| Title: | BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations |
| Journal: | Genome Research |
| Published: | 24 Oct 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41136342/ |
| DOI: | https://doi.org/10.1101/gr.280689.125 |
| URL: | http://genome.cshlp.org/content/early/2025/10/24/gr.280689.125.full.pdf |
Publication 16648
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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.</p>
9 Keywords
- Bayes Theorem
- Genetic Predisposition to Disease
- Genetic Variation
- Genome-Wide Association Study
- Humans
- Machine Learning
- Models, Genetic
- Molecular Sequence Annotation
- Retinal Diseases
5 Authors
- Antonio Nappi
- Liubov Shilova
- Theofanis Karaletsos
- Na Cai
- Francesco Paolo Casale
1 Application
| Application ID | Title |
|---|---|
| 87065 | Computational and machine learning tools for the analysis of genetic and medical imaging data to study human health and disease |
Enabling scientific discoveries that improve human health