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

Autor: Patxot M; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland., Banos DT; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland., Kousathanas A; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland., Orliac EJ; Scientific Computing and Research Support Unit, University of Lausanne, Lausanne, Switzerland., Ojavee SE; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland., Moser G; Australian Agricultural Company Limited, Brisbane, QLD, Australia., Holloway A; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland., Sidorenko J; Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia., Kutalik Z; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.; University Center for Primary Care and Public Health, Lausanne, Switzerland.; Swiss Institute of Bioinformatics, Lausanne, Switzerland., Mägi R; Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia., Visscher PM; Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia., Rönnegård L; School of Technology and Business Studies, Dalarna University, Falun, Sweden.; Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden., Robinson MR; Institute of Science and Technology Austria, Klosterneuburg, Austria. matthew.robinson@ist.ac.at.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2021 Nov 30; Vol. 12 (1), pp. 6972. Date of Electronic Publication: 2021 Nov 30.
DOI: 10.1038/s41467-021-27258-9
Abstrakt: 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.
(© 2021. The Author(s).)
Databáze: MEDLINE
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