BayesW time-to-event analysis posterior outputs and summary statistics

Here, we develop a Bayesian approach (BayesW) that provides probabilistic
inference of the genetic architecture of age-at-onset phenotypes in a
hybrid-parallel sampling scheme that facilitates Bayesian time-to-event
large-scale biobank analyses. We show in extensive simulation work that
BayesW achieves a greater number of discoveries, better model performance
and improved genomic prediction as compared to other approaches. 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.