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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., DOI: 10.1101/gr.280689.125 (2025)
DOI PMC
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
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 1088-9051
e-ISSN 1549-5469
Zeitschrift Genome Research
Verlag Cold Spring Harbor Laboratory Press
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)
POF Topic(s) 30205 - Bioengineering and Digital Health
30202 - Environmental Health
Forschungsfeld(er) Enabling and Novel Technologies
Pioneer Campus
PSP-Element(e) G-540004-001
G-510007-001
DOI 10.1101/gr.280689.125
PubMed ID 41136342
Erfassungsdatum 2025-10-27
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