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Biometrics. 2014 Mar;70(1):73-83. doi: 10.1111/biom.12112. Epub 2013 Dec 18.

Bayesian model selection in complex linear systems, as illustrated in genetic association studies.

Author information

1
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.

Abstract

Motivated by examples from genetic association studies, this article considers the model selection problem in a general complex linear model system and in a Bayesian framework. We discuss formulating model selection problems and incorporating context-dependent a priori information through different levels of prior specifications. We also derive analytic Bayes factors and their approximations to facilitate model selection and discuss their theoretical and computational properties. We demonstrate our Bayesian approach based on an implemented Markov Chain Monte Carlo (MCMC) algorithm in simulations and a real data application of mapping tissue-specific eQTLs. Our novel results on Bayes factors provide a general framework to perform efficient model comparisons in complex linear model systems.

KEYWORDS:

Bayes factor; Genetic association; Linear models; Model comparison; Model selection

PMID:
24350677
PMCID:
PMC3954315
DOI:
10.1111/biom.12112
[Indexed for MEDLINE]
Free PMC Article

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