Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

Sven E. Ojavee (), Athanasios Kousathanas, Daniel Trejo Banos, Etienne J. Orliac, Marion Patxot, Kristi Läll, Reedik Mägi, Krista Fischer, Zoltan Kutalik and Matthew R. Robinson ()
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Sven E. Ojavee: University of Lausanne
Athanasios Kousathanas: University of Lausanne
Daniel Trejo Banos: University of Lausanne
Etienne J. Orliac: University of Lausanne
Marion Patxot: University of Lausanne
Kristi Läll: Institute of Genomics, University of Tartu
Reedik Mägi: Institute of Genomics, University of Tartu
Krista Fischer: Institute of Genomics, University of Tartu
Zoltan Kutalik: University Center for Primary Care and Public Health
Matthew R. Robinson: Institute of Science and Technology Austria

Nature Communications, 2021, vol. 12, issue 1, 1-17

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-22538-w

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