Calibrated Bayes Factors in Assessing Genetic Association Models

Three competing genetic models—additive, dominant, and recessive—are often considered in genetic association analysis. We propose and develop a calibrated Bayes approach for comparing these competing models that has the desired property of giving equal support to the three models when no genetic association is present. The naïve approach with noncalibrated priors is shown to produce misleading Bayes factors. The method is fully developed with simulation studies, real data analyses, and an efficient algorithm based on an asymptotic approximation. An illuminating connection to the Kullback–Leibler divergence is also established. The proposed calibrated prior can serve as a reference prior for a genetic association study or as a common baseline prior for comparing Bayes analyses of different datasets.