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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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/).
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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.
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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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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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DOI: https://doi.org/10.1038/s41562-023-01630-9
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