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Research article - Peer-reviewed, 2021

Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits

Patxot, Marion; Banos, Daniel Trejo; Kousathanas, Athanasios; Orliac, Etienne J.; Ojavee, Sven E.; Moser, Gerhard; Holloway, Alexander; Sidorenko, Julia; Kutalik, Zoltan; Magi, Reedik; Visscher, Peter M.; Ronnegard, Lars; Robinson, Matthew R.;

Abstract

Improving inference in large-scale genetic data linked to electronic medical record data requires the development of novel computationally efficient regression methods. Here, the authors develop a Bayesian approach for association analyses to improve SNP-heritability estimation, discovery, fine-mapping and genomic prediction.We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only <= 10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having >= 95% probability of contributing >= 0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.

Published in

Nature Communications

2021, volume: 12, number: 1, article number: 6972
Publisher: NATURE PORTFOLIO

Authors' information

Patxot, Marion
University of Lausanne
Banos, Daniel Trejo
University of Lausanne
Kousathanas, Athanasios
University of Lausanne
Orliac, Etienne J.
University of Lausanne
Ojavee, Sven E.
University of Lausanne
Moser, Gerhard
Australian Agricultural Company Limited
Holloway, Alexander
University of Lausanne
Sidorenko, Julia
University of Queensland
Kutalik, Zoltan
University of Lausanne
Magi, Reedik
University of Tartu
Visscher, Peter M.
University of Queensland
Dalarna University
Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics
Robinson, Matthew R.
Institute of Science and Technology - Austria

Sustainable Development Goals

SDG3 Good health and wellbeing

UKÄ Subject classification

Bioinformatics (Computational Biology)
Genetics

Publication Identifiers

DOI: https://doi.org/10.1038/s41467-021-27258-9

URI (permanent link to this page)

https://res.slu.se/id/publ/114699