Recent advances in sequencing and genotyping technologies are contributing to a data revolution in genome‐wide association studies that is characterized by the challenging large
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Abstract p smalln problem in statistics. That is, given these advances, many such studies now consider evaluating an extremely large number of genetic markers (p ) genotyped on a small number of subjects (n ). Given the dimension of the data, a joint analysis of the markers is often fraught with many challenges, while a marginal analysis is not sufficient. To overcome these obstacles, herein, we propose a Bayesian two‐phase methodology that can be used to jointly relate genetic markers to binary traits while controlling for confounding. The first phase of our approach makes use of a marginal scan to identify a reduced set of candidate markers that are then evaluated jointly via a hierarchical model in the second phase. Final marker selection is accomplished through identifying a sparse estimator via a novel and computationally efficient maximum a posteriori estimation technique. We evaluate the performance of the proposed approach through extensive numerical studies, and consider a genome‐wide application involving colorectal cancer.