Vink and 

colleagues27 approached this issue using 

a twin-family design. They examined the 

extent to which current smoking behavior 

was associated with the smoking behavior of 

peers and siblings (along with parents and 

spouses). Using a cross-sectional design, 

they found strong evidence of both peer and 

sibling effects on smoking in adolescence; 

these effects were not seen for smoking in 

adulthood. Rather, in adulthood, the most 

important relational predictor of smoking 

was zygosity of co-twin smoking (such that 

having a monozygotic twin who smoked 

conveyed the most prediction of current 

smoking status). They concluded that social 

effects may be most evident in adolescence, 

but lessen in importance in adulthood, when 

genetic factors become a stronger infl uence 

on the likelihood of smoking. Taking this 

a step further, there is also evidence that 

exposure to smoking by parents and peers 

in adolescence and early adulthood, when 

accounted for in the traditional biometrical 

model, substantially reduces the impact 

of genes on smoking behavior, leading to 

the suggestion that environmental factors 

provide the strongest infl uence on smoking 

during these developmental periods.28

Subsequent expansions of this focus on 

interpersonal infl uences have focused on 

direct sibling effects by utilizing more 

differentiated measures of the sibling 

relationship as well as extension of the twin 

paradigm to include siblings of varying 

genetic relatedness (full, half, and unrelated 

siblings) via the genetically informative 

subsample of the National Longitudinal 

Study of Adolescent Health (Add Health).29,30

A fi rst fi nding29 is that monozygotic twins 

have elevated levels of time spent together 

(social contact) as well as mutual friendships 

as compared with all other sibling types. 

However, the interpretation of this fi nding 

is not straightforward. Levels of social 

contact and mutual friendships did not 

follow a dose-response association with 

zygosity once the effect of monozygotic 

twins was considered. Thus, it may be that 

monozygotic twins, compared with all other 

sibling types, have much more commonality 

in their time spent with each other as well 

as with friends. 

That said, the infl ated monozygotic 

concordance for time together and mutual 
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1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

friendships did not alter the estimates of 

heritability of smoking frequency (measured 

as number of days smoked over the last 

30 days). Rather, time spent together and 

mutual friendships both signifi cantly 

moderated the shared environmental 

component.29 These two variables were 

also analyzed along with amount of sibling 

affection to create a construct of sibling 

connectedness, which also moderates 

the shared environment effect but not 

the estimate of heritability.30 The fi nding 

that twins and siblings form connections 

with mutual friends, and that these social 

groupings represent social rather than 

genetic infl uences on smoking, highlights 

the importance of considering broader 

effects of larger social networks as a potent 

infl uence on smoking patterns.31,32

Summary

Overall, the studies reviewed in this section 

provide a good starting point for considering 

how social contextual factors may be 

integrated into genetically informative 

designs. These studies provide solid 

empirical evidence that the estimation and 

interpretation of the descriptive statistic 

of heritability can vary when referenced 

according to important macro- and 

microcontextual factors and represent a 

good starting point for a more realistic 

genetic epidemiological model of smoking. 

Proximal Measures 

of the Social Context 

It has been suggested that moving from 

distal indicators of the social environment to 

more proximal measures will be important 

for improving the resolution of models of 

gene-environment interplay.4–6 Building on 

this suggestion, the realization of adequate 

tests of these models will depend in part on 

careful and forward-looking assessment of 

candidate social contextual factors (as one 

class of environmental factors in models of 

gene-environment interplay). It is becoming 

recognized that accurate measurement 

of the environment is as critical to the 

success of any foray into gene-environment 

interplay as is quality control of genotyping.6

Despite this recognition, there has been a 

tremendous disparity in the attention and 

resources given to “molecular” assessment 

of the environment in genetic studies 

as compared with the effort devoted to 

dissection of the genome,5 despite the strong 

evidence on the potent effects of a number 

of social contextual factors. These include 

social networks as well as the overarching 

social and cultural environment, which 

includes pro- and antitobacco factors.31,32

Dissemination of the multiple levels and 

corresponding constructs of social context 

that could bear upon smoking and nicotine 

dependence would require a separate 

monograph devoted to that purpose. 

In lieu of that, this section will build on the 

candidate social contextual factors reviewed 

in the prior section by illustrating newer 

methodologies that attempt to capture 

proximal social contextual infl uences that 

could be integrated relatively easily into 

most genetically informative designs. 

In general, behavioral genetic studies are 

very well positioned to incorporate both 

macro- and microcontextual measures, and 

two particular features can be exploited. 

First, nearly all ongoing behavioral genetic 

studies rely on large, population-based 

samples. As such, they would provide ideal 

vehicles for introducing specifi c indicators of 

macrocontextual features that may affect the 

role that genes play in pathways to nicotine 

dependence. Second, behavioral genetic 

designs are by defi nition family based. This 

provides enormous opportunities to expand 

the focus on microcontextual infl uences that 

either operate as family process or impinge 

on family members such as twins and 

siblings (e.g., peer groups). Although the 

range of both macro- and microcontextual 

factors that could be included in genetic 
519

Monograph 20. Phenotypes and Endophenotypes

studies is wide-reaching, the focus here 

is on illustrative examples using specifi c 

constructs that have been linked with 

smoking, can be folded with relative ease 

into ongoing genetically informative 

designs, and, perhaps most important, can 

be pursued with measurement strategies 

that attempt to move from the distal to the 

more proximal level.

Socioeconomic Status: Moving 

from Distal to Proximal Infl uence

The few behavioral genetic studies of 

smoking or substance use that have 

attended to macrocontextual factors suggest 

that more detailed quantifi cation of the 

social environment is warranted. This 

section illustrates the potential for genetic 

studies of smoking by highlighting one 

(of many) prominent aspects of the social 

environment with strong relevance for 

smoking in both adolescents and adults: 

socioeconomic status (SES). A number 

of studies provide good examples of links 

between SES and smoking in a wide range 

of populations. SES effects on smoking 

are not limited to adolescent smoking and 

continue into early adulthood.33 Indeed, 

the effects of SES have been observed at 

all stages of smoking—from initiation 

in adolescence through progression to 

regular smoking and smoking persistence 

in adulthood—as linked by continuities 

between childhood (parental) SES and 

adult (individual) SES.34,35 In addition, 

changes in educational attainment in young 

adulthood alter the trajectories of smoking. 

For example, although adolescent smoking 

strongly predicts smoking in adulthood, 

an improvement in SES (moving to a 

higher SES level in adulthood as compared 

to childhood SES) reduces the likelihood 

of progressing to persistent smoking in 

adulthood.36 Although SES effects may 

operate through multiple levels of infl uence, 

including linkages with parental smoking 

and parental behavior, direct links between 

parental education level and offspring adult 

smoking have been found after controlling 

for these factors.37

It is worth noting that at this point 

incorporation of SES—even measured as 

a distal environmental construct—into 

behavioral genetic designs would be a 

step forward for the fi eld. The modeling 

approach that has been used in prior 

behavioral genetic studies to test for cohort 

and cultural variations in the etiological 

architecture of smoking would be well-suited 

to test for evidence of gene-environment 

interplay with SES as a measured contextual 

variable. For example, SES has been shown 

to moderate the heritability of IQ38 and 

cognitive aptitude;39 in both cases, shared 

environmental infl uences are pronounced 

in impoverished families but genetic effects 

predominate in affl uent families. Given the 

wealth of studies linking both childhood 

and adulthood SES to all stages of smoking, 

the dearth of behavioral genetic studies that 

have explored SES as a potential moderator 

of the etiological architecture could easily be 

rectifi ed, especially given that there are solid 

conceptual models that provide a rationale 

for examining such effects.6

Nuanced Approaches to Capture 

Proximal SES Effects

Consistent with the theme of this section, 

there are nuances to the measurement of 

SES that would be instructive for genetically 

informative studies. For example, Unger and 

colleagues40 have shown that two features 

of SES—an objective SES index (based 

on a composite measure of family and 

neighborhood SES) and available pocket 

money—are associated with an increased 

risk of smoking in a sample of 8th-grade 

adolescents. This study is interesting in 

that attention was given to both a more 

proximal indicator in the adolescents (their 

own available spending money) as well 

as a specifi c effect of neighborhood SES 

(as determined by matching zip codes to 

U.S. Census data). Both of these steps refl ect 
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1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

progress in moving toward more proximal 

indicators of the macroenvironment in 

that there are multiple levels of proximal 

infl uence that operate at both the individual 

level and the neighborhood or area 

level (again, see chapter 3 for a similar 

perspective on measurement of phenotypes). 

Diez Roux and colleagues41 used more 

detailed information available from census 

data, including census tracks (subdivisions 

of a county), as well as smaller components 

(or blocks), to measure a number of area 

characteristics. Such measurement of 

socioeconomic disadvantage was highly 

predictive of smoking in young adulthood 

in their study, and as suggested in this 

paper, individual- and area-level indicators 

of SES may capture unique aspects of 

socioeconomic effects. Also, evidence shows 

that area-defi ned economic deprivation 

is predictive of the likelihood of quitting 

smoking.42 These studies provide important 

examples of obtaining more precision on 

macrosocial factors, and clearly a variety of 

other variables—such as cigarette prices and 

presence of smoking restrictions—that are 

geographically linked deserve consideration 

in future studies. 

Proximal Indicators of Area Effects 

on Smoking

The studies above highlight the early steps 

that are being taken to break down the distal 

factor of SES into a number of components, 

with the net result being more specifi c 

indicators of the macrocontext that may 

likely alter the mix of genetic and nongenetic 

infl uences on smoking during both 

adolescence and adulthood. The overriding 

implication for genetic studies of tobacco 

use and nicotine dependence is that there 

are not only crucial macrocontextual 

infl uences that shape patterns of 

smoking (and undoubtedly intersect with 

genetic susceptibility) but also specifi c 

methodologies that permit more precise 

assessment of these factors at a proximal 

level. A number of emerging constructs 

and measurement techniques could be 

relevant as predictors of smoking, including 

a focus on area crime rates,43 neighborhood 

disorder,44 price of cigarettes, and presence 

of a smoke-free law. One illustrative 

example is the density of tobacco retail 

outlets as a specifi c area risk factor linked 

with cigarette smoking, especially in 

youths. A paper by Novak and colleagues45

illustrates the conceptual basis as well as 

a highly detailed methodology as applied 

in the Project on Human Development 

in Chicago Neighborhoods. In this study, 

trained raters videotaped, while driving, 

each side of streets that corresponded to 

selected census tracks. Codes were developed 

to identify retail locations licensed to sell 

tobacco and captured empirically as density 

of retail outlets. Two fi ndings of the study 

are especially relevant: (1) retail tobacco 

outlets were overrepresented in socially and 

economically disadvantaged neighborhoods 

(suggesting a more proximal level of risk for 

smoking via area SES), and (2) youths who 

resided in the high-density areas were at 

increased risk for smoking, especially after 

controlling for confounding variables.

It is unlikely that large-scale behavioral 

or molecular genetic studies would invest 

the resources to physically code diverse 

geographic areas for density of retail tobacco 

outlets. The advancement in methodologies 

such as geographic information systems 

(GISs) provides a cost-effective approach for 

capturing such specifi c social contextual 

factors with relevance for smoking. Croner 

and colleagues provide an informative 

and readable description of the methods 

and utility of GISs, which they describe 

as computer-based programs “supporting 

the collection, storage, retrieval and 

statistical manipulation of spatiallyreferenced observations and events.”46(p1961) 

Fundamentally, any study that collects 

street address data on participants has the 

capacity to extract information from sources 

such as census data (as discussed above in 

studies of area effects on smoking), as well 
521

Monograph 20. Phenotypes and Endophenotypes

as geocode or address match to spatial data 

(i.e., map coordinates), and utilize any 

number of indicators for features of interest 

from relevant databases. 

To demonstrate the utility of this approach, 

this chapter briefl y describes portions of 

ongoing work at the Brown University 

Transdisciplinary Tobacco Use Research 

Center (Brown TTURC) as applied to 

the third (adolescent) generation of its 

three-generation family study of nicotine 

dependence. The study was successful in 

using GISs to match density of tobacco retail 

outlets that correspond to the locations 

of participants. The geocoding process 

required three types of fi les: TIGER/Line, 

census block groups, and address tables. 

TIGER/Line is the term given to fi les 

containing the layout of U.S. streets, and 

census block groups is the term given 

to the fi les containing the layout of the 

U.S. Census block groups, which, at the 

time of these analyses, were the smallest 

geographic unit of measurement of the 

U.S. Census. Lastly, tables containing 

physical addresses of participants and 

cigarette retailers were needed that 

contained U.S. street addresses and zip 

codes. The addresses of cigarette retailers 

for Rhode Island and Massachusetts (the two 

primary states of residence for participating 

families) were obtained electronically 

from the Rhode Island Division of Taxation 

and the Massachusetts Disclosure Offi ce. 

Figures 11.1 and 11.2 present the located 

cigarette vendors within each state as spatial 

points on a map of the state.

A number of variables can be generated from 

these data, including counts of cigarette 

vendors per census block group per state 

and the density of cigarette vendors within 

specifi ed distances of each participant. 

For example, one measure can be created to 

index if there is any outlet in the given area, 

and another can be based on the proportion 

of block faces with a given outlet. Physical 

distance and traveling time to the closest 

and the second-closest tobacco and alcohol 

stores can also be calculated by using 

network analysis in GISs. 

Implications for Incorporating 

Proximal Indicators of the 

Macroenvironment in Genetically 

Informative Studies

Four basic points can be extracted from 

these examples of methods that can be used 

to move to a more precise and “molecular” 

level of understanding the macrocontext 

within genetically informative studies. 

First, genetic and family-based studies 

provide an excellent platform for applying 

relatively new methodologies such as GISs, 

as well as many other approaches now 

used to generate sophisticated indices of 

macrocontextual infl uences with empirically 

demonstrated relevance for smoking. 

Second, integrating these approaches 

into genetically informative designs will 

be most effectively accomplished via 

a transdisciplinary framework,1 which 

facilitates collaboration across a number 

Figure 11.1 Located Cigarette Vendors 

in Rhode Island
522

1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

of disciplines. Third, these proximal 

constructs that can emerge from methods 

such as GISs can be as easily integrated 

into quantitative genetic models as any 

distal measure, such as SES as traditionally 

represented, and would provide needed 

data on the extent to which expression 

of genetic liability to multiple indices 

of smoking (initiation, persistence, 

dependence) is modifi ed by macrocontextual 

factors. Finally, proximal indicators of the 

macrocontext could also serve as putative 

environmental components in a variety 

of models of gene-environment interplay 

(e.g., gene-environment interaction 

and correlation)5,6 when combined with 

measurement of candidate gene markers 

with relevance to nicotine dependence. 

Quantifying the Microsocial 

Context: Moving Toward 

Proximal Measures 

of Interpersonal Infl uences 

on Smoking

As was the case for the macrocontext, 

a multitude of microcontextual factors 

could impinge on likelihood of smoking 

across developmental stages. Building upon 

the behavioral genetic studies reviewed 

earlier that incorporated interpersonal 

infl uences into estimates of the etiological 

architecture of smoking, this section focuses 

on methods that attempt to yield more 

proximal indicators of potential underlying 

social processes. This focus not only refl ects 

an important theme in the behavioral 

genetics of smoking, but also provides a 

logical extension of genetically informative 

designs, given the inherent attention to 

dyadic relationships (e.g., twins and siblings; 

parents and offspring).

Interpersonal Relationships 

as a Context for Smoking

Independently of the behavioral genetic 

literature, tremendous attention has 

been given to interpersonal relationships 

as social contexts for the development 

of multiple forms of substance use,47

including smoking.10,48,49 Indeed, some 

perspectives emphasize the critical 

importance of social networks as an 

infl uence on smoking,32 which have been 

conceptualized as being comparable to 

an “infectious disease” model50 or “social 

contagion.”29 Particular focus has been 

placed on three types of relationships as 

Figure 11.2 Located Cigarette Vendors in Massachusetts
523

Monograph 20. Phenotypes and Endophenotypes

the most salient for smoking: parents, 

peers, and siblings. There is a long history 

of studying parental smoking as a predictor 

of offspring smoking.51 A number of studies 

have provided further evidence of linkages 

between parental smoking and a number 

of smoking outcomes in adolescence and 

adulthood. Parental smoking increases the 

likelihood of experimentation in childhood 

and adolescence52 and regular smoking 

in early adulthood;53 smoking parents 

who provide a smoke-free home for their 

children may confer particular protection 

against smoking initiation.54 Adolescent 

offspring of mothers who smoke regularly 

and/or are nicotine dependent are more 

likely to initiate smoking and to progress to 

nicotine dependence.55 Parental smoking, 

including paternal smoking, also predicts 

an earlier age of onset of tobacco use.56

A number of studies also suggest that 

parental smoking cessation is associated 

with a decreased risk of smoking in 

adolescent offspring.57–60 These studies all 

go to the broader point that social networks 

(and the resultant proximal effects of social 

infl uence) may have a profound effect on 

patterns of smoking.31,32

A similarly large historical literature exists 

for peer infl uences on adolescent cigarette 

smoking, whereas less attention has been 

given to siblings. Reviews by Hoffman and 

colleagues and Kobus describe multiple 

theories of peer infl uences and provide 

a comprehensive longitudinal model.49,61

Hoffman and colleagues61 provide a good 

discussion on the core concepts of peer 

infl uence (as a putative causative factor) 

and peer selection as they pertain to 

smoking.62 The impact of sibling smoking 

has begun to receive more attention, 

especially given the strength of the 

association between siblings,29,51 and the 

overviews of the models of sibling infl uence 

provided by Slomkowski and colleagues.30

Subsequent studies have confi rmed the 

strong predictive value of friend and sibling 

infl uences on adolescent smoking.63–65

Proximal Indicators of Interpersonal 

Infl uence 

The extensive data on interpersonal 

relationships as a social context for the 

development of smoking suggests that 

incorporation of social processes into 

genetic models would be profi table and 

perhaps necessary and is consonant with 

an emphasis in the literature on the effects 

of social networks and infl uences on 

smoking.31,32 As stated earlier, the behavioral 

genetic literature consistently points to 

shared environmental effects on smoking in 

adolescence; one source of infl uence could be 

joint social relationships and direct infl uence 

of smoking behavior within intimate 

relationships. As reviewed earlier, some 

progress has been made in behavioral genetic 

studies suggesting that, as a structural 

variable, both having friends who smoke and 

having a parent who smokes moderate the 

shared environmental effect on adolescent 

smoking. Furthermore, prior studies29,30

that focused on specifi c dimensions of the 

sibling relationship as moderators of shared 

environment have inched toward more 

specifi c indicators of social process. However, 

little attention has been paid to translating 

the typically measured distal factors of 

having a relationship with someone who 

smokes into more proximal measures of 

social infl uence that can be studied within 

the gene-environment interplay framework. 

This section illustrates an approach for 

incorporating proximal indicators of 

interpersonal infl uence into genetically 

informative designs. Although the focus 

here is on interpersonal dynamics, the 

broader point is the need to appropriately 

consider and measure a host of powerful 

social contextual factors into genetic studies, 

including infl uences such as exposure 

to cigarette advertising and smoking in 

movies,66 which may operate via social 

networks such as peers.67

Two rationales based on the empirical 

work available support the relevance for 
524

1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

genetic models of nicotine dependence. 

First, the consistent isolation of sources of 

shared environment in studies of adolescent 

smoking, some of which may be embedded 

in interpersonal dynamics,26 could identify 

candidate “environmental” factors to study 

jointly with both latent genetic indicators as 

well as candidate gene markers for propensity 

for nicotine dependence within the geneenvironment interaction framework.4,5

Second, given evidence that components 

of family and peer relationships may refl ect 

genetic as well as social infl uences,68 it would 

be informative to explore the possibility of 

gene-environment correlation as one source 

of the net genetic effect on smoking and 

nicotine dependence. In addition to fi lling in 

the black box of heritability, such work could 

contribute to identifying multiple sources of 

genetic infl uence on smoking. For example, 

Agrawal and colleagues53 suggest that 

correction for a host of risk factors, including 

parental smoking and features of the parentchild relationship and home environment, 

yields a reduction in the overall heritability 

of regular smoking in young adulthood 

that nonetheless remains signifi cant. They 

draw two important conclusions from these 

results: (1) the reduction in heritability may 

signal that part of the overall parental effect 

refl ects genetic effects, and (2) the residual 

heritability of young adult regular smoking 

may represent more “phenotype-specifi c” 

genetic effects. 

Proximal Indicators of Social 

Infl uence: Methods for Studying 

Real-Time Interaction

The most traction will be made in developing 

gene-environment models focusing on social 

context relationships by using specialized 

methodologies capable of capturing more 

proximal indicators of interpersonal 

processes that are linked with risk for 

smoking and progression of smoking.47

A number of processes could be studied. 

For example, selected parental behaviors 

could be measured and inserted into 

behavioral and molecular genetic studies, 

including parental beliefs and behaviors 

pertaining to smoking,69 parenting style 

and smoking-specifi c parenting practices,70

and antismoking socialization.71 Another 

example with respect to peers would be 

social network analysis, which, as described 

by Hoffman and colleagues,61 can be used 

in longitudinal studies to tease apart 

peer infl uence and peer selection. Other 

interesting methods include using speech 

samples to extract relationship narratives as 

an indicator of mother-child family process72

and sibling-expressed emotion.73

Similar to the strategy of focusing in some 

detail on GIS methods in the prior section, 

this section will provide an illustrative 

example that has been used especially in 

studies of both peers and siblings: the 

use of microsocial coding of real-time 

social interaction as captured by using 

semistructured, videotaped paradigms. 

One paradigm that elicits and records 

relationship dynamics is to observe 

microsocial interaction as it unfolds in real 

time.74 Typically, semistructured discussion 

tasks are constructed and videotaped 

without an observer present. Dishion and 

colleagues have pioneered this work with 

particular reference to microsocial processes 

that convey risk for antisocial behavior and 

substance use,75,76 and similar work has been 

done with siblings.77

Real-Time Social Interaction in a 

Genetically Informative Design

Two features of this methodology warrant 

expansion in terms of immediate relevance 

to the etiological architecture of smoking 

as it changes from adolescence to 

adulthood. First, the genetically informative 

Nonshared Environment in Adolescent 

Development (NEAD) Project, which uses 

monozygotic and dizygotic twins along 

with full, half, and unrelated sibling 

pairs, has provided a wealth of data on the 
525

Monograph 20. Phenotypes and Endophenotypes

genetic and environmental determinants 

of sibling behavior in adolescence via the 

combined use of videotaped interaction 

and multirater reports.78 A key fi nding 

is that a number of indices of sibling 

relationship dynamics are shaped by 

shared environmental, rather than genetic, 

factors as determined by biometrical model 

fi tting. Given the accumulating evidence, 

reviewed earlier, that shared environmental 

factors infl uence adolescent smoking and 

that sibling interaction may moderate, 

in part, the shared environmental effect, 

the elucidation of specifi c interpersonal 

processes derived from microsocial data 

would provide a strong candidate for 

this form of proximal environmental 

infl uence on smoking. As discussed in the 

examples for macrocontextual factors, 

such empirically validated indicators of 

environmental infl uence would serve well 

in gene-environment interaction models of 

adolescent smoking that are optimally tested 

by using proximal measured environmental 

pathogens.4,5 

A second theme from the NEAD Project 

is that shared environmental infl uences 

provide the most robust linkage across 

different types of relationships, including 

covariation between mother-adolescent and 

sibling relationships as well as longitudinal 

associations between adolescent antisocial 

behavior and young adult relationships 

with romantic partners.78 These fi ndings 

are included for consideration as part of 

the thesis that the interpersonal dynamics 

that may underlie both peer and sibling 

infl uences on smoking in adolescence may 

represent enduring relationship styles 

that carry into adulthood and into other 

relationships, including relationships with 

romantic partners. These patterns may be 

especially relevant given the notable evidence 

for assortative mating for a number of stages 

of cigarette smoking that include regular 

smoking and nicotine dependence.79,80

Although assortative mating may primarily 

refl ect selection rather then interpersonal 

infl uence per se, it is worth considering the 

possibility that the continual construction 

of intimate relationships may be infl uential 

in maintaining lifestyle choices across 

developmental periods that promote harmful 

behaviors.81 It is worth reiterating at this 

point that current contact between adult 

twins is associated with twin resemblance 

of nicotine dependence.23 The proposed 

utility of microcontextual measures of 

proximal, interpersonal infl uences may not 

only be useful as a piece of the etiological 

architecture of adolescent smoking but 

also could be expanded to include adult 

relationships as a putative source of 

environmental reinforcement for smoking; 

this could, in principle, interact with 

emerging genetic propensity for nicotine 

dependence. The application of these 

methods would provide the most sensitive 

tests for the role of interpersonal infl uences 

in models of nicotine dependence that posit 

the possibility of gene-environment interplay.

Ecological Momentary 

Assessment

A fi nal, newer methodology available to 

the smoking fi eld is ecological momentary 

assessment (EMA). Indeed, the rationale for 

EMA is now well recognized in the smoking 

literature and has been well explicated.82,83

The “ecological” aspect refers to the use 

of technologies—for example, personal 

digital assistants (PDAs) and cellular 

phones—that allow respondents to report 

their behaviors in real time and in real-life 

settings. The corresponding “momentary 

assessment” of the methodology is the 

emphasis on acquiring instantaneous selfreports to minimize the recall bias and 

memory distortion typically introduced by 

more retrospective accounts. 

EMA Studies of Smoking

A number of published studies have 

used EMA to assess smoking behavior in 
526

1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

adolescents and adults. EMA has been used 

to examine differential smoking patterns in 

adult heavy smokers and chippers.84 Some 

of these studies have assessed antecedents 

of cigarette smoking in adults, especially 

a variety of affective states,85,86 as well as 

prospective indicators of smoking lapses,87–89

which have implications for knowledge 

about the smoking relapse process.90 EMA 

methods have been used successfully with 

adolescents as well; these methods have 

shown links between tobacco use and both 

high levels of anxiety91 and symptoms of 

attention defi cit hyperactivity disorder.92

The interest here in bringing attention to 

the EMA methodology is twofold: it holds 

great promise for merging the microcontext, 

such as interpersonal interactions, with 

internal states and cognitions, and it permits 

a simultaneous level of measurement of 

macrocontextual features. The essence of the 

approach is being able to repeatedly prompt 

participants in “real-time” and “real-life” 

contexts with questions concerning how they 

are feeling, what they are doing, who they 

are with, and where they are. For example, 

Shapiro and colleagues93 reported that adult 

smoking was associated with particular 

activities and locations such as work breaks, 

being in a car, and outdoors, refl ecting the 

increasing restrictions on where smoking 

can take place. Chandra and colleagues94

have found that environmental restrictions 

seem to affect the smoking patterns of some 

individuals more than others.

The interesting studies reviewed above begin 

to highlight the potential for using EMA 

to assess, in an integrated and ecologically 

sensitive manner, actual smoking behaviors 

along with concurrent information on 

affective and cognitive states, interpersonal 

contexts, and broader macrocontexts 

(and although not reviewed here, it is also 

possible to record physiological data as well 

by using ambulatory recording methods) 

within genetically informative samples. 

The intensive, repeated intervals that can be 

used during the day and across days permits 

a complex stream of potential antecedents, 

correlates, and consequences of smoking 

which, when crossed with a genetically 

informative design, will yield a potentially 

overwhelming overlay of proximal variables 

at multiple levels of analysis. Sophisticated 

data analytic tools have been (and continue 

to be) developed to work with such 

“intensive longitudinal data.”95

Illustration of EMA in a Family-Based 

Design

To demonstrate the feasibility of collecting 

EMA data in a family-based design, this 

section contains a brief overview of methods 

and some illustrative data derived from the 

ongoing Sibling Partners Study. This study 

focused on 60 adolescent sibling pairs drawn 

from the New England Family Study who 

have participated in the three-generation 

family study of nicotine dependence by 

the Brown TTURC. The sibling pairs were 

recorded in real time, using programmed 

PDAs, with a variable interval between their 

prompts to minimize subject reactivity while 

permitting logical overlap in the chronology 

of their responses. They were prompted on 

a variable schedule every 30 to 45 minutes, 

starting with the time they typically woke 

and ending with the time they typically went 

to sleep (these times were determined for 

each projected day of recording during an 

intake interview conducted the night before 

data collection began). Participants were 

also allowed to indicate times during the day 

when they structurally could not respond 

to the prompts (e.g., sports practice) and 

were also instructed not to respond to the 

PDA if that behavior could be harmful 

(e.g., while driving). Because this was a 

family-based design with participants from 

multiple states, data were recorded during 

school hours. Participants were asked to 

provide daily responses to the PDA for six 

consecutive days; the same protocol was 

used both 6 months and 12 months after 

the baseline assessment. 
527

Monograph 20. Phenotypes and Endophenotypes

This study illustrates a few aspects of the 

methodology that may be useful for future 

genetically informative designs (such 

as twin studies). First, the compliance 

rate (at each wave and across waves) was 

excellent. Nearly all subjects responded to 

over 80% of the PDA prompts (producing 

on average more than 100 data points across 

the six days of recording at each wave). 

Second, table 11.1 provides examples of 

some of the PDA diary items along with 

response choices to demonstrate how social 

context, mood, interpersonal dynamics, and 

smoking behaviors can be assessed (it takes 

approximately 60 seconds for a participant 

to respond to all 47 prompts). 

Third, preliminary descriptive data are 

presented to show how smoking behavior, 

recorded every 30 to 45 minutes, varies 

according to two levels of social context as 

represented by two diary items: “Location” 

(Where Am I Now?) and “Whom” (Right 

Now, I Am With). Graphs (see fi gure 11.3) 

show the percentage of diary responses 

to each “Cigarettes” prompt (Since Last 

Beep # of Cigarettes Smoked) aggregated 

over the six-day recording period across all 

individuals; note that these data are presented 

descriptively, without application of the 

statistical models suitable to these data, to 

simply show the potential utility of EMA. 

For these purposes, the responses were 

dichotomized, and the fi gures show the 

percentage of epochs in which any cigarette 

smoking was endorsed (as opposed to “none 

at all”) as a function of both “Location” 

and “Whom.” The number of epochs with a 

positive endorsement of any smoking was 

higher when with the sibling partner as 

compared with when alone, with other family 

members, or with other family members plus 

the sibling partner. There are also suggestions 

that “Location” plays a role; for example, 

smoking percentages with a sibling increase 

when at a shopping mall but decrease at this 

location when with other family members 

(with or without the sibling partner). Again, 

these percentages demonstrate how multiple 

levels of both micro- and macrocontext can 

be combined using EMA. To illustrate this 

further, fi gure 11.4 summarizes the pattern 

of endorsed smoking epochs over the six-day 

period for a concordant pair of siblings who 

are often concordant at real points in time. 

For each data point represented, information 

on where they were, whom they were 

with, their moods, and dynamics of their 

interactions with each other are included.

Potential Contribution of EMA 

to Genetically Informative Designs

In summary, newer methodologies such as 

EMA offer untapped potential for genetically 

informative designs from the perspective 

of exploring gene-environment interplay 

because of the unique opportunities to 

gather simultaneous, ecologically valid, 

proximal indicators of social context. 

Application to twin studies (and similar 

genetic designs) would allow a new class of 

questions to be asked on the degree to which 

smoking behavior varies as a function of 

both genetic similarity and social context. 

Furthermore, EMA methods could be 

used to examine or validate differential 

smoking patterns in individuals as a 

function of both candidate gene markers 

and proximal indicators of social context. 

Finally, given the perspective that smoking 

phenotypes will involve multiple levels 

(chapter 3), the simultaneous assessment 

of smoking behavior along with both 

micro- and macrocontextual information 

could eventually yield novel phenotypes 

for genetic studies that are defi ned, in part, 

by the context in which they arise. 

Future Directions

The fi ndings presented in this chapter have 

two key implications. First, it is possible 

that overall population estimates of the 

heritability of smoking could refl ect an 

aggregation of very different etiological 
528

1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

Table 11.1 Sibling Partners Diary Prompts

Location Where am I now?

 Home

 Friend’s

 School or work

 Store or mall

 Event

 Car-bus

 Outdoors

 Somewhere else

Whom Right now, I am with:

 Sib-partner

 Other family

 Both

 Neither

Activity 1 What am I doing now (primary activity)?

 Getting ready

 Homework

 Computer

 TV or music

 Hanging out w/ friends

 More choices

Activity 2 What am I doing now (primary activity)?

 Exercise/sports

 Errands/chores

 Hanging out

 Talking/phone

 Videos or games

 Other activity

 Go back

Activity_with 1 I am doing this:

 Alone

 With someone

Activity_with 2 Who am I doing this with? (check all)

 Sib-partner

 Other siblings

 Mother

 Father

 Other adults

 Friends/others

Activity_with 3 Who (else) is nearby? (check all)

 No one

 Parents

 Other siblings

 Friend(s)

 Other adult(s)

Irritated How irritated/angry am I feeling now?

 Not at all

 Just a little

 Pretty much

 Very much

Relaxed How relaxed am I feeling now?

 Not at all

 Just a little

 Pretty much

 Very much
529

Monograph 20. Phenotypes and Endophenotypes

Table 11.1 Sibling Partners Diary Prompts (continued)

Focused How focused am I feeling now?

 Not at all

 Just a little

 Pretty much

 Very much

Worried How worried am I feeling now?

 Not at all

 Just a little

 Pretty much

 Very much

Sib_with Been w/ your sib-partner in the last 45 minutes?

 Yes

 No

Sib_quality Quality of my interaction w/ sib-partner was (check all):

 Special

 Pleasant

 Neutral

 Uncomfortable

 Confrontational

 No interaction in last 45 min

Sib_annoyed In the last 45 min

 I’m a bit annoyed at my sib-partner

 My sib-partner is a bit annoyed at me

 Both are true

 Neither is true

Sib_talked In the last 45 min my sib-partner & I talked

 Not at all

 Just a little

 Pretty much

 Very much

Sib_feel_good While together he/she made me feel good about myself?

 Not at all

 Just a little

 Pretty much

 Very much

Sib_argued While together we argued/fought

 Not at all

 Just a little

 Pretty much

 Very much

Sib_mischief What we did together might be considered mischievous

 Not at all

 Just a little

 Pretty much

 Very much

Urge_smoke In the last 45 min my urge to smoke:

 Not at all

 Just a little

 Pretty much

 Very much

Cigarettes Since last beep # of cigarettes smoked:

 A few puffs

 1 to 2

 3 to 5

 More than 5

 None at all
530

1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

architectures that vary on the basis of 

measurable social contextual factors. Thus, 

there may be etiological heterogeneity in 

the mix of genes and environments that 

can only be captured by incorporating 

candidate social contextual measures in 

genetically informative designs. Insight into 

the mechanisms underlying such etiological 

heterogeneity will not be achieved without 

considering a broad number of both macroand microsocial factors. Although this 

chapter focused on highly selected social 

contextual factors to illustrate a number 

of concepts and methods of relevance to 

genetically informative designs, the analogy 

can be made that a “whole-environment” scan 

would carry as much importance as a wholegenome analysis in the eventual construction 

of satisfactory mechanistic etiological models 

of nicotine dependence. Second, evaluating 

sources of etiological heterogeneity may 

help in understanding the mechanisms by 

which endophenotypes (chapter 8) become 

salient for smoking behaviors under specifi c 

environmental conditions and not others. 

That is, crossing advanced measurement of 

both endophenotypes and social contexts 

may illuminate core environmental factors 

that dwarf individual-level propensities 

as well as highlight especially prominent 

endophenotypes that convey risk under 

particular environmental conditions.

It is reasonable to assume that a multitude 

of genetic strategies eventually will yield 

replicable fi ndings supporting multiple 

genes with direct relevance to nicotine 

dependence, such as the 2007 report from 

Bierut and colleagues.96 It also is reasonable 

to postulate some direct pathways between 

candidate genes and propensity to develop 

nicotine dependence that may be somewhat 

impervious to the social environment, 

once exposure to nicotine takes place. 

However, it is becoming increasingly 

untenable to ignore social contextual 

factors without sacrifi cing a broader and 

more comprehensive understanding of 

the etiological architecture of complex 

phenotypes such as nicotine dependence. 

Furthermore, taking the perspective that 

nicotine dependence is an end point of 

Figure 11.3 Percentage of Diary Responses Endorsing Cigarette Use Stratifi ed by Social 

Contexts

Sibling Sibling & family Other family Alone

Percentage

0

5

10

15

20

25

30

35 Home

Car-bus

Store-mall
531

Monograph 20. Phenotypes and Endophenotypes

complex behavioral and physiological 

pathways that stretch across multiple periods 

of the life course reinforces the notion 

that empirically supported environmental 

infl uences on earlier stages of smoking play, 

at a minimum, an indirect role in shaping 

the expression of genetic susceptibility.

If the fi eld is to take seriously the proposition 

that gene-environment interplay will play 

a key role in eventually understanding the 

mechanisms by which genes contribute to 

smoking behavior and nicotine dependence,1

a dedicated effort will be needed not only 

to incorporate environmental measures 

with more regularity and vigor but also to 

invest the time, resources, and collaborative 

expertise necessary to provide the best 

available data on the environment.4,5 It is 

worth noting here the Genes, Environment 

and Health Initiative of the National 

Institutes of Health,97 which includes a 

component to develop novel and precise 

measures of exposure to disease-causing 

agents in the environment. 

Summary

The key yield from behavioral genetic studies 

of smoking that have included attention to 

the social context is that they demonstrate 

how the heritability of complex phenotypes 

can fl uctuate, depending on varying social 

factors. Genetic pathways to nicotine 

dependence are not activated if social 

Figure 11.4 Pattern of Endorsed Smoking Epochs over a Six-Day Period for a Concordant 

Sibling Pair

Participant 491 - wave 1

Participant 492 - wave 1

Date–time

Number of cigarettes smoked

0.0

3.5

1.5

3.0

2.5

2.0

1.0

0.5

38575 38581 38576 38577 38580 38578 38579
532

1 1 . I n c o r p o r a t i n g S o c i a l C o n t e x t i n t o G e n e t i c S t u d i e s

conditions dampen the likelihood of smoking 

initiation (as is the case for females in China) 

as would be predicted by application of the 

genetic epidemiological triangle to smoking. 

Certain socioregional characteristics can 

either diminish the impact of heritable 

infl uences on substance use or make genetic 

differences across individuals salient (as is 

the case for adolescents in Finland). Some 

of the predictive power attributed to genes 

in quantitative genetic models may be 

explained away by microsocial infl uences 

such as having peers, parents, and siblings 

who smoke (as is the case for adolescents 

in the Netherlands). These studies provide 

empirical evidence that the extent to which 

genetic differences between individuals 

affect the likelihood of smoking depends in 

part on multiple levels of the social context, 

reinforcing that genetic effects for complex 

phenotypes are not deterministic, but rather 

probabilistic, and best defi ned via reference to 

the social environments in which they arise. 

This chapter has provided a highly selective 

overview of relatively new methods for 

assessing “molecular” aspects of social 

context at both a macro and micro level. 

These methods were chosen to illustrate 

approaches that make conceptual sense in 

typical behavioral genetic or epidemiological 

designs and that can fold into these designs 

with relative ease, given both the types 

of samples studied and the size of such 

samples. Although there are costs involved 

in the application of these methods, these 

need to be weighed against the likelihood 

of their necessity in building more 

comprehensive and realistic models of 

genetic effects on smoking. 

Conclusions

1. Social context infl uences on 

developmental pathways to nicotine 

dependence refl ect gene-environment 

interplay that comprises the elements of 

a traditional epidemiological framework 

including a host (e.g., smokers and 

genetic endowment), environmental 

factors (social network), and an agent 

(e.g., tobacco).

2. Macrocontextual factors such as 

culture, socioregional variables, and 

socioeconomic status can modify or even 

nullify genetic infl uences on nicotine 

dependence. For example, a twin study 

revealed a prevalence rate for smoking 

of less than 1% in Chinese women, 

refl ecting an inhibitory cultural infl uence. 

Family or neighborhood socioeconomic 

status and density of tobacco sales outlets 

are examples of specifi c contextual factors 

that appear to infl uence smoking risk 

among adolescents.

3. Microcontextual approaches have revealed 

factors such as exposure to parental, 

sibling, and peer smoking that may 

moderate genetic infl uence on behavioral 

smoking measures. The genetically 

informative Nonshared Environment in 

Adolescent Development Project, which 

comprised twins as well as other siblings, 

indicated that sibling interaction patterns 

may moderate the shared environmental 

effects that infl uence adolescent smoking.

4. Studies of smoking behavior using 

ecological momentary assessment, 

designed to measure both macroand microcontextual factors, show 

that smoking behavior varies with 

both location and companions. Such 

assessments serve as a possible future 

model for incorporating integrated social 

context issues such as actual clinical and 

public health efforts to reduce tobacco 

use within etiological architectures.

5. Future work incorporating social 

context within gene-environment 

studies of smoking behavior and 

nicotine dependence will benefi t from 

a greater focus on environmental 

factors, including more-fi ne-grained and 

comprehensive assessments of potential 

environmental infl uences.
533

Monograph 20. Phenotypes and Endophenotypes

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539

Existing studies of genetic associations with nicotine dependence frequently do not refl ect 

complex relationships between genetic, environmental, and social factors underlying 

tobacco use. Moreover, the scope of potential genetic variations and their impact on 

analysis pose a conceptual challenge to effective studies of genetic factors.

This chapter examines the potential for the use of hierarchical modeling techniques 

within the framework of an ontology that quantifi es relationships across genotypes and 

phenotypes for nicotine dependence. Topics discussed include

■ An overview of the existing statistical approaches for genetic association studies 

in tobacco use

■ Design and analysis considerations in the use of hierarchical modeling in 

conjunction with stochastic variable selection for future genetic studies of 

tobacco use

■ The use of ontologies for codifying prior knowledge to support effi cient 

computational analysis of such hierarchical models

■ Results of a study of nicotine metabolism using the data from the Northern 

California Twin Registry in conjunction with the Nicotine Pharmacokinetics 

Ontology, showing signifi cant genetic associations with nicotine clearance levels

The results of this pilot study, and the potential of these approaches to overcome the 

methodological issues inherent in existing genetic studies, show promise for these 

approaches as an area for further study.

12

Using Ontologies in Hierarchical 

Modeling of Genes and Exposure 

in Biological Pathways

David V. Conti, Juan Pablo Lewinger, Rachel F. Tyndale,

Neal L. Benowitz, Gary E. Swan, and Paul D. Thomas

The analyses described herein were supported by National Institutes of Health grants CA084735, CA52862, DA18019, 

DA20830, DA02277, DA11070, and HL084705. Analysis support was also provided by University of Toronto, Canada 

grants CAMH, and CIHR MOP53248 and a Canada Research Chair in Pharmacogenetics.
540

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

Introduction

This chapter examines the use of ontologies 

as a framework for creating hierarchical 

models that could support quantitative, 

computationally driven research in 

biological pathways for nicotine dependence, 

as a potential means of linking genetic 

and environmental factors to yield a more 

accurate understanding of why people 

smoke. It highlights a specifi c example 

using data from the Northern California 

Twin Registry1,2 to explore the heritability 

of nicotine metabolism, together with a 

discussion of broader issues involved in 

creating hierarchical models in conjunction 

with ontologies that quantify prior 

knowledge of relationships linking specifi c 

genotypes, endophenotypes, and phenotypes 

for nicotine dependence.

The multistep nature of tobacco use 

progression—from initiation, to episodic 

use, to dependence—provides several 

opportunities for risk factors to act. 

Although distinct factors may affect 

each step, universal factors may also 

create background characteristics for an 

individual throughout use progression. 

In addition, compounding the background 

profi le are large, punctuated events, 

such as intervention programs, that may 

substantially alter an individual’s tobacco 

use—both in isolation and synergistically 

with other factors. That is, smoking behavior 

is a composite consisting of large social 

factors, interpersonal relationships, and 

intrapersonal characteristics. Large social 

patterns substantially infl uence smoking 

behavior through demographic changes, 

fi nancial mechanisms, cigarette availability, 

and perceptions of smoking. Economic 

factors such as unemployment rates, income 

levels, and cigarette prices also affect 

individuals’ ability to purchase cigarettes.3

Although these large social forces often 

affect an individual’s tobacco use, close 

interpersonal relationships have considerable 

infl uence as well. Personal relationships 

with family, friends, peer groups, and 

classmates form immediate surroundings 

and an individual’s attitudes.4

 Especially 

among adolescents, it is within these social 

networks that individuals make behavioral 

choices about tobacco use—choices 

that depend on individuals’ dispositional 

attributes as infl uenced by further biological, 

cognitive, and emotional characteristics 

such as the personality traits of hostility and 

depression.5 Of course, these personality 

traits are also under some genetic infl uence. 

For example, monoamine oxidase (MAO), 

a mitochondrial enzyme consisting of two 

isoforms, MAOA and MAOB, is found in 

neuronal and nonneuronal cells in the 

brain.6 Its main function is the breakdown 

of neurotransmitters; it is therefore a key 

enzyme in the regulation of serotonin and 

dopamine levels in the brain. Mouse models 

indicate that genetic variation within MAO 

are associated with changes in the levels of 

serotonin and dopamine in the brain and 

a change in behavior, especially indicators 

of hostility and depression.7–9 Once an 

individual fi rst smokes, the response to a 

particular acute or chronic dose of nicotine 

is determined in part by the rate of nicotine 

metabolism and the genes that infl uence 

metabolic function. There is a long-term 

physiological and psychological response 

as well.

To simply test the hypothesis of an 

association between a single genetic variant 

and whether an individual currently smokes 

or not ignores any knowledge one might 

have of the underlying etiologic mechanism 

within the analysis framework. But how 

does one incorporate biological information 

into the analysis? Often, the inclusion 

of previous knowledge of the underlying 

biological mechanism has been limited to 

the design phase of a study, only informing 

the selection of potential candidate genes 

and exposures. Thus, the analysis is confi ned 

to determining the independent association 
541

Monograph 20. Phenotypes and Endophenotypes

of each gene via contingency tables or 

regression models. If joint effects are 

suspected, the analysis is expanded to include 

the search for statistical interactions or, 

more specifi cally, the search for departures 

of independent and additive effects on the 

assumed scale of the outcome.10–12 However, 

it is often unclear how suspected or known 

mechanistic and biological joint action will 

manifest in population-based inferences 

relying on epidemiological data. This chapter 

will examine how appropriate hierarchical 

models can move beyond the existing 

approaches to help form a framework for 

examining such interactions at a more 

macro level, which, in turn, may help to 

better understand and describe the role of 

biology in human smoking behavior.

Methodological Issues

When examining factors in nicotine 

dependence, diffi culties in estimating 

and testing effects are compounded with 

the expanding numbers of exposures, 

genotypes, intermediate measures, and 

multiple phenotypes now readily available 

and relatively inexpensive to obtain 

on population samples with modern 

technologies. Such an extent of available 

information may lead an investigator to 

search for interaction effects in fi ner strata 

with limited information from the data or 

to exclude potentially valuable measures. 

For instance, possible “omic” measures such 

as metabolomics (e.g., surrogate measure 

of metabolite concentrations within a 

pathway),13,14 proteomics (e.g., surrogate 

measures of enzyme concentration 

or activity within a pathway),15,16

epigenomics (e.g., DNA methylation),17

and interactome (e.g., protein-protein 

binding interactions)18–20 are ignored in 

conventional gene-disease association 

analyses, or they are treated as an outcome 

in gene to intermediate-phenotype studies. 

Inclusion of all measures (e.g., exposures, 

genes, intermediate phenotypes, and 

disease) in a structured joint analysis may 

provide valuable information in clarifying 

the separate component contributions, 

their aggregate effects in complex pathways, 

and ultimately, determining an individual’s 

overall risk of disease. Furthermore, each 

factor may contribute only a small effect 

that may be detected only when all relevant 

factors are considered together. Here, 

conventional regression models often 

reach their limits in attempting to model 

all these factors jointly.21

These diffi culties have led to the 

development of many data-mining statistical 

Ontologies: A Defi nition

An ontology is a formal structuring of knowledge.a

 For the purposes here, an ontology is a 

formal model of a domain of knowledge and consists of entities and relations between entities. 

An entity is simply a class, or category, of things that one wishes to model. An entity can be 

either a continuant (an object existing at a particular point in time) or an occurrent (an event 

or process occurring over time). Relations can be of many types, depending on the knowledge 

domain being represented. Two of the most common are the “is_a” relation, which specifi es one 

class as a subclass of another (e.g., human is_a mammal), and the “part_of” relation (e.g., fi nger 

part_of hand). Ontologies have their origins in Aristotelian philosophy, but computer science has 

driven a renaissance in ontology development and use—initially, by the problem of representing 

computational knowledge in the artifi cial intelligence fi eld, and subsequently, the Semantic Web.

a

Smith, B., W. Ceusters, B. Klagges, J. Kohler, A. Kumar, J. Lomax, C. Mungall, F. Neuhaus, A. L. Rector, and 

C. Rosse. 2005. Relations in biomedical ontologies. Genome Biology 6 (5): R46.
542

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

techniques aimed at detecting higher-order 

interactions.22 Such approaches include 

tree-based methods based on recursive 

partitioning of the data, such as random 

forests23 and logic regression.24 For these 

methods, the data are split by a single 

binary variable into two subsamples with 

varying trait or outcome characteristics. 

These subsamples are then investigated for 

further splits that may be warranted on the 

basis of additional variables. Higher-order 

interactions are inferred by identifying 

the combination of variables and the 

corresponding splits that identify particular 

subgroups. To avoid overfi tting of the data 

or fi nding splits that may exist only by 

chance, pruning techniques are applied 

to reduce the number of splits according 

to some pruning criteria. While these 

techniques can be effective at identifying 

higher-order interactions, there are some 

limitations as far as interpretability and 

fl exibility in the modeling (e.g., including 

covariates and forcing in certain effects). 

As an alternative within the regression 

framework, Millstein and colleagues 

proposed a method called the “focused 

interaction testing framework.”25 This 

approach tests main effects and interactions 

among multiple candidate genes by using 

a series of orthogonal tests in a staged 

manner. Specifi cally, this approach tests 

the main effect of each candidate gene in 

stage 1, followed by models with two-way 

interactions in stage 2, and with threeway interactions in stage 3. An algorithm 

based on controlling false discovery 

rates (FDRs)26–30 is used to control the 

experiment-wise type I error to a predefi ned 

level (e.g., 0.05). While simulation work 

has shown promise for this method when 

a single polymorphism is present within a 

gene, it is not clear how this would work 

when multiple correlated single nucleotide 

polymorphisms (SNPs) are included for 

several genes.

Data-mining techniques rely solely on the 

data for inference and ignore any prior 

knowledge that may exist regarding the 

factors of interest, specifi cally that these 

factors may be part of a biological pathway. 

An editorial in Cancer Epidemiology, 

Biomarkers & Prevention in 200531 laid 

out the case for pathway-driven research in 

molecular epidemiology and the need for 

further methods development in support of 

such research. The editorial described two 

broad types of approaches: one based on 

mechanistic modeling of specifi c pathways 

of interest, the other based on empirical 

modeling that incorporates what is known 

about the factors involved in a pathway 

in a fl exible manner without requiring 

such strong parametric assumptions. 

The mechanistic approach can be thought 

of as a structural equation model in 

which the topology of the structure is 

specifi ed by biological knowledge. This 

was fi rst introduced in an application 

to a case-control study of colorectal 

polyps in relation to well-done red meat 

consumption, tobacco smoking, and the 

various genes involved in the metabolism 

of polycyclic aromatic hydrocarbons 

and heterocyclic amines that these 

exposures produce.32 Here, the sequence 

of intermediate metabolite equilibrium 

concentrations was modeled in terms of 

a linear pharmacokinetic model, with 

person-specifi c metabolic rate parameters 

that depended upon their genotypes. 

The entire model, comprised of regression 

coeffi cients, individual- and genotypespecifi c population rate parameters, and 

their variances, was fi t by using Markov 

chain Monte Carlo (MCMC) methods. 

While the authors of this chapter are 

continuing to investigate the statistical 

performance of this approach via simulation 

studies in a more general setting, it is 

recognized that the major limitation of 

this approach is the expertise needed to 

construct the topology of the mechanistic 

model. Unfortunately, very few biological 

pathways are understood well enough 

to specify the specifi c mechanism from 

genes to outcome. While extensions exist 
543

Monograph 20. Phenotypes and Endophenotypes

to estimate the topology, these methods 

rely heavily on accurately measured 

intermediates—intermediate measures that 

are often unmeasured in epidemiological 

studies or measured on only a subsample 

of individuals.

As an alternative to these highly parametric 

models, a background and extensions 

to hierarchical modeling are presented 

here. Hierarchical modeling with prior 

covariates aims at stabilizing and informing 

estimation by incorporating similarities 

among regression estimates using categories 

describing biological similarities between 

genes and exposures. To narrow the space 

of possible regression models, the prior 

probability of including any variable as 

a function of known biology is further 

structured. This is accomplished via Bayes 

model averaging using stochastic variable 

selection. Similar to the parametric models, 

these hierarchical models utilize prior 

knowledge and information to aid inference. 

However, in contrast to the highly specifi ed 

mechanistic models, the knowledge only 

specifi es exchangeable classes or sets of 

factors with similarities. Often this reduces 

to a series of indicator variables based on 

expert opinion. Because these opinions 

may be susceptible to subjective infl uences, 

the use of ontologies is proposed. Ontologies 

attempt to represent the knowledge 

base in a computable form to provide 

“a shared and common understanding of 

some domain that can be communicated 

between people and application systems.”33

Thus, ontologies attempt to transform 

implicit knowledge into specifi c and 

explicit relations. Here, a discussion is 

provided of how these relations may be 

incorporated into the hierarchical models 

to aid in model selection, inference, 

and interpretation of conclusions from 

observational studies.

Background on 

Statistical Approaches

Models for Multivariant Data 

within a Candidate Gene

Assume an investigation of G candidate gene 

regions for gene association and possible 

identifi cation of specifi c causal variants 

for an identifi ed outcome. Further assume 

that each gene is independent from the 

others—that is, no linkage disequilibrium 

(LD) or no underlying biological interaction 

between genes. A quantitative trait outcome 

is the focus, but by using a generalized 

linear framework and the appropriate 

link function, the discussed methods 

can be extended to other types of traits.34

In addition to J exogenous or nongenetic 

covariates specifi ed in the design or 

covariate matrix W, assume that there are 

Mg fi nely spaced SNPs within each gene, g ,

and that for each polymorphism, genotypelevel information for all individuals in 

the study is obtained. bgm is used as an 

estimate of the risk from SNP m in gene g. 

For clarity, the modeling on SNPs is mainly 

discussed; however, the following analyses 

are also applicable to the modeling of other 

genetic markers such as microsatellites. 

First, treating the SNPs as independent, 

Mg separate regressions are performed, 

assuming a disease model of the form (1) 

where Xgm indicates the number of variant 

alleles for SNP m (i.e., additive coding) 

in gene g (e.g., Xgm = 1 if heterozygous 

and Xgm = 2 if an individual carries two 

copies of the variant allele), although one 

may also consider dominant or recessive 

genetic models.35 The parameter estimate 

djgm corresponds to the effects of the j th 

covariate on the outcome conditioned on 

SNP m in gene g.

(1)
544

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

Additional information may be incorporated 

if one accounts for the effect of an SNP 

conditional on all other SNPs within the 

candidate region. This is accomplished with 

a joint main effects model of the form (2).

Here, one sums over all the SNPs within a 

gene g but treat each gene as independent. 

The parameter estimate dgj corresponds 

to the effects of the j th covariate on the 

outcome conditioned on the SNPs in 

gene g. By accounting for the correlations 

between SNPs, this model may be useful in 

determining the independent contribution 

of each SNP within a given region, but it 

ignores any effects due to the arrangement 

of SNPs either on the same chromosome 

(i.e., haplotypes) or combinations of SNPs 

within an individual (i.e., interaction).

Aiming to capture synergistic effects 

between SNPs within a single candidate 

gene, the model in equation (2) may be 

extended to incorporate all interaction 

terms between SNPs. This model builds on 

the joint model in equation (2), with the 

form (3) where Xgm*g = Xgm*Xg and “...” 

indicates potential higher-order interaction 

terms. Here, the focus is only on all pairwise 

second-order interactions within a gene, 

although one may expand this model to 

higher-order interactions.

In the above models, a test of the statistical 

signifi cance for association to disease for 

each SNP can be obtained via a Wald test, 

score test, or likelihood ratio test (LRT) of 

each bgm. In addition, for the main effects 

model (2) and interaction model (3), one 

may perform an omnibus LRT comparing 

a full model in which bgm is estimated for 

each of Mg markers, , 

to the null model in which all SNP effects 

within the gene are set to zero. This 

global Mg-degree of freedom LRT provides 

evidence for an overall association of the 

chromosomal segment to disease.

When multiple SNPs are available within 

a gene, an alternative is to analyze the 

association of haplotypes to disease. For a 

given set of haplotypes, Hg , the haplotypic 

risk may be modeled by using a similar 

logistic regression for Hg–1 of the 

haplotypes (4).

Here, Xgh is used as an indicator variable 

denoting the number of haplotypes of type h

that an individual possesses within gene g. 

Usually for the haplotype model, the most 

common haplotype, hg1 , acts as the referent 

haplotype. Similar to the SNP analysis, a 

Wald test, score test, or LRT statistic may be 

calculated for each ggh to test associations 

with each haplotype. In addition, an 

omnibus LRT can test the overall association 

of the gene region to the trait. If haplotypes 

are unknown, one may substitute for Xgh

an expected probability for haplotype h.

36,37

Haplotype-based analysis as outlined in 

equation (4) has been advocated because 

of the potential reduction in the number 

of comparisons made (because there are 

usually fewer common haplotypes than 

common SNPs), the ability of haplotypes 

to better exploit patterns of LD, and the 

capacity to capture causal effects that may 

(2)

(3)

(4)
545

Monograph 20. Phenotypes and Endophenotypes

be due to a combination of variants on the 

same chromosome.38,39 However, to attain 

these potential benefi ts one must often 

narrow each region to identify a limited 

number of haplotypes; this is typically 

done by identifying blocks or continuous 

regions of high LD along the chromosome. 

This, in turn, makes haplotype analysis 

subject to how one determines these 

regions via the underlying LD structure 

and the accompanying uncertainty in that 

determination.40–43

As an alternative to haplotype analysis, 

Conti and Gauderman44 proposed a modifi ed 

pairwise interaction term to capture phase 

information in equation (3) to allow for 

most of the haplotype information in the 

data to be exploited, without having to 

consider all possible haplotype resolutions, 

as required for equation (4). At the genotype 

level within gene g, one can approximate 

haplotype information by modifying the 

second-order interaction terms in model (3) 

to describe the phase between pairwise 

SNPs, m and , and given the two haplotypes 

for individual i, hig1 and hig2. Specifi cally, 

the defi nition is given in equation (5).

The above coding assumes that the cis

confi guration or double variant haplotype 

is additive to disease. However, it is also 

possible that the trans confi guration of the 

variant alleles, as defi ned here, may be at 

higher risk. In this alternative case, one can 

specify the reverse coding for the double 

heterozygotes (i.e., if Xgm*Xg = 1, and hig1

or hig2 is the double variant haplotype, then 

Xgm.g = 1). This parameterization allows 

for separate tests for each SNP effect (bgm ),

pairwise phase term (bgm.g), and the overall 

contribution of the candidate region to 

disease via a global LRT. When the phase is 

unknown, the cis phase term is altered to 

refl ect the probability of a cis haplotype in 

the population for each pair of loci assuming 

Hardy-Weinberg equilibrium. As an example, 

assume two SNPs, A and B, each with two 

alleles, (A, a) and (B, b), respectively, as 

well as the four possible haplotypes, AB, 

Ab, aB, and ab. Thus, one can calculate 

the probability of the cis confi guration 

of the two SNP haplotypes as given in 

equation (6) where P(AB) , P(Ab) , P(aB) ,

and P(ab) are estimated from genotype 

data using the expectation maximization, 

or EM, algorithm.45 This is equivalent to 

altering the phase term in equation (5) 

by setting Xgm.g = if Xgm*Xg = 1. 

Thus, a genotype model with phase 

interaction terms not only avoids long-range 

haplotype estimation but also allows for 

the investigation of which SNPs are driving 

the association within each candidate gene. 

In addition, this model provides a fl exible 

framework for incorporating relations 

among numerous SNPs over several 

candidate genes.

Extensions to Multiple Genes 

and Exposures

The above models present various 

alternatives to the analysis of numerous 

variants within a candidate gene, with the 

(5)

(6)
546

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

increase in complexity aiming to better 

capture the LD and joint effects of multiple 

SNPs. The complexity may be warranted if 

a true causal SNP is not measured, and the 

analysis must rely on how the combination 

of measured SNPs captures the underlying 

effect. Of course, if a true causal variant(s) 

is measured, the most appropriate model 

may be the one that focuses solely on that 

variant(s), ignoring all others. In contrast, 

it may be the combination of several SNPs 

acting together that leads to variation in 

the outcome. In this case, simple tests of 

the marginal effect of each SNP may not 

be suffi cient, and interaction terms may 

be necessary to detect these higher-order 

joint actions. Thus, even within a single 

gene, there are uncertainties regarding 

the most appropriate model to use. 

These uncertainties only increase as one 

attempts to evaluate multiple candidate 

genes, each with multiple polymorphisms. 

The previous models treat each candidate 

gene as independent. This assumption may 

be adequate if the genes are unlinked, and 

therefore, SNPs between candidate genes 

are not in LD. However, because a set of 

candidate genes is most often selected with 

a priori knowledge that they act via an 

underlying biological mechanism or pathway, 

there is a good possibility that interactions 

may be present across genes. Their linear 

modeling framework may be expanded to 

accommodate multiple gene effects as given 

in (7) by summing over all possible genes G

and, within each gene, including all marker 

main effects and phase terms, and including 

interaction terms across genes (7).

For similar reasons, one may also want to 

investigate gene-environment interaction 

with measured covariates. This expands the 

model further as in (8).

The Challenge of Numerous 

Polymorphisms and Exposures

The investigation of associations for 

numerous polymorphisms within a 

single candidate gene and across multiple 

genes can raise concerns about multiple 

comparisons and sparse data bias in 

estimation. As one extreme approach, 

each polymorphism can be treated as 

independent, as in model (1). This approach 

is problematic: these reduced models may 

result in underestimated variance, and they 

do not account for the correlation that 

may exist among the polymorphisms, such 

as two polymorphisms in LD with each 

other within a gene region.46 Furthermore, 

treating each polymorphism as independent 

and relying on statistical tests across 

all polymorphisms can lead to issues of 

multiple comparisons. While one may 

perform adjustment in the declaration of 

signifi cance, such as a Bonferroni correction 

or control of the false discovery rates,27,47,48

these procedures may not accurately 

account for the relations between the 

(7)

(8)
547

Monograph 20. Phenotypes and Endophenotypes

polymorphisms, and they do not yield 

estimates of effect conditional upon other 

polymorphisms and exposures.

At the other extreme, the analyst may 

choose to model all main effects and 

interactions in one single model, as 

described in equation (8). Including all 

genetic polymorphisms and exposures in 

one model can lead to biased and unreliable 

estimates due to sparse data when the 

number of parameters approaches the 

number of individuals in the sample.21,49

These models tend to overfi t the data, 

resulting in estimates that explain the 

observed data well, but will lead to 

unrealistic predictions for any new data or 

biased inferences implied by the estimates. 

While conceptually attractive, in modern 

observational studies this approach quickly 

reaches the limits of the data, especially 

given the relatively large expense of 

enrolling an individual into a study in 

comparison to the rapidly dropping costs 

of obtaining a plethora of genotype-level 

information for a given individual. Often, 

a compromise in analysis approaches comes 

in the form of model selection or using the 

data and/or prior information to determine 

which set of polymorphisms and exposures 

may have substantial effects and only 

include those terms in the model. Models 

(1) through (7) may be viewed as types of 

reduced models in which polymorphisms 

and/or genes are assumed to be independent 

or interacting effects are assumed to be 

nonexistent. The use of knowledge or 

statistical tests is attractive in providing 

the analyst with simplifi ed models in which 

to estimate and interpret. However, it is 

important to realize that, by not including 

a certain term in the model, the analyst is 

implicitly stating a belief that, with 100% 

certainty, that term’s effect estimate is zero. 

Is previous knowledge reliable enough to 

justify the exclusion of a term, or is there a 

level of uncertainty? Clearly, relying solely 

on a priori decisions of what to include 

in the model is limited to the accuracy 

of the prior knowledge and, moreover, 

these a priori decisions ignore the data 

completely. In contrast, model selection 

procedures that use only the data to decide 

which terms to include in the model may 

underestimate the variance for each term 

by not accounting for the uncertainty in 

the selection procedure itself. Furthermore, 

automated procedures are prone to 

increased type I errors (i.e., false positive 

errors) by relying strictly on statistical 

cutoffs in the model-building process.50

Potential Solutions

To address these issues, an approach is 

proposed that uses hierarchical modeling 

and stochastic variable selection. 

Hierarchical modeling allows for the 

construction of complex probability models 

that incorporate higher-level information 

to yield more stable and plausible measures 

of association. Stochastic variable selection 

utilizes both the data and prior knowledge 

to determine which terms to include in 

the models, resulting in a guided model 

search leading to more representative and 

interpretable models. These approaches are 

possible because the hierarchical nature of 

the data—that is, polymorphisms within 

genes and genes within pathways—provides 

an opportunity to formalize a bottomup approach placing more emphasis on 

combinations of polymorphisms within 

a gene in comparison to combinations 

across genes. This hierarchy served as the 

foundation for the development of the 

various approaches outlined in equations 

(1) through (8). This culminates in the 

saturated model (8) in which one fi rst sums 

over SNPs main effects and SNP interaction 

effects within a gene, then SNP interaction 

effects across genes, and fi nally, over 

SNP × covariate interactions. It is proposed 

to formalize the combination of knowledgebased heuristics and model selection 

procedures in deciding which model is most 

appropriate. In this context, hierarchical 

modeling and stochastic variable selection 
548

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

as conventionally applied are briefl y 

introduced, and then these approaches are 

combined in a pathway-based model.

Hierarchical Modeling

A primary motivation for using hierarchical 

modeling and stochastic variable selection 

with structured priors is to describe the 

joint distribution of the underlying genetic 

structure and biological mechanism 

represented by the data and, notably, the 

uncertainty in representing that structure 

and mechanism. In doing so, the parameter 

estimates and corresponding uncertainty 

intervals will better capture the dependency 

between terms; therefore, the resultant tests 

will more effectively refl ect the evaluation of 

multiple polymorphisms and exposures.49–53

This is similar in spirit to an approach 

proposed by Wacholder and colleagues54 in 

which they introduced a Bayesian procedure 

for multiple comparisons that incorporates 

a prior specifi cation of the probability of 

any given polymorphism being associated 

with an outcome. While the notion of 

incorporating prior knowledge into testing 

and estimation is an important one, the false 

positive reporting probability of Wacholder 

and colleagues54 frames the decision into a 

binary choice between the null hypothesis 

and an effect size determined from estimation 

using observed data.55 Alternatively, one can 

specify a prior distribution for the effect size 

via a hierarchical model. By incorporating 

known information regarding the relations 

among the genetic polymorphisms, a joint 

distribution is specifi ed that both stabilizes 

the fi nal effect estimates and incorporates 

dependencies across multiple tests of 

association. Specifi cally, one can model the 

regression coeffi cients bgm from model (9) in 

terms of a regression on a vector of q “prior 

covariates” Zgm = (Zg1,...Zgq ). Thus, a secondlevel model of the form is adopted (9).

The design matrix Zl contains the secondstage covariates refl ecting higher-level 

relations between the polymorphisms, is a 

column vector of coeffi cients corresponding 

to these higher-level effects on the trait 

outcome, U is a column vector of random 

effects capturing the residual variation 

after adjustment by the relations in Zl, 

and R is a covariance matrix specifying any 

residual covariance among the regression 

coeffi cients. This hierarchy results in 

posterior estimates of effect for the 

polymorphisms , which are an inversevariance weighted average between the 

maximum likelihood estimates obtained 

from a conventional regression and the 

estimated conditional second-stage means 

. Thus, the fi nal estimates of effect are 

dependent upon the amount of information 

available. Estimates with less information 

may be unstable and will tend to have 

larger variances. This, in turn, will result 

in a fi nal posterior estimate more heavily 

weighted toward the prior information 

refl ected by the conditional second-stage 

means .

An important assumption here is that 

the modeled parameters, the bs, are 

exchangeable. Formally, this means that 

conditional on the information in Zl, 

the parameters have no prior ordering 

or grouping such that their joint 

distribution, f (b11,...,bgm), is invariant to 

permutations of the indexes g = (1,...,G )

and m = (1,...,M ). If this assumption 

holds, one may assume that the parameters 

are drawn from the same population 

distribution. In practice, the validity of 

this exchangeability assumption requires 

one to both focus on the interpretation 

of the bs and on how to group them via 

the design-matrix Zl. First, in linear 

regression, the bs represent the increase in 

the outcome, Y, given a one-unit increase 

in the independent variable, X. In the 

analysis of SNPs, these effect estimates are 

the increase in Y given one unit in change 

in the variant allele, assuming an additive 

(9)
549

Monograph 20. Phenotypes and Endophenotypes

coding for a selected variant. Here, across 

numerous SNPs, all the bs refl ect similar 

interpretations for their corresponding 

effect estimates, and the design matrix 

Zl may be constructed to incorporate a 

priori knowledge of SNPs having similar 

estimates of effect, for example. However, 

even in this simple example of multiple 

SNPs, care must be taken in how the 

effect estimates are interpreted and the 

impact this interpretation will have on 

the construction of the prior covariates. 

Specifi cally, for a particular SNP, one must 

consider which of the two alleles describes 

the increase in effect.56 If it is known that 

two SNPs might share similar risks—that 

is, are exchangeable classes conditional on 

the design matrix—one is really assuming 

that the two variant alleles as defi ned at the 

two respective SNPs share similar effects 

in the same direction. If one does not have 

knowledge of the direction of effect for 

each variant, then one may incorrectly 

specify sharing of two effects that act 

in opposite directions and are thus not 

from the same population distribution.57

Further complications arise as the analysis 

is expanded to include environmental 

covariates. On what scale does one defi ne 

the effect estimates corresponding to 

environmental covariates? And, is the 

corresponding effect estimate exchangeable 

with other covariate or SNP estimates? 

To address these diffi culties, it is proposed 

that the hierarchical model be modifi ed 

to model the test statistics rather than the 

effect estimates. This will be discussed later 

in the “Methods” section.

Model Selection via Stochastic 

Variable Selection

Although hierarchical modeling can 

stabilize the estimates of effect across 

the numerous terms, it is also of interest 

to highlight the linear combinations of 

SNPs and phase terms that best capture 

the gene-disease relations. Furthermore, 

it is desirable to account for varying prior 

beliefs that each polymorphism or term 

is involved in the trait outcome. That is, 

although all the genes were chosen with 

at least some belief that they are involved 

in the outcome under investigation, some 

genes are more likely to be involved given 

prior functional evidence or knowledge of 

the underlying biology. Similarly, within 

a specifi c gene, some polymorphisms 

are more likely to affect trait variation, 

with some polymorphisms chosen 

because of putative functional evidence 

and others chosen strictly to capture an 

unknown causal effect via LD. Thus, a 

stochastic variable selection approach 

is proposed to stochastically search the 

model space to highlight important SNP 

and phase terms and to average over all 

possible models. This approach has the 

advantage of accounting for uncertainty 

in model selection and allowing for a 

fl exible prior structure in which one can 

incorporate the relations among terms 

when selecting representative models. 

Previously,58 a variation was implemented 

of the stochastic search variable selection 

algorithm, fi rst presented by George and 

McCulloch,59 by introducing a latent 

variable, cv = 0 or 1, indicating whether a 

term, m, is included in a model (10).

The above specifi cation is conventionally 

implemented with a prior second-stage 

normal distribution with a mean of zero. 

While others have discussed the use of 

semi-Bayes or empirical-Bayes approaches 

to prespecify w and r 2,

59–61 a fully Bayesian 

approach can be adopted to integrate 

over posterior distributions using MCMC 

methods as implemented in WinBUGS.44,58,62

(10)
550

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

The posterior probability of c = 1 and the 

set of possible models visited during the 

stochastic search can be used to gauge the 

impact of each term and the combinations 

of SNPs and phase terms that best explain 

the relation of genetic variation to disease. 

These posterior probabilities will depend 

on the specifi ed prior distribution for c. 

The simplest form of the prior is to assume 

a binomial prior distribution for c (11) 

where qv is the probability that cv = 1. 

By assuming that qv is constant for all 

terms, one assumes that the corresponding 

parameters, bv , are exchangeable, as 

indicated in equation (10), and equally 

likely to be included in any given model. 

Specifi cally, main effects and interaction 

terms are equally likely. However, it is 

desirable to structure the prior in equation 

(11) to both guide the stochastic search 

to models that are more parsimonious in 

relation to the number of SNPs or terms 

included in any given model, and also to 

assist the stochastic search in the inclusion 

of phase terms, conditional on the inclusion 

of both “parent” SNPs used to defi ne the 

pairwise interaction term.63 Following Conti 

and Gauderman,44 the level of parsimony can 

be controlled by setting the prior for q for 

SNP main effects as Pr(qSNPs) = Beta(1,3). 

This gives a low expected probability 

(E[qSNPs] = 0.25) of inclusion for any given 

SNP and places emphasis on models with 

fewer SNPs.

Furthermore, following Chipman,63

a conditional probability for inclusion 

of phase terms, cgm.g, is defi ned as (12). 

This conditional prior reduces the model 

space visited by the stochastic search. 

This structure is invoked to refl ect the 

approximation of the underlying haplotype 

architecture with linear combinations 

of SNP and, if necessary, phase terms. 

Introducing a hierarchical dependency of 

phase terms on the “parent” SNP terms 

directs the stochastic search to simpler 

and more stable models, if appropriate. 

To offset this restriction and to encourage 

the exploration of the importance of 

phase, a higher probability is specifi ed for 

the inclusion of phase terms, conditional 

on the inclusion of both “parent” SNPs, 

Pr(qgm.g | cgm = 1, cg = 1) = Beta(3,1). This 

puts a higher prior expected probability 

on the phase terms (E[ qgm.g | cgm = 1, 

cg = 1] = 0.75). However, marginally, the 

prior expected probability for the inclusion 

of any given phase term is lower than the 

SNP main effects, E[ qgm.g] = 0.05. This 

structured prior in equation (12) acts to 

both guide the stochastic search to models 

that are more parsimonious in relation 

to the number of SNPs included in any 

given model, and also assists the stochastic 

search in the inclusion of interaction 

terms, conditional on the inclusion of both 

“parent” main effects used to defi ne the 

interaction term. In a similar fashion, the 

structured priors can be used to limit and/or 

guide the model search to combinations 

of SNPs across genes within subpathways 

and networks, and models can thus be 

summarized.64

Methods

General regression approaches to the 

analysis of multiple SNPs within and across 

candidate genes have been reviewed. Also, 

both hierarchical and model selection 

extensions to these regression models 

have been discussed. As mentioned earlier, 

(12)

(11)
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Monograph 20. Phenotypes and Endophenotypes

it is desirable to combine the benefi ts of 

borrowing information from exchangeable 

classes via hierarchical modeling with 

the ability to search the expansive model 

space via stochastic variable selection. 

However, the combination of these two 

approaches introduces two notable 

practical diffi culties. First, how does one 

reasonably defi ne exchangeable classes 

across SNPs (and possibly microsatellites), 

environmental factors, and all possible 

interaction terms? Second, how does one 

search such a vast space of applicable 

models in a reasonable amount of 

computational time? One can begin by 

fi rst framing the hierarchical model 

on unsigned summary statistics from 

the regression model rather than from 

the effect estimates. Thus, one only has 

to defi ne exchangeable classes for the 

unsigned summary statistics and avoid 

specifi cation for the effect estimates, which 

may vary in scale and direction. In addition, 

an empirical Bayes approach is used to 

regress these summary statistics on prior 

covariates to yield posterior estimates of 

the summary statistics and of the probability 

that a summary statistic is nonzero. 

This probability, in turn, determines the 

probability that a corresponding term is 

included in the model. 

Hierarchical Model

Following Lewinger and colleagues,65 one 

can begin by defi ning a Wald test statistic, 

, for a specifi c term, m,

in a regression model. This test statistic 

has an asymptotic v2 distribution with 

one degree of freedom and a noncentrality 

parameter . Since the interest is in 

defi ning exchangeable classes independent 

of the direction of effect, the focus is on 

the unsigned statistic and the 

corresponding noncentrality parameter 

, resulting in an asymptotic 

distribution for tv as v1(kv) = |N(kv,1)|. 

Specifi cally, this distribution is of the 

form (13) where u is the standard normal 

density. This places a second-stage 

distribution on the test statistics obtained 

from a fi rst-stage regression model. 

Of interest is deciding if this test statistic 

provides evidence for the SNP or factor 

being involved in the outcome of interest. 

If kv = 0, there is no association with the 

outcome. Positive values for kv indicate 

an association with increasing evidence 

as kv grows in magnitude. This can be 

formalized by modeling the ks as a mixture 

between a chi distribution with a positive 

noncentrality parameter and a point mass 

d(0) where kv = 0 (14).

Here, Hv is an indicator of whether a term 

is associated with the outcome and pv

is the corresponding probability of that 

association. Given that there is a true 

association, the expected noncentrality 

parameters are infl uenced by ev and r > 0.

The terms in a regression model are not all 

equivalent in regard to prior information 

that may be available (e.g., is the SNP 

known or predicted to affect gene 

function? Or, how important is the gene 

in a particular pathway?) and in regard to 

the hierarchical structure of the model 

(i.e., main effects versus interactions). 

Recognizing that differences exist across 

terms, both the probability that the 

association is true and magnitude of the 

noncentrality parameter are regressed on 

(13)

(14)
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1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

a set of prior covariates constructed to 

indicate the prior knowledge (15).

The intercept l0 is constrained to be 

nonnegative for identifi ability. For details 

regarding the estimation of the relevant 

parameters, see appendix 1.

Model Selection via Stochastic 

Variable Selection

The above hierarchical model incorporates 

prior information into the estimation 

of the posterior probability that a term 

is associated with an outcome and the 

magnitude of the test statistics. However, 

the model assumes that there is a given 

regression model in which to obtain the 

corresponding test statistics. Given the 

immense space of all possible models 

outlined in equations (1) through (9), 

it is desirable for the priors and the data 

to infl uence which models are examined. 

Following the previous discussion on 

stochastic variable selection, a vector of 

variables, , is introduced that indicates 

if a certain term is included in the model. 

Conditional on the selected terms, the test 

statistic is then calculated as (16).

To allow for both the priors and the data 

to infl uence model selection, one sets 

the probability that a term is included 

in a regression model to be equal to the 

probability that an association is true, 

that is, (17).

Because of the hierarchical nature of the 

terms within a given regression model, 

a similar conditioning as outlined in 

equation (12) for the inclusion of interaction 

terms is included. For more details 

regarding the model selection algorithm, 

see appendix 2.

The prior structure specifi ed via Zl and 

Zp and incorporated into this hierarchical 

model serves two purposes. First, it allows 

the posterior estimates of and to 

borrow information from exchangeable 

classes, and second, via , it will guide the 

stochastic search to regression models that 

include more biologically relevant terms. 

The overall impact of these structured 

priors is to narrow the space of possible 

models searched via the stochastic 

algorithm. Thus, instead of being faced 

with an impossible number of main effect 

and interacting terms and possible models, 

the process is reduced with biological 

knowledge to an informed and guided 

search procedure.

In the process of the stochastic search, 

the data will serve to update the prior 

probability and inform one of the impact 

of each factor via the posterior estimates 

for , , and the posterior probability of 

certain terms being selected. For inference 

regarding the posterior magnitude of the 

test statistic, calculation (18) is made. For 

the posterior probability of an association to 

be true, calculation (19) is made.

Because the fi nal inference regarding the 

importance of each factor via the posterior 

probability of association and the probability 

of each factor being selected must refl ect 

the prior probability structure, one relies on 

Bayes factors for inference.66 Bayes factors 

are the ratio of the posterior probability 

odds, comparing two hypotheses to the prior 

probability odds, and can be thought of as 

a type of marginal likelihood ratio for the 

comparison of two hypotheses. For example, 

calculate as in equation (20).

(15)

(17)

(16)
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Monograph 20. Phenotypes and Endophenotypes

The numerator is calculated by using the 

frequency distribution of cv = 1 from the 

MCMC iterations when examining the 

association to Y. In a similar fashion, the 

denominator is calculated by calculating 

the frequency distribution of cv = 1 under 

the null of no association to Y, or effectively 

removing Y from the conditioning. Also, 

a Bayes factor should be calculated for the 

posterior probability of a true association. 

For each hypothesis comparison, a Bayes 

factor between 3 and 20 can be considered 

as “positive” evidence, 20 to 150 as “strong” 

evidence, and greater than 150 as “very 

strong.”66

Prior Knowledge and Ontologies

The list of candidate genes has been chosen 

because they are involved in biological 

pathways suspected in the trait process. 

Thus, in branching out from assessing 

the impact of a single candidate gene to 

comprehensively evaluating the factors 

within interconnected pathways, one is 

faced with the a priori possibility that many 

interactions, often of higher order, will exist 

between factors (as represented in model [9]).

For genetic association studies, one wants 

to encode in computational form prior 

knowledge that can either (1) estimate the 

likelihood of effects from a specifi c genotypic 

or phenotypic variable or (2) hypothesize 

a relationship between two or more 

variables that would otherwise be assumed 

to be independent: genotype-genotype 

relationships, phenotype-phenotype 

relationships, and genotype-phenotype 

relationships. Knowledge of these types has 

been used in association studies, but only 

in either the study design phase or as an 

independent analysis, not as an integral part 

of a global analysis as proposed here. As a 

familiar example, “coding SNPs” are often 

prioritized in genotyping studies because 

they cause a change in the protein product 

of a gene—either a missense (amino acid 

substitution) or nonsense (premature stop 

codon) change—that is, more likely to 

have a phenotypic effect than a random, 

noncoding SNP. Interactions are often tested 

between polymorphisms within a particular 

gene because they have a relatively high 

likelihood of interacting simply by virtue 

of being in the same gene. The LD provides 

knowledge of haplotype structure in the 

population that can be used to select SNPs 

for genotyping.67

Prior Knowledge About Potential 

Functional Effects of Genetic 

Polymorphisms

A number of prediction algorithms exist 

for estimating the probability that a given 

genetic polymorphism may have phenotypic 

consequences. Most human polymorphisms 

are believed to have little or no detectable 

phenotypic effect;68 it is almost certainly 

true that in any given genetic association 

study, the probability that a randomly 

chosen polymorphism affects the phenotype 

(18)

(19)

(20)
554

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

of interest is vanishingly small. A number 

of computational methods have been 

developed to estimate the probability that a 

polymorphism has a functional effect, such 

as the Sorting Intolerant From Tolerant, or 

SIFT, procedure;69 PolyPhen (polymorphism 

phenotyping);70 and subPSEC (substitution 

position-specifi c evolutionary conservation).71

Most of these methods apply to 

nonsynonymous coding SNPs (SNPs that 

result in an amino acid substitution in the 

protein product of a gene), and they predict 

the probability specifi cally of a deleterious 

effect. The most commonly used methods 

analyze either (1) related protein sequences 

and judge a polymorphism to be deleterious 

if it causes a substitution at a highly 

conserved site (because conservation is due 

to natural selection against substitutions 

at that site)72–74 or (2) how the change may 

disrupt known elements of protein 3D 

structure (e.g., substitutions in the interior 

of proteins are more likely to destabilize 

protein structure).72,75 Figure 12.1 shows 

examples of these analyses, applied to the 

*2 variant of CYP2A6 (cytochrome P450, 

subfamily 2A, polypeptide 6), which changes 

leucine 160 to histidine (L160H). This 

substitution completely inactivates the 

enzyme. This substitution can be predicted 

as deleterious by using evolutionary 

analysis (fi gure 12.1A): all CYP2A (and 2B 

and 2G, not shown) enzymes have either 

leucine, isoleucine, or phenylalanine (large 

hydrophobic amino acids) at that position, 

so histidine, which is polar, would be 

predicted to be deleterious. A structurebased analysis (fi gure 12.1B) shows that 

position 160 is on the interior of the protein, 

also predicting a probable deleterious effect.

Analysis of conservation patterns can also 

be applied to noncoding DNA sequences,76

although there is generally less statistical 

power than for coding SNP analysis. 

Noncoding DNA sequences generally diverge 

faster than protein sequences, and local 

mutation rates can be diffi cult to estimate 

in the absence of known, neutrally evolving 

sites (which in proteins can be estimated 

from synonymous coding changes).

It is important to note that these analyses 

provide prior information about the 

likelihood of a particular genetic change 

resulting in a phenotypic change, although 

not necessarily the phenotypic change 

(or changes) assayed in any particular 

association study.

Systems Biology and Genetic 

Association Studies

Of interest is building a model of the 

system (including both biological and 

environmental variables) that represents 

how a perturbation in any one variable 

will affect other variables in the system. 

The more information in the model, the 

more information can be used to infer effects 

of changes in genetic or environmental 

variables. In genetic association studies, 

the minimal set of variables includes 

genetic polymorphisms and phenotypic 

effects (outcomes). One way to visualize 

this system is in terms of a network model, 

in which nodes represent variables and edges 

represent potential paths for propagating 

perturbations to the system. Examples of 

networks are given in fi gure 12.2. In this 

model, a variant allele (e.g., a polymorphism) 

of a gene is a “perturbation” of the system 

relative to the wild-type allele. An association 

between a polymorphism and a phenotype 

implies that the perturbation due to the 

polymorphism was propagated through 

the system to affect the phenotype. If the 

phenotype is a defi ned event—for example, 

smoking cessation—then the polymorphism 

might affect the probability of occurrence of 

the event. If the phenotype is a quantitative 

trait—for example, cigarettes smoked per 

day—then the polymorphism might affect 

the magnitude or variance of the trait.

In the simplest case, a genetic association 

study will collect data only on one or 
555

Monograph 20. Phenotypes and Endophenotypes

Figure 12.1 Evolutionary and Structural Analyses for the CYP2A6*2 Variant

A.

B.

L160

CYP2A6

L160

Note. In the top fi gure (A), evolutionary conservation in homologs of human CYP2A6 at position 160 predicts that CYP2A6*2 will 

have reduced function. Only large, hydrophobic amino acids (L:leucine, F:phenylalanine, I:isoleucine) are found in all homologues 

while the CYP2A6*2 variant codes for a histidine at this position. The left panel shows a phylogenetic tree of some mammalian 

homologues of human CYP2A6, the middle panel provides information about each sequence including the gene and organism 

(mouse: Mus musculus; rat: Rattus norvegicus; dog: Canis familiaris; pig: Sus scrofa; cow: Bos taurus; macacque: Macaca mulatta;

human), and the right panel shows part of the sequences of the corresponding protein near position 160 in CYP2A6. The tree 

was constructed using nonsynonymous divergence as the distance metric. mRNA = messenger RNA. In the bottom fi gure (B), 

protein structure analysis predicts that CYP2A6*2 will have reduced function. Leucine 160 is buried inside the hydrophobic core 

of CYP2A6, where a substitution to histidine in CYP2A6*2 would be predicted to destabilize the protein. Picture drawn with 

ProteinWorkshop.77
556

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

more phenotypes and one or more genetic 

polymorphisms. If there is no prior 

knowledge about the potential relationships 

between these genetic polymorphisms and 

the phenotype, one implicitly assumes a 

completely connected network, in which 

any polymorphism can affect any phenotype 

by any path. An example of such a network, 

for the genes and phenotypes considered 

in this paper, is shown in fi gure 12.2A. 

In this network, each polymorphism is 

assumed, prior to data analysis, to have 

an equal probability of affecting the 

phenotype. All interaction terms are also 

considered a priori to be equally probable. 

The model makes no assumptions about 

the underlying mechanisms by which 

genetic (or environmental) perturbations 

will propagate to the phenotypes of interest. 

In this sense, it is hypothesis free, although 

in practice most genetic association 

studies focus on “candidate genes” that are 

hypothesized a priori to have a potential 

role in a particular phenotype.

An increasing amount of information is 

becoming available about the underlying 

structure of these systems networks, 

which can be applied to genetic association 

studies. Computational representations are 

now available for a number of biochemical 

pathways,78 modeling detailed (mostly 

intracellular) interactions between proteins, 

genes, and small molecules. One relevant 

example is the nicotine metabolism pathway 

now available in the HumanCyc79 and 

PANTHER Pathways80 databases. Higherlevel representations are also available that 

model the relationships between various 

“constructs” (concepts) in a fi eld, such 

as nicotine dependence, withdrawal, and 

smoking relapse.81 These data sources can 

be used to defi ne a network structure that 

relates genetic and environmental variables 

to phenotypes (and endophenotypes) 

in a detailed manner, as illustrated in 

fi gure 12.2B. This network differs from 

the network in fi gure 12.2A in two main 

aspects. First, the actual number of edges 

(connections) can be much smaller than 

in the “hypothesis free” network, which 

reduces the “search space” of likely models 

for genotype-phenotype effects. Second, 

there are intermediate nodes between 

Figure 12.2 Examples of Networks

CYP2A6

UGT2B7

CYP2D6

UGT1A4

Metabolic

clearance of

nicotine

3HC/COT

CYP2B6

A.

Note. Shown is a generalized network of genes and all possible relations within the nicotine metabolism pathway. 3HC/COT = 

trans 3-hydroxycotinine to cotinine ratio.
557

Monograph 20. Phenotypes and Endophenotypes

Figure 12.2 Examples of Networks (continued)

}

Elimination

of nicotine

Metabolism

of nicotine

Rate

Ratio product:reactant

CYP2A6 UGT1A4

Age 

Gender

Ethnicity Influences

Precedes

Precedes

Catalyzes

Catalyzes Catalyzes

Catalyzes

Renal

excretion

of nicotine

Conversion of

nicotine to 

nicotine iminium ion

Conversion of

nicotine iminium ion

to cotinine

Conversion of 

cotinine to 

3ÕOH cotinine

Conversion of 

cotinine to 

cotinine-glucuronide

Metabolism

of cotinine

3HC/COT

Metabolic

clearance of 

nicotine

Conversion of

nicotine to 

nicotine-glucuronide

{

B.

Note. Shown is a simplifi ed Nicotine Pharmacokinetics Ontology. 3HC/COT = trans 3-hydroxycotinine to cotinine ratio.
558

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

genotype and phenotype that can serve as 

“endophenotypes,” which can be assayed 

to model (and validate) mechanistic 

relationships between genotype and 

phenotype. For the purposes of this paper, 

the network structure can be used to 

estimate prior probabilities of effects and 

interactions that are different for different 

genetic and environmental variables, as well 

as for different interaction terms.

Using Ontologies to Represent Prior 

Knowledge About Relationships 

Between Variables

In genetic association studies, there is 

often prior knowledge of relationships 

that is applied to hypothesis testing. 

The most commonly used relationships 

are physical or genetic distance-based 

Figure 12.2 Examples of Networks (continued)

Metabolic

clearance

of nicotine (CI)

UGT1A4

CYP2B6

CYP2D6

CYP2A6

3 ́-OH-cotinine

to cotinine 

ratio (3HC/COT)

UBT2B7

UGT1A4-3HC/COT T

UGT2B7-3HC/COT T

UGT1A4-CI T CYP2A6-CI T

CYP2A6-3HC/COT T

CYP2A6-UGT1A4 T

C.

Note. Shown is the network characterization of the effect of each gene (via the test statistic T) and its impact on the outcome of 

interest (either nicotine clearance or 3’-OH-cotinine to cotinine ratios) and the relations to each other as specifi ed in the ontology.
559

Monograph 20. Phenotypes and Endophenotypes

relationships between individual singleposition genotypes, such as LD, or, for 

functional relationships, presence in 

the same gene or in a list of “candidate 

genes.” Candidate genes are generally 

selected using prior knowledge, such as 

previously reported associations with the 

same or similar phenotypes or known or 

hypothesized biological relationships. Here, 

it is suggested that ontologies provide a 

useful formalization of these relationships, 

enabling the incorporation of multiple types 

of prior knowledge into computational 

analyses of genetic association data.

The goal of genetic association studies 

is to uncover statistical correlations 

between genetic (germline) variation and 

phenotypic variation. To be useful for 

genetic association studies, an ontology 

must represent concepts (or “entities”) 

in the domains of both genotypes and 

phenotypes and the relations between 

these concepts.

An ontology is a formal structuring of 

knowledge.82 For the purposes here, an 

ontology is a formal model of a domain 

of knowledge, and consists of entities and 

relations between entities. An entity is 

simply a class, or category, of things that 

one wishes to model. An entity can be 

either a continuant (an object existing at 

a particular point in time) or an occurrent

(an event or process occurring over time). 

Relations can be of many types, depending 

on the knowledge domain being represented, 

but two of the most common are the “is_a” 

relation, which specifi es one class as a 

subclass of another (for example, human 

is_a mammal), and the “part_of” relation 

(e.g., fi nger part_of hand). Ontologies have 

their origins in Aristotelian philosophy, 

but computer science has driven a 

renaissance in ontology development 

and use—namely, by the problem of 

representing computational knowledge 

in the artifi cial intelligence fi eld and the 

Semantic Web.

Why Build an Ontology?

Formalizing a particular knowledge 

domain can have two main impacts on a 

scientifi c research fi eld. First, it can help 

to clarify and communicate the (often) 

implicit models used by scientists to 

formulate and test hypotheses. It clarifi es 

the models by making them formal and 

explicit. Trying to formalize an implicit 

model can often be a useful exercise in 

itself, but for knowledge domains that are 

too large or complex for a single scientist 

to be an expert in all relevant subdomains, 

it is critical. A structured representation 

can help to clearly communicate a model 

to other researchers and allow iterative 

community development and revision of 

the model.

Second, an ontology can make expert 

domain knowledge accessible to 

computation. A computer may not yet 

“understand” the knowledge (in the human 

sense, whatever that means), but it can take 

advantage of the relations between entities 

in computational models and numerous 

useful algorithms, such as those aiding 

humans to fi nd relevant information on 

the Web. The focus in this paper is on one 

such application: ontologies can facilitate 

the building of computational models 

for the testing of genotype-phenotype 

associations.

Clearly, then, ontology development 

will have the greatest impact on a given 

scientifi c area of inquiry when the fi eld 

is suffi ciently complex to be beyond the 

expertise of (most) single researchers. 

Such fi elds are interdisciplinary almost 

by defi nition, including or depending on 

many subfi elds of specialized expertise. 

As noted by Karp,83 a scientifi c theory can be 

structured “within a formal ontology so that 

it is available for computational analysis.” 

The resulting computational symbolic 

theory enables “analysis and understanding 

for theories that would otherwise be too 
560

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

large and complex for scientists to reason 

with effectively.”

How To Build an Ontology Relating 

Genotypes and Phenotypes

Ontologies have been developed for a 

number of biomedical domains, including 

anatomy, gene function, biochemical 

pathways and mutant and strain phenotypes 

in experimental model organisms such 

as mouse, zebrafi sh, fruit fl y, and yeast. 

The experiences of these groups, as well 

as groups from other domains, have led 

to a number of proposed best practices for 

ontology development.84–88 Here, the focus 

is on building an ontology that relates 

genotypes and phenotypes. A well-known 

ontology development process89 has been 

adopted for this purpose.

Step 1: Determine the domain and scope.

A genotype-phenotype ontology must 

include concepts from the molecular level 

(such as gene and genetic variation) to the 

phenotypes (such as a disease), including 

any intermediate-level concepts that may 

bridge the genotype-phenotype gap (such 

as biochemical pathways, or particular cell 

types or organs). Obviously, the concepts 

will be specifi c to the phenotype(s) of 

interest. This is a good point in the process 

to defi ne “competency questions”;90 these are 

questions that the ontology, once completed, 

should be able to address. For a genotypephenotype ontology, the competency 

questions should cover such areas as

■ What are the prevailing models for 

disease etiology?

■ What are the relevant phenotypes/

endophenotypes?

■ What biological processes are thought, 

or hypothesized, to be involved?

■ What is known at the molecular level 

about these biochemical pathways and 

underlying genes?

■ Are there any clues from previous 

association studies, or from linkage or 

twin studies?

■ What are possible confounding/

environmental factors?

Step 2: Consider using existing ontologies.

As mentioned above, a number of 

ontologies exist already in the biomedical 

domain. One of the best sources for 

existing ontologies is the Open Biomedical 

Ontologies (OBO) project.91 Existing OBO 

ontologies cover many relevant domains 

such as anatomy (Foundational Model of 

Anatomy92), biological processes (Gene 

Ontology93), molecular “events” such as 

biochemical reactions (Event Ontology 

[EVO]94), phenotype-directed qualities 

(Phenotype and Trait Ontology [PATO]), 

sequence types and features, such as 

genes and genetic variation (Sequence 

Ontology95), and human disease (Disease 

Ontology [DO]). OBO ontologies are 

completely open, and most have ongoing 

active discussion groups and a process for 

community maintenance and expansion of 

the ontology. Of the ontologies mentioned 

above, all but EVO and DO are also part of 

the OBO Foundry project, which ensures 

adherence to strict principles of ontology 

development.91

Step 3: Enumerate important terms.

This step involves simply listing terms that 

are important in the domain of interest. 

At this point, it is not necessary to decide 

whether these terms will become entities 

(a class, or category desirable to model) or 

qualities (inherent “attributes”) of entities. 

These terms will help to refi ne the scope 

of the ontology and to provide the basis for 

formalizing ontology.

Step 4: Defi ne entities (classes) and 

relationships between entities.

At this stage, one begins to defi ne entities 

and relationships between them. When 

possible, terms from existing ontologies 

should be used. When a new entity is 
561

Monograph 20. Phenotypes and Endophenotypes

introduced, it is critical that a defi nition 

also be provided, to ensure that the term 

can be interpreted correctly (preferably 

even by a nonexpert). Most of the necessary 

relationship types already exist in the 

OBO Relation Ontology, although one 

additional relationship, infl uences, was 

found to be useful for describing putatively 

causal relations between entities that are 

critical for a model of the existing domain 

knowledge. For example, in the Nicotine 

Pharmacokinetics Ontology given here 

(NPKO, fi gure 12.2B), age infl uences

metabolism_of_nicotine.

A number of software packages are available 

for simplifying the task of constructing 

ontologies. The added benefi t of using 

one of these packages is that at the end 

of the process, the ontology is stored in a 

standard ontology format, such as the OBO 

format. As a result, the ontology can be 

imported into a number of software tools, 

such as those developed for the Ontology 

Web Language, or OWL,84,96 for analyzing 

the ontology for consistency and for 

computational reasoning over the ontology. 

Among the most popular packages for 

developing biomedical ontologies are OBOEdit97 and Protégé.98

For biochemical pathways, the BioPAX 

Ontology99 is beginning to enter widespread 

use. Well-known pathway resources such as 

BioCyc,100 Kyoto Encyclopedia of Genes and 

Genomes,101 Reactome,102 and PANTHER103

have made a relatively large number of 

pathways available in BioPAX format99

and SBML (Systems Biology Markup 

Language).104 SBML has the advantage of 

being able to specify quantitative data such 

as reaction rate constants, but BioPAX has 

greater expressive capability for genomic 

and protein sequence data that is critical for 

treating genetic variation data. If a relevant 

pathway does not yet exist in suffi cient 

detail in one of these resources, PANTHER 

Pathways has a community curation 

Web site where domain experts can take 

advantage of the CellDesigner105 program’s 

interface to draw a pathway and store a 

formal ontology representation directly 

from the drawing.

Step 5: Defi ne qualities important 

for representing phenotypes.

Once the entities are defi ned, qualities can 

be enumerated for each of the entities. 

The emerging standard for phenotypes 

is the PATO syntax. In this “bipartite” 

entity:quality defi nition, a phenotype 

(e.g., metabolic clearance of nicotine) is 

expressed as a combination of an entity 

(e.g., metabolism of nicotine) and a 

quality inherent in the entity (e.g., rate). 

Phenotypes can be quantitative or 

qualitative. For example, a particular 

chemical reaction type (entity) might 

have a rate (quality), which would then 

be specifi ed by a particular quantitative 

measurement (value).

Most ontology development projects begin 

with the formation of a small working 

group that brings together expertise in the 

relevant knowledge domain, with expertise 

in ontology construction. In the biomedical 

fi eld, the National Center for Biomedical 

Ontology (NCBO) has been established; 

one of its primary missions is to provide 

the ontology construction expertise to 

facilitate development of new ontologies for 

biomedical applications.106 The NCBO is an 

excellent resource for expert guidance and 

software tools for this purpose.

The product of the initial working group is 

a draft ontology. If appropriate, this draft 

ontology can provide a framework and 

starting point for a larger, communitydriven project to expand and refi ne the 

ontology. At this point, the ontology enters 

a completely new phase of development. 

Community projects such as this require 

an infrastructure for managing discussions 

to come to a resolution on proposed 

changes and then rapidly incorporate 

accepted changes to the ontology. The OBO 
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1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

project provides a platform for facilitating 

community ontology projects, leveraging 

resources originally designed to support 

Open Source software development projects, 

such as the SourceForge Web site.107

Example: Nicotine 

Metabolism

Data

As an example, data are used from a study 

involving the volunteer-based Northern 

California Twin Registry.1,2 This study of 

the heritability of nicotine metabolism 

included 278 individuals between the 

ages of 18 and 65 years. Individuals 

were excluded for the following: greater 

than 30% above normal weight range; 

pregnancy; use of known drug metabolismaltering medications (e.g., barbiturates, 

phenytoin, rifampin [or INN, rifampicin]); 

uncontrolled hypertension or diabetes; 

heart, lung and cardiovascular disease; 

cancer; liver and kidney diseases; substance 

abuse or dependence; positive human 

immunodefi ciency virus status; history 

or evidence of hepatitis B or C; and 

discomfort with venipuncture procedures. 

Both nonsmokers and smokers were 

recruited. Further details regarding the 

study description can be found elsewhere.1,2

Quantitative data were obtained to measure 

the impact of genetic variants on the 

disposition kinetics and metabolism of 

nicotine after systemic administration. 

As such, participants of the twin study 

were administered intravenous deuteriumlabeled nicotine and cotinine (the major 

proximate metabolite of nicotine) and blood 

samples were obtained for genotyping. 

From blood concentrations obtained 

at intervals over 72 hours and urinary 

excretion data, pharmacokinetic parameters 

were estimated using model-independent 

methods.108 Here, attention is confi ned to 

two outcomes of interest: the total clearance 

of nicotine, and trans 3-hydroxycotinine 

to cotinine ratio (3HC/COT). Trans 3-

hydroxycotinine is the major metabolite 

of cotinine, and its formation is catalyzed 

almost or entirely exclusively by CYP2A6,

the enzyme that is primarily responsible 

for the metabolism of nicotine. The 

3HC/COT ratio has been used as a marker 

of CYP2A6 activity and of the clearance rate 

of nicotine.109 Because this data set has a 

limited number of smokers, and previous 

analyses have demonstrated that inference 

for pharmacokinetics of nicotine remained 

largely unchanged after controlling 

for smoking status, smoking status is 

not included in the present analysis for 

simplicity. The analysis is limited to only 

Caucasians (N = 211), and age is included 

as the only covariate for demonstration 

purposes. Genotypes available for analysis 

include “wild-type” CYP2A6*1 and its most 

common variants: CYP2A6*2, CYP2A6*4, 

CYP2A6*7, CYP2A6*8, CYP2A6*9, 

CYP2A6*10, CYP2A6*12; four SNPs within 

CYP2B6; a single SNP within CYP2D6;

seven SNPs in UGT1A4; and four SNPs in 

UGT2B7.

Analysis

To begin, a univariate analysis was 

performed by comparing the means for 

the kinetic parameters by each variant 

by using a mixed linear model for the 

fi rst-stage likelihood, , in 

which a random effect is included for 

twins to control for nonindependence. 

For CYP2A6, a previously reported analysis 

was followed,108 and three categories 

were created on the basis of the impact of 

individual genotypes on nicotine clearance, 

fractional clearance, cotinine clearance and 

the 3HC/COT ratio: (A) *1/*1; (B) *1/*9

or *1/*12; and (C) any of the following 

variants: *1/*2, *1/*4, *9/*12, *9/*4, *9/*9

(CYP2A6*7, *8, *10 were not found in this 

data set). Thus, the linear model has two 

dummy variables for groups (B) and (C), 

refl ecting the difference in means relative 
563

Monograph 20. Phenotypes and Endophenotypes

to the referent group (A). For the remaining 

SNPs in the other genes, an additive 

coding representing the number of variant 

alleles was used. For the three SNPs 

with individuals with missing genotypes, 

the expected coding was substituted as a 

function of allele frequency. While this 

may result in an underestimated variance, 

one should not expect an appreciable 

difference in that the number of individuals 

with missing values is small (N = 1, 6, 

or 7, respectively). Age is included as a 

continuous covariate in every model.

For the hierarchical stochastic search, 

the fi rst step was to outline a full model in 

which there are 18 main effects for gene 

polymorphisms (two dummy variables 

for CYP2A6 and 16 SNPs across the 

other four genes), one main effect for 

age, and 170 pairwise interaction terms 

that include within and across gene 

interactions and gene-by-age interactions. 

For interpretability, the two dummy 

variables for CYP2A6 are always included 

in the model together. Because the SNPs 

within a single gene were in relatively 

low LD, only the conventional interaction 

term (i.e., a deviation form additivity) was 

modeled and a phase term as described 

in equation (5) was not created. For the 

stochastic search, a hierarchical constraint 

on interaction terms was included, allowing 

interactions in the model only if their 

parental main effect terms are included. 

For this example, interaction terms were 

included to illustrate the feasibility and 

computational challenge of searching 

over a substantial model space. However, 

in this particular application, one should 

not expect to be able to detect interaction 

effects because of the limited sample size. 

Under favorable assumptions for two genes 

interacting (i.e., common allele frequencies 

and a large effect size—comparable to that 

observed in Benowitz and colleagues108 for 

the main effect of CYP2A6)—the power to 

detect an interaction with this sample is 

about 10%–20%.

Ontology and Incorporation 

into the Hierarchical Stochastic 

Search Model

An Example Ontology Linking 

Genotypes and Phenotypes 

for Nicotine Pharmacokinetics

As part of the Pharmacogenetics of 

Nicotine Addiction and Treatment project 

funded by the National Institute on Drug 

Abuse, the authors of the chapter are 

developing a draft ontology in the areas of 

nicotine pharmacokinetics, dependence, 

and treatment outcomes. Figure 12.2B 

shows part of the initial draft of the NPKO 

relevant to the outcome phenotypes 

addressed in this paper. The ontology 

has several notable properties. First, it is 

hierarchical (more properly, the structure 

is a directed acyclic graph, or DAG, meaning 

that a child class can have more than one 

parent). Second, it spans the range from 

genotype to phenotype, representing 

high-level phenotypes, intermediate-level 

“endophenotypes” down to molecules 

and genotypes. Third, phenotypes are 

represented using the emerging PATO 

standard,110 shown as two adjacent ontology 

terms, an entity (black typeface) and a 

quality (blue typeface) in fi gure 12.2B.

Using the Nicotine Pharmacokinetics 

Ontology to Derive Priors

A discussion follows on how the information 

encoded into the ontology can help to defi ne 

priors in the context of the Bayesian model 

selection process outlined previously.

What does fi gure 12.2B reveal in terms of 

prior information regarding the infl uence 

of genes on the phenotypes? In other 

words, how might the different effect 

estimates as summarized in the test 

statistics be related to each other? The fi rst 

phenotype, 3HC/COT, is the ratio of the 
564

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

concentrations of 3HC and cotinine, and 

therefore variation in any genes connected 

in the network (fi gure 12.2B) to either 

3HC or cotinine, or both, could have an 

effect on this ratio. CYP2A6 catalyzes the 

conversion of 3HC to cotinine, which would 

clearly be expected to have the primary 

effect on the 3HC/COT ratio. However, 

since UGT1A4 activity depletes cotinine by 

conversion to cotinine-glucuronide and 

UGT2B7 activity depletes 3HC by conversion 

to 3HC glucuronide, variation in both 

UGT1A4 and 2B7 could also affect the 

3HC/COT ratio.

The second phenotype, metabolic clearance 

of nicotine, relates to the rate at which 

nicotine is converted to other compounds. 

In the simplifi ed NPKO (fi gure 12.2B), there 

are two pathways for nicotine metabolic 

clearance: nicotine can be converted into 

either cotinine or nicotine glucuronide, 

reactions catalyzed by CYP2A6 and UGT1A4,

respectively. The ontology, therefore, 

specifi es that variation in both CYP2A6

and UGT1A4 would be expected to affect 

nicotine metabolic clearance. One can use 

further prior information—namely, in most 

individuals, more nicotine was found to be 

metabolized through the cotinine pathway 

than the nicotine-glucuronide pathway, by 

a factor of about 15,111 to specify the prior 

belief that CYP2A6 variation will have a 

larger effect on nicotine metabolic clearance 

than does UGT1A4. The relative rates of 

these reactions are stored in the ontology 

in the following form:

conversion_of_nicotine_to_nicotine_

iminium_ion:relative_ _rate

Compar conversion_of_nicotine_to_

nicotine-glucuronide

M 15,

where Compar denotes “in comparison to” 

and M denotes “measurement,” using the 

PATO standard terms.

The relations between genes and 

phenotypes, represented in the ontology 

(fi gure 12.2C), therefore provide a list of 

nonzero priors for the effects of variation in 

each gene on each of the phenotypes. They 

also provide expected relative contributions 

to the phenotype; namely, CYP2A6 is 

expected to have the primary effect on both 

3HC/COT and nicotine metabolic clearance. 

For simplicity, the expected effect of CYP2A6

on the 3HC/COT ratio was set to be four 

times as large as the expected effect of either 

UGT2B7 or UGT1A4. The ontology can also 

provide prior effect estimates for gene-gene 

interactions. CYP2A6 and UGT1A4 are both 

involved in the two phenotypes, nicotine 

clearance and 3HC/COT, so the gene-gene 

interaction term is expected to be nonzero 

for these two genes in both phenotypes.

Finally, relatively little is known regarding 

the specifi c polymorphisms within each 

gene, so a single prior value applicable to 

all SNPs within a gene is assigned. Taken 

together, the ontology yields the following 

matrix of priors:

Incorporating Priors into Statistical 

Analysis

In addition to the above prior covariates for 

each respective analysis, an intercept term 

and a dummy prior covariate are included 

for main effects versus interaction effects. 

The same prior covariates are used for both 

the means and probability portions of the 

Gene

Metabolic 

clearance 

of nicotine

3HC/COT 

ratios

CYP2A6 4 4

CYP2B6 0.5 0.5

CYP2D6 0 0

UGT1A4 1 1

UGT2B7 0 1

CYP2A6-UGT1A4 1 1

All other interactions 0 0
565

Monograph 20. Phenotypes and Endophenotypes

mixed model. Furthermore, in the means 

model, the intercept of the noncentrality 

parameter ev is constrained to be equal to 

the expectation of a chi distribution under 

the null of no associated terms in the 

regression model for identifi ability. This 

causes the interpretation of the remaining 

effects of the prior covariates on the 

magnitude—that is, the ls—to refl ect a 

deviation from the null expectation. In the 

probability portion of the model, p0 = 1

and p1 < 0 are constrained, corresponding 

to the effects of all the terms and the main 

effects on the probability of inclusion. These 

constraints limit the inclusion of main 

effects via p1 and thus guide the stochastic 

search to more parsimonious models 

in terms of the number of main effects 

included in the model. This is important 

in that the relatively small sample size in 

this example (N = 211) prohibits the fi tting 

of models with too many main effects. 

However, once a set of main effects is 

included in a model, one wants to encourage 

the exploration of models with interactions. 

Thus, by setting p0 = 1, the expectation of 

the inclusion of an interaction conditional 

on the inclusion of the parental main effects 

is relatively high (21).

Sensitivity to Prior Specifi cation

To compare and investigate the sensitivity 

of inference to the prior covariate 

specifi cation, two alternative specifi cations 

are used. First, the above prior covariate 

matrix is altered by the assumption that 

CYP2A6 has the same impact as UGT1A4.

This is accomplished by replacing the “4” 

with a “1” in the previously described prior 

covariate matrix. Second, in assuming 

that the prior knowledge is limited, 

a prior covariate design matrix is used 

with fi ve dummy variables indicating the 

gene in which a specifi c polymorphism 

is found. Here, it is assumed that all 

the polymorphisms within a gene are 

exchangeable or share a common mean 

with a different mean for each gene. This 

allows sharing among polymorphisms 

within a gene, but not across genes.

Results

Univariately, polymorphisms for groups 

(B) and (C) for CYP2A6 were signifi cantly 

associated with measured nicotine 

clearance levels as seen in table 12.1 

(tB = 2.15, p-value = 0.03; tC = 3.86, 

and p-value = 0.0002, respectively). In 

addition, SNP 4 within UGT1A4 had a 

statistically signifi cant result (tSNP4 = 2.19, 

p-value = 0.03). Because of the small sample 

size, a model could not be fi tted in which all 

possible polymorphisms were included as 

represented in equation (2), thus limiting 

any further exploration of full joint models 

with interactions without some type of 

model selection procedure.

The hierarchical stochastic search model 

was implemented by using the statistical 

software R.112 Posterior inference was based 

on 50,000 samples from a single chain 

after discarding the fi rst 10,000 samples 

(i.e., burn-in) to ensure that the fi nal 

inference is independent of the starting 

values.113 Visual inspection of time series 

and sensitivity to inference over time 

was used to check for convergence and 

model performance. The burn-in period 

was selected because it was found that the 

constraints in both the means and the 

probability portions of the model allowed 

for a nonzero probability of including any 

given main effect in the model. This results 

in suffi cient mixing within the model 

space and a very limited dependence on 

the starting model. For example, under 

the null of no association between any 

(21)
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1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

polymorphisms and nicotine, the average 

probability of including any term was 3%. 

This encourages sampling the model space, 

but since the probability of including a term 

is nonzero under the null, it also highlights 

the need to compare posterior estimates 

of the probability of a true association and 

the probability of including a given term 

conditional on the data to those under 

the null via Bayes factors to obtain valid 

inference. To guarantee suffi cient mixing 

within the local model space, a random 

walk over 500 iterations was incorporated in 

which an additional main effect to the model 

under evaluation was included.

The focus initially is on the posterior 

estimates of the magnitude of the 

association for nicotine clearance. Recall 

that the ontology specifi ed that the two 

groups of polymorphisms in CYP2A6 would 

have twice the effect of polymorphisms 

in UGT1A4 and that there would be no 

effect for all other variants in other genes. 

Table 12.1 Results for Nicotine Clearance

Gene Variant

Univariate Hierarchical model

T 1 p-value 2 3 4 5

CYP2A6 Group A 

(*1/*1)

— — —— — —

Group B 

(*1/*9 or *1/*12)

2.15 0.03 3.53 0.09 3.16 17.38

Group C

(*1/*2; *1/*4; *9/*12; 

*9/*4; *9/*9)

3.86 0.0002 3.53 0.94 534.37 17.38

CYP2B6 SNP 1 1.22 0.23 2.14 0.02 0.62 0.82

SNP 2 1.82 0.07 2.27 0.04 1.33 0.82

SNP 3 1.56 0.12 1.78 0.04 1.36 0.74

SNP 4 1.69 0.09 2.32 0.03 0.90 0.82

CYP2D6 SNP 1 0.21 0.83 1.51 0.02 0.49 0.70

UGT1A4 SNP 1 0.38 0.70 2.19 0.01 0.40 0.84

SNP 2 0.02 0.98 2.18 0.01 0.40 0.84

SNP 3 0.07 0.94 1.64 0.02 0.56 0.81

SNP 4 2.19 0.031 2.01 0.08 3.90 0.76

SNP 5 0.55 0.58 1.69 0.02 0.52 0.77

SNP 6 1.57 0.12 2.43 0.02 0.75 0.81

SNP 7 0.81 0.42 2.16 0.02 0.65 0.84

UGT2B7 SNP 1 1.22 0.23 1.58 0.03 0.84 0.73

SNP 2 0.12 0.90 1.83 0.02 0.42 0.76

SNP 3 0.50 0.62 1.53 0.02 0.54 0.67

SNP 4 0.05 0.96 2.23 0.02 0.58 0.73

Note. Results were obtained by using a conventional univariate regression analysis and from the hierarchical stochastic search by 

using informative prior covariates derived from the ontology.

1

The absolute value of the v test statistic obtained from the Wald-type test from a univariate regression model.

2

Posterior expectation of the v test statistic.

3

Posterior expectation of the probability that the association is true.

4

Bayes factor for the probability that the association is true.

5

Bayes factor for the inclusion of the corresponding term in the regression model.
567

Monograph 20. Phenotypes and Endophenotypes

This structure is refl ected in the posterior 

estimates for summarized in table 12.1. 

The two groups of polymorphisms in 

CYP2A6 have similar posterior estimates of 

CYP2A6,B = 3.53 and CYP2A6,C = 3.53. Similarly, 

the estimates for the posterior magnitude of 

the test statistics for SNPs within UGT1A4

are shifted toward each other, albeit at a 

lower magnitude. The combined effect of 

the prior covariates is more clearly seen 

in fi gure 12.3, which demonstrates the 

shrinkage of the original test statistic to 

the posterior estimates. First, by grouping 

all main effects via the second covariate in 

Zl , all the posterior estimates are shrunk 

upward toward a group effect. Furthermore, 

within this upward shrinkage, the prior 

covariate based on the ontology allows 

further borrowing of effects within CYP2A6

(the bold solid lines) to be four times the 

magnitude of that of the SNPs within 

UGT1A4 (the thin solid line). All other 

polymorphisms (dashed lines) have upward 

shrinkage based solely on the grouping of 

main effects.

In focusing on the posterior estimates of the 

probability of a true association , it can be 

seen that CYP2A6 group C has a much larger 

probability of being true ( CYP2A6,C = 0.94) in 

comparison with group B ( CYP2A6,B = 0.09) 

despite their similar posterior estimates 

for . This is due to the contribution of 

the data for each polymorphism group 

refl ected through their corresponding fi rststage test statistic of T = 3.86 and T = 2.15, 

respectively. Furthermore, because the 

posterior probability is not zero under the 

null, inference into the signifi cance of this 

estimate should be made via the Bayes 

factor. Here, very strong evidence can be 

seen for a true association for group C in 

Figure 12.3 Shrinkage of Test Statistics

4

Test statistic

0

1

2

3

T ~Ev 

Note. Shown is the shrinkage of the univariate test statistics T by the hierarchical stochastic search to yield the posterior 

estimates of effect size . For each term, the test statistic obtained from the univariate analysis is paired with the posterior 

estimate, demonstrating shrinkage to a conditional mean specifi ed by the prior covariate structure.
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1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

CYP2A6 (BF( CYP2A6,C ) = 534.37) as well 

as positive evidence for an association of 

group B in CYP2A6 (BF( CYP2A6,C ) = 3.16). 

In addition, an indication can be seen for 

positive evidence of an association for 

SNP 4 in UGT1A4 with BF( UGT1A4,4 ) = 3.90. 

Although these conclusions are qualitatively 

similar to conclusions based on the results 

obtained from the univariate analyses, there 

are some notable differences. For example, 

the test statistics obtained for group B 

in CYP2A6 and for SNP 4 in UGT1A4 are 

similar, suggesting comparable evidence 

for an association. However, they have very 

different posterior estimates for with 

CYP2A6,B = 3.53 and UGT1A4,4 = 2.01, refl ecting 

the borrowing of information via the prior 

structure; mainly, the test statistic for 

group B is shrunk upwards toward group C 

within CYP2A6. When one accounts for 

the infl uence of the prior structure and 

focuses on the Bayes factors for a true 

association, there is slightly more evidence 

for SNP 4 in UGT1A4: BF( UGT1A4,6 ) = 3.90 

versus BF( CYP2A6,B ) = 3.16. The evidence for 

group B is tempered because of the strong 

prior for the infl uence of CYP2A6—that is, 

it would have a fourfold increase in effect. 

In contrast, it was believed that UGT1A4

would have a much smaller effect, and thus, 

the impact of the data relative to the prior 

is greater.

The hierarchical stochastic search model 

did not fi nd any evidence for interactions 

between polymorphisms or between the 

polymorphisms with age. Most likely, this 

is mainly a refl ection of the limitations 

for obtaining statistical signifi cance for 

interactions with such a small sample size 

(N = 211) of a narrow age range. However, 

one of the major goals of incorporating 

prior knowledge was to have an effi cient 

stochastic search across the model space. 

In this regard, guiding the stochastic search 

via the proposal distribution as a function 

of the probability that an association is true 

results in a very high acceptance rate during 

the MCMC iterations (across the various 

analyses, on average 90% of the proposed 

models are accepted). At fi rst glance, this 

high acceptance rate may indicate poor 

mixing in the MCMC chain, leaving one 

unable to move around in the model space. 

To some extent, the exploration of the 

entire space is limited, but sampling of 

models believed to be more biologically 

plausible is actively encouraged. Specifi cally, 

the prior structure given from the ontology 

indicates the desirability of investigating 

interactions between CYP2A6 and UGT1A4.

As evidence of a guided search, it was found 

that, conditional on the inclusion of the 

two polymorphism groups within CYP2A6,

interactions with SNPs within UGT1A4 are 

included in 3% of the models searched. 

In contrast, in performing a stochastic 

variable selection and substituting a 

binomial proposal distribution that is not 

dependent on a prior structure but has 

probabilities refl ective of the hierarchical 

model under the null, it is found that 

interactions between CYP2A6 and UGT1A4

are included in less than 0.1% of the 

models searched.

To gauge the sensitivity of the results to 

prior specifi cation, two additional analyses 

were run using different prior covariates. 

To mimic the infl uence of incorrect priors 

and assuming a lack of knowledge for 

CYP2A6, a “1” was substituted in place of 

the “4” for CYP2A6 in the previous prior 

covariate matrix. This had little impact 

on the fi nal inference in regard to the 

posterior estimates corresponding to 

CYP2A6, further indicating that the data are 

driving the results for CYP2A6. However, 

under this prior structure, estimates for 

polymorphisms within UGT1A4 were 

slightly attenuated because they were no 

longer shrunk upward toward the CYP2A6

estimates, for SNP 4 UGT1A4,4 = 1.73. Despite 

the change in estimates, the Bayes factor for 

the posterior probability of a true association 

still indicated some positive evidence for 

SNP 4, BF( UGT1A4,4 ) = 3.81. With the genespecifi c prior covariate matrix that includes 
569

Monograph 20. Phenotypes and Endophenotypes

a set of dummy variables indicating which 

gene an SNP is in, qualitatively similar 

results are found. Of course, inference in 

terms of both posterior estimate and the 

magnitude of the Bayes factors varies, 

refl ecting differences in the borrowing 

of information across polymorphisms as 

specifi ed by the prior structure (results 

not shown).

Results for the analysis in regard to 3HC/COT 

are presented in table 12.2. As before, similar 

patterns are apparent in posterior estimates 

with the most notable evidence provided 

for the two groups of polymorphisms in 

CYP2A6. Of note are the estimates for SNPs 

in UGT2B7. For 3HC/COT, a prior covariate 

matrix was specifi ed that placed a slight 

emphasis on UGT2B7 in conjunction with 

CYP2A6. For the posterior estimation for 

the magnitude , the four SNPs in UGT2B7

are shrunk upward, refl ecting a borrowing 

of information from the larger CYP2A6

estimates.

Table 12.2 Results for 3HC/COT Ratios

Gene Variant

Univariate Hierarchical model

T 1 p-value 2 3 4 5

CYP2A6 Group A 

(*1/*1)

— ——— — —

Group B 

(*1/*9 or *1/*12)

-2.53 0.01 4.39 0.09 2.83 13.87

Group C

(*1/*2; *1/*4; *9/*12; 

*9/*4; *9/*9)

-4.9 4.0E-06 4.39 0.92 291.79 13.87

CYP2B6 SNP 1 -1.47 0.15 1.17 0.06 1.28 1.10

SNP 2 2.39 0.02 1.35 0.14 3.47 1.02

SNP 3 1.31 0.19 1.16 0.04 1.02 0.64

SNP 4 2.18 0.03 1.22 0.09 2.25 1.05

CYP2D6 SNP 1 -0.51 0.61 1.32 0.03 0.58 0.86

UGT1A4 SNP 1 0.13 0.90 0.95 0.03 0.71 1.03

SNP 2 -0.68 0.50 1.00 0.03 0.77 1.03

SNP 3 0.93 0.35 0.98 0.04 1.03 0.97

SNP 4 1.56 0.12 1.02 0.05 1.19 0.86

SNP 5 0.89 0.38 0.95 0.03 0.80 1.00

SNP 6 1.55 0.12 0.97 0.04 1.20 1.06

SNP 7 -0.34 0.74 0.94 0.03 0.71 1.00

UGT2B7 SNP 1 -0.94 0.35 1.03 0.03 0.87 1.05

SNP 2 0.16 0.87 0.86 0.03 0.80 0.97

SNP 3 0.6 0.55 0.95 0.04 1.08 1.05

SNP 4 -2.49 0.01 1.36 0.17 4.90 1.16

Note. Results were obtained by using a conventional univariate regression analysis and from the hierarchical stochastic search by 

using informative prior covariates derived from the ontology. 3HC/COT = trans 3-hydroxycotinine to cotinine ratio.

1

The absolute value of the v test statistic obtained from the Wald-type test from a univariate regression model.

2

Posterior expectation of the v test statistic.

3

Posterior expectation of the probability that the association is true.

4

Bayes factor for the probability that the association is true.

5

Bayes factor for the inclusion of the corresponding term in the regression model.
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1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

Discussion

Statistical modeling does have limits, 

especially when evaluating multiple 

exposures and genes with a limited 

sample size. In light of these limitations, 

conventional univariate analyses can be 

appealing in their ease of implementation 

and straightforward interpretation. However, 

building upon the knowledge that guided 

the initial selection of the SNPs and genes 

for investigating, most researchers feel 

compelled to go beyond the independent 

treatment of each gene and attempt to 

model more complex joint action and 

interactions. Often, this includes ad hoc 

criteria for model building on the basis of 

prior biological knowledge with the analyst 

balancing the complexity of each model 

investigated with real world limitations of 

the data, such as multicolinearity, sparse 

data bias, and instability. Rarely do fi nal 

models accurately refl ect the statistical 

costs in terms of multiple comparisons or 

the uncertainty in arriving at a given “best” 

model. As an alternative, the analyst may opt 

to use strictly data-driven approaches and 

search for signifi cant interactions by using 

statistical criteria. Within this context, the 

method presented here represents the use of 

a hierarchical model together with a means 

of using prior knowledge to guide statistical 

model selection by means of an ontology.

The idea of placing more emphasis on 

more biologically relevant SNPs is not 

new. Several other approaches have 

been presented. The false positive report 

probability uses prior information in the 

form of an investigator’s prior belief that 

an association is true. Likewise, a weighted 

FDR and Bayesian FDR approach have 

been presented to incorporate outside 

information on the a priori impact of a 

particular SNP. However, these approaches 

rely on prespecifi cation of the weight or 

prior for every SNP and interaction term 

without allowing the data to enhance 

or attenuate the infl uence of the prior 

information. In contrast, the hierarchical 

modeling approach discussed here relies on 

prespecifi cation of only how it is believed 

that SNPs and interaction terms are related, 

but it relies on the data to determine 

the degree or the weight of the various 

specifi cations or prior covariates. This has 

the advantage of giving some fl exibility in 

the prior specifi cation, and correspondingly, 

fi nal inference and conclusions may be 

less sensitive to those specifi cations. 

Thus, the posterior estimates for the 

importance of each term and interaction 

are conditional on the prior knowledge, 

and within this modeling framework 

these parameters are naturally interpreted 

in the context of that knowledge. This 

avoids having post hoc justifi cation and 

rectifi cation of conventional results with 

what is known. As knowledge changes, 

the analyses can be rerun to gauge how 

new knowledge combined with the sampled 

data may alter fi nal conclusions. While 

sensitivity analysis is a vital part of any 

comprehensive Bayesian analysis, with 

subjective priors one does not expect the 

results to be quantitatively similar across a 

variety of prior structures. In fact, the goal 

is just the opposite. One would like to use 

subjective knowledge as a guide to models 

that would not have been found otherwise 

or to enhance posterior estimates that may 

have been overlooked without shrinkage 

to other SNPs or genes. But, one must also 

be careful that the fi nal inference does not 

solely refl ect specifi c prior beliefs. The use 

of Bayes factors gauges the evidence for the 

conclusions conditional on the data and in 

the context of the priors.

Ultimately, it is a fi ne line between 

deterministic weights and informative 

priors. The authors of this chapter believe 

that this line is drawn by the quality of 

the prior information. While much has 

been done with hierarchical modeling in 

epidemiological analysis, relatively little 

research has been done on the quality of 
571

Monograph 20. Phenotypes and Endophenotypes

the prior covariate specifi cation. Here, 

an approach is described that attempts 

to formalize the prior knowledge via an 

ontology. Ontologies provide a mechanism 

for investigators to specifi cally structure 

their prior knowledge in a usable format. 

Of course, what is specifi ed in the ontology 

is not the truth, but only refl ective of the 

available state of knowledge. As such, 

ontologies can and should be dynamic. 

In fact, how an ontology changes over 

time is instructive in indicating areas for 

advancement and further research.

Ontologies provide a structure for encoding 

prior knowledge or hypotheses. The existing 

PATO syntax allows for specifying 

relationships between concepts and for 

specifying relative quantities. An example 

has been given of how both relationships 

and relative quantities can be used to 

derive priors in the context of Bayesian 

model selection, which is, as far as known, 

a novel application of biological ontologies. 

The ontology provided a structure for 

estimating the prior probability that a given 

gene is involved in a phenotype of interest, 

as well as the probabilities that different 

pairs of genes interact with each other.

The part of the NPKO used here is based 

on extensive evidence from experimental 

studies, but it would also be possible 

to encode a more speculative, and even 

completely untested, hypothesis into 

an ontology structure to guide model 

selection. These priors would ensure that 

the hypothesis will be tested, with high 

probability, during the model selection step. 

Of course, whether the hypothesis is accepted 

will depend on the posterior probabilities 

after considering the data, and the strength 

of the evidence as refl ected, for example, 

in the Bayes factors reported here.

In the example given, the ontology structure 

has been converted into quantitative 

priors by using expert interpretation. 

The reasoning followed was simple 

and could be straightforwardly coded 

into a computational algorithm. Graph 

connectivity was used between phenotypes 

and genes to determine which priors 

would be nonzero: if a gene was closely 

connected to the phenotype of interest, 

the prior was set to be greater than zero. 

Relative measurements (of reaction rates, 

in this case) was also used from previous 

experiments to set the relative values of 

nonzero priors.

One can expect that one of the most 

valuable contributions of an ontology for 

larger studies will be in prioritizing the 

testing of potential gene-gene and SNP-SNP 

interactions. The sample data set used was 

too small to draw any conclusions regarding 

interactions, but for larger studies that 

assay a large number of polymorphisms, 

prioritizing interactions will be critical. 

Ontologies are one way of estimating a 

priori probabilities of different interactions. 

For instance, genes that are closely 

connected in the ontology relationship 

network can be hypothesized as being more 

likely to interact.

Finally, it is straightforward to extend 

this approach to provide different priors 

for different individual polymorphisms. 

For instance, rather than setting the prior 

expected effects for all polymorphic CYP2A6

alleles to be the same (relative to the *1/*1

homozygote), functional polymorphism 

predictions could have been used to provide 

additional prior information. For instance, 

allele-specifi c priors could have been used 

for CYP2A6. The CYP2A6*9 and CYP2A6*12

alleles are known to have reduced activity 

(*9 reduces gene expression through an SNP 

in the TATA box,114 while *12 includes exons 

from the closely related CYP2A7, resulting 

in 10 amino acid substitutions relative to 

*1 and reduced activity115). The CYP2A6*2

allele116 has a single amino acid substitution 

that completely inactivates the enzyme, 

and, in the CYP2A6*4 allele, the entire gene 

is deleted.117 One could, therefore, have used 
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1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

this prior knowledge (much of which could 

have been predicted from sequence data 

alone, e.g., fi gure 12.1) to specify different 

priors for the different CYP2A6 genotypes, 

with the largest effects expected for 

individuals having the *4 or *2 alleles. Using 

functional information about each SNP 

yields a prior probability that a given SNP 

will affect gene function. To estimate a prior 

for the effect of the SNP on the phenotype 

of interest, one could take the product of 

(1) the conditional prior of the effect of a 

gene on the phenotype of interest (given an 

effect on gene function) estimated from the 

ontology and (2) the prior of the effect of the 

SNP on gene function.

When including ontological knowledge in 

statistical analysis, it is desirable to capture 

potential real world complexities while also 

addressing the practical limitations of the 

data—for example, sample size. It is believed 

that a stochastic variable selection procedure 

via a hierarchical model offers a potential 

approach to knowledge-based pathway 

analyses. Given modeling limitations, one 

can probabilistically restrict the number 

of terms included in any specifi c model via 

constraints on the conditional probabilities 

of including a given term. This limits the 

overall complexity for a regression model 

evaluated for each iteration of the stochastic 

search. However, when inference is averaged 

over all the models, one can begin to describe 

complex relations between SNPs and genes. 

In addition, it was demonstrated how prior 

knowledge can guide the stochastic search 

effi ciently within the model space, yielding 

more biologically plausible models (in terms 

of the defi ned prior covariates). Of course, 

there is a trade-off of directing the search 

too narrowly and possibly missing some 

well-fi tting models or of having a broad, 

nonfocused search in which one may spend 

most of the stochastic search in an area 

in which the models are not biologically 

relevant. Again, this hierarchical framework 

is a fl exible approach that allows multiple 

sources of information (via the prior 

covariates) to be included while having 

the advantage that their actual infl uence 

on posterior estimation and the stochastic 

search does not need to be prespecifi ed but 

can be estimated from the data.

Details of the specifi c performance of 

the statistical model presented here in 

terms of estimation, sensitivity to prior 

covariates, ability to identify signifi cant 

terms, and so on are being pursued in 

a separate, more statistically oriented 

paper. While this statistical framework 

makes use of MCMC methods for the 

stochastic search across the model space, 

for computational effi ciency maximum 

likelihood approaches to estimate the fi rststage generalized linear model parameters 

were chosen. Thus, a simplifi cation is 

made when conditioning on the fi rst-stage 

maximum likelihood estimates when 

modeling the second-stage mixture model. 

Clearly, one can imagine a fully Bayesian 

analysis in which the uncertainty in the 

fi rst-stage estimates is propagated into 

subsequent stages. However, for model 

selection purposes across such a large model 

space, it was decided that computational 

effi ciency trumps subtle refi nement in 

estimation. Likewise, the second-stage 

mixture model uses a maximum likelihood 

estimation procedure as opposed to a 

fully Bayesian approach. Again, this 

decision was made for computational 

effi ciency, and comparisons to the fully 

Bayesian approach for the mixture model 

demonstrated suitable performance.65 With 

these simplifi cations, the computations are 

now on the order of hours as opposed to 

days with actual times depending on the 

specifi c computer. In addition to statistical 

issues surrounding estimation, there are 

also issues with how one deals with missing 

data across all the variables. At the heart 

of this model selection procedure is a 

likelihood comparison that requires the 

likelihoods to be calculated on the same 

number of individuals. Thus, individuals 

cannot be removed across models. In the 
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Monograph 20. Phenotypes and Endophenotypes

nicotine example, analysis was limited to 

individuals with complete data or, for the 

few individuals with missing genotypes, 

an expected score was imputed. As the 

number of polymorphisms examined 

increases, the number of individuals with 

any missing data will also increase, making 

this issue a much more serious concern. 

While the specifi cs of missing data analysis 

is beyond the scope of this particular work, 

the MCMC procedure for model selection 

provides a fl exible framework in which to 

implement an imputation strategy.

Hierarchical modeling and stochastic 

variable selection can offer some robustness 

against multiple comparisons when deciding 

statistical signifi cance. In 2007, Wakefi eld118

formalized the control of false discoveries 

in genetic epidemiology studies via a prior 

specifi cation by presenting a Bayesian False 

Discovery Probability (BFDP). This method 

is relatively simple to implement and has 

the advantage of other proposed methods, 

such as the false positive report probability,54

by specifying distributions for the null and 

alternative hypotheses for a given test of 

association. Furthermore, the BFDP may 

be calibrated to explicitly incorporate the 

costs of false discovery versus the costs 

of nondiscovery. The major limitation of 

this approach is that it treats each test of 

association across all polymorphisms as 

independent. The approach described in 

this chapter overcomes this limitation by 

representing a joint distribution over all the 

test statistics. That is, this method places 

a full distribution upon the test statistics 

(i.e., the second-stage mixture model) 

and allows for the posterior estimation 

of a probability of a true association 

conditional on the prior covariate structure. 

Because the hierarchical nature of the 

data—that is, SNPs within genes and genes 

within pathways—provides an opportunity 

to test from the “bottom up” in this 

analysis procedure, the method places 

more emphasis on tests of main effects 

or combinations of SNPs within a gene 

in comparison to SNP interactions across 

genes. By formalizing the joint distribution 

of all the test statistics, the prior 

beliefs in the relations between them, 

and the uncertainty of the model form, 

the parameter estimates and corresponding 

uncertainty intervals will better capture the 

dependency between terms. This, in turn, 

results in tests that more effectively refl ect 

the evaluation of multiple factors. This is in 

contrast to more conventional approaches, 

such as the Bonferoni correction and 

controlling for false discovery rates, 

in which a uniform adjustment of the 

critical level is made across all p-values. 

By focusing on the posterior estimates 

for fi nal inference, some of the multiple 

comparison pitfalls may be avoided. 

However, when relying on Bayes factors to 

gauge statistical signifi cance, the infl uence 

of the prior structure is removed and the 

focus is solely on what the data tell us. Here, 

one must be careful when determining a 

cutoff level for declaring signifi cance and 

should consider the number of comparisons 

made in deciding what is truly signifi cant.

Summary

An overview has been presented of the 

analysis of numerous SNPs across multiple 

genes in a pathway focusing on the overall 

idea of incorporating prior knowledge via 

ontologies into a Bayesian hierarchical 

framework. The method presented is viewed 

as a unifi ed approach by guiding statistical 

model selection with one’s knowledge. 

In this framework, the method is based on 

the belief that polymorphisms, genes, and 

corresponding interactions vary in their 

biological plausibility and that by formally 

incorporating this differentiation into the 

statistical analysis, some of the diffi culties in 

evaluating numerous factors may be lessened.

While there are many diffi culties in pathwaybased analyses, a pathway perspective has 

considerable promise. Many insights of 
574

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

relations and assumptions may be gained 

by properly representing one’s knowledge of 

the underlying processes via ontologies and 

corresponding graphical representations. 

Furthermore, the formal incorporation 

of one’s knowledge into the statistical 

framework can both guide the model 

search to more relevant models and allow 

interpretation of fi ndings specifi cally in the 

context of one’s knowledge base. Ultimately, 

confi rmation of results by further studies is 

the key to valid conclusions in this area of 

research. However, this hierarchical model 

selection procedure with the incorporation 

of prior knowledge can help not only in 

identifying individual components but also 

in the characterization of the underlying 

complexity of a particular trait’s variation.

Conclusions

1. The available knowledge of nicotine 

dependence arises largely from studies 

that model the independent association 

of candidate genes with outcome 

measures. Such studies often fail to 

refl ect the complexity of interacting 

factors and discrete events that can 

infl uence smoking behavior and, 

therefore, may not provide a clear 

picture of biological mechanisms 

affecting nicotine dependence.

2. A promising approach to the study of 

nicotine dependence involves the use 

of prior biological knowledge about 

the relations between genotypic and 

phenotypic variables in a hierarchical 

modeling framework. This allows 

prior knowledge to aid in estimating 

specifi c genotypic effects and to guide 

a stochastic search over all possible 

statistical models.

3. The use of ontologies is a promising 

new direction for the elucidation of the 

genetic basis of nicotine dependence. 

An ontology is a construct or model that 

represents entities in both genotypic 

and phenotypic domains as well as their 

interrelations. The use of an ontology 

permits the modeling of hierarchical 

relationships by using directed acyclic 

graphs spanning genotypes and 

endophenotypes and phenotypes, while 

taking advantage of prior knowledge to 

quantify these relationships, making 

them amenable to computational 

analysis.

4. A study of nicotine metabolism 

that used data from the Northern 

California Twin Registry to examine 

the total clearance of nicotine and the 

trans 3-hydroxycotinine to cotinine 

ratio, with the Nicotine Pharmacokinetics 

Ontology as a framework, showed a 

signifi cant association between specifi c 

polymorphisms for CYP2A6 and 

measured nicotine clearance levels as 

well as statistically signifi cant results 

for single nucleotide polymorphism 4 

within UGT1A4.

5. Hierarchical modeling combined with the 

use of an ontology defi ning relationships 

between constructs of interest represents 

a promising area for further research 

in studying a possible genetic basis 

for nicotine dependence as well as for 

understanding the interaction between 

genetics and social and environmental 

infl uences on tobacco use and 

dependence.
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Monograph 20. Phenotypes and Endophenotypes

Appendix 12A. Estimation for the 

Hierarchical Model

A two-step estimation procedure is performed. First, for a given regression model, obtain the 

maximum likelihood estimates for and from a generalized linear model likelihood, 

f (Y | X,b), and calculate the corresponding test statistic, tv. Second, conditional on the set of 

test statistics, the contribution to the likelihood for each term in the second-stage model is 

the marginal distribution of tv.

where a() is the chi distribution given in equation (13) and b() is the mixture distribution 

given in equation (14). The full log-likelihood for the second-stage model is then the marginal 

distribution summed over the entire set of test statistics,

and maximized with respect to H = ( ).

Application of the Bayes formula results in expressions for the posterior of the probability of 

an association being true:

and for the posterior magnitude of the association:
576

1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s

where

and U denotes the cumulative distribution function of a standard normal. Use a standard 

numerical maximization algorithm to maximize . The estimated parameters are then 

substituted in the posterior expression to obtain and .
577

Monograph 20. Phenotypes and Endophenotypes

Appendix 12B. Model Selection Algorithm

Assuming that the second-stage mixed model is independent of the regression model 

conditional on the test statistics, fi rst defi ne the posterior probability as

where is the log-likelihood of the fi rst-stage regression model. Because the 

model space is tremendous, one should not attempt to obtain a posterior estimation for the 

s by integrating over all possible models. Instead, adopt an MCMC approach by using a 

Metropolis-Hastings algorithm.113 Thus, during the iterations of the Markov chain, accept a 

new vector of s, * at iteration (i + 1) with probability

Here, a proposal distribution (PD) is defi ned as a function of , the probability that a term is 

associated with the outcome. Specifi cally, PD is defi ned as

where = Pr(kv > 0 |tv, ,Zl,Zp;H). That is, the probability of a proposed vector of s is 

dependent upon the probability that the terms are associated with the outcome given the 

vector of s at iteration i.

The MCMC algorithm is
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1 2 . U s i n g O n t o l o g i e s i n M o d e l i n g B i o l o g i c P a t h w a y s
579

Monograph 20. Phenotypes and Endophenotypes

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585

This fi nal part explores future directions for genetic studies of nicotine dependence, 

taking into account social and environmental infl uences as well as complex G×E 

interactions. It presents chapter-specifi c and cross-cutting recommendations for future 

research in tobacco genetics.

Future Directions

Part

6

587

Although a predominant scientifi c theory exists on how nicotine leads to dependence, 

further study is needed to explore the relationships between genetic, environmental, 

and social infl uences on dependence. This concluding chapter summarizes how 

tobacco genetics may affect basic and clinical research and provides summaries and 

recommendations for each part of the monograph and cross-cutting recommendations 

for the entire volume.

■ Continued research on genetics may enhance the understanding of how 

nicotine’s positive and negative effects lead to smoking relapse and nicotine 

dependence, the role genetic variation plays in acquiring dependence, and how 

to develop more effective treatments for nicotine dependence.

■ The next generation of nicotine-dependence investigators are encouraged to 

conduct research at varying levels of analysis and approaches to studying 

genotype-phenotype associations, take into account the environmental context 

as well as G×E interactions, use a broader range of more homogenous groups of 

tobacco users, and to clearly communicate their results to lay audiences and the 

media in ways that will not be used to stigmatize subgroups of the population.

This research has promise to refi ne existing nicotine-dependence treatments and identify 

new ones. Understanding the role of genetic susceptibility to nicotine dependence within 

the context of what is already working in the fi eld of tobacco control should help to 

design and implement more effective treatments for nicotine dependence and enhance 

tobacco prevention and control policies.

13

Future Directions
588

13. Future Directions

Introduction

This chapter begins with comments on how 

continued research in the area of genetics 

and nicotine dependence may infl uence 

future research at the basic and clinical 

levels by enhancing our understanding of 

the involvement of dopaminergic pathways 

responsible for nicotine dependence, 

the role of genetic variation in the initial 

acquisition of nicotine dependence, and the 

development of more effective treatments 

for nicotine dependence. The second portion 

of the chapter provides summaries and 

recommendations from parts 2–5 of the 

monograph and concludes with several 

crosscutting suggestions for the future.

Genetics and 

Nicotine Dependence: 

Implications for Basic 

and Clinical Research

Nicotinic and Dopaminergic 

Receptors

Tobacco smoke contains more than 

5,000 compounds (many of which are 

of unknown impact with regard to 

dependence), and nicotine is widely 

considered to be the most addictive of 

these.1 The predominant theory concerning 

how nicotine leads to dependence posits 

that acute nicotine binds to nicotinic 

receptors located on dopaminergic neurons 

in the ventral tegmental area (VTA) of the 

substantia nigra. The resultant dopamine 

release is associated with the experience 

of pleasure and the enhancement of some 

cognitive functions, such as sustained 

attention and vigilance, through neuronal 

projections from the VTA to the nucleus 

accumbens, frontal cortex, and striatum. 

Unfortunately, for the chronic tobacco 

user, long-term use results in a reduced 

function of nicotinic and dopaminergic 

receptors and more nicotine is required 

to maintain the same effects on mood 

and cognition. At the same time, cues 

in the environment become conditional 

triggers to smoke. When a regular tobacco 

user attempts to quit, withdrawal from 

nicotine is associated with a concurrent 

upregulation of nicotinic receptors 

and downregulation of dopaminergic 

receptors thereby leading to unpleasant 

symptoms, dysphoria, and in some cases, 

a wide array of cognitive decrements. 

The simultaneous avoidance of negative 

symptoms (negative reinforcement) 

and the pursuit of the positive effects of 

nicotine (positive reinforcement) lead to 

a harmful and recurring cycle of relapse 

back to smoking.2

 Because nicotinic and 

dopaminergic receptors are but two of 

many pathways (such as glutamatergic, 

opioid, and serotonergic) implicated 

in the neurobiology of dependence, 

the complete picture of reward and 

dependence is undoubtedly much more 

complex and likely involves secondmessenger systems.

A number of lines of evidence support 

the dopamine hypothesis of dependence. 

Various abused drugs, including nicotine, 

result in measurable and, in some cases, 

substantial increase in dopamine in terminal 

dopaminergic fi elds, particularly the nucleus 

accumbens.3 Signifi cant changes in regional 

cerebral blood fl ow have been observed in 

the nucleus accumbens, hippocampus, and 

orbitofrontal cortex of smokers in response 

to the fi rst cigarette of the day following 

overnight abstinence.4 In individuals with 

nicotine dependence, cue-induced changes 

dependent on blood oxygen level in brain 

regions along major dopaminergic pathways 

provide further neuroanatomical support to 

the hypothesis.5 Continued advances can be 

expected in the elucidation and validation 

of the dopaminergic basis for nicotine 

dependence as new phenotypic measures, 

such as those observed with functional 
589

Monograph 20. Phenotypes and Endophenotypes

magnetic resonance imaging, are examined 

in relation to novel genetic variants.

Nicotine Dependence—A Note 

on Developmental Pathways

In addition to strides made in understanding 

the basic neurobiological pathways to 

nicotine dependence, evidence indicates that 

the neurobiological and social processes 

by which young people exposed to tobacco 

products become addicted to nicotine are 

likely to be different from those responsible 

for the maintenance of dependence. Because 

the developing child’s brain is dynamic, 

it is possible that the brains of young 

people may be more susceptible than those 

of adults to the addictive properties of 

nicotine. For example, as the brain matures, 

the amount of gray matter on the cortical 

surface of the brain decreases from back to 

front as synaptic pruning occurs.6 It has also 

been shown that frontal and temporal lobe 

volumes are smaller in adolescents than in 

adults.7 Evidence from animal models shows 

that preadolescents, as compared with older 

adolescents, show increased upregulation 

of nicotinic receptors (including a5 and 

a6, and b2) and increased nicotine selfadministration following preexperimental 

exposure to nicotine.8 This suggests that 

age-gene-environment interactions may be 

operating to heighten the risk for entering 

into a tobacco use trajectory that ultimately 

leads to chronic tobacco use in adulthood. 

The use of developmental trajectories to 

describe how different people become 

dependent as adults (see chapters 4–6 in this 

volume) is a new area of research receiving 

increased attention.9,10 In the future, it will 

be possible to examine genomic differences 

among trajectory subgroups.

Genomic Studies of Nicotine 

Dependence in Adults

Early work involving twins suggested that 

genetic factors may account for more 

than 50% of interindividual variation 

in smoking initiation and nicotine 

dependence. Subsequent studies have 

sought to understand how genetic variation 

may affect the underlying neurobiology 

of nicotine dependence.9

 New analytic 

capacity will permit more powerful 

efforts to identify genetic variants key to 

nicotine dependence and its treatment. 

More than 25 whole-genome linkage scans 

involving nicotine dependence (and/or 

related phenotypes, such as number of 

cigarettes smoked per day and maximum 

cigarettes ever smoked in a single day) 

have been reported. A study in 200611

identifi ed a linkage peak on chromosome 6 

for scores from the Fagerström Test for 

Nicotine Dependence (FTND).12 Swan 

and colleagues11 also found suggestive 

linkage peaks on chromosomes 8 (nicotine 

dependence related to criteria of the 

Diagnostic and Statistical Manual of Mental 

Disorders)

13 and 15 (report of a previous 

reason for relapse of “enjoyed smoking too 

much”). All three peaks are near candidate 

genes of interest in the nicotinic and opioid 

pathways. Signifi cantly, a genome-wide 

association study in 2007 of 32,000 single 

nucleotide polymorphisms (SNPs) in 

nicotine-dependent cases (n = 1,050; 

FTND score more than 4 when smoking 

at maximum) and nondependent controls 

(n = 879; smoked at least 100 cigarettes 

lifetime but had a score of 0 on the FTND) 

provided evidence that some of the strongest 

associations were with variants in nicotinic, 

opioid, and dopamine genes, several of 

which were close to candidate chromosomal 

regions.14,15 What is apparent from this 

work is that convergence across studies 

and methodologies is now being seen.16

Great progress will continue to be made in 

identifying specifi c gene variants that play 

a role in nicotine dependence.

Figure 13.1 represents a schematic of 

what the future might hold as the pieces 

of the puzzle of nicotine dependence are 

identifi ed, certifi ed, and assembled. On the 
590

13. Future Directions Figure 13.1 Some Examples of Hypothetical Gene to Phenotype Pathways Tobacco dependence Smoke >20 CPD Sample Pathway 1 Relapse Sample Pathway 2 Frequency Sample Pathway 3 Dimensions (NDSS) Drive Continuity Stereotypy

Motivations (WISDM) Pos. reinforcement Cog. enhancement Automaticity

Endophenotypes Nicotine reward Executive function Pharmacokinetics

Biological processes Activation of reward Prefrontal activation Metabolism

Gene expression Gene-gene, gene-environment interactions

Environmental factors Family discord Academic demands Protobacco ads

Candidate genes DRD2, OPRM nAChR CYP2A6, B6

Note. CPD = cigarettes smoked per day; NDSS = Nicotine Dependence Syndrome Scale (Shiffman, S. A. Waters, and M. Hickcox. 2004. The Nicotine Dependence Syndrome Scale: A multidimensional measure of nicotine dependence. Nicotine & Tobacco Research 6 (2): 327–48.); WISDM = Wisconsin Index of Smoking Dependence Motives (WISDM-68) (Piper, M. E., T. M. Piasecki, E. B. Federman, 

D. M. Bolt, S. S. Smith, M. C. Fiore, and T. B. Baker. 2004. A multiple motives approach to tobacco dependence: The Wisconsin Inventory of Smoking Dependence Motives (WISDM-68). Journal of Consulting and Clinical Psychology 72 (2): 139–54.). From Swan, Lessov-Schlaggar, and Brigham, 2005, SRNT Annual Meeting.
591

Monograph 20. Phenotypes and Endophenotypes

far left of the fi gure is the arrangement, 

in ascending order, of components of 

a pathway that begins with the impact 

of candidate genes through their 

expression in response to the environment 

(e.g., tobacco, tobacco advertising, peer 

or parental smoking, cigarette design, 

or other environmental pressures to 

use or not use tobacco). The result of 

the gene-environment interaction is to 

initiate a biological process that can be 

measured in some fashion (e.g., nicotine 

metabolism, reward, cognition)—that is, 

the endophenotype. The biological process, 

in turn, must contribute in some way 

to motivate an individual to continue 

smoking. While the primary motivations to 

smoke may be different across individuals, 

they must, in turn, be associated with any 

or all dimensions of nicotine dependence 

and, fi nally, to observable tobacco use 

behavior.

One of the key features in this progression 

of events is that every step in the path is 

potentially measurable. Another feature 

is the implied association between 

components of the pathway, testable 

through a planned series of experimental 

studies to determine the validity of the 

pathway. The temporal sequencing of 

components in the pathway to confi rm 

causality could be investigated through 

the use of any of a variety of tools. Finally, 

the arrows extending through the rows of 

the diagram suggest a progression over 

time in which any individual, given his 

or her initial variation across a number 

of genes and subsequent variation across 

environments, could come to exhibit 

behavioral variation in a number of indices 

of nicotine dependence, including tobacco 

use trajectories.

Figure 13.1 provides three hypothetical 

pathways, each of which could be examined 

empirically. To illustrate, the fi rst example 

begins with variation in genes involved 

in the reward pathway that are activated 

through interaction with an environment 

that provides few reinforcers. Individuals 

with this confl uence of initial conditions 

would be expected to experience 

heightened reward from nicotine and 

acquire the need for sustained positive 

reinforcement—a motivation to continue 

to smoke. The need for sustained 

reinforcement would result in an increased 

drive to smoke and an increase in number 

of cigarettes smoked per day. The remaining 

two pathways, initiated through interaction 

between variation in the nicotinic receptors 

or metabolic genes, can be viewed as 

leading, ultimately, to different components 

of nicotine dependence. Investigators of the 

future should use this schematic, or one 

like it, to place their efforts in a theoretical 

context that permits empirical tests of 

hypothesized connections in a nicotine 

dependence pathway (see chapter 3 for 

another formulation of potential pathways 

and chapter 12 for an analytic approach for 

studying pathways). Such an approach will 

help others make sense of the plethora of 

results likely to continue to emerge over 

the coming years.

Future Directions

This part summarizes the discussions of 

the research presented in the monograph 

(parts 2 through 5) and suggests future 

research agenda items.

Part 2—Theoretical 

Considerations

Research presented in part 2 from 

investigators who examined both competing 

and distinct models of dependence 

demonstrated that nicotine dependence 

is multidimensional and that numerous 

theories on its development are available.

Guided by their own hypotheses about the 

nature, manifestation, and development of 

dependence, investigators recommended 
592

13. Future Directions

that incorporating a more comprehensive 

portrayal of dependence development 

may help fi nd the link between genes 

and behavior for genetic dependence 

susceptibility.

Part 2 of this monograph also examined 

issues surrounding the complex genetic 

and behavioral measures that combine and 

contribute to nicotine dependence gleaned 

through studies of inbred mouse strains. 

Available evidence used to translate the 

validity of the mouse fi ndings to humans 

holds promise in coming to understand 

individualized responses to nicotine.

Additional areas recommended for future 

research include the following:

■ Phenotypic assessments are needed that 

refl ect the different stages in progression 

to dependence (intermediate and 

transitional phenotypes) (chapter 3).

■ Future research should address the 

extent to which the different types 

of tolerance (e.g., acute or chronic, 

behavioral or dispositional) are related 

to core features of dependence, such as 

a pervasive pattern of drug use. Further 

understanding of the neural and genetic 

substrata of tolerance, and how these 

compare with other causal infl uences 

on dependence, may elucidate the role 

of tolerance in dependence development 

(chapter 4).

■ Given the tremendous potential created 

by the availability of well-characterized 

mouse strains and both knockout 

and knockin preparations, the use of 

such tools is needed to explore genetic 

infl uences on phenotypes that provide 

additional insight into the processes 

involved in nicotine dependence. 

Additional assays, both physiological and 

behavioral, should be used to expand 

understanding of the genetic contributors 

to the critical motivational processes of 

dependence (chapter 4).

Part 3—Developmental 

Trajectories of Tobacco Use 

and Their Relation to Tobacco 

Dependence

Over time, the paths that smoking 

behavior take vary widely. These patterns 

of development are an important basis for 

genetic studies of nicotine dependence. This 

third part of the monograph focused on 

issues related to studying these trajectories. 

One chapter focused on the development of 

smoking patterns from adolescence (when 

smoking and other substance use are most 

commonly initiated) to adulthood. Data 

collected from related individuals were 

used to examine the association between 

smoking initiation and progression in an 

effort to understand the etiology of nicotine 

dependence. Genetic data indicating the 

existence of an overlapping developmental 

pathway between smoking and other 

substance use in identical and fraternal 

twins were also examined.

Suggested topics for continued study 

include the following:

■ Future research should link hypothesized 

preexposure endophenotypes to 

trajectories that might constitute 

dynamic phenotypes of cigarette smoking. 

These studies should also consider other 

forms of tobacco use (chapter 5).

■ Better specifi cation is needed of the 

relation between trajectories of smoking 

behavior and the development of nicotine 

dependence, as well as the relation 

between adolescent and adult trajectories. 

Moreover, further research (using animal 

and human models) is required to 

understand the mechanisms underlying 

age-specifi c effects of initial nicotine 

exposure, which have shown a signifi cant 

relation between an early age of onset 

and steeper acceleration over time 

(chapter 5).
593

Monograph 20. Phenotypes and Endophenotypes

■ It is important to determine whether 

a particular individual feature of a 

trajectory (e.g., age of onset or steepness 

of acceleration) is the important 

phenotype or whether it is more useful 

to consider an entire trajectory group. 

Moreover, different research approaches 

are needed to determine whether 

phenotypes are best considered as 

categorical “groups” or as representations 

of an underlying continuous dimension. 

Future research will help determine if 

there are important ethnic differences in 

these groups (chapter 5).

■ Efforts should be made to develop reliable 

and valid methods to retrospectively 

reconstruct trajectories in addition to 

pursuing a range of longitudinal study 

designs (chapter 5).

■ Future development and applications of 

genetic latent growth curve models and 

genetic latent class models promise to 

improve the understanding of the role 

of genes and environment in smoking 

trajectories and transitions from 

nonsmoker to smoking dependence 

(chapter 6).

■ Future genetic research should jointly 

examine the extent to which different 

trajectories (combinations, of course) 

and the use of multiple substances 

(comorbidity) are genetically infl uenced. 

If it can be shown that phenotypes 

represented by broader substance-use 

trajectories are equally or more heritable 

than are single-substance trajectories, 

both phenotypic and genetic work can 

proceed more effi ciently. Findings would 

have implications for whether researchers 

should take a more genetic approach in 

preventing and treating substance-use 

disorders (chapter 7).

Part 4—Endophenotypes

Characteristics present at or before exposure 

to nicotine may help to identify individuals 

genetically susceptible to nicotine 

dependence. Similarly, measures in smoking 

persistence may help predict the success of 

cessation attempts among chronic smokers. 

Part 4 of the monograph focused on data 

indicating that genetic risk for nicotine 

dependence may also be affected by the 

presence of several psychological factors, 

such as approach-, avoidance-, and controlrelated smoking risk variables, at or prior 

to smoking and nicotine exposure. Chronic 

smokers were also a focus. Data presented 

indicate that the path to persistent smoking 

should include not only measurement 

of genetic factors but also motivational, 

sensory, cognitive function, craving, 

and other behavioral elements.

Additional areas recommended for future 

research include the following:

■ Higher-order trait domains (approach, 

avoidance, control, and affi liation/

empathy) all have some promise in 

relation to smoking risk. However, 

these traits are best understood in 

relation to lower-level neural systems, 

which, in turn, point to more molecular 

cognitive or physiological measures that 

can be examined as endophenotypes. 

More research is needed to evaluate a 

range of context-sensitive physiological 

measures as candidates of these lower 

systems (chapter 8).

■ There is great potential for future 

research to provide the evidence for 

or against the criteria important for 

endophenotype measures of nicotine 

dependence and to inform the debate 

about the utility of endophenotypes in 

genetic research (chapter 9).

■ Differences in quitting motivation 

between laboratory research participants 

and smokers in clinical studies may affect 

the development and validation of brief 

laboratory-based behavioral procedures 

that may serve as endophenotypes. 
594

13. Future Directions

Future endophenotypic research should 

take these motivational differences into 

consideration (chapter 9).

Part 5—Epidemiological and 

Methodological Considerations

In part 5 of the monograph, methodological 

and epidemiological issues related to 

the future direction of genetic studies 

on nicotine dependence are discussed. 

An epidemiological approach for modeling 

smoking phenotypes, models that 

incorporate social context factors, and 

hierarchical modeling techniques were 

presented.

Using an epidemiologic approach to defi ning 

smoking phenotypes, three analyses were 

presented that demonstrated that using 

more tightly defi ned comparison groups 

would yield more consistent fi ndings 

about the role of genetics in smoking 

behavior. Other analyses that incorporated 

social context into genetic studies of 

nicotine dependence indicated that genetic 

susceptibility to smoking may be infl uenced 

by the social context and environment—

such as having peers, parents, and siblings 

who smoke. How the genetic variation 

affects the analysis and interpretation of the 

role of genetics in tobacco use closes out 

this part of the monograph.

Results from a pilot study using hierarchical 

modeling techniques demonstrated that 

formally incorporating different phenotypes 

and genotypes into the statistical analysis 

may help to lessen some of the diffi culties 

experienced in evaluating the numerous 

factors affecting nicotine metabolism.

Research agenda items for future 

consideration include:

■ Researchers should be encouraged to 

use more tightly defi ned phenotypes 

of smoking behavior that are based on 

transitions on the smoking trajectory and 

adequate prior exposure, as these have 

the potential to reduce misclassifi cation 

bias and the lack of specifi city inherent 

in broader existing phenotypes such as 

current smoking status (chapter 10).

■ There may be etiological heterogeneity 

in the mix of genes and environments that 

can be captured only by incorporating 

candidate social contextual measures 

in genetically informative designs. 

To understand the mechanisms underlying 

such etiological heterogeneity, researchers 

should be encouraged to examine a 

broad number of both macro- and 

microsocial factors (i.e., conduct a “whole 

environment” scan) (chapter 11).

■ Evaluating sources of etiological 

heterogeneity may help in 

understanding the mechanisms by 

which endophenotypes become salient 

for smoking behaviors under specifi c 

environmental conditions but not 

others. Therefore, future research should 

use advanced measurement of both 

endophenotypes and social contexts to 

potentially illuminate core environmental 

factors that dwarf individual-level 

propensities as well as highlight 

especially prominent endophenotypes 

that convey risk under particular 

environmental conditions (chapter 11).

■ It is becoming increasingly untenable 

to ignore social contextual factors 

without sacrifi cing a broader and more 

comprehensive understanding of the 

etiological architecture of complex 

phenotypes such as nicotine dependence. 

Therefore, if the fi eld is to take seriously 

the proposition that gene-environment 

interplay will play a key role in eventually 

understanding the mechanisms by 

which genes contribute to smoking 

behavior and nicotine dependence, 

a dedicated effort will be needed not 

only to incorporate environmental 

measures with more regularity and vigor 

but also to invest the time, resources, 
595

Monograph 20. Phenotypes and Endophenotypes

and collaborative expertise necessary to 

provide the best available data on the 

environment (chapter 11).

■ Since complex traits are the results 

of many factors acting in concert, 

statistical analysis needs to be rich 

enough to identify sets of factors acting 

synergistically. One approach is to use 

ontologies in hierarchical modeling in 

conjunction with stochastic variable 

selection for future genetic analyses of 

tobacco use.

Crosscutting Issues for 

Future Research in Nicotine 

Dependence

In developing this monograph and 

examining continuing developments in the 

fi eld, the editors identifi ed several higher 

level recommendations for future research 

in nicotine dependence that cut across the 

content of this volume.

■ A comprehensive approach to examining 

and reporting genotype-phenotype 

associations should be adopted; 

single-gene, single-variant association 

studies should be discouraged unless 

accompanied by reports of replication 

and validation.

■ Researchers working in the fi eld of 

genetics and nicotine dependence 

should be mindful of the potential 

for misinterpretation of results by lay 

audiences. Efforts to communicate 

results to the media should include the 

limitations of the work along with the 

extent to which the results are reliable 

and generalizable. Doing so will minimize 

the chances of stigmatizing subgroups in 

the population.

■ An ontology-based approach to nicotine 

dependence, with specifi cation of 

expected relations within and between 

phenotypic domains, will provide an 

interpretive context and more focused 

hypotheses for future research; this will 

lead to an ongoing refi nement of the 

ontology as new information becomes 

available.

■ A greater use of strategies that combine 

differing levels of analysis is needed. 

The incorporation of measured genetics 

into genetic latent growth curve and/or 

latent class models in extended twin 

designs, for example, will provide 

information on the extent to which 

variation in one or more genes plays a 

role in the overall estimate of genetic 

variation in any particular phenotype. 

In addition, a nicotine reward phenotype 

may be characterized via behavioral 

measures of self-administration, selfreport assays, and imaging measures of 

activity in brain regions associated with 

reward processing. This, in turn, could 

spur the hunt for more genetic variants 

and gene-gene or gene-environment 

interactions to account for more of the 

overall genetic variation estimated in the 

biometric models. Inclusion of quantifi ed 

life events, cultural factors, and extant 

clinical and public health efforts in 

tobacco control and prevention in genetic 

studies is also warranted.

■ Genome-wide association analysis of 

phenotypes considered to be risk factors 

for the adoption or maintenance of 

nicotine dependence would lead to 

further understanding of the pathways 

by which children progress to adult 

nicotine dependence.

■ Given the enormous social, health, 

and economic impacts of nicotine 

dependence, the coordinated effort of 

multiple research teams to address the 

many opportunities for further research 

identifi ed in this volume is warranted.

■ There is a need to examine the association 

between gene variants and phenotypes 

of relevance in both the presence and 

absence of environmental risk factors. 

Emerging evidence from longitudinal 
596

13. Future Directions

studies of adolescents suggests that 

genetic associations with indices of 

nicotine dependence may be stronger 

and more robust when acting in the 

absence of environmental pressure 

to not use tobacco. Another way in 

which gene-environment interactions 

may infl uence nicotine dependence 

is during and/or following attempts 

to quit the use of nicotine-containing 

products. For example, variation in genes 

responsible for drug metabolism could 

interact with the dosing or duration 

of pharmacotherapy for nicotine 

dependence to reduce drug effi cacy. 

A third possibility for further exploration 

of gene-environment interactions involves 

the period following smoking cessation. 

The relationship between genetic 

variation and the likelihood of relapse 

back to nicotine dependence could well be 

dependent on the presence of conditioned 

cues to smoke or environmental stress.

■ Epigenetic methodologies promise 

to further understanding of the 

impact of the environment on the 

differential expression of gene variants. 

One possible approach, described in 

chapter 2, involves the comparison, 

at the genomic and/or expression level, 

of lymphoblastoid cell lines from identical 

twins discordant for nicotine dependence 

or other characteristics such as nicotine 

metabolism. Informative measures of 

environmental exposures will enhance 

the power of this approach to account 

for monozygotic twin discordance.

■ Much of the tobacco literature examines 

genetic susceptibility to smoking 

initiation and cessation only among very 

broad groups, without an understanding 

of the complexities or variations within 

these categories in patterns of smoking 

behavior. Combining very different 

subgroups of smokers into a few 

common phenotypes and then using 

such heterogeneous groups in research 

studies may be hindering progress in 

understanding the role of genetics in 

complex behaviors such as smoking. 

Moreover, standard defi nitions of 

smoking behavior from epidemiological 

surveys are not commonly used, making 

it diffi cult to compare results among 

genetics studies and to put these results 

into the context of knowledge gained 

from other disciplines. Therefore, 

researchers should be encouraged to 

use existing standardized defi nitions 

and measures of tobacco use behavior 

and to examine the role of genetics and 

environment in a greater number and 

broader range of more homogeneous 

groups of tobacco users.

■ Epidemiologists and surveillance 

researchers should be encouraged 

to contribute more to the 

conceptualization, identifi cation, 

defi nition, and operationalization of 

potential phenotypes of tobacco use 

behavior and then to demonstrate the 

utility, reliability, and validity of these 

potential phenotypes by using data from 

representative national surveys.
597

Monograph 20. Phenotypes and Endophenotypes

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599

Index

A

a4b2 nAChRs, 166, 168

a7 nAChR receptors, 168

a7 nAChR subunit knockout mice, 159

abstainers (nonsmokers)

characteristics of, 22

inclusion versus exclusion of, 222–223, 235, 323

nicotine patches, 377

abstinence

in behavioral economics effects, 420

defi cits, 350–351

effects of, 405, 408

event-related potential, 429

abstinence-induced craving, 438

effects of, 405

measurements, 403

during smoking cessation, 441

a-bungarotoxin binding, 165, 167

ACE model, 249–250

acetaldehyde (alcohol studies), 86–87

acoustic startle refl ex. See startle response

active avoidance, 435

acute stress mimicry of withdrawal symptoms, 448

acute tolerance, 162–163

ADA (Americans with Disabilities Act), 49

Add Health (National Longitudinal Study of 

Adolescent Health), 197–198, 261, 517

addiction. See also nicotine dependence; 

substance use

clinical manifestations of, 79

as dependence, 77

DRD2 gene and, 32

versus habituation, 24

models of, 293

versus smoking, 24

stress and, 36

additive components, 511

adenosine knockout mice, 159

adenosine systems, in nicotine reinforcement, 

159–160

ADH1*2 alleles (alcohol studies), 86–87

ADHD. See attention defi cit hyperactivity disorder

adipose tissue, nicotine concentrations in, 145

ad libitum (ad lib) self-administration, 415–416, 

419

administration, 145–149, 418. See also selfadministration

in drinking water, 148, 172

intravenous, 146, 156, 410

oral, 148, 154, 412

adolescent(s)

alcohol use by, 516

cognitive control, 358, 380

delay discounting choices, 350

depression in, 351–352

event-related potential, 429

extraversion in, 349

fi rst mood effects, 375–376

neuroticism in, 351, 352

nicotine deprivation learning defi cits, 436

nicotine response in, 194–195

novelty seeking behavior, 27, 348

P300 amplitude in, 360

physiological changes in, 589

protective factors, 343

research limitations, 367

social infl uences on, 346–347, 517

substance-use vulnerability of, 195, 199, 200–

201, 212, 233–234, 261, 292–293

transition to adulthood, 195

use of genetic information by, 46–47

adolescent developmental trajectories, 189–235, 

592

age of smoking onset, 200–201

empirically identifi ed, 202–214

example of, 223–233

future research directions, 233–234

psychopathology, 191–202, 292–293

statistical models, 214–223

substance use, 295–296 (See also substanceuse comorbidity; specifi c substance)

adolescent nicotine dependence, 191–195

animal models of, 155, 194–195

biological vulnerability for, 100, 193–195, 

200–201, 233

future research directions, 233

genetic studies of, 86, 264–266, 342

individual symptoms of, 192

measurement of, 192, 230–231, 264

time and exposure required for, 192–193

withdrawal symptoms, 192

adolescent smoking, 371

antisocial behavior and, 200, 202, 211, 232

environmental infl uences on, 196–197

ethnic differences in, 213–214, 279
600

Index

gender differences in, 196, 199, 260, 263–264, 

342

gene-environment interactions in, 197–200, 

259

genetic research on, 195–200, 259–269

heterogeneity in, 190, 233

as indicator of adult nicotine dependence, 

230–231

latency between cigarettes, 371

molecular genetic studies of, 198–199

parental smoking and, 196–197, 200

peer smoking and, 197

prevalence of, 191

twin studies of, 196, 259–262

adolescent smoking initiation, 191

age range in, 261–262

heterogeneity of, 196, 201–202, 233

progression to dependence, 341

genetic studies of, 263–264

rate of acceleration, 201–202

psychosocial factors, 200, 202, 211

risk profi le, 211–212, 232

shared environmental factors in, 260–261, 

264, 280

adoption studies, 196, 279

ADRA1A gene, 42

adulthood, transition to, 195

adult nicotine dependence, adolescent smoking 

indicators of, 230–231

adult-onset events, 100

adult smoking phenotype, limitations of, 190

advertising

costs of, 21

in movies, 7, 20, 523

novelty seeking as response to, 348

protobacco, 7, 20, 30, 348

smoking index variable and, 30

aerosols, nicotine, 147

affective coping, 112–113

affective response, 373–376

future research directions, 456

physiological measures of, 377–378, 445–446

regulation of, 358, 403, 443–449

affi liation/empathy system, 362

African Americans

adolescent smoking in, 213–214

genotypes linked to dependence, 47

linkage study focused on, 267

age effects, 170–171

factor loadings by, 271–276

in smoking initiation assessment, 261–262, 

279, 281, 322

in substance-use comorbidity, 322–323

age-gene-environment interactions, 589

age of onset, 26, 100, 371–372

developmental trajectories by, 200–201

age-related macular degeneration, 46

age-specifi c risk, measurement of, 35

aggregate effects in complex pathways, 541

aggression, 357

agonists (activators), 143

AHe mice, 435

AIC (Akaike Information Criterion), 274, 312

A inbred mice, 154, 165, 412, 422

A/J mice, 435

A/J×NMRI cross-bred mice, 154, 412

Akaike Information Criterion (AIC), 274, 312

alcohol use

adolescent, 516

in ATBC analysis, 496

Edwards’s theory of, 82–83

Iowa gambling task as predictor, 350

nasal spray use correlated with, 373

in NHANES III analysis, 502

policies infl uencing, 294–295

as secondary reinforcement, 413

side effects, 86–87

tobacco use concurrent with, 98, 290, 296–298

empirical examples of, 307–323, 496, 502

health effects of, 290

modeling, 299–305

nicotine-dependence correlation, 369, 406, 

411–412, 420

trajectories of, 295

ALDH2*2 alleles (alcohol studies), 86–87

alertness, 361–362

allele(s)

identical by descent (IBD), 257, 258

variants of, 554

allele frequency, 48, 258

alpha subunits, 153

Alpha-Tocopherol, Beta-Carotene Cancer 

Prevention Study (ATBC), 487, 494–497

alternative reinforcement, 417

Americans with Disabilities Act (ADA), 49

amphetamine, 412

analysis of variance (ANOVA), 215

analytic methods. See also specifi c method

developmental trajectories, 214–223

molecular genetic studies, 257–259

phenotypic research, 96–103, 113–118
601

Monograph 20. Phenotypes and Endophenotypes

anger, palliative effects on, 357

animal studies. See also mouse models; rat 

models; specifi c strain or study

ad lib administration in, 415

adolescent nicotine exposure, 194–195, 589

affective regulation, 443

dependence, 134–135

fetal nicotine exposure, 357

impulsivity research in, 449–450

reward studies, 372–373

transfer to, 350

ANOVA (analysis of variance), 215

antagonists (inhibitors)

CB1 receptors, 160

for mu opioid receptors, 160

muscarinic receptors, 141

nicotine as, 143

for nicotine dependence, 159

anti-infl ammatory effects of nicotine, 148–149

antinociception, 162

antisaccade task, 359

antisocial behavior

adolescent smoking and, 200, 202, 211, 232

substance use and, 292–293, 304

antitobacco stimuli, 20

anxiety

adolescent smoking and, 201

nicotine linkage with, 352–353, 445

anxiogenic effects of nicotine, 168

apolipoprotein E testing, 48

approach, versus impulsivity, 378–379

approach-related risk, 339, 346–349, 362

arousal, 378

ASN40ASP polymorphism, 407

aspartame, 412

association analysis, 258–259, 268–269, 280

assortative mating, 251, 259, 280

ATBC (Alpha-Tocopherol, Beta-Carotene Cancer 

Prevention Study), 487, 494–497

ATR (Australian Twin Registry), 197, 260–261, 267

attention, 361, 378, 432–434

attentional bias, 440, 456

attention defi cit hyperactivity disorder (ADHD), 

354–357

adolescent smoking and, 201, 211

as risk factor, 350, 433

substance use and, 292–293, 304

attrition biases, 224

Australian Twin Registry (ATR), 197, 260–261, 267

Automaticity subscale, 90–91

aversive mood symptoms, 443

avoidance-related risk, 339, 351–354, 362

B

backcrossed mice, 151

bacterial contamination, during administration, 

145

BALB/cBy mice, 164–165, 430–431, 435

Barratt Impulsivity Scale, 450

Bayes factors, 552, 570

Bayesian analysis, 548–549, 570, 572

Bayesian False Discovery Probability (BFDP), 573

Bayesian Information Criterion (BIC), 310, 312

Bayesian model averaging, 117

Bayes model, 543

BAY K 8644, 167

behavior

analysis of, 163–164

antisocial

adolescent smoking and, 200, 202, 211, 232

substance use and, 292–293, 304

drug-motivated, 404

measurements of, 103

nicotine and, 151–157

phenotypes, 171

response systems, 362

smoking indices, 80

substance-use comorbidity and, 322

tolerance, 168

traits, 344

undercontrol, 98–99

behavioral economics, 417, 420, 454

behavioral genetics

phenotypes in, 492

of self-administration, 153–155

in social context, 514–518

behavioral modeling

methodological issues with, 247–248

of parental smoking, 246

BFDP (Bayesian False Discovery Probability), 573

BIC (Bayesian Information Criterion), 310, 312

bioavailability of nicotine, 7, 20

biochemical indices of smoking, 80, 415

biochemical measures of self-administration, 97

biochemical pathways, 561

BioCyc, 561

biological pathways, candidate, 105

biological plausibility

affective regulation, 443–445
602

Index

attention/vigilance, 432–433

craving, 438–439

event-related potential, 427–428, 429

impulsivity, 449–450

mood effects, 374

reinforcement, 368–369, 410–414

resting EEG activity, 425–426

rewards, 372–373, 421–423

startle response, 430–431

working memory, 434–436

biological vulnerability, in adolescent nicotine 

dependence, 194–195

biomedical ontologies, 561

biometric factor model, 253

biometric modeling, 37–45, 86, 88–89, 514

BioPAX Ontology, 561

bitter taste, 84, 148

BKW mice, 443

blood pressure, 448–449

b2 nAChRs, 153

b2 nAChR subunit knockout mice, 159, 166

Bonferoni correction, 546, 573

brain

nicotine concentrations in, 145

stimulation, 373

upregulation in, 144

of young people, 589

brain imaging, 360

breakpoint

drug use, 418

nicotine use, 420

preferring to wait, 350

breast milk, nicotine concentrations in, 145

breeding

mice, 150–151, 411

rats, 411

Brown University Transdisciplinary Tobacco Use 

Research Center (TTURC), 89, 521, 526

BUB/Bn mice, 165, 412

BUB inbred mice, 154

buzz, 374

C

calcium, 136, 139–140

calcium calmodulin protein kinase II, 168

calcium channel blockers, 162, 167, 168

calcium signaling, 167–168

Canadian National Longitudinal Survey of 

Children and Youth, 212

cancer genetics, 50

candidate biological pathways, 105

candidate gene studies, 40–45

adolescent smoking, 342

discordant phenotype associations, 36

epigenetic differences, 37

linkage analysis, 32, 40–42, 267–268, 280–281, 

589

multivariant data, 543–546

nicotine dependence, 42

prior knowledge of, 559

smoking association with, 24

SNP relationships over, 545

substance-use comorbidity, 325

trait pathways, 553

variants, 25, 35

candidate neural systems, 343–346

cannabis. See marijuana use

Card Arranging Reward Responsivity Objective 

Test, 350–351

CART (cocaine- and amphetamine-regulated 

transcript), 36

cases, in association analysis, 258

catechol-O-methyl transferase (COMT) gene, 269, 

408–409, 430

categorical covariates, 276–278

causal contingent common (CCC) pathway model, 

253, 263, 278–279

causal differences between groups, 492

causal paths

phenotypic research, 102, 107–110

sensitivity to, 105

smoking as, 352

types of, 111–113

CBA mice, 435

CB1 knockout mice, 160, 422

C57BL/6 inbred mice

conditioned place preference in, 159, 422

dose-dependent effects, 165

fear conditioning in, 169, 432

fi ve choice serial reaction time task in, 433

nicotine consumption, 155, 159

nicotine-dependence risk, 161

nicotine effects in, 435

novelty-seeking behavior in, 156

oral self-administration in, 412–413

prepulse inhibition in, 430–431

strain comparisons, 153–154, 164–165

in tolerance, 167

C57BL/10 mice, 435
603

Monograph 20. Phenotypes and Endophenotypes

C57BR/cd mice, 435

CCC (causal contingent common) pathway model, 

253, 263, 278–279

CCK gene, 269

CD-1 mice, 443

CellDesigner, 561

cell signaling, 161, 167–168

Center for Antisocial Drug Dependence, 260

Centers for Disease Control and Prevention, 498

central nervous system (CNS), 344, 348, 353, 354

centroid. See mean

chain smoking, 37

children

effortful control in, 358

secondhand smoke from parental smoking, 246

self-control in, 344

sleep problems in, 357

smoking by, 371

children of twins (COT) design, 251–252, 279, 511

C3H inbred mice

dose-dependence, 165

nicotine effects in, 431, 435

oral self-administration, 412

strain comparisons, 154, 164–165

tolerance in, 162

chippers (light smokers)

adolescent, 213, 232

delay discounting, 350

genetic factors and, 29

versus heavy smokers, 43, 81, 83–84, 90, 94

prevalence of, 193

choice procedure, 369–370, 416, 419–420

cholecystokinin (CCK) gene, 269

Christchurch, New Zealand (CNZ) study, 266

CHRNA2 gene, 42, 376–377

CHRNA3 gene, 376–377

CHRNA4 gene, 268, 407–409, 412

CHRNA5 gene, 342, 376–377

CHRNA7 gene, 408, 409, 428

CHRNA5-A3-B4 haplotypes, 100, 407

CHRNA5-CHRNA3-CHRNB4 nicotinic receptor 

genes, 43

CHRNB2 gene, 268, 407, 408, 409

CHRNB3 gene, 342

CHRNB3-CHRNA6 nicotinic receptor locus, 43

chronic exposure, 405, 449

chronic smoker endophenotypes, 403, 404–406

affective regulation, 443–449

cognitive control, 432–438

craving, 438–443

electrophysiological measures, 425–432

future research directions, 452–457

impulse control, 449–452

investigation rationale, 406–410

motivational mechanisms, 410–424

summary/conclusions, 457–458

chronic tolerance, 163

CIDI (Composite International Diagnostic 

Interview), 81

cigarette(s)

availability of, 99

consumption of, 4, 20

design of, 7, 20

pricing of, 20, 21, 520

vendor locations, 521, 522

Cigarette Dependence Scale, 79n

Cigarette Evaluation Scale, 423

cigarettes per day (CPD)

in ATBC analysis, 496

with CYP2A6 gene variants, 418

delay discounting correlation, 452

as dependence measure, 79, 80

in factor analysis, 88–89

in NHANES III analysis, 502

nicotine metabolism and, 406

predictive value of, 80

recall reliability, 26–27

as smoking cessation predictor, 81, 413

in TUS-CPS analysis, 498, 499

cigarettes per month, 91

class extraction, model misspecifi cation related 

to, 222

classic dependence criteria, 84, 86

classifi cation bias, 493

class I–IV phenotypes, 28–31

class membership, for familial resemblance, 

256–257

clinical preventive services, 4

Clinical Research Support System, 415

cluster analysis

developmental trajectories, 211, 217–223

discrete versus continuous phenomena in, 219

static versus dynamic, 219–220, 232

within-class variability in, 220–221, 233

CNS (central nervous system), 344, 348, 353, 354

CNZ (Christchurch, New Zealand) study, 266

cocaine, 374, 456–457

cocaine- and amphetamine-regulated transcript 

(CART), 36

coexpression of receptors, 141, 142
604

Index

COGA (Collaborative Studies on Genetics of 

Alcoholism), 266

cognition changes, 163–164

cognitive control, 403, 424–425, 432–438

during adolescence, 380

alertness in, 361

electrophysiological measures, 425–432

as endophenotype measurements, 358, 360

impairment, 113

impulsivity and, 112, 378–379

physiological basis of, 381

cognitive defi cits reversal, 434–435

cognitive measures, of craving, 440

cohorts

effects of, 515

research models for, 519

Collaborative Studies on Genetics of Alcoholism 

(COGA), 266

colorectal polyps, 542

commercial testing, 50

common pathway model, 252, 270

common-vulnerability model, 291–294, 306, 

315–316

communication of genetic fi ndings, issues in, 

45–50

comorbidity

psychiatric (See psychiatric comorbidity)

substance-use (See substance-use comorbidity)

complementary dimensions of dependence, 97

complex traits

defi ning features, 31

genetic factors in, 35

genome-wide association studies for, 46

multiple determinations of, 22

replication diffi culties, 45

sensitive genetic measures, 341

similarity of, 24–25

Composite International Diagnostic Interview 

(CIDI), 81

compulsive smoking, 80

computational symbolic theory, 559–560

COMT gene, 269, 408–409, 430

concurrent choice procedure, 416

conditional independence, 256

conditional triggers to smoke, 588

conditioned place preference (CPP)

biological plausibility of, 372–374, 421

in mice, 157–158, 161, 422

in rats, 423

conditioning, contextual, 163–164

conduct disorder

adolescent smoking and, 201, 211, 232

defi nition of, 357

substance use and, 292–293, 304

confounding factors, 32

consensus, across indicators, 77

constitutional hypothesis, 24

construct

defi nition of, 75

emerging, 520

proximal, 522

refi nement of, 25–27

construct properties, 75

construct validation, 33, 34, 35, 75–78

consumption level

during adolescence, as indicator of adult 

dependence, 230–231

adolescent nicotine dependence and, 193

assessment of, 415, 418

contamination, during administration, 145

context-sensitive physiological measures, 363

contextual conditioning, 163–164

contingency table, smoking-drinking, 313, 

315–316

contingent factors, 247

continuant, 541, 559

continuous factors, 256

continuous-level information, for behavioral 

modeling, 247

continuous performance task (CPT), 362, 378, 

433

continuous phenomena, versus discrete 

phenomena, 219

control, endophenotype measures of, 357–362

control-related risk, 339, 354–357, 362

controls, in association analysis, 258

copy-number variation, 36

core criteria, 76, 77

core dependence dimension, 87–95

core factors, 97, 168

core strategy, 510

cortisol, 447

cosegregation of smoking behaviors, 24

cost(s)

genetic testing, 50

smoking cessation, 593

tobacco use, 4, 21

COT (children of twins) design, 251–252, 279, 511

cotinine

biological activity of, 148

clearance factors, 39–40
605

Monograph 20. Phenotypes and Endophenotypes

as nicotine by-product, 406

covariance, 32, 95–96, 341–342

in latent growth curve models, 254–255

in structural equation modeling, 248, 250

CPD. See cigarettes per day

CPP. See conditioned place preference

CPT (continuous performance task), 362, 378, 

433

craving, 438–443

abstinence-induced (See abstinence-induced 

craving)

in adolescents, 192

cue-induced (See cue-induced craving)

dopaminergic systems associated with, 111

effects of, 405

measurement of, 403, 439–440

Craving subscale, 90–91

CREB activation, 160–161

CREB mice, 422

critical constituents, 25

cross-cultural differences, 515–516

cross-sectional studies, of substance-use 

comorbidity, 291

cross-species analysis, 348

cue(s)

in animal studies, 411

relapse, 99

reward signaling, 350

cue-induced craving, 438–439

with dependence, 441–443

effects of, 405

future research directions, 456–457

measurements, 403

physiological basis of, 111, 588

procedures to elicit, 440–441

research limitations in, 456

cue-self-administration response, 94

cultural transmission

adolescent smoking and, 198

effects of, 515

as environmental factor, 21

phenotypic, 251

research models for, 519

twin studies of, 262

cumulative effects, of pathogens, 103

cumulative risk, measurement of, 35

CYP2A6 gene

adolescent smoking and, 199

analysis of, 554, 555

association analysis of, 268

coding for, 408–409

mood effects with, 376

in nicotine metabolism, 22, 39–40, 149, 342, 

406

in smokers, 413

variants in, 371, 418

Cyp2a5 gene variants, 149, 155, 413

CYP2B6 gene, 39

CYP2D6 gene, 39

Cys-Cys pairs, 136

cytochrome P-450 (CYP) system, 268

D

danger-alarm responses, 353, 354

data-mining techniques, 541–542

data sets

ATBC analysis, 495

NHANES III analysis, 500–501

smoking cessation analysis, 493–494, 494

TUS-CPS analysis, 498

DAT/SLC6A3 (dopamine transporter protein), 268

DBA/2 inbred mice

conditioned place preference, 422

dose-dependent effects, 165

IV self-administration, 156

nicotine consumption, 159

nicotine effects, 435

oral self-administration, 412

prepulse inhibition in, 430–431

strain comparisons, 153–154, 164–165

tolerance in, 162, 163

DDC (DOPA decarboxylase), 269

defi nition variables, in structural equation 

modeling, 248

dehydroepiandrosterone (DHEA), 447

delay discounting

defi nition of, 348

in impulsivity, 349, 449, 450–452

physiological basis of, 379

study subjects for, 350

deleterious effect prediction, 554

dependence. See also nicotine dependence

addiction as, 77

core features of, 592

defi nition of, 75

distal measures of (See distal measures)

maintenance of, 598

model evaluation of, 78

patterns, 94
606

Index

severity with comorbidity, 98

study of, 24

depression, 351–352

nicotine amelioration and, 443

smoking association with, 444

subthreshold, 352

desensitization, 146

developmental pathways, 100, 589

impact on trajectory study, 222

developmental psychopathology, 191–202, 

292–293

developmental trajectories. See adolescent 

developmental trajectories

DHbE, 159, 169

DHEA (dehydroepiandrosterone), 447

diabetes mellitus, 46

Diagnostic and Statistical Manual of Mental 

Disorders (DSM) criteria

adolescent smoking, 192, 264

attention defi cit hyperactivity disorder, 356

dependence, 37–38, 40, 81, 86

poor agreement with FTND, 25–26

scales in, 79

substance-use disorder, 291

as syndromal medical model, 80–81

diagnostic criteria, heritability of, 30, 38

diagnostic inferences, 77

Diagnostic Interview Schedule (DIS), 501

diary prompts/responses, 527, 528–529, 530

Digit Span test, 433

diltiazem, 168

direct drug infusion, 152–153

direct-to-consumer marketing, 50

DIS (Diagnostic Interview Schedule), 81, 501

disadvantaged youth, 520

discrete phenomena, versus continuous 

phenomena, 219

discrimination, against nicotine dependence, 

46–47, 49

discriminative validity, 77

discussion groups (OBO Foundry project), 560

Disease Ontology (DO), 560

disease risk, 541, 550

disinhibition, common trait of, 292–293

disorder markers, 107

dispositional tolerance

acquisition of, 162

versus behavioral tolerance, 164

in tolerance, 168

distal infl uence, versus proximal infl uence, 

519–522

distal measures

agreement among, 88

early versus mature states, 74

genetic mapping, 78–81, 86–87, 103–105

versus proximal measures, 516

distress tolerance, 448

distributional assumptions

in growth curve models, 255

in structural equation modeling, 249

DNA methylation, 36

DNA sequences analysis, 554

DO (Disease Ontology), 560

DOPA decarboxylase (DDC), 269

dopamine

in attention defi cit hyperactivity disorder, 356

attention-vigilance associations with, 434

impulsivity linkage with, 451–452

inactivation of, 430

mesotelecephalic, 171

midbrain circuits, 348, 349, 350

in nicotine binding role, 406–407

in nicotine dependence, 43, 410

receptors, 156, 343

regulation of, 540

reinforcement role of, 374, 407

reward pathways, 342, 349, 352

signaling, 160

dopamine b-hydroxylase (DbH), 269

dopamine hypothesis of dependence, 588

dopaminergic neurons, in ventral tegmental area, 

588

dopaminergic systems

adolescent smoking and, 198–199, 201

association analysis of, 268–269

craving associated with, 111

dopamine transporter protein (DAT/SLC6A3), 268

dopamine transporter (SLC6A3) *9-repeat allele, 

441

dose of nicotine, 161, 165

differences in, 367, 416

distribution of, 370

standardization, 152

dose-response curve, 162, 444

double variant haplotype, 545

downregulation, 144–145, 588

downstream processes, 81–82, 106

DRD4 (dopamine receptor), 268

DRD5 (dopamine receptor), 268

DRD4 exon III polymorphism, 199
607

Monograph 20. Phenotypes and Endophenotypes

DRD2 gene, 409

addiction association with, 32

coding for, 408

commercial testing for, 50

dependence association with, 407

in craving, 441

variants, 198

DRD4*7-repeat allele, 376

drinking. See alcohol use

drinking-water administration, 147–148, 172

drug addiction. See addiction; substance use; 

specifi c drug

drug-motivated behavior, 404

drug response comparison, 490–492, 491

DSM. See Diagnostic and Statistical Manual of 

Mental Disorders criteria

dual-trajectory model, of smoking-drinking 

trajectories, 315–316

dynamic clustering

versus static clustering, 219–220, 232

within-class variability in, 220–221, 233

E

early-emergent motive, 91

early-onset smokers

risk for persistence, 200, 212–213, 230–231

substance use and, 292, 296–297, 315, 320

early smoking experiences (ESE), 375

early tobacco exposure, 101, 155

ecological momentary assessment (EMA), 255, 

525

economic deprivation, 520

educational attainment, adolescent smoking and, 

227–228, 232

Edwards’s theory of alcohol dependence syndrome, 

82–83

EEA (equal environments assumption), 516–517

EEG (electroencephalogram), 354, 403, 425–427

effortful control, 358

elasticity of demand, 417

elation, 374

electroencephalogram (EEG), 354, 403, 425–427

electromyography (EMG), 431

electrophysiological measures, 378, 425–432

EMA (ecological momentary assessment), 255, 

525

EMG (electromyography), 431

empirical-Bayes approach, 549, 551

empirical search strategies, 117

employment discrimination, 49

employment status, of hard-core smokers, 35

encoding prior knowledge, 571

endogenous cannabinoid systems, 159, 160

endogenous enkephalin system, 160

endogenous event-related potentials (ERPs), 

427

endophenotypes, 5, 408–410

caveats, 110–111

characteristics of, 107–110

in chronic smokers (See chronic smoker 

endophenotypes)

conceptual issues, 381–383

criteria, 413

disorders associated with, 106

future research directions, 455–457, 594

gene linking in, 347, 409

measurement of, 349–351, 353–354, 355, 

357–362

motivational effects, 452, 454–455

in network models, 558

nicotine dependence, 409, 453

phenotypes associated with, 5, 33–34

pre-exposure risk, 340–347

in psychiatric genetics research, 25

replicability of, 27

transitional, 107, 108, 200, 233

types of, 340

enhanced clearance. See dispositional tolerance

entities, 541, 559, 560

environmental factors, 99–103. See also social 

context; specifi c factor

adolescent smoking, 196–197

comorbidity, 99

cue-induced craving, 438–439

enrichment, 36

in experimentation, 31

gender differences in, 38

gene expression variation from, 36–37

genetic factors in, 35, 515

importance of, 29

linkage analysis, 258

measurement of, 35

in nicotine dependence, 22, 23

nicotine use, 158

relative contribution of, 30

in smoking decline, 20

substance use, 294–295

twin studies, 251, 262, 279, 280

environmental pathogens, 5, 25, 35
608

Index

epidemiology, 31–37

future research in, 594

genetic, 257

extended, 250–252, 262–269, 279, 280

hierarchical modeling in, 570–571

perspectives from, 514

phenotypic defi nitions in, 493

public health outcomes in, 492

triangle, 513

epigenetics, 36

epistasis, 32–33

epistemology, 74, 77

equal environments assumption (EEA), 516–517

equifi nality, 191, 220

ERPs (event-related potentials), 403, 427

ESE (early smoking experiences), 375

ethanol, 412. See also alcohol use

ethnic differences, in developmental trajectories, 

213–214, 279

etiological architecture, 510–511, 515–518, 519, 

527, 530

etiology

diverse, 82, 106

matrix of, 509

of phenotypic assay, 83

of symptoms, 78

euphoria, 374

Event Ontology (EVO), 560

event-related potentials (ERPs), 403, 427

EVO (Event Ontology), 560

exchangeable classes, 551

excitatory tone, 141

executive function

cognitive control and, 379

defi nition of, 361

nicotine dependence and, 34

Executive Order 13145, 49

exogenous event-related potentials (ERPs), 427

experimental design, basics of, 149–150

experimentation

infl uences on, 31

progression from, 30

exposure model, 365

extended structural equation modeling (XSEM), 

249

extended twin family studies, 250–252, 262, 279, 

280

extra-nicotinic mechanisms, 156–157

extraversion, 346, 348–349

extreme group membership, 96, 97

extreme groups

alternatives to, 116–118

constructing, 114–116

eyeblink response, 448

F

factor analysis, 37–38, 265

consistency, 88

correlation among, 80, 87

DSM-IV correspondence with, 103

factor loadings, 271–278

factor mixture model (FMM), 256

factor models, 256

Fagerström Test for Nicotine Dependence (FTND), 

79–80

as assessment tool, 405

dependence criteria, 37–38

FTQ as precursor to, 24

linkage analysis, 40–42, 589

poor agreement with DSM, 25–26

reliability and validity of, 26, 79–80

scales in, 79

visuospatial attention association with, 434

Fagerström Tolerance Questionnaire (FTQ), 

79–80

for adolescent smoking, 192, 230–231, 264

as physical dependence measure, 79

startle response inconsistency, 448

in susceptibility loci mapping, 24

test-retest reliability, 26

false discovery rates (FDRs), 542, 546, 570, 573

false positive reports, 570

familial resemblance, class membership for, 

256–257

family-based studies

design of, 518

ecological momentary assessment in, 526–527

heritability documentation, 28–29

new methodologies in, 521

family dysfunction scores, 33

family environment, adolescent smoking and, 

196–197

family history analysis, example of, 225, 230, 

232

fast-ionotropic nicotinic receptors, 136

FDRs (false discovery rates), 542, 546, 570, 573

fear conditioning, 169, 432, 435

fear responses, 353, 354

feeder stream infl uences, 81–82, 106
609

Monograph 20. Phenotypes and Endophenotypes

female smokers. See also gender differences

adolescent, 343

blood pressure changes in, 448

nicotine-dependence factors, 37, 38, 99

OPRM1 gene in, 419

statistics on, 21

twin studies, 515, 517

fetal nicotine exposure, 357

FHS (Framingham Heart Study), 266

fi nal common pathway, 82, 93, 106

fi nite mixture model, 256

Finnish Twin Registry, 262

Finn Twin16-25 study, 307–323

methods, 307–310

results, 310–323

fi rst experience with smoking. See initial 

sensitivity

fi rst-stage estimates, 572

Fisher, Ronald Aylmer, 22, 24

fi ve choice serial reaction time task (5CSRTT), 

432

fi ve-class solution, example of, 226

fi xed effects, 215–216

fl unarizine, 168

FMM (factor mixture model), 256

focused interaction testing framework, 542

forced choice procedure, 416

formal model, 541, 559

Fosb knockout mice, 161

Foundational Model of Anatomy, 560

four-point Likert scale, 375

Framingham Heart Study (FHS), 266

F344 rats, 423

FTND. See Fagerström Test for Nicotine 

Dependence

FTQ. See Fagerström Tolerance Questionnaire

future research. See also specifi c topics

crosscutting issues, 595–596

implications of, 588–591

understanding, 588–596

G

c-aminobutyric acid (GABA), 157, 406–407

c-aminobutyric acid receptors, 43

GABA (c-aminobutyric acid), 157, 406–407

GABAergic interneurons, 142

gateway theory of substance use, 292

GAW (Genetic Analysis Workshops), 266

gender differences. See also female smokers; 

male smokers

adolescent smoking, 196, 199, 227–228, 260, 

263–264, 342

animal studies, 155, 164

cross-cultural, 515

factor loadings by, 271–276

nasal spray use, 373

nicotine-dependence factors, 38, 99

nicotine-dependence heritability estimates, 

279, 281

OPRM1 gene, 419

smoking initiation, 267

startle response, 378

substance-use comorbidity, 304

twin studies, 262, 517

gender heterogeneity, 273–274, 276–278

gene(s). See also candidate gene studies

endophenotype linkage risks, 347, 409

in nicotine dependence, 32, 43

gene-environment interaction, 33, 515

adolescent smoking, 197–200, 259, 346

biological process initiation, 591

environmental pathogens in, 25

in etiology, 509

investigation of, 546

substance-use comorbidity, 320

underuse of, 5

gene expression, 36–37

gene-gene interaction, 199, 343, 546

gene-nicotine dependence associations, 45–46

Gene Ontology, 560

gene-pathogen relations, 100–101

general growth mixture modeling (GGMM), 

308–311

generational changes, in smoking, 515

Genes, Environment and Health Initiative, 531

genetically informative designs, 527

genetically modifi ed mice, 444

Genetic Analysis Workshops (GAW), 266

genetic architecture, 510

genetic association studies, 554, 556–559

genetic drift, 150

genetic epidemiology, 257

extended, 250–252, 262–269, 279, 280

genetic factors

acute tolerance, 162–163

conditioned place preference, 159

craving, 441

detection of, 493
610

Index

in experimentation, 31

importance of, 29

measured, 4

in nicotine effects, 22, 39–40, 539

quantitative models, 512

in reinforcement, 418

relative contribution of, 30

selecting for, 96–97

genetic heterogeneity, 32

adolescent smoking initiation, 196, 201–202, 

233

in developmental trajectories, 190, 233–234

gender, 273–274, 276–278

phenotypes, 341

population, 217

estimating, 218, 221–222

receptor, 139

genetic heterogeneity models, difference in fi t 

between homogeneity models and, 274–275

Genetic Information Nondiscrimination Act 

(GINA), 49

genetic latent class models. See latent class 

analysis

genetic latent growth curve models. See latent 

growth curve models

genetic mapping, 73–75. See also phenotypic 

research

analytic strategies, 96–103

construct validation, 75–78

core dependence dimension, 87–95

covariation among measures, 95–96

distal measures of dependence, 78–81, 86–87, 

103–105

multidimensional measures, 81–86

person factors implications in, 97–98

genetic modeling, 245–281

methodological and conceptual issues, 247–248

statistical framework for, 248–259

genetic polymorphism effects, 553–554

genetic substrata, associated with tolerance, 162

genetic testing, 46, 50

genetic variants

biological processes associated with, 109

causal, 546

disease association with, 550

evaluation context for dependence, 76

phenotypes with, 102, 106, 109, 111

pleiotropic associations of, 47–50

selection of, 101

value of, 110

gene-to-phenotype infl uence, 78

gene-transcription cascades, 169

genome(s)

candidate genes in, 24

data, 561

studies of, 589

genome markers, linkage analysis, 257–258

genome scan, 266–267

genome-wide association studies (GWAS), 25, 44, 

269

event-related potentials, 428, 430

FTND, 42–45

genetic variant fi ndings, 589

genotyping technologies used in, 258–259

potential of, 45–46

results from, 342

susceptibility loci identifi cation, 407–408

genotyping

effects of variables on, 553

mouse strains, 150

P450, 39

with phase interaction, 545

phenotypes and, 560–562

technologies, 257–259

geographic information systems (GIS), 520–521

GGMM (general growth mixture modeling), 

308–311

GINA (Genetic Information Nondiscrimination 

Act), 49

GIS (geographic information systems), 520–521

global use, 4, 21

GluR (glutamate receptor), 157, 169

glutamate receptor (GluR), 157, 169

GMM (growth mixture modeling), 202, 218, 221, 

308

go/no-go task, 358–359, 451

government policies, 7, 20

effect on adolescent smoking, 193

substance use, 294–295

grant funding, 45–46

graph connectivity, 571

grouping variables, for growth curve modeling, 

216–217, 232–233

group membership, stability of, across statistical 

models, 229–230

growth curve, nonlinear, 255

growth curve mixture modeling, 215–217, 

248–249

growth mixture modeling (GMM), 202, 218, 221, 

308
611

Monograph 20. Phenotypes and Endophenotypes

growth process, random effects for, 220–221

gum. See nicotine gum

gustatory reaction to tobacco, 75, 84

GWAS. See genome-wide association studies

H

habituation, versus addiction, 24

half-life of nicotine, 147

haplotypes

dependence and, 100

disease association with, 544

double variant, 545

hard-core smokers, characteristics of, 35–36

Hardy-Weinberg equilibrium, 545

head rush, 374

health care access, disparities in, 47

health effects of smoking, statistics on, 4, 21

heart rate, 351, 354, 440, 447

Heaviness of Smoking Index (HSI)

components of, 37, 413

predictive value of, 80, 89–90

scales in, 79

zero-order correlations in, 80

heavy smokers, 93

delay discounting, 350

diagnostic variance in, 89

genetic factors in, 29

versus light smokers, 43, 81, 83–84, 90, 94

substance use and, 296–297, 315

Heavy Smoking Index, 265

hedonic impact of nicotine, 158, 372, 424

heritability

adolescent nicotine dependence, 86, 342

antisocial scores, 33

anxiety, 445

delay aversion, 351

dependence, 37–38, 86, 433–434

diagnostic criteria, 30, 38

endophenotypes, 107

estimates, 29–30

event-related potential, 428, 429–430

factors in, 28–29

gender differences in, 279, 281

impulsivity, 451

neuroticism, 101

nicotine metabolism/clearance, 38–40

P450 genotype, 39

prepulse inhibition startle response, 431–432

response inhibition, 359

resting EEG, 426

smoking cessation, 406

smoking heaviness, 90

withdrawal symptoms, 30

working memory, 437

heterogeneity. See genetic heterogeneity

hierarchical modeling

estimation for, 575–576

with ontologies, 551–552

with prior knowledge, 570

for statistical modeling, 117

stochastic variable selection and, 547–549, 572, 

573

weighting in, 570

high-affi nity nAChRs, 156, 159

higher-order joint actions, 546

high genetic proneness, 96

hippocampal activity, 141, 142

HISTONE proteins, 36

home smoking bans, 99

homogeneity models, 271

difference in fi t between heterogeneity models 

and, 274–275

homogeneous population

assumption of, 248

for growth curve modeling, 216–217

Hooked on Nicotine Checklist, 26

Horn-Russell Scale, 34

hostility, 357, 362, 377

Household Adult Questionnaire, 501

HSI. See Heaviness of Smoking Index

HTR5A gene, 42

5-HTT gene, 33, 269, 441

5-HTTLPR gene, 101, 199, 409

in adolescent girls, 343

in affective response, 112, 446

coding for, 408

human clinical research

affective regulation, 444–445

electrophysiological measures, 426

event-related potential, 428

impulsivity, 450

reinforcement, 413–414

HumanCyc database, 556

human genome, similarity with mice genome, 

134

3-hydroxycotinine, 406

hyperactivity, 354, 356

hypertensive rats, 449
612

Index

I

IBD (identical by descent), 257, 258

ICD-10 criteria, 80–81, 192

ICR mice, 159, 162, 163

ICSS (intracranial self-stimulation), 372–373, 421

identical by descent (IBD), 257, 258

illicit substances. See substance use; specifi c drug

impulse control, 449–452

impulsivity, 348

in attention defi cit hyperactivity disorder, 354, 

356

clinical research, 433, 450–451

cognitive control and, 112, 378–379

delay discounting with, 349, 350, 449, 450–

452

heritability, 451

measurement of, 450–451

neural incentive system association, 346

preclinical research, 449–450

in response inhibition, 359

incubation effect of initial exposure, 371

independent pathway model, 253

Indiana University Smoking Survey, 223–233

data analysis, 225–226

discussion, 231–233

measures, 224–225

procedures, 224

results, 226–231

individual pathways to mature state, 105, 106

“infectious disease” model, 522

infl ammation masking, 145

inhibitors. See antagonists

inhibitory interneurons, 141, 142

initial exposure response measures, 339

initial sensitivity, 27, 363–364

future research directions, 380–381

innate sensitivity, 364–368

other responses, 373–380

reinforcement, 368–372

rewards, 372–373

innate sensitivity, 364–368

instrumental learning, 158

insurance companies, genetic testing and, 49

integrative model of nicotine dependence, 22, 23

integrative theory of triadic infl uence, 293

intercept, distribution of, 219, 306, 310

intercept models, initiation-based, 322

intermediate phenotypes, 341, 342. See also

endophenotypes

International Statistical Classifi cation of Diseases 

and Related Health Problems, Tenth Revision

(ICD-10) criteria, 80–81, 192

interpersonal dynamics, 517, 522–525

interval level, 247

intracranial self-stimulation (ICSS), 372–373, 421

intrathecal administration, 162

intrauterine events, 100

intravenous administration, 146

humans, 415

rodents, 152–157, 410–412

in utero nicotine exposure, 357

inveterate smokers, 87

ion-channel receptors, 143

Iowa gambling task, 350

IRT. See item response theory

item diffi culty, 270

item response theory (IRT), 254

empirical example of, 269–279

versus sum score approach, 278

J

Jarvik, Murray, 24

journal publishing requirements, 45–46

K

kinship model, twin studies extended to, 250–

252, 262, 279, 280

k-means clustering, 217

knockout mice, 159, 160, 161, 166, 422

Kraepelinian approach to diagnosis, 291

Kyoto Encyclopedia of Genes and Genomes, 561

L

laboratory-based measurements

attention defi cit hyperactivity disorder, 356

consumption, 418

endophenotypes, 349–351, 353–354, 357–362

late emergent symptoms, 90–91

latency to fi rst puff, 415

latent class analysis, 80, 92, 98, 248–249, 256–257, 

266, 280

latent class growth analysis (LCGA), 218, 226–229, 

248–249

latent growth curve (LGC) models, 254–256, 266, 

280
613

Monograph 20. Phenotypes and Endophenotypes

association data integrated into, 259

substance-use comorbidity, 306, 308, 321

latent phenotype model, 252, 270

latent profi le modeling, 90–91

latent trait, 270

latent variables, 77, 248, 256

substance-use comorbidity, 306, 321, 324

LCGA (latent class growth analysis), 218, 226–229, 

248–249

LD (linkage disequilibrium), 543, 545–546

learning associations, 349

learning differences, 158, 169

letter cancellation task, 433

level, in latent growth curve models, 254

Lewis rats, 411, 423

LGC. See latent growth curve models

liability models, of smoking behavior stages, 264, 

280

lifetime regular smoking, defi nition of, 29

ligand-activated ion channels, 143

light smoking. See chippers

likelihood-based approaches, in cluster analysis, 

217–218

likelihood ratio tests, 310, 312–313

linear growth, assumption of, 255

linear model, 116

linear regression, in structural equation modeling, 

248

linear relations, 25

linkage analysis, 257–258, 266–268

candidate gene studies, 32, 40–42, 267–268, 

280–281, 589

environmental factors, 258

genome markers, 24, 257–258

nicotine-dependence indices, 40–42

linkage disequilibrium (LD), 543, 545–546

Lister rats, 432–433, 444

lithium-chloride conditioned place aversion, 161

liver cytochrome P-450 enzyme CYP2A6. See 

CYP2A6 gene

location of smoking, 527

loci segregation, 40

locomotor activity, 369, 422

logarithm of odds (LOD) score, 40–42, 258, 267

logistic regression curves, 91

Long-Evans rats, 411

longitudinal data

growth curve modeling of, 218–219, 234

on substance-use comorbidity, 291, 320

Loss of Control subscale, 90–91

low genetic proneness, 96

LPAAT-delta gene, 42

lung, nicotine concentration in, 145

lung cancer, predisposition to, 44, 50

M

macrocontextual factors, 509, 514

as moderators, 515–516

macroenvironment proximal indicators, 521–522

macular degeneration, age-related, 46

magnetic resonance imaging (MRI), 351, 439, 

456–457

maintenance of dependence, 589

male smokers. See also gender differences

adolescent, 342, 348

blood pressure changes in, 449

nicotine-dependence factors, 37, 38, 99

OPRM1 gene in, 419

statistics on, 21

twin studies, 515, 517

manifestations of dependence, 75

Mannheim Study of Risk Children, 199

MAO. See monoamine oxidase

MAP3K4 gene, 42

marginalization

of smoking, 4, 21

of social groups, 47

marginal nonnormality, 221

marijuana use

early pleasurable, 374

tobacco use concurrently with, trajectories of, 

296–298

modeling, 304–305

trajectories of, 295–296

marketing direct-to-consumer, 50

Markov chain Monte Carlo (MCMC) methods, 542

masking etiology, 82

masking of causal factors, 74

maternal care, 36

mature subphenotypes, 82, 110–111

maximum acute tolerance, 162, 165

maximum price assessment, 418

McGill University Study on the National History 

of Nicotine Dependence, 199

MCMC (Markov chain Monte Carlo) methods, 542

mean (centroid)

cluster analysis, 217

growth curve modeling, 255

structural equation modeling, 248–249
614

Index

measured genetic factors, 4

measurement invariance, 247, 255, 276, 280

mecamylamine, 159

mediation

of conditioned place preference, 161

by endophenotypes, 107–108

of nicotine, 160

memantine, 156, 171

memory, 379, 434–438

Mendelian randomization, 118

mesolimbic dopaminergic system, 410, 411

metabolic tolerance, 97

metabotropic glutamate receptor 5 (mGluR5), 156

methodological issues

assessment precision, 520

behavioral modeling, 247–248

family-based studies, 521

future research directions, 383–384, 594

genetic modeling, 247–248

innate sensitivity research, 366–368

real-time interaction, 524–525

research limitations, 366–368, 376, 383–384

substance-use comorbidity, 321–323

methyllycaconitine citrate (MLA), 159

mGluR5 (metabotropic glutamate receptor 5), 156

mice. See mouse models

microchip analysis, 170, 258–259

microcontextual factors, 509, 514

coding of real-time interaction, 524

as moderators, 516–518

microsatellites, 543, 551

microsocial context, quantifying, 522–525

midbrain dopamine circuits, 348, 349

Mid-South Tobacco Family (MSTF), 267

migration levels, 516

Minnesota Nicotine Withdrawal Scale, 446

Minnesota Twin Family Study (MTFS), 196, 260, 

360

mirror tracing, 448

misleading claims, 50

misspecifi cation, model, 222

mixed models, developmental trajectories, 215

MLA (methyllycaconitine citrate), 159

model(s)

clarifi cation of, 559

searching, 551

selection of, 547

with stochastic variable selection, 549–550, 

551–552

model fi t, evaluation of, 310, 312–313

modeling. See also specifi c types of modeling

phenotype (See phenotype modeling)

with prior knowledge, 570

selection algorithm, 577–578

uncertainties in, 546, 547, 549

model misspecifi cation, 222

moderation of relationships, 248

modifi ed pairwise interaction, 545

modulation

dopamine receptors, 156

nicotine rewards, 160

molecular genetic studies, 266

 of adolescent smoking, 198–199

analytic framework for, 257–259

Monitoring the Future project, 299, 304

monoamine oxidase (MAO)

in anxiety disorders, 353

neuroticism and, 351

in neurotransmitter breakdown, 540

monoamine oxidase (MAOA/MAOB) gene, 269

mood effects, 373–376, 380–381

in ATBC analysis, 497

measures of, 372

of nicotine, 366

Mood Form of Diener and Emmons, 374–375, 446

morning smoking. See time to fi rst cigarette

morphine, as nicotine substitute, 153

Morris water maze, 435

mortality statistics, 4, 21

motivational mechanisms, 84, 403, 408

reinforcement, 410–420

rewards, 420–424

Mouse Genome Informatics database, 150

mouse models, 134–135, 418

adolescent exposure, 194–195

behavioral changes, 151–157

future research directions, 168–172

nicotine administration, 145–149

nicotine dependence, 149–151

nicotinic receptors, 135

customizing, 141–143

functional diversity of, 136–141

molecular biology of, 136

nicotine as agonist/antagonist, 143–144

upregulation, 144–145

reward, 157–161

startle inconsistency, 444

strains, 134 (See also strain-specifi c differences; 

specifi c strain)

research options with, 592

selection of, 150–151
615

Monograph 20. Phenotypes and Endophenotypes

tolerance, 162–168

mouse-rat differences, 150

movies, smoking in, 7, 20, 523

MRI (magnetic resonance imaging), 351, 439, 

456–457

MSTF (Mid-South Tobacco Family), 267

MTFS (Minnesota Twin Family Study), 196, 260, 

360

multidimensional measures, of nicotine 

dependence, 81–86

multifi nality, 191

multilevel analysis, diffi culties of, 248

multiple trajectories, developmental, 191–202, 

232–234

multivariate analysis

developmental trajectories, 215

latent growth curve models, 255

substance-use comorbidity, 315–316, 321

twin studies, 262–263

multivariate factor model, 252–253

multivariate normal distribution

in growth curve models, 255

in structural equation modeling, 249

multivariate normality, within-class, 221, 233

mu opioid knockout mice, 422

mu opioid receptors

in conditioned place preference, 160

in nicotine replacement therapy, 407

reward mediation, 419

in tolerance, 167, 168

muscarinic acetylcholinergic systems, 166, 168

muscarinic receptors

in aging, 170

blockading, 140–141

metabotropic, 136

muscle tension, 377–378

mutations

rate predictions, 554

for tolerance, 166

Muthén, Bengt, 218

N

nAChRs. See nicotinic acetylcholine receptors

Nagin, Daniel, 218

naloxone, 160

nasal spray. See nicotine nasal spray

National Cancer Institute, 498

National Center for Biomedical Ontology (NCBO), 

561

National Comorbidity Study, 445

National Health and Nutrition Examination 

Survey (NHANES III), 487, 494, 500–503

National Institute of Mental Health Diagnostic 

Interview Schedule (DIS), 81, 501

National Institute on Drug Abuse Genetics 

Consortium, 43

National Institutes of Health, 531

National Longitudinal Study of Adolescent Health 

(Add Health), 197–198, 261, 517

National Survey on Drug Use and Health, 93

N-back task, 436

NCBO (National Center for Biomedical Ontology), 

561

NDSS. See Nicotine Dependence Syndrome Scale

NEAD (Nonshared Environment in Adolescent 

Development) Project, 509, 524–525

Netherlands Twin Register, 262

Netherlands Twin Study of Anxious Depression 

(NETSAD), 266–267

network models, 554, 556–558

neural analysis, 349–351, 353–354, 357–362

neural incentive system, 346

neural networks modeling, 340–341

neural substrata, associated with tolerance, 162

neural systems, candidate, 343–346

neurexin 1 (NRXN1) gene, 43

neurobiological analysis, 348

neurobiological dependence pathways, 43

neurobiological systems, 344

neuroendocrine response, to stress, 354

neuroimaging, 360, 379–380

neuropeptide systems, 159, 160

neuroprotection, 170

neuroticism, 101, 351–353

neurotransmitter systems

in chronic tolerance, 166–167

in conditioned place preference, 159–161

New England Family Study, 526

NHANES III (National Health and Nutrition 

Examination Survey), 487, 494, 500–503

nicotine

administration of (See administration)

age-related response differences, 194–195

as agonist/antagonist, 143–144

anti-infl ammatory effects of, 148–149

anxiogenic effects of, 168

behavioral changes from, 151–157

bioavailability of, 7, 20

enforcement timing, 369

in free-base form, 148
616

Index

frequency of use, 4, 21, 369–371, 413, 420

hedonic impact of, 158, 372, 424

neuronal activity induced by, 43

physical changes from, 162

pre-exposure risk (See pre-exposure risk)

pretreatment, 420

reinforcement (See reinforcement)

rewards and (See reward)

tolerance (See tolerance)

nicotine aerosols, 147

nicotine-binding sites, 135, 144

nicotine choice, 369

nicotine choice procedure, 416, 419–420

nicotine cigarette choice paradigm, 419

nicotine clearance

dispositional tolerance, 162, 164, 168

genetic factors in, 39–40, 539

nicotine dependence, 20–22, 149–151

in adolescence (See adolescent nicotine 

dependence)

concurrent with substance use (See substanceuse comorbidity)

construct refi nement, 25–27

craving associated with (See craving)

crosscutting issues, 595–596

developmental pathways in, 589

distal measures of (See distal measures)

endophenotypes in, 409, 453

epidemiological concepts, 31–37

future research directions, 455–457

heritability of (See genetic factors; heritability)

historical perspective of, 22–25

inference of, 77

mouse models of (See mouse models)

phenotype (See phenotype(s))

progression research, 592

psychiatric disorders correlated with, 98

risk with, 375–376

smoking compared with, 87

versus tobacco dependence, 75

treatment of (See smoking cessation)

understanding of, 588–596

nicotine-dependence measures, 26, 28, 37–45, 73, 

78, 79

adolescents, 192, 230–231, 264

example of, 225

invariance, 276, 280

Nicotine Dependence Syndrome Scale (NDSS), 

82–84

abbreviated, 500

as assessment tool, 405

subscales of, 90

in TUS-CPS analysis, 498

nicotine deprivation memory defi cits, 436

nicotine gum

as consumption assessment, 415

effect on EEG activity, 426

in memory effects, 435

versus placebo, 370

nicotine metabolism

association analysis of, 268

catabolism of, 149

CPD variation and, 34

CYP2A6 gene in, 39–40, 149, 342, 406, 418

in dependence risk, 22, 342

in ontology example, 562–569

pathway, 556

nicotine nasal spray

aversion with DRD4*7-repeat allele, 376

as consumption assessment, 415

in current smokers, 374

memory effects of, 435–436

versus placebo, 369–370

pleasurable responses to, 373

nicotine patches

effect on EEG activity, 426

in memory effects, 435

on nonsmoking adults, 377

Nicotine Pharmacokinetics Ontology (NPKO), 

539, 561, 571

nicotine replacement therapy (NRT), 406. See 

also smoking cessation

nicotinic acetylcholine receptors (nAChRs), 134

association analysis, 268

as attention factor, 362

beta2-subunit (CHRNB2), 268

binding to, 588

blockading, 140–141, 143–144

chromosomal regions, 43, 44, 50

in chronic tolerance, 166–167

coding for, 408

customizing, 141–143

desensitization of, 146

fast-ionotropic, 136

functional diversity of, 136–141

high-affi nity, 157

illustration, 409

as impulsivity mediator, 449

inactivation of, 143

inferences from, 152–153
617

Monograph 20. Phenotypes and Endophenotypes

initial sensitivity response with, 376–377

molecular biology of, 136

in nicotine binding role, 406–407

in nicotine dependence, 411

structure of, 136–138, 138

tissue-specifi c responses, 71, 133

in tolerance, 168

upregulation of, 135, 144–145, 588

nicotinic receptor subunits, 136–140

CHRNA5/CHRNB3 genes, 342

composition of, 139

functional variants of, 140

limiting expression, 141–143

structure of, 136, 137

nimodipine, 162, 167, 168

nitric oxide, in conditioned place preference, 161

7-nitroindazole, 161

N-methyl-D-aspartic acid (NMDA) glutamate 

receptors, 156

NMRI outbred mice, 154, 412, 430

nomological net, 76

noncoding DNA sequences, 554

nonlinear growth curves, 255

nonnicotinic systems, 166

nonnormality, 221

Nonshared Environment in Adolescent 

Development (NEAD) Project, 509, 524–525

nonsmokers (abstainers)

characteristics of, 22

inclusion versus exclusion of, 222–223, 235, 323

nicotine patches, 377

Northern California Twin Registry, 539, 540

novelty seeking, 155–156, 199, 348–349

by adolescents, 27

nasal spray use in, 373

neural incentive system association with, 346

in substance use, 292–293, 348–349

NPKO (Nicotine Pharmacokinetics Ontology), 539

NRT (nicotine replacement therapy), 406. See 

also smoking cessation

NRXN1 gene, 43

NRXN3 gene, 43

nutritional cancer prevention, 497

O

OBO (Open Biomedical Ontologies), 560

OBO-Edit, 561

OBO Foundry project, 560

OBO Relation Ontology, 561

observed variables, in structural equation 

modeling, 248

occurrent, 541, 559

oddball stimulus, 429

olanzapine, 456

ontologies, 539–541

defi nition of, 541, 559

development process, 560–562

discussion of, 570–574

methods, 541–543, 550–562

nicotine metabolism, 562–569

statistical approaches, 543–550

Ontology Web Language (OWL), 561

Open Biomedical Ontologies (OBO), 560

Open Source software, 561

OPRM1 gene, 409

coding for, 408

gender differences in, 419

in nicotine replacement therapy, 407

in smokers, 423

support interval proximity, 42

oral administration, 147–149, 172, 412–413

oral mucosa exposure to nicotine, 148

ordinal data, analytical framework for, 247, 255

osmotic minipump, 147

outcomes of dependence, 75

outliers, controlling for, 281

OWL (Ontology Web Language), 561

oxotremorine, 166–167

P

P3 amplitude, 294

panic disorder, 353

PANTHER Pathways databases, 556, 561

parent(s)

educational level of, 519

twin studies extended to, 251, 262, 279, 280

parental monitoring, as smoking counterforce, 22

parental smoking

adolescent smoking and, 196–197, 200, 262, 

346

behavioral modeling of, 246

nasal spray non-response, 377

smoke-free home with, 523

socialization effects from, 517

parenting behavior, 342

partition variation, 270, 280

passive avoidance, 435

patch. See nicotine patches
618

Index

path diagrams, in structural equation modeling, 

248–250

pathogen modeling, 100–101, 103

PATO (Phenotype and Trait Ontology), 560, 571

Pavlovian learning, 158

PBT (problem behavior theory), 198, 292–294, 

322

PDAs (personal digital assistants), 525–527

peers

as infl uence, 100, 346, 517

as smoking predictors, 523

peer smoking, effect on adolescent smoking, 197

penetrance, incomplete, 31

peripheral nervous system (PNS), 344, 348

P50 ERP, 427–428

persistence

drug use, 417–418

negative affect with, 447

smoking (See smoking persistence)

personal digital assistants (PDAs), 525–527

personalized health care, 49–50

person factors, 97–99

PET (positron emission tomography), 439

P300 event-related potential (ERP), 359–360, 

429–430

P450 genotype, 39

pharmacokinetics, 37, 39, 118, 149, 542, 590

pharmacokinetics ontology, 539, 561, 571

phenotype(s)

assays, 83

association of, 33

behavioral, 171, 492

characteristics of, 109

components, 341

developmental progression of, 74

effects of variables on, 553

as endophenotypes, 5, 33–34

environmental, 34–36

framework, 27–31

genetic mapping, 96–103

genetic variants to, 102

genotyping and, 560–562

heterogeneity, 341

intermediate, 341, 342 (See also

endophenotypes)

of mouse strains, 150

pathways, 23, 590

as points in smoking trajectory, 490

research pitfalls, 489

stages, 109

substance-use disorders, 316–318, 321, 324

Phenotype and Trait Ontology (PATO), 560, 571

phenotype modeling, 487, 488–492

examples, 493–503

methods, 492–494

summary, 503–505

phenotypic assortative mating, 251

phenotypic cultural transmission, 251

phenotypic research, 105–106

analytic strategies, 113–118

causal paths, 107–110, 111–113

caveats, 110–111

summary, 118–119

phenylthiocarbamide (PTC) haplotype, 84

phosphatase and tensin homolog (PTEN) gene, 

269

physical aggression, 357

physical responses to nicotine, 169

symptoms, 26

in tolerance, 162–163, 164–165

physiological measures of response reward, 351

physiological startle. See startle response

physiology

of affect, 377–378

of behavioral traits, 344, 345, 346

Pittsburgh Youth Study, 214

placebo, versus nicotine, 369–370, 420

placenta, 145

plasma nicotine levels

with IV injection, 146

tissue nicotine levels compared with, 145

in tolerance studies, 163–164

pleiotropy, 32, 47–50

PNS (peripheral nervous system), 344, 348

point mutation, 444

policy interventions, 34–35

polymorphisms, 553–554. See also single 

nucleotide polymorphism

associations with, 116

different priors for, 571

in dopamine reward pathway, 342

emphasis within genes, 547

investigation of, 546–547

numerous, challenges of, 546–547

perturbations from, 554

trait variation effect of, 549

PolyPhen (polymorphism phenotyping), 554

polysubstance use, 43, 296

population

frequency of genetic factors in, 86
619

Monograph 20. Phenotypes and Endophenotypes

for growth curve modeling, 216–217, 234

homogeneous, assumption of, 248

in latent class analysis, 256

response distribution in, 370, 373, 376–380

in structural equation modeling, 248–249

population heterogeneity, 217

estimating, 218, 221–222

Positive and Negative Affect Schedule, 374–375, 

446

positron emission tomography (PET), 439

postural hypotension, 449

PPI (prepulse inhibition), 378, 403, 430–432

preclinical research. See also animal studies; 

mouse models

affective regulation, 443–444

electrophysiological measures, 425–426

event-related potential, 427–428

impulsivity, 449–450

reinforcement, 410

rewards, 421–423

precursors, class III phenotypes as, 30

predictive validity

of genetic testing, 50

of primary motives scales, 90–91

pre-exposure risk, 339, 340. See also smoking 

initiation and progression risk

endophenotypes, 340–347

future research directions, 381–385

initial sensitivity endophenotypes (See initial 

sensitivity)

preproenkephalin knockout mice, 160, 422

prepulse inhibition (PPI), 378, 403, 430–432

price-demand curve, 417

pricing, of tobacco products, 20, 21, 520

primary motive scales, as predictors, 90–93

prior covariate specifi cation, 570–571

prior knowledge, ontologies and, 553–562

PR (progressive ratio) measures, 417–418, 420, 

454

probability discounting, 450–451

problem behavior theory (PBT), 198, 292–294, 

322

problem use, 196, 198, 263, 294

Profi le of Mood States, 374–375, 446

programmed lapse procedure, 454

progression to smoking, 491

progressive ratio (PR) measures, 417–418, 420, 454

Project on Human Development in Chicago 

Neighborhoods, 520

“proof of concept” analyses, 493, 494

protective factors

in adolescents, 343

versus vulnerabilities, 87, 114

protein sequence data, 561

protobacco advertising, 7, 20, 30, 348

prototypes, of nicotine-dependence research, 

27–28

proximal indicators, 509

proximal infl uence, 519–522

proximal measures

versus distal measures, 516

of social context, 518–527

psychiatric comorbidity, 81, 98–99

with DSM-IV dependence, 26, 81

empirical examples of, 496, 502

resolving, 115

psychiatric genetics research, 25, 27

psychoactive alkaloid, 22

psychological traits

approach-related risk, 346–349

avoidance-related risk, 351

control-related risk, 354–357

psychometric common factor model, 252

psychopathology

developmental, 191–202, 292–293

indices, 32

physiological basis of, 344

substance use and, 292–293, 304

psychophysiological responses

to acute stressors, 448–449

craving, 440

psychosocial factors

in adolescent nicotine dependence, 195

in adolescent smoking initiation, 200, 202, 

211

PTC (phenylthiocarbamide) haplotype, 84

PTEN gene, 269

public health messages, 47

public health outcomes, 492

public settings, smoking in, 4, 20–21

putative endophenotypes, 341

Q

QSU (Questionnaire on Smoking Urges), 439

QTL (quantitative trait locus), 258

quantitative genetic models, 512, 522

quantitative trait locus (QTL), 258

quantity smoked measures, 28

Questionnaire on Smoking Urges (QSU), 439
620

Index

R

racial background, 439. See also African 

Americans

racism, associated with genetic information, 47

random effects, 216

within-class, 220–221, 233

rapid-decision context, 358

Rapid Visual Information Processing (RVIP) task, 

433

rat models

adolescent nicotine exposure in, 194–195

alcohol/nicotine correlation, 369

conditioned place preference in, 423

self-administration of electrical stimulation, 

421

sensitivity in, 364

strains, 411 (See also specifi c strain)

Reactome, 561

real-time contexts, 526

real-time interaction, 524–525

recall, 379

receptor heterogeneity, 139

recovery, from acute tolerance, 162

regression models, 248, 542, 543, 550–551, 572

regular smoking

defi nition of, 29

genetic factors in, 29–30

reinforcement, 151–152, 155–156, 366–372, 380

alternative, 417

in cognitive control, 349

enhancing, 413

genetic infl uences in, 418

in initial sensitivity, 368–372

measurement of, 414–420

motivational mechanisms, 410–420

secondary, 411, 413

relapse

environmental infl uences in, 95

physiological basis of, 588

predictors of, 77, 413, 434, 442, 444–445

time to fi rst cigarette as predictor of, 80, 89

relations between entities, 541, 559, 560

relative measurements, 571

reliability. See also test-retest reliability

of developmental trajectory research, 234

of nicotine-dependence measures, 26, 79–80

recall, 26–27

reliability coeffi cients, 79

religiosity, adolescent smoking and, 198

repeated-measures data, developmental 

trajectories, 215, 222

replication, of gene-nicotine dependence 

associations, 45–46

research fi ndings, communication of, issues in, 

45–50

research limitations

adolescent smoking, 367

adult smoking phenotype, 190

ATBC analysis, 497, 504

behavioral measures, 104

cue-induced craving, 456

data, 572

distal measures, 103

DSM, 103

extreme groups, 114–116

methodology, 366–368, 376, 383–384

NHANES III analysis, 503–505

nicotine-dependence measures, 103–104

Nicotine Dependence Syndrome Scale, 84

retrospective reporting, 367

self-report measures, 367

smoking cessation research, 454–455

statistics modeling, 570

tobacco dependence assessments, 95

TUS-CPS analysis, 500, 504

twin studies, 279–280

Wisconsin Inventory of Smoking Dependence 

Motives, 85–86

residual familial factors (F), 258

residual item variances, 270

respiratory sinus arrhythmia (RSA), 361

response inhibition, 358–359

resting EEG activity, 425–427

retail tobacco outlets, 520, 521

retrospective reporting limitations, 367

reward, 157–161

defi nition of, 349

for depressed smokers, 352

future research directions, 454–455

immediacy over magnitude, 348, 349

in initial sensitivity, 372–373

measurement of, 351, 366, 372–373, 380, 

423–424

modulation of, 160

motivational mechanisms, 420–424

mu opioid receptor mediation, 419

preclinical studies, 157–161, 372–373, 421–423

signaling, 350

reward and pleasure pathways, 22

reward-discounting tasks, 350
621

Monograph 20. Phenotypes and Endophenotypes

rimonabant, 160

risk. See also pre-exposure risk; smoking 

initiation and progression risk

age-specifi c, measurement of, 35

approach-related, 339, 346–349, 362

avoidance-related, 339, 351–354, 362

control-related, 339, 354–357, 362

cumulative, measurement of, 35

disease, 541, 550

with nicotine dependence, 375–376

nicotine metabolism and, 22, 342

risk factors

adolescent smoking initiation, 211–212, 232, 

350, 433

substance use, 306, 340

risk-taking behavior, by adolescents, 195, 199

RNA analysis, 142

rodent models. See also mouse models; rat models; 

specifi c strain

adolescent nicotine exposure, 194–195

adolescent sensitivity, 371

intravenous self-administration, 410

nicotine effects, 368, 435

strain-specifi c differences, 418

RSA (respiratory sinus arrhythmia), 361

*RS578776 subunit gene, 43

*RS16969968 subunit gene, 43

Russell, M.A.H., 24

RVIP (Rapid Visual Information Processing) task, 

433

S

saccharin, 148, 154, 159

S allele, 112, 113

sample size/followup

in ATBC analysis, 497

for developmental trajectory research, 234

in TUS-CPS analysis, 498

saturated model, 547

SBML (Systems Biology Markup Language), 561

schizophrenia, 428, 443

secondary criteria of nicotine dependence, 76, 77

secondary motives scales, 92–93

secondary reinforcement, 411, 413

secondhand smoke, 20, 246

second-stage mixture model, 572

self-administration

ad libitum (ad lib), 415–416, 419

genetic effects on, 146

intravenous, 146, 152–157, 410–412

oral, 412–413

self-control, in children, 344

self-insuring fi rms, 49

self-report measures

of affect, 446–447

components of, 79

of craving, 439–440

ecological momentary assessment in, 525

limitations of, 367

SEM. See structural equation modeling

semi-Bayes approach, 549

semistructured paradigms, 524

Sensation Seeking Scale, 377

sensitivity

of measurements, 341

modeling, 363–364, 364

periodic, 96

sensitivity analysis, 570

sensory measures, 403

Sensory Questionnaire, 423

sequential process model, of substance-use 

comorbidity, 304

serotonin

association analysis, 268–269

genetic variation in, 343

metabolism of, monoamine oxidase in, 353

regulation of, 540

smoking cessation and, 407

SES. See socioeconomic status

seven-point Likert scale, 372

shared environment effects

in adolescent smoking, 197, 260–261, 264, 280

twin studies, 251, 280

Shiffman-Jarvik Withdrawal Scale, 440

Shiffman Nicotine Dependence Syndrome Scale. 

See Nicotine Dependence Syndrome Scale

sibling(s)

IBD confi gurations for, 257–258

smoking epochs of, 531

as smoking predictors, 523

socialization studies, 517

twin studies extended to, 251, 262, 279, 280

Sibling Partners Study, 526

sickle cell discrimination, 47

side effects

from drinking-water administration, 172

from intravenous administration, 146

SIFT (Sorting Intolerant From Tolerant) 

procedure, 554
622

Index

simultaneous effect of genes, 32–33

single-factor dependence, 79n

single-factor structure, 81

single-group growth curve model, 219

single nucleotide polymorphism (SNP)

candidate gene variants, 25

disease association with, 544

genotyping, 43, 257–259

nonsynonymous coding, 554

as refl ection of underlying effects, 546

relationships over candidate genes, 545

relevance of, 570

in whole-genome research, 4

situational dependence, 27

six-class solution, example of, 227–228

skin conductance, 354, 377–378

skin temperature, 440

SLC6A3 gene, 198

SLC6A4 gene, 32, 101, 112, 113

sleep problems, in children, 357

slope

distribution of, 219

factor loading and, 275–276

in latent growth curve models, 254, 306, 310

129S6 mice, 430

SMOFAM (Smoking in Families Study), 267

smoke-free laws, 520

smoke-free settings, 20, 30, 523

smokeless tobacco, 147

smokers, characteristics of, 22

smoking

bans on, 99

decline in, 20

developmental phenotypes (See adolescent 

developmental trajectories)

fi rst experience with (See initial sensitivity)

frequency of, 369–371, 413, 420

during illness, 79, 80

nicotine dependence compared with, 87

quantitative genetic model and, 512

status, 28

transition levels, 42–43, 488–490

smoking cessation

age-related changes in, 170

barriers to, 4–5, 46

CHRNB2 gene in, 407

commercial testing, 50

comparison groups changes, 493

data sets in, 493–494, 494

defi nition of, 492

delay discounting factor in, 350

demand for, 21

diffi culties of, 97

drugs for, 160

failures in, 44–45

FTND predictions of, 80

future research directions, 454

gender differences in, 38

heritability in, 406

monoamine oxidase decrease during, 351

research limitations in, 454–455

serotonin pathway and, 407

subthreshold pretreatments, 162

success predictors, 81, 89, 413, 434

symptoms of, 447

tailored, 48

smoking index variable, 30

Smoking in Families Study (SMOFAM), 267

smoking initiation, 31, 42

adolescent (See adolescent smoking initiation)

assessment of, age effects in, 279, 322

defi nition of, 29

gender differences in, 267

linkage analysis of, 267

versus persistence, 406

smoking initiation and progression risk, 346, 491. 

See also pre-exposure risk

approach-related, 346–349

avoidance-related, 351–353

control-related, 354–357

endophenotypic measures, 349–351, 353–354, 

357–362

future research directions, 362–363

smoking level

measurement of, example of, 224–225

substance-use comorbidity and, 308, 322

smoking pattern, factor analysis of, 265

smoking persistence, 29, 30, 406

defi nition of, 29

early-onset smokers’ risk for, 200, 212–213, 

230–231

smoking topography devices, 415, 418

SNP. See single nucleotide polymorphism

social context, 509, 510–511

adolescent smoking, 193, 198

behavioral genetics in, 514–518

future research directions, 527–532

proximal measures of, 518–527

rationale for, 511–514

substance use, 294–295
623

Monograph 20. Phenotypes and Endophenotypes

social development model, 293

socioeconomic status (SES)

adolescent smoking and, 232

distal to proximal infl uence, 519–522

of hard-core smokers, 35

nuanced approaches to, 519–520

socioregional infl uences, 515–516

“softening” of smoking, 193

software

association analysis, 259

item response theory, 270

linkage analysis, 258

ontology, 561, 562

Sorting Intolerant From Tolerant (SIFT) 

procedure, 554

SourceForge Web site, 562

species-specifi c responses, 134

specifi c-factor models, of substance use, 306–307, 

315–316

speed congenics, 151

spinal cord minipumps, 163

spouses, socialization effects from, 517

Sprague-Dawley rats, 411, 431, 432–433

stage models, developmental trajectories, 233

startle-probe measures, 113

startle response

as affective response, 447–448

in humans, 377

increases in, 444

prepulse inhibition of, 378, 403, 430–432

test-retest reliability of, 446–447

state laws, against genetic discrimination, 49

static clustering, versus dynamic clustering, 

219–220, 232

statistics

approaches to, 543–550

combining genetic studies with, 248

developmental phenotypes, 214–231

modeling, 248–259, 570

ontological knowledge in, 572

ST/b inbred mice, 154, 155, 412–413

stem cells, 151

Sternberg Memory Task, 379, 436–437

stigma, nicotine dependence as, 46–47

stochastic variable selection

hierarchical modeling and, 547–548, 572, 573

model selection with, 549–550, 551–552

stop-go task, 359

strain-specifi c differences

aging, 170

conditioned place preference, 158

DNA markers, 151

fi ve choice serial reaction time task, 432–433

genetic, 150

mouse models, 153–154, 164–165, 412–413

nicotine effects of, 135, 161, 169, 418

nicotinic acetylcholine receptors, 172

rat models, 411

tolerance, 164

unraveling of, 142

strain surveys, 422

stress, 351

as infl uence, 100

infl uences on, 36

neuroendocrine response to, 354

response mediators, 143

Stroop interference task, 379, 440, 451, 452

Stroop paradigm, 113

structural equation modeling (SEM), 248–249

combined with latent class models, 256

of developmental trajectories, 215

linkage analysis and, 257–258

for twin data, 249–257

study participants, selection of, 490–492

subcutaneous administration, 147, 172

subpopulation

in latent class analysis, 256

in structural equation modeling, 248–249

subPSEC (substitution position-specifi c 

evolutionary conservation), 554

substance use. See also specifi c substance

adolescent, shared environmental infl uences 

in, 261

age-dependent vulnerability to, 195, 198, 

200–201, 212, 233–234, 292–293

attention defi cit hyperactivity disorder in, 

356

common-vulnerability model, 291–294, 306, 

315–316

dopamine in, 410, 588

early pleasurable use, 374

environmental factors infl uencing, 294–295

interpersonal dynamics in, 522

Iowa gambling task as predictor, 350

modeling of, 43

nasal spray non-response, 377

novelty seeking in, 292–293, 348–349

prevention of, research approaches, 294–295

risk factors for, 340

specifi c-factor models, 306–307, 315–316
624

Index

substance-use comorbidity, 289–325

association between smoking trajectories and, 

296–298

empirical examples of, 307–323, 496, 502

future research directions, 323–324

gender differences in, 304

gene-environment interactions in, 320

importance of studying, 290–292

informative phenotypes for, 316–318, 321, 324

literature review, 295–296

mechanisms underlying, 291–292

methodological issues, 321–323

modeling, 298–307

risk factors, 306

two-stage models of, 323

substance-use disorders

common versus specifi c liability to, 292–295

diagnosis of, 291

shared genetic risk for, 294

substitution position-specifi c evolutionary 

conservation (subPSEC), 554

subthreshold depression, 352

sucrose, 154–155, 412

support interval, 40, 42

Surgeon General’s Report (1964), 24

Surgeon General’s Report (1979), 24

susceptibility loci mapping, 24. See also candidate 

gene studies

sweat gland activity, 440

Swedish Twin Registry, 515

Swiss mice, 435

Swiss-Webster mice, 158, 161

switching, between trajectory groups, 255–256

systems biology, genetic association studies and, 

554, 556–559

Systems Biology Markup Language (SBML), 561

T

targeted treatment, 21–22

task performance, with nicotine deprivation, 

436

Taste/Sensory Processes subscale, 84

taxon, nicotine dependence as, 93–94

team sports, as protective factor, 343

temperament-based model, 343–344

test performance, defi nition of, 75

test-retest reliability

acoustic startle refl ex, 446–447

ad lib smoking, 415

diagnostic tools, 81

event-related potential, 429

mood effects tests, 375

nicotine-dependence measures, 26–27, 29

prepulse inhibition startle response, 431

thapsigargin, 167

theta rhythm (slow-wave activity), 362

TH (tyrosine hydroxylase) gene, 198–199, 269

threshold

factor loading and, 275–276

as “smoker,” 42

time to fi rst cigarette (TTFC)

during adolescence, as indicator of adult 

nicotine dependence, 230–231

correlations with, 90

factor analysis of, 88–89

nicotine metabolism association, 34

as physical dependence measure, 79

as quitting predictor, 80

time to maximum tolerance, 162

tissue levels of nicotine, versus plasma levels, 

145

Tobacco Craving Questionnaire, 440

tobacco dependence, versus nicotine dependence, 

75

tobacco industry, 24

tobacco policies, 7, 20

effect on adolescent smoking, 193

substance use policies and, 294–295

tobacco settlement dollars, 4

tobacco use. See also nicotine

frequency of, 4, 21, 369–371, 413, 420

history of, 27–28

smoke compounds, 588

Tobacco Use Supplement, U.S. Census Bureau’ 

Current Population Survey (TUS-CPS), 487, 

494, 498–500

tolerance, 162–168

genetic effects on, 146

to repeated doses, 158

Tolerance subscale, 90–91

TPH gene, 269

trace fear conditioning, 435

transcription factor CREB, 160–161, 422

transcription factor Fosb, 161

transcription levels, alterations in, 40

transdisciplinary framework, 521

Transdisciplinary Tobacco Use Research Center 

(TTURC), 89, 521, 526

transitional endophenotypes, 107, 108, 200, 233

translational validation, 172
625

Monograph 20. Phenotypes and Endophenotypes

TRPC7 gene, 43

129T2/SvEmsJ mice, 431

TTFC. See time to fi rst cigarette

TTURC (Transdisciplinary Tobacco Use Research 

Center), 89, 521, 526

TUS-CPS (Tobacco Use Supplement, U.S. Census 

Bureau’s Current Population Survey), 487, 

494, 498–500

twin studies. See also specifi c study

adolescent smoking, 196, 259–262

affective regulation, 446–447

cross-substance concordance, 317, 319–320

CYP2A6 effect, 40

delay aversion, 351

ecological momentary assessment, 527

epigenetic regulation, 36

equal environments assumption in, 516–517

event-related potential, 428, 429

extended family, 250–252, 262, 279, 280

factor analysis, 88–89

factor mixture models, 256

genomic studies, 589

heritability of dependence, 28–29, 86, 342, 

406

impulsivity, 451

limitations of, 279–280

multivariate, 252–253, 262–263

P300 amplitude, 360

prepulse inhibition startle response, 431

resting EEG, 426

smoking habits, 22, 24

smoking initiation, 31

structural equation modeling, 249–257

substance-use comorbidity, 307–323, 324

tobacco use history, 38–39

working memory, 437

two-factor structure, 81

two-stage genetic models, initiation versus

progression, 323

tyrosine hydroxylase (TH) gene, 198–199, 269

U

univariate analysis, substance-use comorbidity, 

322

unmeasured genetic factors, 4, 511

upregulation, of nicotinic receptors, 135, 144–

145, 588

U.S. Task Force on Community Preventive 

Services, 4

V

validity

of developmental trajectory research, 234

discriminative, 77

predictive, 50, 90–91

variables

independent, 495–496, 498–499

observable, 77

ontologies to represent knowledge about, 

558–559

perturbations from, 554

in structural equation modeling, 248

variance

in latent growth curve models, 254

partitioning, 270, 280

ventral tegmental area (VTA), 152–153, 588

verapamil, 167, 168

VET (Vietnam Era Twin) Registry, 263–264

videotaped paradigms, 524

Vietnam Era Twin (VET) Registry, 263–264

vigilance, 432–434

Virginia 30,000 Study, 262

Virginia Twin Registry, 264, 269–279

measures, 269–270

methods, 270

results, 270–276

study conclusions, 276–279

subjects, 269

Virginia Twin Study of Adolescent Behavioral 

Development, 260

visuospatial attention, 434

VLMR LR (Vuong-Lo-Mendell-Rubin likelihood 

ratio) test, 310, 312

VTA (ventral tegmental area), 152–153, 588

vulnerability

of adolescents, 343

differences in, 22

modeling, 363–364

protective factors, 87, 114

Vuong-Lo-Mendell-Rubin likelihood ratio 

(VLMR LR) test, 310, 312

W

Wald test, 544

watershed model, 78–79, 81–82

WCST (Wisconsin Card Sorting Test), 378, 436

Wechsler Adult Intelligence Test-Revised, 433

whole-environment scan, 530
626

Index

whole-genome association studies, 4

whole-genome linkage scans, 589

whole-genome quantitative transcript screening, 

170

wild-type mice

conditioned place preference in, 159, 160, 161, 

422

in CREB activation studies, 160

in nicotine reinforcement, 159

WinBUGS, 549

Wisconsin Card Sorting Test (WCST), 378, 436

Wisconsin Inventory of Smoking Dependence 

Motives (WISDM), 84–86, 85

as assessment tool, 405

subscales of, 90–93

Wistar rats, 425, 431, 449

withdrawal symptoms

in adolescents, 192, 194

cellular changes, 147

environmental infl uences in, 95

heritability of, 30

negative affect, 443

physical dependence inference from, 77

physiology of, 588

severity of, 97, 413, 447

support interval for, 40

within-class variability, estimation of, 220–221, 

233

working memory, 434–438

World Health Organization, 80–81

World Mental Health Survey Initiative, 81

X

XSEM (extended structural equation modeling), 

249

Z

zygosity, function of, in substance-use 

phenotypes, 316–317, 320