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Activity Number: 658 - Recent Statistical Advances in Genomic and Genetic Data Analysis
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #329864
Title: A Likelihood Ratio Test for Gene (G)-Environment (E) Interaction Based on the Trend Effect of a Genotype Under an Additive Risk Model Using the G-E Independence Assumption
Author(s): Summer Han* and Nilanjan Chatterjee
Companies: Stanford University and Johns Hopkins University
Keywords: gene-environment interaction; additive model; additive risk model; gene-environment independence; case-control design; GWAS

Several statistical methods have been proposed for testing G-E interactions under additive risk models using genome-wide association study data. However, these approaches have strong assumptions on the underlying genetic model such as dominant or recessive effects that are known to be less robust when the true genetic model is unknown. Our goal is to develop a robust trend test employing a likelihood ratio test for detecting G-E interaction under an additive risk model, also incorporating the G-E independence assumption to increase power. We used a constrained likelihood approach to impose two sets of constraints: (i) the linear trend effect of a genotype and (ii) the additive joint effects of G and E, exploiting a saturated logit model. To incorporate the G-E independence assumption, a retrospective likelihood was used. Numerical investigation of power suggests that the proposed trend test is more powerful compared to those assuming a dominant, recessive, or general model under various parameter settings. Incorporation of the independence assumption enhances the efficiency of the test by 2.5- to 3-fold. We illustrate this method by applying it to Alzheimer disease GWAS data.

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

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