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Nappi, A. ; Cai, N. ; Casale, F.P.

Bayesian aggregation of multiple annotations enhances rare variant association testing.

In: (Research in Computational Molecular Biology). Berlin [u.a.]: Springer, 2025. 428-431 (Lect. Notes Comput. Sc. ; 15647 LNBI)
Postprint DOI
Open Access Green
Gene-based rare variant association tests (RVATs) are essential for uncovering disease mechanisms and identifying candidate drug targets, yet existing frameworks lack flexibility in integrating multiple variant annotations. Here, we introduce BayesRVAT, a Bayesian framework for RVAT which models variant effects using priors informed by multiple annotations. We show that BayesRVAT outperforms state-of-the-art burden test strategies in both simulations and an analysis of 12 blood traits from the UK Biobank.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Research in Computational Molecular Biology
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15647 LNBI, Heft: , Seiten: 428-431 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)