Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.

Détails

ID Serval
serval:BIB_DD352C34FFE4
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.
Périodique
Nature communications
Auteur(s)
Ojavee S.E., Kousathanas A., Trejo Banos D., Orliac E.J., Patxot M., Läll K., Mägi R., Fischer K., Kutalik Z., Robinson M.R.
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Statut éditorial
Publié
Date de publication
20/04/2021
Peer-reviewed
Oui
Volume
12
Numéro
1
Pages
2337
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches.
Pubmed
Open Access
Oui
Création de la notice
26/04/2021 8:34
Dernière modification de la notice
30/04/2021 5:38
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