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 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