Patxot, Marion and Banos, Daniel Trejo and Kousathanas, Athanasios and Orliac, Etienne J. and Ojavee, Sven E. and Moser, Gerhard and Holloway, Alexander and Sidorenko, Julia and Kutalik, Zoltan and Magi, Reedik and Visscher, Peter M. and Rönnegård, Lars and Robinson, Matthew R.
(2021).
Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits.
Nature Communications. 12
, 6972
[Research article]
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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.
Authors/Creators: | Patxot, Marion and Banos, Daniel Trejo and Kousathanas, Athanasios and Orliac, Etienne J. and Ojavee, Sven E. and Moser, Gerhard and Holloway, Alexander and Sidorenko, Julia and Kutalik, Zoltan and Magi, Reedik and Visscher, Peter M. and Rönnegård, Lars and Robinson, Matthew R. | ||||||
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Title: | Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits | ||||||
Series Name/Journal: | Nature Communications | ||||||
Year of publishing : | 2021 | ||||||
Volume: | 12 | ||||||
Article number: | 6972 | ||||||
Number of Pages: | 16 | ||||||
Publisher: | NATURE PORTFOLIO | ||||||
ISSN: | 2041-1723 | ||||||
Language: | English | ||||||
Publication Type: | Research article | ||||||
Article category: | Scientific peer reviewed | ||||||
Version: | Published version | ||||||
Copyright: | Creative Commons: Attribution 4.0 | ||||||
Full Text Status: | Public | ||||||
Subjects: | (A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > 10203 Bioinformatics (Computational Biology) (applications to be 10610) (A) Swedish standard research categories 2011 > 1 Natural sciences > 106 Biological Sciences (Medical to be 3 and Agricultural to be 4) > Genetics (medical genetics to be 30107 and agricultural genetics to be 40402) | ||||||
URN:NBN: | urn:nbn:se:slu:epsilon-p-114699 | ||||||
Permanent URL: | http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-114699 | ||||||
Additional ID: |
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ID Code: | 26297 | ||||||
Faculty: | VH - Faculty of Veterinary Medicine and Animal Science | ||||||
Department: | (VH) > Dept. of Animal Breeding and Genetics | ||||||
Deposited By: | SLUpub Connector | ||||||
Deposited On: | 10 Dec 2021 10:25 | ||||||
Metadata Last Modified: | 10 Dec 2021 10:31 |
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