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Activity Number: 156 - Statistical Interactions – Making an Impact in Health Science
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract #304540 Presentation
Title: Detection of Set-Based Gene-Environment Interactions for Substance Use Disorders
Author(s): Saonli Basu* and Brandon Coombes and Matt McGue
Companies: University of Minnesota, Biostatistics SPH and Mayo Clinic and University of Minnesota
Keywords: Gene-Environment Interaction; Dimension Reduction

The development of substance use disorders (SUDs) is an intricate dynamic process controlled by a network of genes as well as by environmental factors. The detailed study design of Minnesota Center for Twin and Family Research (MCTFR) samples provides an opportunity to investigate such gene-environmental interactions in SUDs. One way to increase power for detection of G-E interaction is to improve the effect size(s) by aggregating the DNA polymorphisms (SNPs) in what we call SNP-sets, which also reduces the multiple-testing problem. We propose a test for detection of interaction between a SNP-set and a group of correlated environmental factors by using a likelihood-based dimension reduction approach within a random-effect model framework. The proposed approach employs a parsimonious model to capture the effect of a group of interacting SNPs and environmental exposures on the disease. We illustrate our model and compare the performance with existing methods to detect G-E interactions through simulation studies and with application to MCTFR data. We show that the performance of these methods vary widely based on the directionality and sparsity of the interaction effects.

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

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