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.本研究开发了名为BayesW的贝叶斯方法，依托混合并行采样方案对发病年龄表型的遗传架构开展概率推断，从而支撑贝叶斯事件发生时间视角下的大规模生物样本库分析。我们通过大量模拟实验证实，相较于其他方法，BayesW可识别出更多的显著遗传关联，具备更优异的模型性能与更出色的基因组预测能力。在英国生物样本库（UK Biobank）中，我们发现多达数千个常见基因组区域与高血压（high blood pressure, HBP）、心脏疾病（cardiac disease, CAD）及2型糖尿病（type-2 diabetes, T2D）的发病年龄相关，且上述疾病的发病遗传基础恰好反映了个体罹患疾病的内在遗传易感性。绝经年龄与初潮年龄同样属于高度多基因调控的表型，但其表型变异更多由低频变异贡献。针对爱沙尼亚生物样本库数据开展的基因组预测分析显示，BayesW的预测精度显著优于其他方法。