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

Details

Serval ID
serval:BIB_9710808BCDCD
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits.
Journal
Nature communications
Author(s)
Patxot M., Banos D.T., Kousathanas A., Orliac E.J., Ojavee S.E., Moser G., Holloway A., Sidorenko J., Kutalik Z., Mägi R., Visscher P.M., Rönnegård L., Robinson M.R.
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Publication state
Published
Issued date
30/11/2021
Peer-reviewed
Oui
Volume
12
Number
1
Pages
6972
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: epublish
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.
Keywords
Bayes Theorem, Body Height, Body Mass Index, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Genetic Techniques, Genetic Variation, Genome-Wide Association Study, Genomics, Genotype, Humans, Introns, Models, Statistical, Multifactorial Inheritance/genetics, Open Reading Frames, Phenotype, Software
Pubmed
Web of science
Open Access
Yes
Create date
03/12/2021 10:55
Last modification date
22/01/2022 7:32
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