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Activity Number: 53
Type: Invited
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #318084
Title: Set-Based Association Analysis of Gene-Environment Interaction Using Mixed Effects Models
Author(s): Li Hsu*
Companies: Fred Hutchinson Cancer Research Center
Keywords: gene-environment interaction ; mixed effects model ; genome-wide association studies ; adaptive weighted score test

The development of next generation sequencing technologies has allowed researchers to study comprehensively the involvement of genes and environment in complex diseases. However, analysis of GxE (gene-environment interaction) poses considerable challenges because few subjects carry the variants while being exposed. To tackle this challenge, we propose a mixed-effects model to jointly assess the GxE effects of a set of variants in a gene or regulatory region leveraging the information across the variants. Under this model, GxE is modeled by two components: fixed effects for incorporating functional or screening characteristics of variants as weights to calculate the weighted sum of variants interacting with E and random effects for the residual GxE. We develop score statistics for both the fixed and random effects and establish their asymptotic distributions. Extensive simulation results show that the proposed test statistics maintain the correct type I error and the power is comparable to or better than existing methods under a wide range of scenarios. We illustrate the methods by an exome-wide analysis of GxE with NSAIDS use in colorectal cancer.

Authors who are presenting talks have a * after their name.

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