  Because of the decreasing cost and high digital resolution, next-generation
sequencing (NGS) is expected to replace the traditional hybridization-based
microarray technology. For genetics study, the first-step analysis of NGS data
is often to identify genomic variants among sequenced samples. Several
statistical models and tests have been developed for variant calling in NGS
study. The existing approaches, however, are based on either conventional
Bayesian or frequentist methods, which are unable to address the multiplicity
and testing efficiency issues simultaneously. In this paper, we derive an
optimal empirical Bayes testing procedure to detect variants for NGS study. We
utilize the empirical Bayes technique to exploit the across-site information
among many testing sites in NGS data. We prove that our testing procedure is
valid and optimal in the sense of rejecting the maximum number of nonnulls
while the Bayesian false discovery rate is controlled at a given nominal level.
We show by both simulation studies and real data analysis that our testing
efficiency can be greatly enhanced over the existing frequentist approaches
that fail to pool and utilize information across the multiple testing sites.
