| Title: | Integration of variant annotations using deep set networks boosts rare variant association testing |
| Journal: | Nature Genetics |
| Published: | 25 Sep 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39322779/ |
| DOI: | https://doi.org/10.1038/s41588-024-01919-z |
Publication 13441
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Abstract
Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.</p>
13 Authors
- Brian Clarke
- Eva Holtkamp
- Hakime Öztürk
- Marcel Mück
- Magnus Wahlberg
- Kayla Meyer
- Felix Munzlinger
- Felix Brechtmann
- Florian R. Hölzlwimmer
- Jonas Lindner
- Zhifen Chen
- Julien Gagneur
- Oliver Stegle
3 Applications
| Application ID | Title |
|---|---|
| 25214 | Investigating multi-locus epistatic interactions underlying coronary artery disease |
| 44108 | Predictive value of the structural connectome using machine learning |
| 81358 | Deep-learning-based genetic analysis and phenotype prediction using variants of any frequency based on UK Biobank sequencing datasets |
Enabling scientific discoveries that improve human health