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• W3083610424 abstract "Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-diagnosis of disease and time-to-event phenotypes. We show in extensive simulation work that our method provides insight into genetic effects underlying disease progression, achieving a greater number of discoveries, better model performance and improved genomic prediction as compared to other approaches. We develop a hybrid-parallel sampling scheme facilitating age-at-onset analyses in large-scale biobank data. In the UK Biobank, we find evidence for an infinitesimal contribution of many thousands of common genomic regions to variation in the onset of common complex disorders of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of age-at-onset reflecting the underlying genetic liability to disease. In contrast, while age-at-menopause and age-at-menarche are highly polygenic, we find higher variance contributed by low frequency variants. We find 291 LD-independent regions for age-at-menarche with ≥ 95% posterior inclusion probability of contributing 0.001% to the genetic variance, 176 regions for age-at-menopause, 441 regions for age-at-diagnosis of HBP, 67 regions for CAD, and 108 regions for T2D. Genomic prediction into the Estonian Genome Centre data shows that BayesW gives higher prediction accuracy than other approaches." @default.
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• W3083610424 date "2020-09-07" @default.
• W3083610424 modified "2023-10-18" @default.
• W3083610424 title "Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis" @default.
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• W3083610424 doi "https://doi.org/10.1101/2020.09.04.20188441" @default.
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