Ojavee, Sven E; Kousathanas, Athanasios; Trejo Banos, Daniel; Orliac, Etienne J; Patxot, Marion; Lall, Kristi; Magi, Reedik; Fischer, Krista; Kutalik, Zoltan; Robinson, Matthew RichardIST Austria
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 higher statistical power 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 360 independent 50kb regions for age-at-menarche with ≥ 95% posterior inclusion probability of contributing 0.001% to the genetic variance, 115 regions for age-at-menopause, 246 regions for age-at-diagnosis of HBP, 32 regions for CAD, and 56 regions for T2D. Genomic prediction into the Estonian Genome Centre data shows that BayesW gives higher prediction accuracy than other approaches.
This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria and the University of Lausanne; the work of KF was supported by the grant PUT1665 by the Estonian Research Council. We would like to thank Mike Goddard for comments which greatly improved the work, the participants of the cohort studies, and the Ecole Polytechnique Federal Lausanne (EPFL) SCITAS for their excellent compute resources, their generosity with their time and the kindness of their support.
Ojavee SE, Kousathanas A, Trejo Banos D, et al. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. medRxiv. doi:10.1101/2020.09.04.20188441
Ojavee, S. E., Kousathanas, A., Trejo Banos, D., Orliac, E. J., Patxot, M., Lall, K., … Robinson, M. R. (n.d.). Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. medRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.09.04.20188441
Ojavee, Sven E, Athanasios Kousathanas, Daniel Trejo Banos, Etienne J Orliac, Marion Patxot, Kristi Lall, Reedik Magi, Krista Fischer, Zoltan Kutalik, and Matthew Richard Robinson. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” MedRxiv. Cold Spring Harbor Laboratory, n.d. https://doi.org/10.1101/2020.09.04.20188441.
S. E. Ojavee et al., “Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis,” medRxiv. Cold Spring Harbor Laboratory.
Ojavee SE, Kousathanas A, Trejo Banos D, Orliac EJ, Patxot M, Lall K, Magi R, Fischer K, Kutalik Z, Robinson MR. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. medRxiv, 10.1101/2020.09.04.20188441.
Ojavee, Sven E., et al. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” MedRxiv, Cold Spring Harbor Laboratory, doi:10.1101/2020.09.04.20188441.