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Leveraging the genetics of psychiatric disorders to prioritize potential drug targets and compounds

Neuropsychopharmacology (2026)Cite this article

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Abstract

Genetics can inform biologically relevant drug development and repurposing, which may improve patient care. Here, we leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the genome-wide association studies of four psychiatric disorders [attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] and genes encoding drug targets. We conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We conducted multiple molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets associated with a single or multiple disorders that implicate glutamate signaling. Drug prioritization indicated genetic support for psychotropic medications, including several top-ranked antipsychotics for schizophrenia. We also observed genetic support for commonly used psychotropics for psychiatric treatment (e.g., clozapine, duloxetine, and lithium). Revealed opportunities for drug repurposing included cholinergic drugs for ADHD, estrogen modulators for depression, and matrix metalloproteinases for ADHD and depression. Our findings indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Our extensive analyses highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders, providing novel opportunities for improving patient outcomes.

Depicted is the series of analyses conducted to generate a list of prioritized drug targets and compounds. First pairings of genome-wide association study (GWAS) traits with drugs are generated using enrichment analyses. Next, a series of molecular trait analyses is conducted to generate and rank a list of potential drug targets for each GWAS trait. Finally, enrichment and molecular trait results are combined to generate a ranked list of prioritized drugs for each GWAS trait based on supporting genetic evidence. ADHD = Attention deficit hyperactivity disorder, BIP = Bipolar disorder, DEP = Depression, SCZ = Schizophrenia, DBP = Diastolic blood pressure, T2D = Type 2 diabetes, RNA = ribonucleic acid, XWAS = both transcriptome and proteome-wide association studies, MR = Mendelian randomization, coloc = colocalization.

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Fig. 1: Overlap of enriched drugs for psychiatric disorders.
Fig. 2: Transcriptome- and proteome-wide association study results.
Fig. 3: Mendelian randomization results.
Fig. 4: Prioritization of candidate drug targets.
Fig. 5: Prioritization of drug compounds.

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Data availability

All GWAS summary statistics and molecular trait data included in this study are publicly available. We generated PWAS weights using proteomic data from the UK Biobank and have shared those weights on figshare [https://doi.org/10.6084/m9.figshare.31487881]. The GWAS summary statistics from the Million Veteran Program were downloaded from the dbGaP website, under accession phs001672.

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Acknowledgements

We thank all participants, staff, and providers of the publicly available data used in this study, including individuals from the UKB, psychiatric genomics consortium, Million Veterans Program, PsychENCODE, ARIC, MetaBrain, ROS/MAP, eQTLGen, and GTEx.

Funding

Funding was provided by the US National Institutes of Health (grants U24DA041123, R01AG076838, U24DA055330, OT2HL161847 and 5R01MH124839-02), the Research Council of Norway (grants 296030, 273291, 273446, 300309, 324252, 326813, 334920), the South-East Regional Health Authority (grant 2022–073), EEA-RO-NO-2018–0573, EEA-RO-NO-2018–0535, EU’s Horizon 2020 Research and Innovation Programme (grant 847776; CoMorMent and 964874 REALMENT), Novo Nordisk Foundation (grant NNF23OC0099658), the Marie Skłodowska-Curie Actions Grant 801133 (Scientia fellowship). This work was performed on Services for sensitive data (TSD), University of Oslo, Norway, with resources provided by UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway (NS9666S).

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Author notes

  1. These authors contributed equally: Olav B. Smeland, Ole A. Andreassen.

Authors and Affiliations

  1. Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway

    Nadine Parker, Elise Koch, Alexey A. Shadrin, Julian Fuhrer, Guy F. L. Hindley, Sara E. Stinson, Piotr Jaholkowski, Markos Tesfaye, Oleksandr Frei, Kevin S. O’Connell, Olav B. Smeland & Ole A. Andreassen

  2. Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway

    Elise Koch

  3. Department of Psychiatry, Lovisenberg Diaconal Hospital, Oslo, Norway

    Guy F. L. Hindley

  4. Department of Clinical Science, University of Bergen, Bergen, Norway

    Markos Tesfaye

  5. Multimodal Imaging Laboratory, University of California, San Diego, La Jolla, CA, USA

    Anders M. Dale

  6. Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA

    Anders M. Dale

  7. Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA

    Anders M. Dale

  8. Department of Radiology, University of California, San Diego, La Jolla, CA, USA

    Anders M. Dale

  9. Department of Neurology, University of California, Davis, Sacramento, CA, USA

    Thomas S. Wingo

  10. Department of Psychiatry, University of California, Davis, Sacramento, CA, USA

    Aliza P. Wingo

  11. Division of Mental Health, VA Medical Center, Mather, CA, USA

    Aliza P. Wingo

  12. Department of Pharmacy, Section for Pharmacology and Pharmaceutical Biosciences, University of Oslo, Oslo, Norway

    Oleksandr Frei

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Contributions

NP and OBS conceived of the study, and EK, AAS, GFLH, SS, PJ, KSO, and OAA were involved in study design. NP, EK, AAS, TSW, and APW were involved in data acquisition and processing. NP, EK, AAS, and JF were involved in data analysis. NP had access to all data and drafted the initial manuscript. All authors contributed to data interpretation and editing of the manuscript and accepted responsibility to submit the manuscript for publication.

Corresponding authors

Correspondence to Nadine Parker or Ole A. Andreassen.

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Competing interests

OAA has received speaker fees from Lundbeck, Janssen, Otsuka, and Sunovion and is a consultant to Cortechs.ai. and Precision Health. AMD is a Founding Director and holds equity in CorTechs Labs, Inc. (DBA Cortechs.ai), Precision Pro, Inc., and Precision Health and Wellness, Inc. Dr. Dale is the President and a Board of Trustees member of the J. Craig Venter Institute (JCVI) and holds an appointment as Professor II at University of Oslo in Norway. OF is a consultant to Precision Health. TSW is a co-founder of revXon, which is disclosed and approved by the University of California, Davis, per its conflict-of-interest policy. All other authors report no biomedical financial interests or potential conflicts of interest.

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Parker, N., Koch, E., Shadrin, A.A. et al. Leveraging the genetics of psychiatric disorders to prioritize potential drug targets and compounds. Neuropsychopharmacol. (2026). https://doi.org/10.1038/s41386-026-02380-8

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