title: Bayesian model comparison in genetic association analysis: linear mixed modeling and SNP set testing.
abstract: We consider the problems of hypothesis testing and model comparison under a flexible Bayesian linear regression model whose formulation is closely connected with the linear mixed effect model and the parametric models for Single Nucleotide Polymorphism (SNP) set analysis in genetic association studies. We derive a class of analytic approximate Bayes factors and illustrate their connections with a variety of frequentist test statistics, including the Wald statistic and the variance component score statistic. Taking advantage of Bayesian model averaging and hierarchical modeling, we demonstrate some distinct advantages and flexibilities in the approaches utilizing the derived Bayes factors in the context of genetic association studies. We demonstrate our proposed methods using real or simulated numerical examples in applications of single SNP association testing, multi-locus fine-mapping and SNP set association testing.
authors: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
authors: Xiaoquan
authors: X
authors: Wen
mesh_terms: D017360:Arabidopsis / Q000235:genetics; D001499:Bayes Theorem; D056726:Genetic Association Studies / Q000379:methods*; D016014:Linear Models; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide
keywords: Bayes factor; Genetic association; Linear mixed model; Model comparison; SNP set analysis