ArticleNature communications2021
Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Can Psychiatric Genetics Advance Without Incorporating a Life Course Perspective?Biological psychiatry · 2026Review
- Genetic association studies using disease liabilities from deep neural networks.American journal of human genetics · 2025Article
- Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.Statistics in medicine · 2024Article
- Genetic association studies using disease liabilities from deep neural networks.medRxiv : the preprint server for health sciences · 2024Article
- Polygenic risk scores for cervical HPV infection, neoplasia and cancer show potential for personalised screening: comparison of two methods.Infectious agents and cancer · 2023Article
- Genetic insights into the age-specific biological mechanisms governing human ovarian aging.American journal of human genetics · 2023Article
- Polygenic Risk Score Predicts Modified Risk inCancers · 2023Article
- Accounting for age of onset and family history improves power in genome-wide association studies.American journal of human genetics · 2022Article
- Significant sparse polygenic risk scores across 813 traits in UK Biobank.PLoS genetics · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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 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.
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