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Abstract Details
Activity Number:
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441
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #300354 |
Title:
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A Bayesian Model Averaging Approach to Gene-Environment Interaction in a GWAS
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Author(s):
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Dalin Li and David V. Conti*+
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Companies:
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University of Southern California and University of Southern California
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Address:
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, , ,
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Keywords:
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Gene-Environment Interaction ;
Genetic Association ;
Bayesian Model Averaging
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Abstract:
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Scans for gene(G)-environment(E) interaction have been mostly neglected in current GWAS. The conventional approach, the case-control analysis, suffers from low power. The alternative case-only analysis can be more powerful but violation of the assumption of independence can greatly bias the results. We propose a model averaging approach for GxE interaction leveraging the fact that most SNPs in a GWAS are not associated with either the outcome nor the environmental factor. When there is no G-E association or main effect of G, removing the corresponding terms in the log-linear model reduces the variance of the interaction estimate. Since there is uncertainty, the method averages over the sub-models with and without the G-E or main effect G terms. A final estimate of effect is the average over the models weighted by the posterior probability of each model. In a single-marker analysis this approach can be more powerful than the case-only approach when the GxE independence assumption holds. When the assumption is violated, the Type I error rate is more robust than the case-only analysis. In genome-wide simulations, this approach has better overall performance.
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