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

Marion Patxot, Daniel Trejo Banos, Athanasios Kousathanas, Etienne J. Orliac, Sven E. Ojavee, Gerhard Moser, Alexander Holloway, Julia Sidorenko, Zoltan Kutalik, Reedik Mägi, Peter M. Visscher, Lars Rönnegård and Matthew R. Robinson ()
Additional contact information
Marion Patxot: University of Lausanne
Daniel Trejo Banos: University of Lausanne
Athanasios Kousathanas: University of Lausanne
Etienne J. Orliac: University of Lausanne
Sven E. Ojavee: University of Lausanne
Gerhard Moser: Australian Agricultural Company Limited
Alexander Holloway: University of Lausanne
Julia Sidorenko: University of Queensland
Zoltan Kutalik: University of Lausanne
Reedik Mägi: University of Tartu
Peter M. Visscher: University of Queensland
Lars Rönnegård: Dalarna University
Matthew R. Robinson: Institute of Science and Technology Austria

Nature Communications, 2021, vol. 12, issue 1, 1-16

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-27258-9

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