TY  - JOUR
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
Y1  - 2025/12/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 2682 
EP  - 2690 
DO  - 10.1101/gr.280689.125 
VL  - 35 
IS  - 12 
UR  - http://genome.cshlp.org/content/35/12/2682.abstract 
N2  - 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. 
ER  -