Antidepressants exhibit a considerable variation in efficacy, and increasing evidence suggests that individual genetics contribute to antidepressant treatment response. Here, we combined data on antidepressant non-response measured using rating scales for depressive symptoms, questionnaires of treatment effect, and data from electronic health records, to increase statistical power to detect genomic loci associated with non-response to antidepressants in a total sample of 135,471 individuals prescribed antidepressants. We performed genome-wide association meta-analyses, leave-one-out polygenic prediction, and bioinformatics analyses for genetically informed drug prioritization. We identified two novel loci associated with non-response to antidepressants and showed significant polygenic prediction in independent samples. In addition, we investigated drugs that target proteins likely involved in mechanisms underlying antidepressant non-response, and shortlisted drugs that warrant further replication and validation of their potential to reduce depressive symptoms in individuals who do not respond to first-line antidepressant medications. These results suggest that meta-analyses of GWAS utilizing real-world measures of treatment outcomes can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants.

The authors have declared no competing interest.

We would like to thank the research participants and employees of 23andMe, Inc. for making this work possible. This work was partly performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT). Computations were also performed on resources provided by UNINETT Sigma2, the National Infrastructure for High Performance Computing and Data Storage in Norway (NS9666S). Computations of the UKB data were enabled by resources in project sens2017519 provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at UPPMAX, funded by the Swedish Research Council through grant agreement no. 2022-06725. We gratefully acknowledge support from the Research Council of Norway (RCN) (296030, 223273, and 334920) and from the National Institutes of Health (NIH 5R01MH124839-02). This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 964874 (REALMENT), as well as from the US NIMH (grant R01 MH123724) and from the European Unions Horizon 2021 research project Psych-STRATA (EU HORIZON-HLTH-2021, grant 101057454). The AGDS was primarily funded by National Health and Medical Research Council (NHMRC) of Australia grant 1086683, with additional funding from MRFF1200644 and MRFF2024891. BLM and SEM were supported by Australian NHMRC Investigator grants (APP2017176 and APP1172917 respectively). YL was supported by the European Research Council grant (grant agreement No: 101042183) and the Swedish Research Council grant (2021-02615 VR). MER thanks the support of the Rebecca L Cooper Medical Research Foundation through an Al & Val Rosenstrauss Fellowship (F20231230). GB, TH, and YL declare that this study represents independent research part-funded by the National Institute for Health and Social Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and Kings College London. The views expressed are those of the author(s) and not necessarily those of the UK National Health Service, the NIHR or the Department of Health. We gratefully acknowledge the participation of all South London and Maudsley NIHR BioResource volunteers within the National NIHR BioResource and thank the South London and Maudsley BioResource staff for their help with volunteer recruitment.

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↵# The Estonian Biobank Research Team includes Andres Metspalu, Tõnu Esko, Reedik Mägi, Mari Nelis, and Georgi Hudjashov

Data produced in the present study are available upon reasonable request to the authors.