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Advanced Genetic Epidemiology Methods for Researchers

Explore quantitative methods in genetic epidemiology, GWAS techniques, genetic association, gene-environment interaction, and statistical analysis in this comprehensive course. Learn from assigned papers on macular degeneration, psoriasis, and colorectal cancer studies. Gain insights into various study designs, testing methods, and statistical corrections in genetic research. Enhance your skills using logistic regression, trend tests, and allele-based analyses for robust conclusions.

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Advanced Genetic Epidemiology Methods for Researchers

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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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