  The discovery of rare genetic variants through Next Generation Sequencing is
a very challenging issue in the field of human genetics. We propose a novel
region-based statistical approach based on a Bayes Factor (BF) to assess
evidence of association between a set of rare variants (RVs) located on the
same genomic region and a disease outcome in the context of case-control
design. Marginal likelihoods are computed under the null and alternative
hypotheses assuming a binomial distribution for the RV count in the region and
a beta or mixture of Dirac and beta prior distribution for the probability of
RV. We derive the theoretical null distribution of the BF under our prior
setting and show that a Bayesian control of the False Discovery Rate (BFDR) can
be obtained for genome-wide inference. Informative priors are introduced using
prior evidence of association from a Kolmogorov-Smirnov test statistic. We use
our simulation program, sim1000G, to generate RV data similar to the 1,000
genomes sequencing project. Our simulation studies showed that the new BF
statistic outperforms standard methods (SKAT, SKAT-O, Burden test) in
case-control studies with moderate sample sizes and is equivalent to them under
large sample size scenarios. Our real data application to a lung cancer
case-control study found enrichment for RVs in known and novel cancer genes. It
also suggests that using the BF with informative prior improves the overall
gene discovery compared to the BF with non-informative prior.
