Harold Snieder

1. Finding genes for complex biomedical and behavioral traits. The promise of genome-wide association studies Harold Snieder Unit of Genetic Epidemiology & Bioinformatics, Dept. of Epidemiology, University Medical Center Groningen, University of Groningen

2. “..there is a wide range of problems in the human sciences where an understanding of both sociology and human genetics, their concepts and their methods, is necessary.” • “Whether one is concerned with structure and behaviour of individuals or of populations one is dealing with the results of complex inter- actions between environmental and genetic determinants, and it is rarely, if ever, justifiable to neglect completely one or other of these.” • “Yet this has been repeatedly done because: • the overall problems are so complex that workers have felt they could only make any progress by considering components in isolation • there is so little communication and collaboration between social and biological scientist.” • “..of particular concern is that there is practically no-one in the country who has sufficient professional expertise in both sociology and human genetics to research in this interdisciplinary area, which is not only one of great academic interest but is of importance in many practical problems of human affairs.”

3. Problem areas: • G/E determination of quantitative variation • Genetic & social structure of human populations • Natural selection in human populations • Recommendations for research: • Make better use of routinely collected official information (eg census) • Establish a birth registry of twins • More study of measured intelligence • Recommendations for education & training: • Integrate training in social science and human biology • At both under- and postgraduate level

4. Tutorial book chapter Nolte IM, McCaffery JM, Snieder H. Candidate gene and genome-wide association studies in behavioral medicine. In: Steptoe A, editor. Handbook of behavioral medicine: methods and applications. New York: Springer; 2010.

5. Genetic Epidemiology Genetic epidemiology seeks to elucidate the role of genetic factors and their interaction with environmental factors in the occurrence of disease in populations Khoury, Beaty & Cohen : Fundamentals of genetic epidemiology, 1993

6. Overview of presentation • Finding genes for complex traits • Advent of the genomewide association study (GWAS) • Example: Finding genes for blood pressure and hypertension by GWAS • GWAS of Social Science variables • Future directions

7. Localizing genes for Mendelian (single-gene) disease • Familial aggregation? • Family studies: Large pedigrees • How is the disease transmitted in families (mode of inheritance)? • Segregation Analysis • Do genetic markers co-segregate with disease in pedigrees? Where is the gene located? • Classic linkage analysis: • Model based: mode of inheritance, penetrance & gene frequencies

8. Pedigree of Huntington Disease

9. Hypertension: a complex disease • Mix of environmental and genetic influence • Environmental lifestyle exposures: Dietary sodium intake Alcohol consumption Excess body weight Stress • Heritabilities of SBP and DBP range from 40-60%

10. Challenges in genetics of complex traits • Identifying genes of small relative effect against a background of substantial variation • Definition and measurement of phenotype • Age-related disease/trait expression • Gene-environment interaction • Heterogeneity (eg Hypertension)

11. Localizing genes for complex traits & diseases • Familial aggregation? Genetic or environmental factors? • Twin study • Adoption Study • Is a major gene involved? • Complex segregation analysis in pedigrees (transmission, penetrance, gene frequencies) • Where is the gene located? • Linkage - eg Genome scan • Association - candidate gene studies - whole genome association

12. Single Nucleotide Polymorphism Finding genes for complex traits and diseases: a three stage process • 2001: Sequencing of the human genome • 2005: Characterization of genetic variants in human populations • Public Databases • Whole genome: HapMap • Candidate genes: SeattleSNPs

13. Candidate gene studies Genomewide Association Studies Finding genes for complex traits and diseases: a three stage process • Now: Genetic epidemiology or Biobank phase

14. Hypothesis based gene finding:Candidate gene study • Direct: Candidate SNP analysis between a putatively functional variant and disease risk • Indirect: • Test a dense map of SNPs for disease association • Risk polymorphism genotyped directly or in strong LD with one of the genotyped tagging SNPs

15. Hypothesis free gene finding:Genomewide Linkage (positional cloning)

16. Limitations of linkage analysis for gene finding in complex traits • Limited power to identify genes of small relative effect against a background of substantial variation • Limited resolution • Often hundreds of genes under the linkage peak • Fine mapping difficult • Solution: Whole genome association scan?

17. From Public Databases to Finding GenesGenome-wide approach Increasing number of SNPs per reaction

19. From Public Databases to Finding Genes Genome-wide approach First successful application Science, April 2005

20. Challenges for genome-wide association • Genotyping • Number of SNP markers needed to provide adequate coverage of variation in the genome • Estimates range from 500,000 to 1 million • Accompanying cost of genotyping these numbers in adequate sample sizes (eg: 2000 cases vs. 2000 controls) • Statistical analysis • How to correct for multiple testing? • How to test for gene-gene and gene-environment interaction?

21. Nature: “The race to discover disease-linked genes reaches fever pitch.” Science: “After years of chasing false leads, gene hunters feel that they have finally cornered their prey. They are experiencing a rush this spring as they find, time after time, that a new strategy is enabling them to identify genetic variations that likely lie behind common diseases.” Genomewide Association Study

22. 2007 second quarter 2008 first quarter 2007 third quarter 2007 first quarter 2007 fourth quarter 2006 2005 Manolio, Brooks, Collins, J Clin Invest, 2008.

23. Published Genome-Wide Associations through 03/2011, 1,319 published GWA at p≤5x10-8 for 221 traits NHGRI GWA Catalog www.genome.gov/GWAStudies

24. Total number of GWA studies: 1001 Total number of loci found: ~4000 Thanks to Teri Manolio. From http://www.genome.gov/GWAstudies/

26. Impact on practice of medicine • Better understanding of etiology • Prediction • Prevention • Personalised medicine

27. Overview of presentation • Finding genes for complex traits • Advent of the genomewide association study (GWAS) • Example: Finding genes for blood pressure and hypertension by GWAS • GWAS of Social Science variables • Future directions

28. Why study blood pressure and/or hypertension? • High prevalence of hypertension • NHANES: >50 million Americans have high blood pressure (BP) • Worldwide: 1 billion hypertension patients • Major causes of mortality • 7.1 million deaths/ year due to hypertension • WHO: Suboptimal BP (>115mm Hg SBP and 75mm Hg DBP) is the number one attributable risk for death

29. The classical twin study ‘Perfect natural experiment to distinguish shared genes from shared environment’

30. Heritability of Systolic Blood Pressure 0.54 Wang & Snieder, 2010

31. Genomewide linkage 16 genome scans of BP/hypertension have yielded 27 loci, but few are replicated

32. 2,000 cases and 3,000 controls genotyped for 500,568 SNPs

33. Global BPgen & CHARGE consortia

34. Seven loci (SNPs) identified for SBP(N>29,000) Newton-Cheh C, et al. Nat Genet, 2009. Levy D, et al. Nat Genet, 2009.

35. Ten loci (SNPs) identified for DBP(N>29,000) Newton-Cheh C, et al. Nat Genet, 2009. Levy D, et al. Nat Genet, 2009.

36. Maher, Nature, 2008

37. Blood pressure 13 <1%

38. Overview of presentation • Finding genes for complex traits • Advent of the genomewide association study (GWAS) • Example: Finding genes for blood pressure and hypertension by GWAS • GWAS of Social Science variables • Future directions

39. Defined as: self employment • Rotterdam study discovery cohort (n = 15000) • Working group within CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) n=50,000 • Not yet successful

40. Social Science Genetic Association Consortium • Launched in Feb 2011 by: • Daniel Benjamin (Cornell), David Cesarini (NYU) & Philipp Koellinger (Erasmus) • Goal: identify genes through meta-GWAS in 100,000 subjects or more • Educational attainment 1st phenotype pursued • Next workshop in Oct. 2011: extension to other phenotypes • Over 35 cohorts participate: n>100,000 • All cohorts analyze their data according to a standard analysis plan

41. Social Science Genetic Association Consortium

42. Social Science Genetic Association Consortium

43. Social Science Genetic Association Consortium

44. Will GWAS work in the social sciences? • Heritability of social outcomes well established • Path from causal variant to final outcome is very complex • Social phenotypes are distal: expected effect sizes extremely small • Success for some behavioral traits offers some hope

45. Discovery in 8 cohorts (n>18,000), replication in LifeLines (n=8000). • The coffee categories were defined as: • 0–2 cups per day • 3–4 cups per day • (3) 5–6 cups per day • (4) 7–9 cups per day • (5) >10 cups per day

47. Overview of presentation • Finding genes for complex traits • Advent of the genomewide association study (GWAS) • Example: Finding genes for blood pressure and hypertension by GWAS • GWAS of Social Science variables • Future directions

48. Even larger GWAS meta-analyses: more common variants 4/10

49. Prospective cohort study

50. Gene-environment interaction studies Prospective cohort study: GxE • Who will develop disease? • Improve prediction • Targeted prevention efforts