BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations
- Antonio Nappi1,2,3,4,
- Liubov Shilova1,3,5,
- Theofanis Karaletsos6,
- Na Cai2,7,8 and
- Francesco Paolo Casale1,2,3,4
- 1Institute of AI for Health, Helmholtz Zentrum München – German Research Center for Environmental Health, 85764 Neuherberg, Germany;
- 2Helmholtz Pioneer Campus, Helmholtz Zentrum München – German Research Center for Environmental Health, 85764 Neuherberg, Germany;
- 3School of Computation, Information and Technology, Technical University of Munich, 85748 Garching, Germany;
- 4AI Resident, Chan Zuckerberg Initiative, Redwood City, California 94063, USA;
- 5Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany;
- 6Chan Zuckerberg Initiative, Redwood City, California 94063, USA;
- 7TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, 81675 Munich, Germany;
- 8Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland
Abstract
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.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.280689.125.
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Freely available online through the Genome Research Open Access option.
- Received March 24, 2025.
- Accepted October 22, 2025.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
