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Clarke, B.* ; Holtkamp, E. ; Öztürk, H.* ; Mück, M.* ; Wahlberg, M.* ; Meyer, K.* ; Munzlinger, F.* ; Brechtmann, F.* ; Hölzlwimmer, F.R.* ; Lindner, J.* ; Chen, Z.* ; Gagneur, J. ; Stegle, O.*

Integration of variant annotations using deep set networks boosts rare variant association testing.

Nat. Genet., DOI: 10.1038/s41588-024-01919-z (2024)
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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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Clonal Hematopoiesis; Uk Biobank; Mutation; Disease; Cataract; Hsf4
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 1061-4036
e-ISSN 1546-1718
Journal Nature Genetics
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants Model Exchange for Regulatory Genomics project (MERGE)
Initiative and Networking Fund of the Helmholtz Association
State Parliament of Baden-Wurttemberg for the Innovation Campus Health+Life Science Alliance Heidelberg Mannheim
Helmholtz Association
Deutsche Forschungsgemeinschaft (DFG
German Research Foundation)
German Bundesministerium fur Bildung und Forschung (BMBF) through the ERA PerMed project PerMiM
UKBB
DOI 10.1038/s41588-024-01919-z
WOS ID 001319489300001
Scopus ID 85205065164
PubMed ID 39322779
Erfassungsdatum 2024-10-29
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