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

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 t

SUBMITTER: Ojavee SE 

PROVIDER: S-EPMC8058085 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature