A powerful and efficient set test for genetic markers that handles confounders.

ABSTRACT:

Motivation

Approaches for testing sets of variants, such as a set of rare or common variants within a gene or pathway, for association with complex traits are important. In particular, set tests allow for aggregation of weak signal within a set, can capture interplay among variants and reduce the burden of multiple hypothesis testing. Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger datasets are used to increase power.

Results

We introduce a new approach for set tests that handles confounders. Our model is based on the linear mixed model and uses two random effects-one to capture the set association signal and one to capture confounders. We also introduce a computationa

SUBMITTER: Listgarten J 

PROVIDER: S-EPMC3673214 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature