RT Journal
A1 Nappi, Antonio
A1 Shilova, Liubov
A1 Karaletsos, Theofanis
A1 Cai, Na
A1 Casale, Francesco Paolo
T1 BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations
JF Genome Research 
JO Genome Research 
YR 2025 
FD December 01 
VO 35 
IS 12 
SP 2682 
OP 2690 
DO 10.1101/gr.280689.125 
UL http://genome.cshlp.org/content/35/12/2682.abstract 
AB Gene-level rare variant association tests (RVATs) are essential for uncovering disease mechanisms and identifying therapeutic targets. Advances in sequence-based machine learning have generated diverse variant pathogenicity scores, creating opportunities to improve RVATs. However, existing methods often rely on rigid models or single annotations, limiting their ability to leverage these advances. Here, we introduce BayesRVAT, a Bayesian rare variant association test that jointly models multiple annotations. By specifying priors on annotation effects and estimating gene- and trait-specific posterior burden scores, BayesRVAT flexibly captures diverse rare-variant architectures. In simulations, BayesRVAT improves power while maintaining calibration. In UK Biobank analyses, it detects 10.2% more blood-trait associations and reveals novel gene–disease links, including PRPH2 with retinal disease. Integrating BayesRVAT within omnibus frameworks further increases discoveries, demonstrating that flexible annotation modeling captures complementary signals beyond existing burden and variance-component tests.