title: Bayesian model selection in complex linear systems, as illustrated in genetic association studies.
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.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors.forename: Xiaoquan
authors.initials: X
authors.lastname: Wen
mesh_terms: D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000379:methods; D003198:Computer Simulation; D056726:Genetic Association Studies; D006801:Humans; D016014:Linear Models; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D040641:Quantitative Trait Loci / Q000235:genetics*
keywords: Bayes factor; Genetic association; Linear models; Model comparison; Model selection