{"_id":"8430","article_number":"2337","language":[{"iso":"eng"}],"citation":{"ieee":"S. E. Ojavee <i>et al.</i>, “Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis,” <i>Nature Communications</i>, vol. 12, no. 1. Nature Research, 2021.","mla":"Ojavee, Sven E., et al. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” <i>Nature Communications</i>, vol. 12, no. 1, 2337, Nature Research, 2021, doi:<a href=\"https://doi.org/10.1038/s41467-021-22538-w\">10.1038/s41467-021-22538-w</a>.","short":"S.E. Ojavee, A. Kousathanas, D. Trejo Banos, E.J. Orliac, M. Patxot, K. Lall, R. Magi, K. Fischer, Z. Kutalik, M.R. Robinson, Nature Communications 12 (2021).","ista":"Ojavee SE, Kousathanas A, Trejo Banos D, Orliac EJ, Patxot M, Lall K, Magi R, Fischer K, Kutalik Z, Robinson MR. 2021. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. Nature Communications. 12(1), 2337.","chicago":"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.” <i>Nature Communications</i>. Nature Research, 2021. <a href=\"https://doi.org/10.1038/s41467-021-22538-w\">https://doi.org/10.1038/s41467-021-22538-w</a>.","apa":"Ojavee, S. E., Kousathanas, A., Trejo Banos, D., Orliac, E. J., Patxot, M., Lall, K., … Robinson, M. R. (2021). Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. <i>Nature Communications</i>. Nature Research. <a href=\"https://doi.org/10.1038/s41467-021-22538-w\">https://doi.org/10.1038/s41467-021-22538-w</a>","ama":"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. <i>Nature Communications</i>. 2021;12(1). doi:<a href=\"https://doi.org/10.1038/s41467-021-22538-w\">10.1038/s41467-021-22538-w</a>"},"publication_identifier":{"eissn":["20411723"]},"external_id":{"isi":["000642509600006"]},"has_accepted_license":"1","author":[{"last_name":"Ojavee","first_name":"Sven E","full_name":"Ojavee, Sven E"},{"full_name":"Kousathanas, Athanasios","last_name":"Kousathanas","first_name":"Athanasios"},{"full_name":"Trejo Banos, Daniel","last_name":"Trejo Banos","first_name":"Daniel"},{"first_name":"Etienne J","last_name":"Orliac","full_name":"Orliac, Etienne J"},{"full_name":"Patxot, Marion","last_name":"Patxot","first_name":"Marion"},{"first_name":"Kristi","last_name":"Lall","full_name":"Lall, Kristi"},{"last_name":"Magi","first_name":"Reedik","full_name":"Magi, Reedik"},{"last_name":"Fischer","first_name":"Krista","full_name":"Fischer, Krista"},{"first_name":"Zoltan","last_name":"Kutalik","full_name":"Kutalik, Zoltan"},{"first_name":"Matthew Richard","last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813"}],"publication":"Nature Communications","doi":"10.1038/s41467-021-22538-w","project":[{"_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181"}],"ddc":["570"],"file_date_updated":"2021-05-04T15:07:50Z","acknowledgement":"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.","isi":1,"year":"2021","volume":12,"intvolume":"        12","abstract":[{"text":"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.","lang":"eng"}],"department":[{"_id":"MaRo"}],"issue":"1","publication_status":"published","date_published":"2021-04-20T00:00:00Z","date_updated":"2023-08-04T11:00:17Z","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"article_processing_charge":"No","month":"04","day":"20","type":"journal_article","file":[{"checksum":"eca8b9ae713835c5b785211dd08d8a2e","relation":"main_file","content_type":"application/pdf","access_level":"open_access","creator":"kschuh","file_name":"2021_nature_communications_Ojavee.pdf","date_updated":"2021-05-04T15:07:50Z","success":1,"file_id":"9372","date_created":"2021-05-04T15:07:50Z","file_size":6474239}],"status":"public","related_material":{"link":[{"description":"News on IST Homepage","relation":"press_release","url":"https://ist.ac.at/en/news/predicting-the-onset-of-diseases/"}]},"title":"Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis","quality_controlled":"1","scopus_import":"1","publisher":"Nature Research","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","date_created":"2020-09-17T10:53:00Z","oa_version":"Published Version","oa":1}