ADuLT: An efficient and robust time-to-event GWAS

Emil M. Pedersen (), Esben Agerbo, Oleguer Plana-Ripoll, Jette Steinbach, Morten D. Krebs, David M. Hougaard, Thomas Werge, Merete Nordentoft, Anders D. Børglum, Katherine L. Musliner, Andrea Ganna, Andrew J. Schork, Preben B. Mortensen, John J. McGrath, Florian Privé and Bjarni J. Vilhjálmsson ()
Additional contact information
Emil M. Pedersen: Aarhus University
Esben Agerbo: Aarhus University
Oleguer Plana-Ripoll: Aarhus University
Jette Steinbach: Aarhus University
Morten D. Krebs: Copenhagen University Hospital - Mental Health Services CPH
David M. Hougaard: Statens Serum Institut
Thomas Werge: Copenhagen University Hospital - Mental Health Services CPH
Merete Nordentoft: Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH
Anders D. Børglum: Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH
Katherine L. Musliner: Aarhus University
Andrea Ganna: University of Helsinki
Andrew J. Schork: Copenhagen University Hospital - Mental Health Services CPH
Preben B. Mortensen: Aarhus University
John J. McGrath: Aarhus University
Florian Privé: Aarhus University
Bjarni J. Vilhjálmsson: Aarhus University

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

Date: 2023
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DOI: 10.1038/s41467-023-41210-z

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