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Activity Number: 350
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #316105 View Presentation
Title: Detecting Rare Haplotype-Environment Interaction Under Uncertainty of Gene-Environment Independence Assumption
Author(s): Yuan Zhang* and Swati Biswas and Shili Lin
Companies: The University of Texas at Dallas and The University of Texas at Dallas and The Ohio State University
Keywords: GXE ; G-E independence ; LBL ; MCMC ; Missing Heritability ; Rare variants
Abstract:

Detecting gene-environment interactions (GXE) where G is a rare haplotype variant is a challenging research problem. A common assumption made in such analyses is independence of G and E. We consider the recently proposed method based on logistic Bayesian LASSO (LBL) for detecting GXE, which assumes G-E independence - we refer to it as LBL-GXE-I. It has inflated type I errors when the assumption is violated. Here we propose a way of relaxing this assumption by modeling the haplotype frequencies as functions of E - we refer to it as LBL-GXE-D. It successfully controls type I error rates in all situations. However, LBL-GXE-D has reduced power than LBL-GXE-I when G-E independence holds. To address this problem, we propose a unified approach by employing reversible jump Markov chain Monte Carlo method - we refer to this method as LBL-GXE. Simulation studies show that LBL-GXE has power similar to that of LBL-GXE-I in case of G-E independence and at the same time has controlled type I errors in all situations. Finally, we apply LBL-GXE to a lung cancer dataset.


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