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