# Presentation: 031157

## Prioritizing Studies for Whole Genome Association Genotyping

**Panel V**
**Multi-IC Symposium on Application of Genomic Technologies to Population-Based Studies**
- Christopher J. O’Donnell, MD MPH
- DHHS/NIH/NHLBI

## Panel V:  Prioritizing Studies for WGA Genotyping

**Determining when WGA genotyping is appropriate**
**Determining when replication/expansion of WGA genotyping is needed**
**Need for diversity in population genetic and environmental backgrounds**
**Appropriate studies to follow-up on WGA findings**
- Assuring appropriate DNA collection, proper human subjects protection, appropriate quality of DNA samples
- Pooling of cohort DNA samples, genotype data, and phenotypes; assuring that phenotype criteria allow pooling
- Determining sufficient power to detect an association
- Pooling DNA samples before genotyping to reduce N of genotype assays that needs to be done

## Working Group on GWA in NHLBI Cohorts: September 2005

**Working Group Discussion Areas**
- September 12, 2005, NHLBI
- Criteria for Selecting Cohorts to be Genotyped
- Informatics Needs/Data Management
- Statistical Analysis Issues
- Data Sharing, Access, Consent, Confidentiality, Reporting
- Approaches to Genotyping, Assessment of Platforms, Quality Control
- Sample Acquisition and Types
**Summary at www.nhlbi.nihl.gov**

**Working Group Members**
- Eric Boerwinkle, Ph.D., Gregory L. Burke, M.D., M.S., Aravinda Chakravarti, Ph.D., Christopher G. Chute, M.D., Dr.P.H., Mark J. Daly, Ph.D., Seth E. Dobrin, Ph.D., Kelly A. Frazer, Ph.D., Stacey Gabriel, Ph.D., Gary Gibbons, M.D., Gerardo Heiss, M.D., Eduardo Marbán, M.D., Betty S. Pace, M.D., Susan Redline, M.D., M.P.H., Christine Seidman, M.D., Belgaum S. Thyagaraja, Ph.D., Russell P. Tracy, Ph.D., Bruce S. Weir, Ph.D., Scott T. Weiss, M.D., M.S.

## Working Group on WGAS in NHLBI Cohorts: Recommendations

- Large-scale GWA projects should proceed.
- Existing cohorts with large amount of phenotype and exposure data should be used.
- >1 cohorts should be included, to catalyze data and repository harmonization and maximize diversity by age, sex, race, ethnicity; consider opportunities for replication.
- Well-defined, quantified phenotypes of public health importance should be analyzed.
**Immediate access should be provided **to data held in a centralized data repository.
- A mixture of study designs should be supported-- studies of families, trios, & unrelated individuals.
- Information about cohorts should be quickly disseminated, allowing easy application for data.

## Translation from Whole Genome Association to Genomic Medicine

**Whole Genome **
**Association**

**Independent Replication**

**Fine Mapping**

**Sequencing**

**Functional  Studies**

**Applications for Genomic Medicine**

**Predict**

**Prevent**

**Personalize**

## NHLBI WGA Portfolio: Planned and Ongoing WGA Studies

- SNP Typing for Association with Multiple Phenotypes in Existing Epidemiology Data (STAMPEED)
- Up to 6 WGAS’s for heart, lung, blood and sleep disorders
- RFA-HL-06-012
- WGAS in Women’s Health Initiative (WHI)
- Staged design with pooled cases vs controls in Stage 1
- Three phenotypes: CHD, stroke and breast cancer
- Framingham Heart Study: Framingham SNP Health Association Resource (SHARe) + other SHARe Projects
- WGAS in 9000 subjects from three generations of FHS
- Collaboration between NHLBI, BU and NCBI
- Phenotypes: **all available** contract & ancillary study data
- 2 additional cohorts ~7200 subjects (RFP NHLBI-PB-2006-091)

## Pooled WGA Scans in the WHI for CHD, Stroke, Breast CA

- Stage 1: Pooled DNA, threshold p<.02
- Stage 2: Individual DNA samples, p=.0004 or p<.02 in both Stage 1 & 2
- Stage 3: Individual DNA samples, threshold p<.00001 for 360K SNPs yields 3.6 FP’s

**Stage 1**
**1000 cases                                1000 controls**
**Perlegen 360K SNPs**

**Stage 2***
**~800 cases                       ~800 controls**
**10,000 SNPs tested**

**Study 3****
**~300 cases               ~300 controls**
**~300 SNPs tested**

**Identify SNPs from cumulative data from all 3 Stages tested at .00001 level, Stages 2 & 3 will test for interaction with HRT.**

## SHARe WGA Studies in Framingham and Other NHLBI Population-Based Cohorts

***Framingham SHARe Study***
***Other SHARe Cohorts***
***Total ~18,000 Subjects***

## Simple WGAS Mathematics

- 9000 subjects
- x 500,000 SNPs
- x 1000 Phenotypes
- x Two Adjustments (Age-adjusted and Multivariable)
- x Four Genetic Models
**= 36,000,000,000,000 Association Tests**

## Features of Framingham SHARe

- All participants provided written informed consent for genetic research, genetic data sharing (DNA Committee) in place for a decade, ongoing dialogue with participants
- Consent includes questions regarding opt-in for genetics, commercial use, other disease areas (eg, cancer)
- DNA samples backed up by “cell lines“ (majority)
- SHARe Oversight Committee and Steering Committee
- Community Ethics Advisory Board and OSMB consulted
- Participants informed via newsletters and focus group
- Data Access Committee authorizes distribution of data after review of application, IRB, distribution agreement
- NCBI provides access to authorized Users
- 12 month embargo on publications except for FHS Investigators and collaborators

## NHLBI WGAS Portfolio: Design, Analysis and Follow-up Genotyping

- Design and Analysis of WGAS
- ENhancing Development of WGAS MEthods (ENDGAME RFA-HL-05-011)
- 11 projects on methods of analysis for WGAS data
- Large-Scale Genotyping
- Candidate Gene Association Resource (CARE)
- Genotyping of up to 50,000 subjects from 8 cohorts:
- ● MESA	 	● Cooperative Study for Sickle Cell Cohort
- ● ARIC		 ● Sleep Heart Health Study
- ● CHS		 ● Jackson Heart Study
- ● CARDIA	 ● Framingham Heart Study
- Broad Institute (Cambridge, MA) contracted to conduct:
- Modest-sized WGAS (n=1500)
- Genotype ~10 SNPs in ~1700 candidate genes
- Plan for follow-up of available WGAS findings

## Other NHLBI Genetic and Genomic Resources Relevant to WGAS

- NHLBI Programs and Services:
- Resequencing and Genotyping Service
- Programs for Genomics Applications (PGA)
- Program on Interactions of Genes and Environment in Shaping Risk Factors for Heart, Lung, Blood and Sleep Disorders (PROgram for GENetic Interaction, PROGENI)

## Translation from Whole Genome Association to Genomic Medicine

**Whole Genome **
**Association**

**Independent Replication**

**Fine Mapping**

**Sequencing**

**Functional  Studies**

**Applications for Genomic Medicine**

**Predict**

**Prevent**

**Personalize**

## Panel V:  Prioritizing Studies for WGA Genotyping

- Determining when WGA genotyping is appropriate
- Determining when replication/expansion of WGA genotyping is needed
- Need for diversity in population genetic and environmental backgrounds
- Appropriate studies to follow-up on WGA findings

## Determining that WGAS is Appropriate for HLBS Phenotypes

**Types of cohorts and types of traits**
- Cohorts for Study
- Population-based:  prospective versus cross-sectional
- Disease-based:  case-control
- Traits for Study
- Qualitative
- Heart, lung, blood or sleep outcomes
- Extremes of quantitative traits
- Quantitative
- Examination measures, risk factors, biomarkers
- Subclinical disease measures

## WGAS: Qualitative Traits (eg, CVD) in Case-Control, Population-Cohorts

## Case-Control Studies of MI: Survey of Missense Gene Variants

- Study 1: Pooled DNA, threshold p<.05
- Study 2: Pooled DNA, threshold p<.05 in both Studies 1 and 2
- Study 3: Individual DNA samples, threshold FDR<10% for accepting SNPs

**Shiffman et al. AJHG 2005;77:596.**

**Study 1**
**340 cases                                346 controls**
**11, 053 SNPs tested**

**Study 2**
**445 cases                         606 controls**
**637 SNPs tested**

**Study 3**
**457 cases                 820 controls**
**31 SNPs tested**

**6 SNPs in Palladin, ROS1, TAS2R50, OR13G1, ZNF627**
**Odds Ratio 1.2-1.4, P<0.05, FDR<10%**

## WGAS: Quantitative Traits (eg, HDL-Chol) in Population-Based Cohorts

**Validation**

**Cohort A**

_**Start with Extremes**_

_**Single (Large) Cohort**_

**Fine Map?**

**Replication**

**Cohort B, C, etc**

**Replication**

**Cohort B, C, etc**

**Sequencing**

**Cohort A**

**Cohort A**

**Cohort B**

_**Pool**_** **_**>**_** **_**2 Cohorts**_

**Replication**

**Cohort C, D, etc**

**Sequencing**

**Functional Studies**

**Sequencing**

**Fine Map?**

**Fine Map?**

## Screen Extremes, Replicate in Cohorts:  CAPON Variant (NOS1 Regulator) and QTc

- Screen Extremes, Replicate in Cohorts:  *CAPON* Variant (*NOS1* Regulator) and QTc

- N=100

- N=100

- Stage I:~100K SNPs,
- Women Only KORA I

- N=300

- N=300

- Stage II: Top 10 SNPs,
- Women Only KORA I

**NOS1AP (CAPON)**
**QTc_5.3**
**FGFR2**
**QTc_14.1**
**KCNK1**
**ITPR1**
**CACNA2D1**

## 100K Quantitative Trait Study in Cohort of Families: INSIG2 Variant and BMI/Obesity

## Framingham 100K Screen Identifies Prior Association with Factor 7 Locus

- Framingham Offspring (Gen2)
- _~1800 Unrelated_        _~1350 in Families_

**Circulating Factor VII Antigen**
- Phenotype: Covariate-Adjusted Level
- Genotype:  htSNPs spanning F7 Locus

**Circulating Factor VII Antigen**
- Phenotype: Covariate-Adjusted Level
- Genotype:  100K Affymetrix CHIP

***Explains ~10% of Variation**

*****

- Of 84633 tests for adjusted FVII trait, strongest association is a SNP (P=5*10-16) highly correlated with Arg/Gln F7

**Kathiresan et al. ATVB 2006.**

## Determining that WGAS is Appropriate for HLBS Phenotypes

**Phenotypes**
- Disease phenotypes relevant to public health
- Common, complex cardiovascular, lung and blood dz
- Clinical outcomes:  eg, CVD, COPD, OSA
- Quantitative phenotypes: eg, SBP, LDL-C
- Rarer disease phenotypes: eg, HCM, SSD, CF
- Intermediate phenotypes that predict disease
- Significant genetic component
- Measurable, reproducible
- Prediction or treatment of phenotype may prevent future occurrence of disease

## Determining that WGAS is Appropriate for HLBS Phenotypes

| Program | Phenotype | Criteria |
| --- | --- | --- |
| FHS SHARe | HLBS+Endo, Bone, CA
Risk Factors,Biomarkers
Subclinical Disease
Clinical Disease | Major Diseases
Longitudinal
Large N
3 Gen Families
Well-phenotyped |
| STAMPEED | HLBS Diseases
Quantitative Traits or Disease Traits | Major Diseases
Peer Review
Well-phenotyped |
| WHI | CHD
Stroke
Breast CA | Major Diseases
Large N
Well-phenotyped |

## Diversity in Population Genetic and Environmental Backgrounds in WGAS

- Given differences in LD noted in HapMap for major ethnic groups, general approach is to confine WGAS to cohorts with adequate sample size within a single genetic background (eg, Caucasian or African American), then test in other backgrounds
- Fortunately, a number of NHLBI cohorts have been ascertained based upon major ethnic groups

## Family Blood Pressure Program Networks, Ethnic Diversity

| Network | Population |  Major Phenotype Interests  |
| --- | --- | --- |
| GenNet | African American, Hispanic, and Caucasian:
N~2000 | Pre-hypertension, biochemical testing |
| GENOA | African American, Hispanic, and Caucasian:  N~5000 | Context dependent genetic effects |
| HyperGEN | African-American, Caucasian: Sib pairs w/ HTN (N= 1,836)
Offspring, no HTN (N=1200) | Intermediate phenotypes for HTN in pedigrees |
| SAPPHIRe | Chinese, Japanese ancestry: 
1200 Sib pairs, HTN families, 1⁄2 concordant, 1⁄2 discordant | Insulin resistance syndrome, metabolic syndrome |

## Factors Determining Whether to Attempt to Replicate WGAS Findings

- Power, False Discovery Rate
- Phenotype
- Quality, Reproducibility
- Heritability
- Heterogeneity
- Strength of Association
- Potential Generalizability to Other Populations
- Genotypic Background:  Ethnicity
- Environmental Background
- Prior Probability, Prior Knowledge
- Known Candidate Locus or Linkage Peak
- Biological Plausibility

## Factors Determining Whether Replication is Valid

- How do we Define:
- “Validation” vs “follow-up” vs “replication”
- When has Replication been Achieved?  No firm agreement on criteria other than “more is better”. Criteria might include:
- Statistical significance and pre-test probability
- Similar ascertainment and phenotype definition in replication study(s)
- Control for confounding, eg pop’n stratification
- Comparisons of _>_1 genotyping platform
- Number & sample size of replication(s)
- Consistency in magnitude, direction of genetic effect
- How Large?
- Again, not clear what size, but certainly replication studies should be powered to reliably be able to exclude a null finding for the initially observed magnitude of effect and genetic model.

## Appropriate Studies to Follow-Up on WGAS Findings

- Wealth of Resources from NHLBI Cohorts
- Eg, CARE, anticipated total N~50,000:
- Multiethnic Study of Atherosclerosis (MESA)
- Atherosclerosis Risk in Communities (ARIC)
- Cardiovascular Health Study (CHS)
- Coronary Artery Risk Devel. in Young Adults (CARDIA)
- Framingham Heart Study (FHS)
- Jackson Heart Study (JHS)
- Sleep Heart Health Study (SHHS)
- Cooperative Study for Sickle Cell (CSSC)

## Panel V:  Prioritizing Studies for WGA Genotyping--Additional Considerations

- Criteria for assuring appropriate DNA collection, proper human subjects protection, and appropriate quality of DNA samples
- Pooling of cohort DNA samples, genotype data, and phenotypes; assuring that phenotype criteria allow pooling
- Determining there is sufficient power to detect an association
- Pooling DNA samples before genotyping to reduce N of genotype assays that needs to be done