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Epidemiology 719 Quantitative methods in genetic epidemiology

Epidemiology 719 Quantitative methods in genetic epidemiology Bhramar Mukherjee and Sebastian Zoellner bhramar@umich.edu szoellne@umich.edu. Acknowledgements. Peter Kraft (HSPH) Ken Rice (UW) Nilanjan Chatterjee (NCI) Stephen Channock (NCI) Lu Wang (UM) Nan Laird (HSPH)

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Epidemiology 719 Quantitative methods in genetic epidemiology

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  1. Epidemiology 719 Quantitative methods in genetic epidemiology Bhramar Mukherjee and Sebastian Zoellner bhramar@umich.eduszoellne@umich.edu

  2. Acknowledgements • Peter Kraft (HSPH) • Ken Rice (UW) • Nilanjan Chatterjee (NCI) • Stephen Channock (NCI) • Lu Wang (UM) • Nan Laird (HSPH) • Goncalo Abecasis (UM)

  3. A brave new world Course Overview

  4. Reverse Effects

  5. Central Course Theme Genetic Association and Gene-Environment Interaction

  6. Course Advice for You:

  7. Assigned Paper 1

  8. Assigned Paper 1 • GWAS of Age-related macular degeneration • Initial GWAS identified four loci explaining one-half of the heritability. Appreciable predictive power. • Additional GWAS to explain remaining heritability. Combined scan vs replication. Meta-Analysis.

  9. Assigned Paper 2

  10. Assigned Paper 2 Collaborative Association Study of Psoriasis Examined ~1,500 cases / ~1,500 controls at ~500,000 SNPs • Examined 20 promising SNPs in extra ~5,000 cases / ~5,000 controls Outcome: 7 regions of confirmed association with psoriasis

  11. Assigned Paper 3

  12. Assigned Paper 3 • Meta-analysis of colorectal cancer (COGENT study) . • A thorough evaluation of ten confirmed loci for colorectal cancer. Very detailed. Supplementary material also available online. • Interesting combination of various study design.

  13. Tests for Association

  14. Basic principle of GWAS

  15. Depends on study design • Case-control study • Family-based study: case-parent triad, case-sib pairs being popular choices • Longitudinal Cohort Study • Looking at a secondary outcome under case-control sampling

  16. The GWAS Mantra!

  17. Primary Analysis • Single marker association tests • Genetic susceptibility model - Dominant, recessive, co-dominant • Which test to use • Multiple testing correction

  18. Case-Control Study: Standard Analysis

  19. Pros and Cons • Simple, Complete. • Robust to misspecification of the true dominance pattern • Less powerful. • Unreliable for sparse table

  20. Pros and Cons • Test statistic has single df, so more powerful. • Simple to report. • Not robust to true mode of dominance • Does not present entire information in the data.

  21. Armitage’s trend test • Test linear trend in log(OR) with # A allele • Test statistic still has single d.f. • Simplicity, use information from the 2 x 3 array • More robust than 2 x 2 tests, but less robust than the 2 d.f. test.

  22. Allelic test • Previous tests were based on genotype • Can also treat allele as the unit of observation. • You have doubled the sample size!!

  23. But… • Serious impact on Type 1 error under departures from HWE • Interpretation becomes trickier.

  24. Example AIC: Akaike information criterion, lower the value, better is model fit

  25. Using logistic regression • Trick: Just code genotype differently • Dominant: G=1 if AA or Aa, 0 otherwise • Recessive: G=1 if AA, 0 otherwise • Trend: G=# A alleles, thus G=2 if AA, =1 if Aa and 0 if aa • Two df test: Create two dummy variables: G1=1 if Aa and 0 otherwise G2=1 if AA and 0 otherwise Perform likelihood ratio test of full (G1 and G2) vs reduced model (No G1, G2). • Adjust for other variables, fit a multivariate model

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