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Nappi, A. ; Shilova, L. ; Karaletsos, T.* ; Cai, N. ; Casale, F.P.

BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations.

Genome Res. 35, 2682-2690 (2025)
Verlagsversion Postprint DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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. We introduce BayesRVAT, a Bayesian rare variant association test that jointly models multiple annotations. By specifying priors on annotation effects and estimating genetrait-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 genedisease 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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Allelic-series; Protein; Common; Algorithm; Diseases
ISSN (print) / ISBN 1088-9051
e-ISSN 1549-5469
Zeitschrift Genome Research
Quellenangaben Band: 35, Heft: 12, Seiten: 2682-2690 Artikelnummer: , Supplement: ,
Verlag Cold Spring Harbor Laboratory Press
Verlagsort 1 Bungtown Rd, Cold Spring Harbor, Ny 11724 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Human-Centered AI (HCA)
Helmholtz Pioneer Campus (HPC)
Förderungen Chan Zuckerberg Initiative
Friedrich-Alexander-Universitt Erlangen-Nrnberg
Free State of Bavaria's Hightech Agenda
UK Biobank Resource