Improving gene-based analysis of rare variants through Bayesian variable selection

A common approach for detecting rare variants (RVs) associated with complex human diseases is to perform a gene-based analysis or a region-based test of association. Typically, in a gene-based analysis, all the RVs pertaining to the gene considered are included in the statistical test, which potentially reduces its power since most RVs are likely not associated with the outcome of interest. As paradigm shift, we propose to add a variable selection step to choose the RVs that compose the gene-based test statistic as a way to enhance the power of the test. We propose a Bayes factor (BF) test statistic derived from the generalized linear model and its conjugate prior. A key component of our approach is the selection of the important RVs within a gene, which is performed through a birth-death Markov Chain Monte Carlo algorithm. Our approach can also leverage functional annotation at the RV level through the elicitation of the conjugate prior. Through simulation studies, we show that the proposed BF outperformed competing approaches both in terms of gene ranking and power to detect gene-based associations. The power of BF was improved by the use of the variable selection procedure and this gain of power was more substantial when functional annotation was included in this procedure. The application of our new approach to a large whole-exome sequencing data set comparing 1658 individuals with lung cancer to 1492 healthy controls was able to identify new genes associated with lung cancer and pointed towards interesting cancer-related pathways.