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

Nappi A, Shilova L, Karaletsos T, Cai N, Casale FP (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Genome Research Cold Spring Harbor Laboratory Press

Book Volume: 35

Pages Range: 2682-2690

Journal Issue: 12

DOI: 10.1101/gr.280689.125

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.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Chan Zuckerberg Initiative (CZI) US United States (USA) (US)

How to cite

APA:

Nappi, A., Shilova, L., Karaletsos, T., Cai, N., & Casale, F.P. (2025). BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations. Genome Research, 35(12), 2682-2690. https://doi.org/10.1101/gr.280689.125

MLA:

Nappi, Antonio, et al. "BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations." Genome Research 35.12 (2025): 2682-2690.

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