Similarity-Based Marker-Set Association Tests for Gene-Environment Effects in Case-Control Studies

• Activity Number: 253
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Guolin Zhao*+ and Daowen Zhang and Jung-Ying Tzeng
• Companies: North Carolina State University and North Carolina State University and North Carolina State University
• Keywords: gene-environment ; similarity-based regression ; variant ; score test

Abstract

Recent researches suggested that gene-environment interactions may play a significant role in uncovering the missing heritability of genes to complex traits. Given the power advantage brought by the collapsing approaches for marker-set analysis, in this work, we present a similarity-based regression method for testing the gene-environment effects with binary traits from case-control studies. The genetic information of the marker set is summarized by collapsing the similarity status across multiple markers. The association is evaluated by regressing trait similarity on genetic similarity and its interaction with environment factors. We construct a score test for examining the gene-environment interactions that can be shown equivalent to a variance component in a generalized linear mixed model, and derive an EM algorithm to estimate the nuisance variance components. We illustrate the utility of the proposed method through simulations and real data application.