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September 2011 Bayesian variable selection regression for genome-wide association studies and other large-scale problems
Yongtao Guan, Matthew Stephens
Ann. Appl. Stat. 5(3): 1780-1815 (September 2011). DOI: 10.1214/11-AOAS455

Abstract

We consider applying Bayesian Variable Selection Regression, or BVSR, to genome-wide association studies and similar large-scale regression problems. Currently, typical genome-wide association studies measure hundreds of thousands, or millions, of genetic variants (SNPs), in thousands or tens of thousands of individuals, and attempt to identify regions harboring SNPs that affect some phenotype or outcome of interest. This goal can naturally be cast as a variable selection regression problem, with the SNPs as the covariates in the regression. Characteristic features of genome-wide association studies include the following: (i) a focus primarily on identifying relevant variables, rather than on prediction; and (ii) many relevant covariates may have tiny effects, making it effectively impossible to confidently identify the complete “correct” subset of variables. Taken together, these factors put a premium on having interpretable measures of confidence for individual covariates being included in the model, which we argue is a strength of BVSR compared with alternatives such as penalized regression methods. Here we focus primarily on analysis of quantitative phenotypes, and on appropriate prior specification for BVSR in this setting, emphasizing the idea of considering what the priors imply about the total proportion of variance in outcome explained by relevant covariates. We also emphasize the potential for BVSR to estimate this proportion of variance explained, and hence shed light on the issue of “missing heritability” in genome-wide association studies. More generally, we demonstrate that, despite the apparent computational challenges, BVSR can provide useful inferences in these large-scale problems, and in our simulations produces better power and predictive performance compared with standard single-SNP analyses and the penalized regression method LASSO. Methods described here are implemented in a software package, pi-MASS, available from the Guan Lab website http://bcm.edu/cnrc/mcmcmc/pimass.

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Yongtao Guan. Matthew Stephens. "Bayesian variable selection regression for genome-wide association studies and other large-scale problems." Ann. Appl. Stat. 5 (3) 1780 - 1815, September 2011. https://doi.org/10.1214/11-AOAS455

Information

Published: September 2011
First available in Project Euclid: 13 October 2011

zbMATH: 1229.62145
MathSciNet: MR2884922
Digital Object Identifier: 10.1214/11-AOAS455

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 3 • September 2011
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