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Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy

Nature Human Behaviour volume 7, pages 1584–1600 (2023)Cite this article

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Abstract

Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the ‘pleiotropy-informed’ multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association studies of the Big 5 personality traits and cognitive function on our multivariate findings, boosting genetic discovery in other personality traits and improving polygenic prediction. These findings advance our understanding of the polygenic architecture of these complex mental traits, indicating a prominence of pleiotropic genetic effects across higher order domains of mental function such as personality and cognitive function.

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Fig. 1: Conceptual overview of study design and main findings.
Fig. 2: Heatmap of genetic and phenotypic correlations across mental traits.
Fig. 3: Boosting the signal of genetic association for 35 mental traits by leveraging pleiotropy.
Fig. 4: Patterns of pleiotropic genetic associations at the SNP-level.
Fig. 5: MAGMA tissue-specific gene expression and gene-set enrichments.
Fig. 6: Leveraging multivariate analysis to boost discovery and polygenic prediction of personality and cognitive function.

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Data availability

Summary statistics for our primary MOSTest analysis are publicly available on the GWAS catalog at the following link: https://www.ebi.ac.uk/gwas/studies/GCST90270074. Individual-level UKB data are available through a publicly accessible application via UKB (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Please visit https://research.23andme.com/collaborate/#dataset-access/ for more information and to apply to access the data. CHARGE general cognitive function summary statistics are available on request to the chair of the NeuroChARGE Cognitive Working Group (email: jan.bressler@uth.tmc.edu). UKB, 23andMe and CHARGE datasets are not freely available as there are specific ethical requirements associated with the use of these data. Tissue specificity was determined using GTEx v.7 eQTL database, which is publicly available at https://www.gtexportal.org/home/datasets. Gene-set analysis was based on curated gene sets from MsigDB, publicly available at https://www.gsea-msigdb.org/gsea/msigdb/.

Code availability

We performed hierarchical clustering on pairwise genetic correlations using AgglomerativeClustering algorithm with distance function 1 − |rg|, as implemented in sklearn Python package v.1.1.2. Phenotypic correlations were computed using Spearman rank correlation as implemented in the Python package SciPy. Plink2 was applied to perform item-level genotype–phenotype association (https://www.cog-genomics.org/plink/2.0/). Hierarchical clustering of univariate GWAS z-scores was produced using AgglomerativeClustering algorithm with Euclidian distance, as implemented in sklearn Python package v.1.1.2. Gene mapping, gene-set and tissue specificity analyses were performed using MAGMA v.1.09b (https://fuma.ctglab.nl/). PGSs were constructed using PRSice (https://choishingwan.github.io/PRSice/). Miami plots, circos plots and circular manhattan plots were generated using the matplotlib package v.3.6.1 in Python3. The cFDR and PGS plots were generated using the ggplot2 package in R v.4.1.2 as implemented in rstudio v.2021.09.2. All analyses run in Python were implemented in Python v.3.10.6. Code for MOSTest, MiXeR v.1.3, cFDR, PRSice, MTAG and PRS-CS are publicly available at https://github.com/precimed/mostest/tree/mental, https://github.com/precimed/pleiofdr, https://github.com/choishingwan/PRSice, https://github.com/JonJala/mtag and https://github.com/getian107/PRScs, respectively.

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Acknowledgements

We thank the research participants, employees and researchers of the UK Biobank, 23andMe, CHARGE and TOP for making this research possible. This work was partly performed on the TSD (Services 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. Computations were also performed on resources provided by UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway. We gratefully acknowledge support from the American National Institutes of Health (NS057198 (O.A.A.), EB000790 (O.A.A.), 1R01MH124839 (K.O. and O.A.A.)), the Research Council of Norway (324499 (O.A.A.), 324252 (O.A.A.), 300309 (G.H. and N.P.), 273291 (O.A.A.), 223273 (O.A.A.), 248980 (O.A.A.)), the South-East Norway Regional Health Authority (2019-108 (O.A.A.), 2022-073 (O.A.A.)), European Economic Area and Norway grants (no. EEA-RO-NO-2018-0573 (A.A.S.), KG Jebsen Stiftelsen (SKGJ-MED-021 (O.A.A.) and the University of Oslo (B.H.)). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement nos 847776 (O.A.A.) and 964874 (O.A.A.) and 801133 (W.C., N.P.) (Marie Sklodowska-Curie grant agreement). W.D.H. is supported by a Career Development Award from the Medical Research Council (MR/T030852/1). Infrastructure for the CHARGE Consortium is supported in part by the National Heart, Lung, and Blood Institute grant R01HL105756 (J.B., S.S.) and for the NeuroCHARGE Cognitive Working Group is supported in part through the National Institute on Aging grant R01AG033193 (J.B. and S.S.). I.J.D. and G.D. are with the Lothian Birth Cohorts group, which is supported by the Biotechnology and Biological Sciences Research Council and the Economic and Social Research Council (BB/W008793/1), Age UK (Disconnected Mind project), the Medical Research Council (MR/R024065/1) and the University of Edinburgh. The background vectors used in Fig. 1 were created by freepik www.freepik.com and smart.servier.com. Freepik images were made by storyset and Freepik on Freepik. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/).

Author information

Author notes

  1. These authors contributed equally: Guy Hindley, Alexey A. Shadrin.

Authors and Affiliations

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

    Guy Hindley, Alexey A. Shadrin, Dennis van der Meer, Nadine Parker, Weiqiu Cheng, Kevin S. O’Connell, Shahram Bahrami, Aihua Lin, Naz Karadag, Børge Holen, Thomas Bjella, Torill Ueland, Olav B. Smeland, Oleksandr Frei & Ole A. Andreassen

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

    Guy Hindley

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

    Alexey A. Shadrin, Srdjan Djurovic & Ole A. Andreassen

  4. School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands

    Dennis van der Meer

  5. Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

    Thomas Bjella

  6. Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK

    Ian J. Deary, Gail Davies & W. David Hill

  7. Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

    Jan Bressler

  8. Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA

    Sudha Seshadri

  9. Framingham Heart Study, Framingham, MA, USA

    Sudha Seshadri

  10. Department of Neurology, Boston University School of Medicine, Boston, MA, USA

    Sudha Seshadri

  11. Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA, USA

    Chun Chieh Fan & Anders M. Dale

  12. Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA

    Chun Chieh Fan

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

    Torill Ueland

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

    Srdjan Djurovic

  15. NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway

    Srdjan Djurovic

  16. Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway

    Oleksandr Frei

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

    Anders M. Dale

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

    Anders M. Dale

  19. Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA

    Anders M. Dale

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

    Anders M. Dale

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  1. Guy Hindley
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  23. Ole A. Andreassen
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G.H., A.A.S., D.v.d.M., A.M.D. and O.A.A. conceived this work. A.A.S, D.v.d.M, O.F. and A.M.D. developed the methodology. A.A.S., G.H., N.P. and W.C. conducted the formal analysis. T.B., I.J.D., G.D., W.D.H., J.B., S.S., T.U., S.D., O.B.S., O.F. and O.A.A. obtained resources. A.A.S., D.v.d.M., T.B., O.F. and O.B.S. were responsible for data curation. G.H. and A.A.S. wrote the orginal draft and G.H., A.A.S., D.v.d.M., N.P., W.C., K.S.O, S.B., A.L., N.K., B.H., T.B., I.J.D., G.D., W.D.H., J.B., S.S., C.C.F., T.U., S.D., O.B.S., O.F., A.M.D. and O.A.A. were involved in reviewing and editing. G.H. and A.A.S. developed the visualizations. A.M.D. and O.A.A. undertook supervision. O.A.A. was responsible for project administration. A.M.D. and O.A.A. acquired funding. The funders had no role in the conceptualization, design, data collection, analysis, decision to publish or preparation of manuscript.

Corresponding authors

Correspondence to Guy Hindley, Alexey A. Shadrin or Ole A. Andreassen.

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Competing interests

O.A.A. has received a speaker’s honorarium from Lundbeck and is a consultant for Healthlytix. A.M.D. is a founder of and holds equity interest in CorTechs Labs and serves on its scientific advisory board. He is also a member of the Scientific Advisory Board of Healthlytix and receives research funding from General Electric Healthcare (GEHC). The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. I.J.D. is a participant in UKB. S.S. has consulted for Biogen. The remaining authors declare no competing interests.

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Nature Human Behaviour thanks Zachary Gerring, Miguel Renteria and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary information

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Supplementary Methods, Results and Figs. 1–21.

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Supplementary Tables 1–25.

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Hindley, G., Shadrin, A.A., van der Meer, D. et al. Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy. Nat Hum Behav 7, 1584–1600 (2023). https://doi.org/10.1038/s41562-023-01630-9

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