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.

The data consists of the posterior distributions of running BayesW model on five phenotypes in UK Biobank: age-at-menopause, age-at-menarche, and age-at-diagnosis of coronary artery disease, high blood pressure, or type-2-diabetes. The posterior distributions give the effect size estimates and the corresponding effect size mixture classification for each of the markers analysed. 

In addition we provide summary statistics for each of the markers analysed, giving posterior means, standard deviations and inclusion probabilities.

The information included in the ReadMe file.