An efficient group test for genetic markers that handles confounding. (arXiv:1205.0793v1 [q-bio.GN])
by Jennifer Listgarten, Christoph Lippert, David Heckerman

Approaches for testing groups of variants for association with complex traits are becoming critical. Examples of groups typically include a set of rare or common variants within a gene, but could also be variants within a pathway or any other set. These tests are critical for aggregation of weak signal within a group, allow interplay among variants to be captured, and also reduce the problem of multiple hypothesis testing. Unfortunately, these approaches do not address confounding by, for example, family relatedness and population structure, a problem that is becoming more important as larger data sets are used to increase power. We introduce a new approach for group tests that can handle confounding, based on Bayesian linear regression, which is equivalent to the linear mixed model. The approach uses two sets of covariates (equivalently, two random effects), one to capture the group association signal and one to capture confounding. We also introduce a computational speedup for the two-random-effects model that makes this approach feasible even for extremely large cohorts, whereas it otherwise would not be. Application of our approach to richly structured GAW14 data, comprising over eight ethnicities and many related family members, demonstrates that our method successfully corrects for population structure, while application of our method to WTCCC Crohn’s disease and hypertension data demonstrates that our method recovers genes not recoverable by univariate analysis, while still correcting for confounding structure.