Hierarchical modeling of the relation between sequence variants and a quantitative trait: addressing multiple comparison and population stratification issues.

When analyzing the relation between genetic sequence information and disease traits, false-positive associations can arise due to multiple comparisons and population stratification. In an attempt to address these issues, we incorporate into a conventional analytic model higher-level--or "prior"--models that use additional information to improve estimates while allowing for differing population structures. We apply this hierarchical model to simulated data from the Genetic Analysis Workshop 12. We focus on the effects of common candidate gene sequence variants on quantitative risk factor 5 (Q5) levels. In particular, we compare the regression coefficients (and 95% confidence intervals) obtained from conventional (one-stage) analyses versus the corresponding results from the hierarchical analyses. When examining either the marry-ins or all subjects in the general and isolate populations, the conventional model detected numerous sites in candidate genes 1-5 and 7 that had statistically significant regression coefficients (alpha level = 0.05). In contrast, our hierarchical model primarily only detected associations for variants in candidate gene 2, which is the casual gene for Q5.