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

A Bayes Factor Approach with Informative Prior for Rare Genetic Variant Analysis from Next Generation Sequencing Data