Bayes Factor Based on a Maximum Statistic for Case-Control Genetic Association Studies

Abstract

We study a Bayes factor based on a robust test statistic, MAX3, for case-control genetic association studies. MAX3 is the maximum of three trend tests derived under the recessive, additive and dominant genetic models, respectively. The proposed Bayes factor, denoted as BFM, models the asymptotic distributions of MAX3 under the null and alternative hypotheses. It is compared to an existing Bayes factor based on Bayesian model averaging (BMA). Through simulation studies, we show that both BFM and BMA are robust under genetic model uncertainty. They both depend on specifying the prior distribution for the genetic model, and have similar performance when common objective priors are used. When the prior places a large probability on the true (wrong) genetic model, BMA (BFM) is more powerful. Applications to real data from a genome-wide association study are presented to illustrate their use and show their sensitivity under genetic model uncertainty.