Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.
Nat Commun
; 12(1): 2337, 2021 04 20.
Article
em En
| MEDLINE
| ID: mdl-33879782
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Genoma Humano
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Idade de Início
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Herança Multifatorial
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Modelos Genéticos
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article