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March 2014 Bayesian methods for genetic association analysis with heterogeneous subgroups: From meta-analyses to gene–environment interactions
Xiaoquan Wen, Matthew Stephens
Ann. Appl. Stat. 8(1): 176-203 (March 2014). DOI: 10.1214/13-AOAS695

Abstract

Genetic association analyses often involve data from multiple potentially-heterogeneous subgroups. The expected amount of heterogeneity can vary from modest (e.g., a typical meta-analysis) to large (e.g., a strong gene–environment interaction). However, existing statistical tools are limited in their ability to address such heterogeneity. Indeed, most genetic association meta-analyses use a “fixed effects” analysis, which assumes no heterogeneity. Here we develop and apply Bayesian association methods to address this problem. These methods are easy to apply (in the simplest case, requiring only a point estimate for the genetic effect and its standard error, from each subgroup) and effectively include standard frequentist meta-analysis methods, including the usual “fixed effects” analysis, as special cases. We apply these tools to two large genetic association studies: one a meta-analysis of genome-wide association studies from the Global Lipids consortium, and the second a cross-population analysis for expression quantitative trait loci (eQTLs). In the Global Lipids data we find, perhaps surprisingly, that effects are generally quite homogeneous across studies. In the eQTL study we find that eQTLs are generally shared among different continental groups, and discuss consequences of this for study design.

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Xiaoquan Wen. Matthew Stephens. "Bayesian methods for genetic association analysis with heterogeneous subgroups: From meta-analyses to gene–environment interactions." Ann. Appl. Stat. 8 (1) 176 - 203, March 2014. https://doi.org/10.1214/13-AOAS695

Information

Published: March 2014
First available in Project Euclid: 8 April 2014

zbMATH: 06302232
MathSciNet: MR3191987
Digital Object Identifier: 10.1214/13-AOAS695

Keywords: Bayes factor , Bayesian hypothesis testing , gene–environment interaction , Heterogeneity , Meta-analysis

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 1 • March 2014
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