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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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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 1088-9051
e-ISSN 1549-5469
Journal Genome Research
Publisher Cold Spring Harbor Laboratory Press
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)
POF-Topic(s) 30205 - Bioengineering and Digital Health
30202 - Environmental Health
Research field(s) Enabling and Novel Technologies
Pioneer Campus
PSP Element(s) G-540004-001
G-510007-001
DOI 10.1101/gr.280689.125
PubMed ID 41136342
Erfassungsdatum 2025-10-27
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