Genome-wide Association Studies

1. Genome-wide Association Studies John S. Witte

2. Candidate Gene or GWAS Association Studies Hirschhorn & Daly, Nat Rev Genet 2005

3. Affymetrix Array Genome-wide Association Studies Altshuler & Clark, Science 2005

4. Genome-wide Assocation Studies (GWAS)

5. # Markers # Samples Discovery: Multi-stage GWAS+ Time GWAS+ Strategy Clarification: Sequencing+ Confirmation / Characterization: Follow-up Genotyping+

6. GWAS+ Strategy # Markers # Samples Discovery: Multi-stage GWAS+ Time Clarification: Sequencing+ Confirmation / Characterization: Follow-up Genotyping+

7. One- and Two-Stage GWA Designs Two-Stage Design One-Stage Design SNPs SNPs 1,2,3,……………………………,M 1,2,3,……………………………,M 1,2,3,………………………,N 1,2,3,………………………,N samples Stage 1 Samples Samples Stage 2 markers

8. One-Stage Design SNPs Samples Two-Stage Design Joint analysis Replication-based analysis SNPs SNPs Samples Stage 1 Stage 1 Samples Stage 2 Stage 2

9. Multistage Designs • Joint analysis has more power than replication • p-value in Stage 1 must be liberal • Lower cost—do not gain power • http://www.sph.umich.edu/csg/abecasis/CaTS/index.html

10. QC Steps • Filter SNPs and Individuals • MAF, Low call rates • Test for HWE among controls & within ethnic groups. Use conservative alpha-level • Check for relatedness. Identity-by-state calculations.

11. Analysis of GWAS • Most common approach: look at each SNP one-at-a-time. • Possibly add in multi-marker information. • Further investigate / report top SNPs only. • Or backwards replication… P-values

12. GWAS Analysis • Most commonly trend test. • Log additive model, logistic regression. • Adjust for potential population stratification.

13. Quantile-Quantile (QQ) Plot

14. Example: GWAS of Prostate Cancer chromosome http://cgems.cancer.gov Multiple prostate cancer loci on 8q24 Witte, Nat Genet 2007

15. Prostate Cancer Replications Witte, Nat Rev Genet 2009 Modest ORs

16. Prostate Cancer Replications Witte, Nat Rev Genet 2009 Modest ORs

17. SNPs Missed in Replication? 24,223 smallest P-value! Witte, Nat Rev Genet, 2009

18. Prostate Cancer www.genome.gov/gwastudies Manolio et al. Clin Invest 2008

19. Population Attributable Risks for GWAS Smoking & lung cancer BRCA1 & Breast cancer Jorgenson & Witte, 2009

20. Limitations of GWAS Example: AUC for Br Cancer Risk Gail = 58% SNPs = 58.9% G + S = 61.8% Wacholder et al. NEJM 2010 • Not very predictive Witte, Nat Rev Genet 2009

21. Limitations of GWAS • Not very predictive • Explain little heritability • Focus on common variation • Many associated variants are not causal

22. Where’s the Heritability? Common disease rare variant (CDRV) hypothesis: diseases due to multiple rare variants with intermediate penetrances (allelic heterogeneity) Many more of these? See: NEJM, April 30, 2009 McCarthy et al., 2008

23. Will GWAS results explain more heritability? • Possibly, if… • Causal SNPs not yet detected due to power / practical issues (e.g., not yet included in replication studies). • Stronger effects for causal SNPs: Associated SNP may only serve as a marker for multiple different causal SNPs.

24. Imputation of SNP Genotypes • Estimate unmeasured or missing genotypes. • Based on measured SNPs and external info (e.g., haplotype structure of HapMap). • Increase GWAS power. • Allow for combining data across different platforms (e.g., Affy & Illumina) (for replication / meta-analysis).

25. Imputation Example Study Sample HapMap/ 1K genomes Gonçalo Abecasis

26. Identify Match with Reference Gonçalo Abecasis

27. Phase chromosomes, impute missing genotypes Gonçalo Abecasis http://www.sph.umich.edu/csg/abecasis/MACH

28. Imputation Application TCF7L2 gene region & T2D from the WTCCC data Observed genotypes black Imputed genotypes red. Chromosomal Position Marchini Nature Genetics2007 http://www.stats.ox.ac.uk/~marchini/#software

29. Genome-wide Sequence Studies • Trade off between number of samples, depth, and genomic coverage. Goncalo Abecasis

30. Near-term Design Choices • For example, between: • Sequencing few subjects with extreme phenotypes: • e.g., 200 cases, 200 controls, 4x coverage. Then follow-up in larger population. • 10M SNP chip based on 1,000 genomes. • 5K cases, 5K controls. • Which design will work best…?

31. Many weak associations combine to risk? Score model: where ln(ORi ) = ‘score’ for SNPi from ‘discovery’ sample SNPij = # of alleles (0,1,2) for SNPi, person j in ‘validation’ sample. Large number of SNPs (m) xj associated with disease? Polygenic Models ISC / Purcell et al. Nature 2009

32. Application of Model Purcell / ISC et al. Nature 2009

33. Application to CGEMs PCa GWAS Witte & Hoffman 2010 • 1,172 cases, 1,157 controls from PLCO Trial • Oversampled more aggressive cases. • Illumina 550K array. • PCa & stratified by disease aggressiveness. • Split into halves, resampling: • one as ‘discovery’ sample; • other as ‘validation’. • LD filter: r2 = 0.5.

34. Results for Prostate Cancer

35. Common Polygenic Model for Prostate and Breast Cancer? • CGEMs GWAS data on prostate and breast cancer. • Use one cancer as ‘discovery’ sample, the other as ‘validation’. Nat Rev Cancer 2010;10:205-212

36. Results for PCa & BrCa

37. Complex diseases Physical activity Genetic susceptibility Obesity Hyperlipidemia Diet Diabetes Complex diseases: Many causes = many causal pathways! Vulnerable plaques Hypertension MI Atherosclerosis

38. Pathways • Many websites / companies provide ‘dynamic’ graphic models of molecular and biochemical pathways. • Example: BioCarta: http://www.biocarta.com/ • May be interested in potential joint and/or interaction effects of multiple genes in one pathway.

39. Systems Biology Moving Beyond Genome Transcriptome: All messenger RNA molecules (‘transcripts’) Proteome: All proteins in cell or organism Metabolome: all metabolites in a biological organism (end products of its gene expression).