Bayesian Aggregation of Multiple Annotations Enhances Rare Variant Association Testing

Gene-level rare variant association tests (RVATs) are essential for uncovering disease mechanisms and identifying potential drug targets. Advances in sequence-based machine learning have expanded the availability of variant pathogenicity scores, offering new opportunities to improve variant prioritization for RVATs. However, existing methods often rely on rigid models or analyze single annotations in isolation, limiting their ability to leverage these advances. To address this, we introduce BayesRVAT, a Bayesian framework for RVATs that models variant effects using multiple annotations. By specifying priors on variant effects and estimating gene-trait-specific burden scores through variational inference, BayesRVAT flexibly accommodates diverse genetic architectures. In simulations, BayesRVAT improved power while maintaining statistical calibration. When applied to real data from the UK Biobank, BayesRVAT detected 10.2% more associations with blood traits than the next-best method and uncovered novel gene-disease associations in analyses of eight disease traits, including a link between PRPH2 and retinal disease. These results highlight BayesRVAT's potential to enhance rare variant discovery and improve gene-trait association studies.