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Psychiatric disorders and brain white matter exhibit genetic overlap implicating developmental and neural cell biology

Molecular Psychiatry volume 28, pages 4924–4932 (2023)Cite this article

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Abstract

Improved understanding of the shared genetic architecture between psychiatric disorders and brain white matter may provide mechanistic insights for observed phenotypic associations. Our objective is to characterize the shared genetic architecture of bipolar disorder (BD), major depression (MD), and schizophrenia (SZ) with white matter fractional anisotropy (FA) and identify shared genetic loci to uncover biological underpinnings. We used genome-wide association study (GWAS) summary statistics for BD (n = 413,466), MD (n = 420,359), SZ (n = 320,404), and white matter FA (n = 33,292) to uncover the genetic architecture (i.e., polygenicity and discoverability) of each phenotype and their genetic overlap (i.e., genetic correlations, overlapping trait-influencing variants, and shared loci). This revealed that BD, MD, and SZ are at least 7-times more polygenic and less genetically discoverable than average FA. Even in the presence of weak genetic correlations (range = −0.05 to −0.09), average FA shared an estimated 42.5%, 43.0%, and 90.7% of trait-influencing variants as well as 12, 4, and 28 shared loci with BD, MD, and SZ, respectively. Shared variants were mapped to genes and tested for enrichment among gene-sets which implicated neurodevelopmental expression, neural cell types, myelin, and cell adhesion molecules. For BD and SZ, case vs control tract-level differences in FA associated with genetic correlations between those same tracts and the respective disorder (rBD = 0.83, p = 4.99e-7 and rSZ = 0.65, p = 5.79e-4). Genetic overlap at the tract-level was consistent with average FA results. Overall, these findings suggest a genetic basis for the involvement of brain white matter aberrations in the pathophysiology of psychiatric disorders.

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Fig. 1: Genetic architecture of psychiatric disorders and average FA.
Fig. 2: Genetic overlap between psychiatric disorders and average FA.
Fig. 3: Gene-level analyses.
Fig. 4: Tract-level FA genetic associations with psychiatric disorders.
Fig. 5: Tract-level genetic overlap beyond genetic correlation.

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Code availability

All tools used in this study are publicly available including: MiXeR v1.3 (https://github.com/precimed/mixer), cond/conjFDR (https://github.com/precimed/pleiofdr), LD score regression (https://github.com/bulik/ldsc), bedtools (bedtools.readthedocs.io), FUMA GWAS (https://fuma.ctglab.nl/).

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Acknowledgements

Funding was provided by the Research Council of Norway [grants 223273, 300309, 324252, 326813, 324499], the South-East Regional Health Authority [grant 2022-073], EEA and Norway [grant EEA-RO-NO-2018-0573], European Union’s Horizon 2020 Research and Innovation Programme [Grant 847776, 964874, the Marie Skłodowska-Curie Actions Grant 801133], and part of the convergence environment (MultiModal Mental Models [4MENT]) at the University of Oslo (UiO) Life Science. The authors have also received internationalization support from UiO:Life Science.We would like to thank the research participants for each GWAS. We additionally thank the staff at 23andMe, Inc and the Psychiatric Genomics Consortium for making this work possible and providing summary statistics. This work was performed on resources provided by Sigma2 (the National Infrastructure for High-Performance Computing and Data Storage in Norway) and the TSD (Tjeneste for Sensitive Data) facilities.

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Authors and Affiliations

  1. NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Nadine Parker, Weiqiu Cheng, Guy F. L. Hindley, Pravesh Parekh, Alexey A. Shadrin, Olav B. Smeland, Lars T. Westlye, Oleksandr Frei & Ole A. Andreassen

  2. Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King’s College London, London, UK

    Guy F. L. Hindley

  3. KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway

    Alexey A. Shadrin

  4. Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway

    Ivan I. Maximov

  5. Department of Medical Genetics, Oslo University Hospital, Oslo, Norway

    Srdjan Djurovic

  6. NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway

    Srdjan Djurovic

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

    Anders M. Dale

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

    Anders M. Dale

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

    Anders M. Dale

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

    Anders M. Dale

  11. Department of Psychology, University of Oslo, Oslo, Norway

    Lars T. Westlye

  12. K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway

    Lars T. Westlye

  13. Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway

    Oleksandr Frei

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  1. Nadine Parker
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Contributions

NP and OAA conceived the study. NP conducted analyses and wrote the initial draft of the manuscript. NP, WC, GFLH, PP, OF, and OAA were involved in study design and provided analytical input. All authors contributed to data interpretation and editing of the manuscript.

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Correspondence to Nadine Parker or Ole A. Andreassen.

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Competing interests

OAA reported personal fees from Lundbeck, Janssen, Sunovion (speaker’s honorarium), Biogen (consultant) outside the submitted work and is a consultant to Cortechs.ai (stock options). AMD is a founder of and holds equity interest in Cortechs.ai and serves on its scientific advisory board. He also receives research funding from General Electric Healthcare (GEHC).

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Parker, N., Cheng, W., Hindley, G.F.L. et al. Psychiatric disorders and brain white matter exhibit genetic overlap implicating developmental and neural cell biology. Mol Psychiatry 28, 4924–4932 (2023). https://doi.org/10.1038/s41380-023-02264-z

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