  Motivated by the genomic application of expression quantitative trait loci
(eQTL) mapping, we propose a new procedure to perform simultaneous testing of
multiple hypotheses using Bayes factors as input test statistics. One of the
most significant features of this method is its robustness in controlling the
targeted false discovery rate (FDR) even under misspecifications of parametric
alternative models. Moreover, the proposed procedure is highly computationally
efficient, which is ideal for treating both complex system and big data in
genomic applications. We discuss the theoretical properties of the new
procedure and demonstrate its power and computational efficiency in
applications of single-tissue and multi-tissue eQTL mapping.
