# Presentation: 302169

## NIDA Genetics:  An Update 

**NIDA Genetics:  An Update **

**Jonathan Pollock, PhD**
**Branch Chief, Genetics and Molecular Neurobiology Research Branch**
**DBNBR**
**And**
**Joni L Rutter, PhD**
**Associate Director for Population and Applied Genetics**
**DBNBR**

## Individual Differences in Vulnerability to Addiction 

- Not everyone who takes drugs becomes addicted
- “has characteristics of chronic disease”       		Piazza _*Science*_ 13 August 2004
- There are individual differences
- Chronic exposure to drugs combined with a vulnerability phenotype leads to addiction
- Genetics contributes to some of these individual differences

## Drug Addiction Is Influenced by Interactions of Genes and Environment

**Twin studies consistently show that there is a ****heritable**** component to drug abuse and addiction.**

**Now we are able to examine ****genetic variants****, or single nucleotide polymorphisms that  ****contribute to addiction vulnerability**

**Notes:**
But, what we do not understand is why some people become addicted and others do not.  We know from NIDA supported Twin studies that there is a heritable component to drug addiction.  

And now with the sequencing of the human genome we are able to ask what are the gene variants associated with drug addiction and what is their functional significance?

We have taken a two-pronged approach to begin to answer this question:

First NIDA’s intramural program is looking at genetic variants in heterogeneous populations of drug addicted individuals.

Second  our extramural research program through NIDA’s Genetic consortium and a contract with Perlegen Sciences has just completed a whole genome wide association study to uncover genetics variations associated with Tobacco dependence.

A similar approach to uncover the genetic basis of disease now also moving forward under the NIH GAIN (Genetic Association Information Network) and GEI (Genes and Environment Initiative) 

## Goal: Identify Genetic Contributions to Drug Abuse

- Genome-wide Association Studies (GWAS) or
- Linkage

- Fine-
- mapping

- Low-density
- Markers

- Genotyping/
- Resequencing

## NIDA Genetics: Identify Genetic Contributions to Drug Abuse

**2 Main Programmatic Themes**
- Trans-NIH initiatives
- Leverage ongoing programs
- Roadmap, GAIN, GEI, PGRN, etc
- Trans-NIDA programs
- Coordination among divisions
- Multidisciplinary/Interdisciplinary
- Data

## Human GeneticsTrans-NIH Involvement

- Roadmap 1.0
- National Centers for Biomedical Computing (NCBC) - NIDA Lead, Karen Skinner
- i2b2 (informatics for integrating biology and the bedside)
- Zak Kohane, PI--evaluate smoking data in medical records
- forge collaborations on genetics and bioinformatics of smoking
- Roadmap 1.5 – Epigenomics
- Nora Volkow, John Satterlee, Christine Colvis, Genevieve de-Almeida-Morris, Jonathan Pollock, David Shurtleff, Joni Rutter

- _[http://nihroadmap.nih.gov/epigenomics/](http://nihroadmap.nih.gov/epigenomics/)__ _

## Human GeneticsTrans-NIH Involvement

- Pharmacogenetics Research Network (PGRN)
- A collaboration studying the effects of genes on people’s responses to medicines
- Supported by 9 NIH ICs
- Pharmacogenetics of Nicotine Addiction and Treatment (PNAT) – Neal Benowitz, PI
- Addresses pharmacogenetics of nicotine addiction
**NRTs**
**Bupropion**
**Varenicline**
**Rimonabant**

## Human GeneticsTrans-NIH Involvement

- Genetic Association Information Network (GAIN)
- Public-private partnership led by Foundation for NIH
- Genome-wide association study policy development for NIH-- Jonathan Pollock
- Genes, Environment and Health Initiative (GEI)
*Genetics Program-* Pipeline for analyzing genetic variation in groups of patients with specific illnesses
- Genetics of Addiction – Laura Bierut, PI
*Exposure Biology Program-* Measuring environmental exposures
- NIDA RFA: Technologies measuring exposure to psychosocial stress and addictive substances--Kay Wanke, Kevin Conway

## NIDA Genetics: Identify Genetic Contributions to Drug Abuse

**2 Main Programmatic Themes**
- Trans-NIH initiatives
- Leverage ongoing programs
- Roadmap, GAIN, GEI, PGRN, etc
- Trans-NIDA programs
- Coordination among divisions
- Multidisciplinary/Interdisciplinary
- Data

## NIDA Genetics: A Coordinated Effort

- Human

**NIDA Genetic Coordinating Committee (NGCC)**

- _Scientific Goals & Services_

**Beth Babecki**
**Kevin Conway**
**Ahmed Elkashef**
**Steve Grant**
**Rita Liu**
**Raul Mandler**
**Cindy Miner**

**JJ Pan**
**Amrat Patel**
**Jonathan Pollock**
**Joni Rutter, Chair**
**Larry Stanford**
**Kay Wanke**
**Naimah Weinberg**

- _Members_

**Written ARA, supplement, and funding recommendations to the Director**
**Recommend genetic applications assignment**
**Provide standards for data collection, data sharing, and informed consent**
**Evaluate human genetics program**
**Serve as the NGC Steering Committee**

## Human GeneticsTrans-NIDA Genetics

- NIDA Genetics Consortium (NGC)
- NIDA Division Reps (+1 NCI rep)
- >20 PIs; 24 studies; 1 contract
- _[http://](http://zork.wustl.edu/nida/)__[zork.wustl.edu/nida](http://zork.wustl.edu/nida/)__[/](http://zork.wustl.edu/nida/)_
-  ~30,000 samples in Repository
- DNA and clinical information (DSMIIIR or IV)
- Nicotine, Opioids, Cocaine, Polysubstance
- Data to be publicly available through controlled access
- NIDA Phenotyping Consortium (NPC)
- DESPR-led initiative (Kevin Conway); 5 PIs
- Produce precise and specific phenotypes for drug abuse

_**Addiction “Bioseverity”**_
- Develop a research instrument measuring addiction severity
- Validated with biological processes (ex. Genetic, imaging, etc)
- Michael Vanyukov (NGC), Gary Swan (NGC), Michael Neale (NPC)
- Kevin Conway (DESPR), Janet Levy (DESPR), Joni Rutter (DBNBR)

## Human GeneticsTrans-NIDA Genetics

- Collaboration between NGC, CTN, DPMCDA
- “START” Study – Starting Treatment on Agonist Replacement Therapies –*added pharmacogenetics*
- Targeting ~600 opioid dependent persons
- Randomized, open-labeled CTN multi-site design to either buprenorphine or methadone
- Primary outcomes are:
- Liver toxicity (parent trial)
- Individual genetic variation in treatment effectiveness (Pharmacogenetics)
- Wade Berrettini and Lindsay DeVane

## Collaboration among NIDA Divisions
Genes, Environment, and Development Initiative (GEDI I & II) (DBNBR, DCNBR, DESPR)
Support research that investigates interplay among genetic, environmental, and developmental factors in the etiology of substance abuse and related phenotypes
Notice in NIH Guide for re-issue – Naimah Weinberg

- Genes, Environment, and Development Initiative (GEDI I & II) (DBNBR, DCNBR, DESPR)
- Support research that investigates interplay among genetic, environmental, and developmental factors in the etiology of substance abuse and related phenotypes
- Notice in NIH Guide for re-issue – Naimah Weinberg

**Human Genetics****Trans-NIDA Genetics**

## Converging Genetics Findings:NIDA IRP & NGC Data on Nicotine

**Uhl GR, et al., Biochem Pharmacol (2007)**

- Smoking-related phenotypes

## Smoking Phenotype

**Number of Subjects**

**Nicotine Dependence Score**
**(Fagerstrom Test – FTND, scale 0-10)**

**4**

**0**

**10**

**5**

**7**

**6**

**9**

**8**

**Controls** (lifetime >100 cigarettes; FTND=0)

**Cases **(FTND 4 or more)

**Notes:**
The control subjects were non-smokers who  had tried at least 100 cigarettes in a lifetime. And had a Fagorstrom Score of 0.
The Fagorstrom test is a standard index of nicotine dependence, and is used as a smoking phenotype. Individuals are asked a series of questions about there smoking behavior and  receive a score from 0-10 based on their answers.  

The cases had a Fagorstrom score of 4 or more. Usually, patients with Fagorstrom score of 6 or more need treatment to quit smoking.

## Whole Genome-wide Association Study Targeting Nicotine Dependence

** ****71 SNPs show the most difference among the cases and controls**

- 6 Million Common SNPs in Genome

**Whole Genome-wide Association Study Targeting Nicotine Dependence**

**71**

**Notes:**
We know that there are approximately 6milion commonly occurring SNPS in the human genome with an allele frequency of 5% or more.  In this study we screened for 2.2M SNPS and examined the top 40,000.

Of those top 40,000, 81 were significantly different between heavy smokers and control non-smokers.

- 71 SNPS different between case and control
- Many were not the “usual suspects”
- Several nicotinic receptors implicated :
******nicotinic receptor**
**β****3 nicotinic receptor**
******5 nicotinic receptor**** SNP is highly associated with nicotine dependence**

**Several SNPS Found to be Different Between Cases and Control**

- **CHRNA5 non-synonymous SNP D398N
- Individuals with this SNP have 2-fold increase risk of developing nicotine dependence OR=1.9 (1.4-2.6)

- Saccone et al. *HMG* 2007
- Bierut et al. *HMG* 2007

## CHRNA5 D398 is Conserved Across Species

- D398N

- Function?

## The human CHRNA5 D398N polymorphism alters nicotinic receptor function in vitro 

- **

**P < 0.01**

- Courtesy of Jerry Stitzel, PhD

- Variant allele

- Common allele

**Notes:**
HEK293T cells were transfected with alpha4 and beta2 cDNAs and either alpha5D398 or alpha5N398. Response is epibatidine-evoked change in intracellular calcium as measured previously by Karadsheh et al. (2004). 

## CHRNA5 encodes the nicotinic receptor alpha5 subunit

*Figure from Gotti et al., 2006*

## Summary

- Trans-NIH involvement
- Roadmap, GAIN, GEI, PGRN, others
- NIDA staff play leadership roles
- NIDA PIs positioned to take advantage of programs
- NIDA Genetics
- Divisional coordination, programmatic synergy
- Phenotype, environment, development, family, clinical, animal models
- Results are converging Functional information  Mechanism
- Inform treatment, prevention, intervention approaches

**Molecular Genetics of Drug Addiction and Related Co-Morbidities (R01)**_[PA-07-073 (R01)](http://grants.nih.gov/grants/guide/pa-files/PA-07-073.html)__ - November 20, 2006_
**Functional Genetics and Genomics of Drug Addiction**_[PA-07-166 (R01)](http://grants.nih.gov/grants/guide/pa-files/PA-07-166.html)__, __[PA-07-167 (R21)](http://grants.nih.gov/grants/guide/pa-files/PA-07-167.html)__, __[PA-07-168 (R03)](http://grants.nih.gov/grants/guide/pa-files/PA-07-168.html)__ - December 14, 2006_
**Genetic Epidemiology of Substance Use Disorders**_[PA-07-413 (R01)](http://grants.nih.gov/grants/guide/pa-files/PA-07-413.html)__, __[PA-07-415 (R21)](http://grants.nih.gov/grants/guide/pa-files/PA-07-415.html)__, __[PA-07-414 (R03)](http://grants.nih.gov/grants/guide/pa-files/PA-07-414.html)_ - **July 27, 2007 **

## Drug Abuse

- Model
- organisms

**Environment/**
**Epidemiology**

**Drug Abuse**
**Genetics**

- Define genetic contribution
- Define environmental and developmental factors
- Understand neurobiological mechanisms
- Improve treatment and prevention/intervention approaches

## Genetic model organisms, such as the mouse, can provide clues to the genetic basis of complex diseases

## Three Different Methods to Identify Gene Function in Mice

- Haplotype Associated Mapping of Natural Variation in Inbred Strains
- Selective Breeding
- Induced Mutations, e.g. Knockouts

## Three Different Methods to Identify Gene Function in Mice

- Haplotype Associated Mapping of Natural Variation in Inbred Strains
- Selective Breeding
- Induced Mutations, e.g. Knockouts

## Power of Inbred Strains

- Inbred strains are a way to determine environmental effects through development across defined genotypes under controlled conditions
- Confidence in the underlying genotype is strengthened when phenotypes map to homologous chromosomal loci in mice and humans

## C57BL/6J

**C57BL/6J**

**C57L/J**

**DBA/2J**

**C3H/HeJ**

**CE/J**

**I/LnJ**

**A/J**

**LP/J**

**C57BR/cdJ**

## Haplotype Associated Mapping of Natural Variation in Inbred Strains

- Many mouse genomes have been sequenced e.g. C57BL6/J, AJ, 129
- Genetic variation in over 15 strains of mice have been identified
- Use variation in gene expression profiling as a “signature” for a trait

## Strain Distribution of Percent Time Spent in Center of Open Field

**Strain Distribution of Percent Time Spent in Center of Open Field**

- Courtesy of Tim Wilshire

**Mouse Strain**

**Time Spent in Center**

## Differential expression of Grm7 in 4 brain regions 

**High “Anxiety”**
**Low  “Anxiety”**

**Low Anxiety -decreased expression of Grm7- increase in percent  time spent in center of open field**

**Gene Expression**

- Brain Region

## Three Different Methods to Identify Gene Function in Mice

- Haplotype Associated Mapping of Natural Variation in Inbred Strains
- Selective Breeding
- Induced Mutations, e.g. Knockouts

## Mapping Genes Using Selective Breeding

- Takes advantage of the mouse genome and gene variants (SNPs) among different mouse strains
- NIH proposed project will increase genetic diversity in inbred recombinant mice making it possible to map any trait in the mouse (i.e., The “Collaborative Cross”)

## Selection for Locomotor Stimulant Response to Methamphetamine in Mice

- Kamens et al. Genes, Brain & Behavior, 2005

**Locomotor response to MA (2 mg/kg)**

##  Csnk1e Gene Expressed More in Methamphetamine Sensitive Mice 

**The most differentially expressed gene was Csnk1e (10 fold different)**
** **
**There is a massive eQTL for Csnk1e**

- Palmer et al. Mammalian Genome, 2005

## Csnk1e Variant Enhances Dopamine Signaling Through Darpp-32 Signaling Protein 

**Csnk1e Variant Enhances Dopamine Signaling Through Darpp-32 Signaling Protein **

**Palmer et al. Mammalian Genome, 2005**

## Translational Genetic Models of Methamphetamine Sensitivity 

- Humans vary in methamphetamine sensitivity
- Differences in sensitivity are partially under genetic control in both mice and humans
- Differences in initial effects of drugs linked to future use
- An predictor or *intermediate phenotype* to addiction in humans

## Effect of CSNK1E Polymorphisms on Response to Methamphetamine

- 3 polymorphisms in CSNK1E in humans
- All 3 SNPs occur at high frequency
- Analyzed their relationship with 3 primary outcome measures:
- DEQ “Feel Drug”
- ACRI “Euphoria”
- POMS “Anxiety”

**Veenstra-VanderWeele et al. Neuropsychopharm., 2005**

## *

- Veenstra-VanderWeele et al. Neuropsychopharmacology, 2005

## Chromosomal Regions are Conserved for Drug Response in Mice and Humans

- Formin
- LRP2
- Alpha 6 integrin
- NFI/B
- NPAS2
- NIMA-related 7 Kinase

- Uhl et al, 2007  *Biochemical Pharmacology*

## Three Different Methods to Identify Gene Function in Mice

- Haplotype Associated Mapping of Natural Variation in Inbred Strains
- Selective Breeding
- Induced Mutations, e.g. Knockouts

## NIH Knockout Mouse Project (KOMP) 

- KOMP is a trans-NIH project involving 18 Institutes
- The NIH has committed ~ $52 M over 5 years
- KOMP is cooperating with other international efforts to minimize overlap

## Why are Knockouts and Transgenic Animals Useful?

- Help identify essential molecules in reward pathways mediating drug abuse
- Help separate cellular signaling pathways that mediate different phenomena associated with addiction (e.g., withdrawal, tolerance)

## Mice Lacking the Β2 Nicotinic Receptor Do Not Exhibit Nicotine Place Preference  . 

**Walters CL, et al Psychopharmacology (Berl). 2006 Mar;184(3-4):339-44**

## Global Gene Knockout Programs

**Global Gene Knockout Programs**

## Model 

- Epigenetics

**Environment**

**Drug Abuse**
**Genetics**

- Model
- Organism
- Genetics

- Define genetic contribution
- Define environmental and developmental factors
- Understand neurobiological mechanisms
- Provide genetic resources to community
- Test putative medications