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Genome-Wide Association Studies

Genome-Wide Association Studies. Xiaole Shirley Liu Stat 115/215. Association Studies. Association between genetic markers and phenotype Especially, find disease genes, SNP / haplotype markers, for susceptibility prediction and diagnosis

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Genome-Wide Association Studies

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  1. Genome-Wide Association Studies Xiaole Shirley Liu Stat 115/215

  2. Association Studies • Association between genetic markers and phenotype • Especially, find disease genes, SNP / haplotype markers, for susceptibility prediction and diagnosis • Influences individual decisions on life styles, prevention, screening, and treatment

  3. Mike Snyder’s iPOP Reveals Diabetes

  4. Warfarin and CYP2C9: SNPs in Pharmacogenomics • Warfarin anticoagulant drug; CYP2C9 gene metabolizes warfarin. • A patient requiring low dosage warfarin compared to normal population, has an odd ratio of 6.21 for having  1 variant allele • Subgroup of patients who are poor metabolisers of warfarin are potentially at higher risk of bleeding • Aithal et al., 1999, Lancet.

  5. Genome-Wide Association Studies • Two strategies: • Family-based association studies • Population-based case-control association studies • Quality Control • Unusual similarity between individual • Wrong sex • Trio has non-Mendelian inheritance • Genotyping quality

  6. Quality Control: SNP calls Bad calls! Good calls!

  7. Family-based Association StudiesTDT: Transmission Disequilibrium Test • Look at allele transmission in unrelated families and one affected child in each • Could also compare allele frequency between affected vs unaffected children in the same family Like coin toss

  8. Case Control Studies • SNP/haplotype marker frequency in sample of affected cases compared to that in age /sex /population-matched sample of unaffected controls • Size matters Visscher, AJHG 2012

  9. From Genotyping to Allele Counts

  10. Test Significant Associations • Expected: • (24 + 278) * (24 + 86) / (24 + 278 + 86 + 296) = 49 • (278+296) * (86+296) / (24 + 278 + 86 + 296) = 321 • 2 = 27.5, 1df, p < 0.001 • Multiple hypotheses testing?

  11. GWAS Pvalues

  12. GWAS Pvalues for Type II Diabetes • Bonferroni correction: most common, typically p < 10-7 or 10-8 • Split samples to improve power McCarthy et al, Nat Rev Genetics, 2008

  13. Association of Alleles and Genotypes of rs1333049 (‘3049) with Myocardial Infarction • OR = 1, no disease association • OR > 1, allele increase risk of disease • OR < 1, allele decrease risk of disease Samani N et al, N Engl J Med 2007; 357:443-453.

  14. Manolio et al., Clin Invest 2008

  15. Pitfalls of Association Studies • Not very predictive

  16. Pitfalls of Association Studies • Not very predictive • Explain little heritability • Poor reproducibility • Poor penetrance (fraction of people with the marker who show the trait) and expressivity (severity of the effect) • Focus on common variation • Difficult when several genes affecting a quantitative trait • Many associated variants are not causal • No available intervention for many disease risks

  17. Reproducibility of Association Studies • Most reported associations have not been consistently reproduced • Hirschhorn et al, Genetics in Medicine, 2002, review of association studies • 603 associations of polymorphisms and disease • 166 studied in at least three populations • Only 6 seen in > 75% studies

  18. Cause for Inconsistency • What explains the lack of reproducibility? • False positives • Multiple hypothesis testing • Ethnic admixture/ stratification • False negatives • Lack of power for weak effects • Population differences • Variable LD with causal SNP • Population-specific modifiers

  19. Population Stratification • Population stratification • e.g. some SNP unique to ethnic group • Need to make sure sample groups match • Hidden environmental structure • Two populations have different disease frequency, and different allele frequency. • Association picks up they are different populations! Balding, Nature Reviews Genetics 2010

  20. Genotyping Principal Components (PCs) Can Model Population Stratification • Li et al., Science 2008

  21. Causes for Inconsistency • A sizable fraction (but less than half) of reported associations are likely correct • Genetic effects are generally modest • Beware the winner’s curse (auction theory) • In association studies, first positive report is equivalent to the winning bid • Large study sizes are needed to detect these reliably

  22. Should we Believe Association Study Results? • Initial skepticism is warranted • Replication, especially with low p values, is encouraging • Large sample sizes are crucial • E.g. PPARg Pro12Ala & Diabetes

  23. Replication, Replication, Replication • Meta-analysis of multiple studies to increase GWAS power • Combine data from different platforms / studies • Impute unmeasured or missing genotypes based on LD (e.g. HapMap haplotypes or 1000 Genomes) • Analyze all studies together to increase GWAS power

  24. Missing Heritability? Visccher, AJHG 2011

  25. Detection Power of GWAS

  26. Acknowledgement • Tim Niu • Kenneth Kidd, Judith Kidd and Glenys Thomson • Joel Hirschhorn • Greg Gibson & Spencer Muse • Jim Stankovich • Teri Manolio

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