MESA Family Genetics Committee Report

1. MESA Family Genetics Committee Report Jerome I. Rotter Monday, Sept. 18, 2006

2. Outline • MESA and MESA Family • “Prior” Candidate Gene Analyses • Phase 2 genotyping • Phase 1 genotyping and analysis • GenePages (Mychaleckyj presentation) • Publications Committee (Taylor presentation) • MESA Family • Recruitment status (Operations Committee) • Planned analyses (Raffel and Pankow presentations) • Genome-wide linkage analysis • Operational issues: MESA Genetics Committee • CARE • NIH RFI on Data Sharing

3. Overview of ProgressMESA and MESA Family • 6 manuscripts from “prior” candidate genes analyses in or pending publication (prior to MESA Family large-scale genotyping effort) • Phase 2 genotyping project underway • Analysis of Phase 1 candidate genes • Genepage development underway • Proposed genetic specific P&P committee for candidate gene proposals (parallel to MESA P&P)

4. “Prior” Genetic Studies MESA (parent study) Candidate Genes

5. History Genetic Studies in MESA Family • MESA Family grant originally proposed to genotype 6-8 candidate genes with ~80 SNPs in MESA (parent study) and • Genome scan in MESA Family subjects • Rapid technological developments allow a project of greater scope for the same budget

6. Candidate Genes • “Prior” Genotyping (Tsai) • Phase 1 • 120 candidate genes • 1536 SNPs including 96 AIMs • 4 ethnic groups (~720 in each) • Phase 2 • Selection of another 1536 SNPs • Same study subjects as Phase 1 • Newly identified and untyped Phase 1 genes

7. Phase 2 Genotyping • Additional SNPs to be genotyped using older technologies (being considered in the future) • ACE insertion/deletion, APOE • 1536 SNPs using Illumina run • DNA already at Illumina from Phase 1 • Some of same genes with additional SNPs • Additional genes proposed since Phase 1 was completed • Next 50 genes on the Phase 1 list • Additional AIMs

8. Phase 2 Gene List

9. Phase 1 Genotyping • Candidate genes and analyses • Focus of Mychaleckyj presentation (MESA Family Steering Committee meeting and MESA SC mtg) • High degree of performance • Few SNPs eliminated • High accuracy • Specific genes: focus of Bowden & Tsai presentations (MESA Family SC mtg) • Ancestry Informative Markers • 97 selected, 96 successfully genotyped • Focus of Arnett presentation (MESA Family SC mtg)

10. Genome Wide Linkage Scan • Originally planned to utilize Mammalian Genotyping Service • Issue: no more Mammalian Genotyping Service • Plan: Supplemental funding request

11. Supplemental Funding Request • Bids for genome screen solicited from deCode, Prevention Genetics, Illumina • Recommended 6000 SNP Illumina panel to NHLBI: • 47 parent visits/565 families • Increased SNP density for information content when parents are not available* *Hum Mol Genet 2004 13:1943-9

12. Comparison of 400 Microsatellites with 4763 SNPs Hum Mol Genet 2004 13:1943-9

13. MESA Genetics Operational Issues • Handling small genotyping projects (Tsai, Taylor, Bowden subcommittee proposal) • Handling collaborations when genes/ markers are unknown at onset

14. Proposed Small-Scale (1 to 100 SNPs) Genotyping Policy • Sending DNA of MESA out to investigators is an inefficient use of the available resource for genotyping of only a few (1 to 100) SNPs: • Amount of DNA is finite and large amounts will be wasted • Too labor-intensive for a large number of requests • Therefore genotyping will be performed at one of the three MESA genotyping laboratories by Bowden, Tsai, or Taylor • Investigator(s) will be responsible for costs

15. Proposed Small-Scale (1 to 100 SNPs) Genotyping Policy • Preferred method for conservation of DNA: • Illumina technology in batch: • 6 Illumina runs use the same amount of DNA as one TaqMan MGB run (for 1 SNP) • Genotyping 1536 SNPs uses the same amount of DNA as 96 SNPs (but costs a lot more)

16. Proposed Small-Scale (1 to 100 SNPs) Genotyping Policy • Investigator(s) will make request to MESA Genetics Committee • Genotyping Sub-Committee is chaired by Mike Tsai, with Don Bowden and Kent Taylor as members • Genotyping Sub-Committee will: • Decide best way to perform the genotyping (where & how) • Combine requests into fewer Illumina runs in order to save DNA resource • Work with MESA Family genotyping committee • If Illumina design not possible, then TaqMan MGB is available. Check feasibility at dbSNP • If these designs are not possible, then MESA family genetics committee will determine the best way to genotype the particular SNP. Emphasis will be placed on conservation of DNA

17. Proposed Small-Scale (1 to 100 SNPs) Genotyping Policy • MESA Genotyping Sub-Committee will consider work load/ scheduling and decide where genotyping will be done • Supply costs to be born by the investigator (costs change constantly): • 96 snp run using Illumina: ~12 cents/genotype • TaqMan MGB: ~50-80 cents/genotype • Other snps depend on technology required (eg restriction enzyme, dye-primers, gel or polymer, etc.) • There will likely be DCC costs and there may be overhead and technician costs, depending on the project

18. Overview of CARE Study • To test a large number of candidate genes in NHLBI cohort studies • Goal: 1700 candidate genes (8-10 SNPs per gene); 15,000 markers on ~50,000 participants from eight NHLBI funded-studies • Comparatively smaller sample for genome-wide association

19. Eight NHLBI CARE Cohorts • ARIC - Atherosclerosis Risk In Communities • CARDIA - Coronary Artery Risk Development in Young Adults • CHS - Cardiovascular Health Study • CSSCD - The Cooperative Study of Sickle Cell Disease • FHS - Framingham Heart Study • JHS - Jackson Heart Study • MESA - Multi-Ethnic Study of Atherosclerosis • SHHS - Sleep Heart Health Study

20. Organization of CARE Study • RFP to establish a Genotyping/ Coordinating Center • coordination and utilization of genetic and phenotypic data • from well-characterized NHLBI cohorts • Center funded at the Broad Institute Center for Genotyping and Analysis

21. CARE Genotyping and Analysis Center Goals 1. Receipt and management of DNA samples and phenotypic information to facilitate cross study analysis 2. Design genotyping experiments with software tools integrating sample management, SNP selection, and SNP genotyping platform 3. Support genotyping experiment execution in candidate genes and a set of genome-wide SNPs 4. Manage data and develop and apply statistical methods required to identify associations between genotypes and HLB phenotypes

22. Larry Atwood, Boston, MAFHS Eric Boerwinkle, Houston, TXARIC Richard Fabsitz, Bethesda, MDNHLBI Myriam Fornage, Houston, TXCARDIA Stacey Gabriel, Cambridge, MABroad Joel Hirschhorn, Cambridge, MABroad Ronald Krauss, Oakland, CAHeart Abdullah Kutlar, August, GABlood Deborah Meyers, Winston-Salem, NCLung Emanual Mignot, Palo Alto, CASleep Dina Paltoo, Bethesda, MDNHLBI Susan Redline, Cleveland, OHSHHS Jerome Rotter, Los Angeles, CAMESA Jeanne Smith, Englewood, NJCSSCD Russell Tracy, Colchester, VTCHS James Wilson, Jackson, MSJHS CARE Steering Committee

23. Mediawiki-based web pagehttp://www.broad.mit.edu/gen_analysis/care/index.php/Main_Page • Numerous conference calls • Steering Committee Meeting, July 25,2006 • Discussion Items • Define pilot project (phenotypes & SNPs) • Establish principles of data release • Discuss genotyping study design • Select phenotypes to be analyzed

24. Susan Heckbert Craig Johnson Richard Kronmal Kiang Liu Joe Mychaleckyj James Pankow Wendy Post Bruce Psaty Stephen Rich Jerome Rotter Kent Taylor Russell Tracy Michael Tsai MESA CARE Working Group

25. Data Release/IRB - James Wilson DNA Transfer/Genotyping - Larry Atwood Analysis/Study Design - Stephen Rich Candidate Gene/SNP Selection - Myriam Fornage Phenotypes - Bruce Psaty- Susan Heckbert Informatics - Joe Mychaleckyj Publications- to be determined CARE Subcommittees & Chair

26. Data Release/IRB - Rotter, Lui DNA Transfer/Genotyping - Tsai Analysis/Study Design - Rich, Pankow Candidate Gene/SNP Selection - Taylor, Tsai Phenotypes - Heckbert Informatics - Mychaleckyj Publications- Post CARE Subcommittees & MESA Rep

27. General CARE Timeline • Set up infrastructure at Broad and Steering/ Subcommittees • Determine protocol for Pilot Study • Pilot Study • Candidate Gene Study • Whole Genome Association Study

28. Current CARE Discussion Items • Pilot Study • Phenotype List • Candidate Gene List • IRB, Data Distribution Agreement/ Policy • Design/Analysis • Informatics • Publications Policy

29. Proposed for Pilot • Diabetes – yes, no, pre, unknown • Dyslipidemia – Total, HDL, LDL, TG • Hypertension – sitting SPB and DBP, history • Obesity – height, weight • Medications associated with phenotypes • How were phenotypes measured • General covariates – age, sex, race, tobacco • 22 Candidate genes (from prior literature),1-4 SNPs per gene

30. CARE Draft Data Distribution Policy and Data Access Agreement • NHLBI goal • Comprehensive genotype and phenotype data set • Broadly accessible to the scientific community • Protect interest of study participants • Promote productivity of CARE Cohort Investigators • Based on Framingham SHARE policy • Will be submitted to each Cohort’s Ancillary Studies Committee and local IRBs for approval

31. CARE Draft Data Distribution Policy and Data Access Agreement • If approval can not be reached, options include • Re-consent if funding available • Data Enclave model (investigator-driven analysis but not raw genotyping download) • Withdrawal of the study from CARE • CARE will provide final documents and IRB talking points

32. NIH RFI on Data Sharing in GWAS • NIH is requesting comments on proposed policy for sharing data obtained in NIH-supported or conducted genome-wide association studies (GWAS) • GWAS defined as “any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits or presence or absence of a disease of condition” • Proposed policy addresses: • Data sharing procedures • Data access principles • Intellectual property • Protection of research participants • Comments are due Oct. 31.

33. NIH RFI on Data Sharing in GWAS • Requesting input on overall concept and specific questions: • Benefits and risks of sharing de-identified data • Additional protections to minimize risks to research participants beyond de-identification of data • Advantages and disadvantages of proposed • Centralized repository • Approach to data submission • Approach to scientific publication • Approach to intellectual property • Additional resources needed by investigators to meet the goals of the proposed policy

34. NIH RFI on Data Sharing in GWAS Principles • NIH believes that full value of GWAS can be realized only if the data is made available as rapidly as possible to a wide range of scientific investigators • Rapid and broad data access is important • Significant resources involved • Challenges of analyzing large datasets • Opportunities for making comparisons across multiple studies • Protection of research participants is a fundamental principle and NIH is committed to responsible stewardship of the data

35. NIH RFI on Data Sharing in GWASApplicability • Draft policy would apply to active research applications identified by applicants or NIH staff as GWAS per Notice to Applicants for NIH Genome-Wide Association Studies (NOT-OD-06-071, release date 05/15/06)

36. NIH RFI on Data Sharing in GWAS Data Management: Data Repository • Central GWAS data repository, NCBI-NLM • Single point of access for • Basic information on NIH-supported GWAS • Genotype-phenotype datasets • Repository will not be exclusive source of GWAS data • Repository will access GWAS datasets from other, non-NIH-supported sources

37. NIH RFI on Data Sharing in GWAS Data Management: Data Submission (1) • All investigators who receive NIH support for GWAS “are expected to submit” descriptive information about GWAS • Included in open access portion of repository • Must include • Protocol • Questionnaires • Study manuals • Variables measured • Other supporting documentation

38. NIH RFI on Data Sharing in GWAS Data Management: Data Submission (2) • NIH “strongly encourages” submission of curated and coded • Phenotype data • Exposure data • Genotype data • Pedigree data as soon as QC procedures have been completed at local institution • Data made available through controlled access process

39. NIH RFI on Data Sharing in GWAS Data Management: Data Submission (3) • To minimize risks to study participants • Data will be submitted without “identifiable information and using a random, unique code” • Keys to codes will be held by submitting institutions • Certification by submitting institution that identities will not be disclosed to repository or secondary users without appropriate approvals • Research participants should not expect return of individual research results

40. NIH RFI on Data Sharing in GWAS Data Management: Data Submission (4) • Submissions must include • Certification from IRB that submission to repository has been approved; specifically • Inclusion in repository and sharing of data is consistent with informed consent • Identification of uses specifically excluded by the informed consent • Statement from the institution that submission of data is in accord with all laws and regulations

41. NIH RFI on Data Sharing in GWAS Data Management: Data Access (1) • NIH Data Access Committee (DAC) • Regulates access to • Genotype-phenotype datasets • Pre-computed analyses • simple genotype-phenotype associations • Variants in LD with variants showing significant association with a phenotype or trait • DACs may be established based on programmatic areas of interest/expertise • All DACs will operate under common principles for transparency

42. NIH RFI on Data Sharing in GWAS Data Management: Data Access (2) • Data Use Certificate from investigators seeking data must include stipulations • Data will be used only for approved research • Researcher will protect data confidentiality • Applicable laws, local institutional policies and procedures will be followed • No attempt to identify participants will be made • Dataset will not be sold or shared with third parties • Researcher will provide annual progress report • Certificate must be Co-signed by Institution • Certificate will be reviewed/approved by DAC

43. NIH RFI on Data Sharing in GWAS Publications • For a “defined period of time” following release of a given dataset, submitting investigators should retain exclusive right to publish • NIH can grant access to others during this period, but they are expected not to publish • Period of exclusivity proposed at 9 months although shorter may be requested by NIH • Following expiration of exclusivity, any investigator with approved access to the data can publish • Contributing investigators, funding sources should be acknowledged in publications

44. NIH RFI on Data Sharing in GWAS Intellectual Property • The “hope of the NIH” that associations and conclusions from the data remain unencumbered by intellectual property claims • Encourages patenting technology suitable for private investment and development of products that address public needs • Filing of patent applications or enforcement of patents “could substantially diminish the utilization of information and the potential public benefit they could provide”

45. NIH RFI on Data Sharing in GWAS Summary • Investigators funded for GWAS will be expected to • Provide descriptive information about their studies • Investigators submitting data will be expected to submit • De-identified genotype, phenotype, covariate data • Limitations to use of data based on consent • Certification from IRB, Institutional assurances • Investigators requesting data will be expected to submit • Description of proposed project • Data Use Certification (protection of data confidentiality) • Annual progress reports

46. NIH RFI on Data SharingConcerns (1) • Does this apply prospectively or retroactively? • If retroactive, current MESA consent is not consistent with proposed policy

47. NIH RFI on Data SharingConcerns (2) • Extensive covariate data makes a subject potentially identifiable

48. NIH RFI on Data SharingConcerns (3) • “Forensic-equivalent” genome-wide data makes a subject and their relatives potentially identifiable

49. NIH RFI on Data SharingConcerns (4) • Consent to include genetic, phenotypic, and covariate data in a government controlled database might be challenging • Particularly in minority groups that have been previously abused by research

50. NIH RFI on Data SharingConcerns (5) • Monitoring of data security delegated to investigators and IRBs with no direct interest in study participants