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

Détails

ID Serval
serval:BIB_9710808BCDCD
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits.
Périodique
Nature communications
Auteur⸱e⸱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
Statut éditorial
Publié
Date de publication
30/11/2021
Peer-reviewed
Oui
Volume
12
Numéro
1
Pages
6972
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Résumé
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.
Mots-clé
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
Oui
Création de la notice
03/12/2021 10:55
Dernière modification de la notice
22/01/2022 7:32
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