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Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank

Nature Mental Health volume 2, pages 960–974 (2024)Cite this article

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A Publisher Correction to this article was published on 09 July 2024

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Abstract

Mental health conditions are characterized by higher-order transdiagnostic factor structures, which may contribute to the high levels of comorbidity observed in psychopathology. However, the phenotypic and genetic structures of various psychopathology diagnoses may differ, raising questions about the validity and utility of these factors. Here we study the phenotypic and genetic factor structures of ten psychiatric conditions using UK Biobank and public genomic data. Although the factor structure of psychopathology was generally genetically and phenotypically consistent, conditions related to externalizing (for example, alcohol use disorder) and compulsivity (for example, eating disorders) exhibited cross-level disparities in their relationships with other conditions, possibly due to environmental influences. Domain-level factors, especially thought disorder and internalizing factors, were more informative than a general psychopathology factor in genome-wide association and polygenic index analyses. Collectively, our findings enhance the understanding of comorbidity and shared etiology, highlight the intricate interplay between genes and environment, and offer guidance for psychiatric research using polygenic indices.

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Fig. 1: Overview of the data and models tested in the present study.
Fig. 2: Comparison of phenotypic and genetic correlations among ten psychiatric conditions.
Fig. 3: Cross-level comparison of the phenotypic and genetic factor structure of psychopathology.
Fig. 4: Comparison of p-factor effects relative to domain-level factor effects.

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Data availability

No new data were gathered for this study; instead, data from pre-existing studies or cohorts were utilized. The essential dataset needed to understand, replicate, and expand on the findings, specifically the GWAS summary statistics for the psychopathology GWAS, are available at an Open Science Framework repository (https://osf.io/unkym/). The UK Biobank and the Mass General Brigham Biobank have access restrictions in place to safeguard participant privacy. The UK Biobank data are accessible to researchers with an ongoing application in the UK Biobank (https://www.ukbiobank.ac.uk/). This study was conducted on the basis of the UK Biobank application 46007. We used reference data from the 1000 Genomes phase 3 (version 5) (https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.html) and HapMap 3 (revision 2) (https://mathgen.stats.ox.ac.uk/impute/data_download_hapmap3_r2.html). Publicly available GWAS summary statistics included in the meta-analyses include alcohol use disorder (https://pubmed.ncbi.nlm.nih.gov/29058377/), anorexia (https://doi.org/10.1176/appi.ajp.2017.16121402), generalized anxiety (https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2733149), bipolar disorder (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956732/), depression (https://www.nature.com/articles/s41588-018-0090-3), suicide attempt (https://pubmed.ncbi.nlm.nih.gov/30116032/), obsessive–compulsive disorder (https://pubmed.ncbi.nlm.nih.gov/28761083/), panic disorder (https://www.nature.com/articles/s41380-019-0590-2), post-traumatic stress disorder (https://pubmed.ncbi.nlm.nih.gov/28439101/), and schizophrenia (https://pubmed.ncbi.nlm.nih.gov/28439101/). Cortical maps were obtained from the neuromaps toolbox (https://www.nature.com/articles/s41592-022-01625-w). Gene expression datasets from GTEx8 and Brainspan are available through FUMA (https://fuma.ctglab.nl/).

Code availability

The code for the present study is available on OSF (https://osf.io/unkym/). This wraps fastGWA-GLMM from the GCTA tool (version 1.94.1) (https://yanglab.westlake.edu.cn/software/gcta/#fastGWA-GLMM), Genomic SEM (version 0.0.5) (https://github.com/GenomicSEM/GenomicSEM), METAL (version 2020-05-05) (https://genome.sph.umich.edu/wiki/METAL_Documentation), PRS-CS (version October 20, 2019) (https://github.com/getian107/PRScs), sBayesR (version 2.05) (https://cnsgenomics.com/software/gctb/#Overview), plink package (version 1.5.1) (https://www.cog-genomics.org/plink/1.9/rserve), and R (version 4.1.0) (https://cran.rstudio.com/). Bioannotation analyses were conducted using FUMA (version 1.3.5e) (https://fuma.ctglab.nl/).

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Acknowledgments

We thank the UK Biobank staff, researchers, and volunteers. Funding: ANR-17-EURE-0017 (F.R.), ANR-10-IDEX-0001-02 PSL (F.R.), NIH T32HG010464 (T.T.M.), and NIH K08MH135343 (T.T.M.).

Author information

Author notes

  1. These authors contributed equally: Travis T. Mallard, Franck Ramus.

Authors and Affiliations

  1. Laboratoire de Sciences Cognitives et Psycholinguistique, Département d′Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France

    Camille M. Williams, Hugo Peyre & Franck Ramus

  2. Population Research Center, The University of Texas at Austin, Austin, TX, USA

    Camille M. Williams

  3. Department of Psychology, University of Texas at Austin, Austin, TX, USA

    Camille M. Williams

  4. Centre de Ressources Autisme Languedoc-Roussillon et Centre d’Excellence sur l’Autisme et les Troubles Neuro-développementaux, CHU Montpellier, Montpellier, France

    Hugo Peyre

  5. TeamDevPsy, Inserm, CESP, University Paris–Saclay, UVSQ, Villejuif, France

    Hugo Peyre

  6. Faculty of Sociology, Bielefeld University, Bielefeld, Germany

    Tobias Wolfram

  7. Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Younga H. Lee, Tian Ge, Jordan W. Smoller & Travis T. Mallard

  8. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Younga H. Lee, Tian Ge, Jordan W. Smoller & Travis T. Mallard

  9. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA

    Younga H. Lee, Tian Ge, Jordan W. Smoller & Travis T. Mallard

  10. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA

    Jakob Seidlitz

  11. Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Jakob Seidlitz

  12. Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA

    Jakob Seidlitz

  13. Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA

    Jakob Seidlitz

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  1. Camille M. Williams
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C.M.W., H.P., F.R., and T.T.M. designed the study. T.G. conducted the polygenic score analyses with PRS-CS. Y.H.L. conducted the phenome-wide association analyses. J.S. conducted the correlational analyses of cortical maps. C.M.W. conducted all other analyses. T.W. and T.T.M. provided statistical guidance. C.M.W., J.W.S., F.R., T.G., and T.T.M. guided interpretation of key findings. All named authors reviewed, edited, and approved the submission.

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Correspondence to Camille M. Williams.

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Nature Mental Health thanks Wei Cheng, Jeggan Tiego, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–34 and Sections 1–14.

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Supplementary Data 1

Supplementary Tables 1–17.

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Williams, C.M., Peyre, H., Wolfram, T. et al. Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank. Nat. Mental Health 2, 960–974 (2024). https://doi.org/10.1038/s44220-024-00272-8

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