Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.07.12.548506v1?rss=1
Authors: Clarke, B., Holtkamp, E., Ozturk, H., Muck, M., Wahlberg, M., Meyer, K., Munzlinger, F., Brechtmann, F., Holzlwimmer, F. R., Gagneur, J., Stegle, O.
Abstract:
Rare genetic variants can strongly predispose to disease, yet accounting for rare variants in genetic analyses is statistically challenging. While rich variant annotations hold the promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here, we propose DeepRVAT, a set neural network-based approach to learn burden scores from rare variants, annotations and phenotype. In contrast to existing methods, DeepRVAT yields a single, trait-agnostic, nonlinear gene impairment score, enabling both risk prediction and gene discovery in a unified framework. On 21 quantitative traits and whole-exome-sequencing data from UK Biobank, DeepRVAT offers substantial increases in gene discoveries and improved replication rates in held-out data. Moreover, we demonstrate that the integrative DeepRVAT gene impairment score greatly improves detection of individuals at high genetic risk. We show that pre-trained DeepRVAT scores generalize across traits, opening up the possibility to conduct highly computationally efficient rare variant tests.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
