Abstract
Schizophrenia genome-wide association studies (GWASes) have identified >250 significant loci and prioritized >100 disease-related genes. However, gene prioritization efforts have mostly been restricted to locus-based methods that ignore information from the rest of the genome. To more accurately characterize genes involved in schizophrenia etiology, we applied a combination of highly-predictive tools to a published GWAS of 67,390 schizophrenia cases and 94,015 controls. We combined both locus-based methods (fine-mapped coding variants, distance to GWAS signals) and genome-wide methods (PoPS, MAGMA, ultra-rare coding variant burden tests). We extracted genes that 1) are targeted by existing drugs that could potentially be repurposed for schizophrenia, 2) are predicted to be druggable, or 3) may be testable in rodent models. We prioritized 101 schizophrenia genes, including 15 that are targeted by approved or investigational drugs (e.g., DRD2, GRIN2A, CACNA1C, GABBR2). Of these, 7 have never been tested in clinical trials for schizophrenia or other psychiatric disorders (e.g., AKT3). Seven genes are not targeted by any existing small molecule drugs, but are predicted to be druggable (e.g., GRM1). We prioritized two potentially druggable genes in loci that are shared with an addiction GWAS (PDE4B and VRK2). We curated a high-quality list of 101 genes that likely play a role in the development of schizophrenia. Developing or repurposing drugs that target these genes may lead to a new generation of schizophrenia therapies. Rodent models of addiction more closely resemble the human disorder than rodent models of schizophrenia. As such, genes prioritized for both disorders could be explored in rodent addiction models, potentially facilitating drug development.
Data availability
ChEMBL Database: https://www.ebi.ac.uk/chembl/, HRC reference release 1.1: https://ega-archive.org/datasets/EGAD00001002729, Gencode release 44: https://www.gencodegenes.org/human/release_44.html, OpenTargets platform: https://platform-docs.opentargets.org/, The PGC3 GWAS core dataset is available through the PGC data access portal: https://pgc.unc.edu/for-researchers/data-access-committee/data-access-portal/, Summary statistics of the PGC3 GWAS are freely available for download: https://pgc.unc.edu/for-researchers/download-results/
Code availability
Custom code used in the presented study is stored at https://github.com/kheilbron/cojo_pipe and https://github.com/kheilbron/brett, Additional software and code: COJO: https://yanglab.westlake.edu.cn/software/gcta/#COJO coloc: https://github.com/chr1swallace/coloc MAGMA: https://cncr.nl/research/magma/ PLINK 1.9: https://www.cog-genomics.org/plink/ PoPS: https://github.com/FinucaneLab/pops PsyOPS: https://github.com/Wainberg/PsyOPS
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Acknowledgements
We thank SURF (www.surf.nl) for the support in using the Snellius National Supercomputer. JK and SR were supported by the German Center for Mental Health (DZPG). AB, JK, AFP, and SR were supported by the European Union’s Horizon program (101057454, “PsychSTRATA”). AB and SR were supported by The German Research Foundation (402170461, grant “TRR265”). DP and MS were supported by The Netherlands Organization for Scientific Research (NWO Gravitation: BRAINSCAPES: A Roadmap from Neurogenetics to Neurobiology - Grant No. 024.004.012). DP was supported by The European Research Council (Advanced Grant No ERC-2018-AdG GWAS2FUNC 834057). AFP, NB, and DP were supported by the European Union’s Horizon program (964874, “REALMENT”). AFP was supported by an Academy of Medical Sciences “Springboard” award (SBF005\1083). KH was supported by a Humboldt Research Fellowship from the Alexander von Humboldt Foundation. GP, SA, DP, SR, and the research reported in this publication were supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH124873. The content is the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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JK: software, methodology, validation, formal analysis, writing - original draft, visualization. AB: software, methodology, validation, formal analysis, writing - original draft, visualization. SA: validation, writing - review & editing. GP: validation, writing - review & editing. MS: validation, writing - review & editing. NB: validation, writing - review & editing. DP: supervision, resources, funding acquisition, writing - review & editing. AFP: validation, writing - review & editing. The Schizophrenia Working Group of the Psychiatric Genomics Consortium provided all schizophrenia genome-wide association study results. SR: conceptualization, validation, supervision, resources, funding acquisition, writing - review & editing. KH: conceptualization, methodology, software, validation, formal analysis, writing - original draft, visualization, supervision.
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JK, AB, SA, GP, MS, NB, DP, and SR have nothing to disclose. AFP reports receiving a grant from Akrivia Health for a project unrelated to this submission. KH is a former employee of 23andMe, Inc. and owns 23andMe, Inc. stock options.
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Kraft, J., Braun, A., Awasthi, S. et al. Identifying drug targets for schizophrenia through gene prioritization. Transl Psychiatry (2026). https://doi.org/10.1038/s41398-026-03813-0
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DOI: https://doi.org/10.1038/s41398-026-03813-0