TY - JOUR
T1 - Integration of variant annotations using deep set networks boosts rare variant association testing
AU - Clarke, Brian
AU - Holtkamp, Eva
AU - Öztürk, Hakime
AU - Mück, Marcel
AU - Wahlberg, Magnus
AU - Meyer, Kayla
AU - Munzlinger, Felix
AU - Brechtmann, Felix
AU - Hölzlwimmer, Florian R.
AU - Lindner, Jonas
AU - Chen, Zhifen
AU - Gagneur, Julien
AU - Stegle, Oliver
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85205065164&partnerID=8YFLogxK
U2 - 10.1038/s41588-024-01919-z
DO - 10.1038/s41588-024-01919-z
M3 - Article
C2 - 39322779
AN - SCOPUS:85205065164
SN - 1061-4036
JO - Nature Genetics
JF - Nature Genetics
ER -