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Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults

Nature Human Behaviour (2024)Cite this article

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Abstract

While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene–phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.

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Fig. 1: Outline of the study.
Fig. 2: Results of the exome-wide rare-variant association analyses of neuropsychiatric diseases and traits.
Fig. 3: Results of the exome-wide common-variant association analyses of neuropsychiatric diseases and traits.
Fig. 4: Genetic overlap among neuropsychiatric diseases and traits.
Fig. 5: Clinical insights on significant genes revealed by Cox proportional hazard regression analyses.
Fig. 6: Biological annotation of the identified neuropsychiatric genes.
Fig. 7: Biological insights from multi-omics analysis.

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Data availability

The main data used in this study were accessed from the UKB (https://biobank.ndph.ox.ac.uk/) under application number 19542. Summary results of the main analyses have been made accessible through https://doi.org/10.6084/m9.figshare.25342486.v1 (ref. 70) and https://doi.org/10.6084/m9.figshare.25342201.v1 (ref. 71). The snRNA-seq data were obtained from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE173731.

Code availability

All software and R packages used to perform the analyses in this work are freely available online: SAIGE-GENE+ v.1.1.6.2, https://github.com/saigegit/SAIGE; PLINK v.2.0, https://www.cog-genomics.org/plink/2.0/; SnpEff v.5.l, https://pcingola.github.io/SnpEff/se_introduction/; ANNOVAR, https://annovar.openbioinformatics.org/en/latest/; BHR v.0.1.0, https://github.com/ajaynadig/bhr; LDSC v.1.0.1, https://github.com/bulik/ldsc/; FUMA platform, http://fuma.ctglab.nl/; SynGO, https://www.syngoportal.org/. The scripts used to conduct the main analyses are available at https://github.com/wubsfudaner/neuropsychiatric-diseases-and-traits-wes.

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Acknowledgements

We thank all the participants and researchers from the UKB. We also thank the SynGO Consortium. This study was supported by grants from the STI2030-Major Projects (no. 2022ZD0211600 to J.-T.Y.), the National Natural Science Foundation of China (nos 82071201, 82271471 and 92249305 to J.-T.Y.; no. 82071997 to W.C.), the Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01 to J.-F.F.), the Research Start-up Fund of Huashan Hospital (no. 2022QD002 to J.-T.Y.), the Excellence 2025 Talent Cultivation Program at Fudan University (no. 3030277001 to J.-T.Y.), Shanghai Talent Development Funding for the Project (no. 2019074 to J.-T.Y.), the Shanghai Rising-Star Program (no. 21QA1408700 to W.C.), the 111 Project (no. B18015 to J.-F.F.), the National Key Research and Development Program of China (no. 2023YFC3605400 to W.C.), the Postdoctoral Innovation Talents Support Program (no. BX20230087 to S.-D.C.; no. BX20230089 to Y.-R.Z.) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, and the Shanghai Center for Brain Science and Brain-Inspired Technology, Fudan University. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. These authors contributed equally: Yue-Ting Deng, Bang-Sheng Wu, Liu Yang, Xiao-Yu He.

Authors and Affiliations

  1. Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China

    Yue-Ting Deng, Bang-Sheng Wu, Liu Yang, Xiao-Yu He, Wei-Shi Liu, Xin-Rui Wu, Ya-Ru Zhang, Shi-Dong Chen, Yi-Jun Ge, Yu-Yuan Huang, Qiang Dong, Wei Cheng & Jin-Tai Yu

  2. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Ju-Jiao Kang, Ze-Yu Li, Jian-Feng Feng & Wei Cheng

  3. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China

    Ju-Jiao Kang, Ze-Yu Li, Jian-Feng Feng & Wei Cheng

  4. Department of Computer Science, University of Warwick, Coventry, UK

    Jian-Feng Feng

  5. Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Ying Zhu

  6. Department of Neurosurgery, Huashan Hospital Fudan University, Shanghai, China

    Ying Mao

  7. Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China

    Wei Cheng

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Contributions

All authors had full access to the data in the study and accepted responsibility to submit it for publication. J.-T.Y. designed the study. Y.-T.D. and B.-S.W. conducted the primary analyses and drafted the manuscript. L.Y., J.-J.K., W.-S.L., Z.-Y.L., X.-R.W. and X.-Y.H. contributed to the biological annotation analyses and brain structures, proteomics and inflammatory marker association analyses. J.-T.Y., W.C., Y.Z., J.-F.F., Y.M., Q.D., Y.-R.Z., S.-D.C., Y.-J.G. and Y.-Y.H. critically revised the manuscript, and all authors approved the final version.

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Correspondence to Wei Cheng or Jin-Tai Yu.

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Extended data

Extended Data Fig. 1 Burden heritability of neuropsychiatric diseases and traits.

a. The burden heritability of neuropsychiatric diseases calculated by burden heritability regression (Methods). The x-axis indicates the specific neuropsychiatric disease and the y-axis indicates the heritability based on rare variants. b. The burden heritability of neuropsychiatric traits calculated by burden heritability regression (Methods). The x-axis indicates the specific neuropsychiatric disease and the y-axis indicates the heritability based on rare variants. c. The distribution of heritability based on different annotation groups in neuropsychiatric diseases (n = 46). Each color represents one annotation group as indicated in the plot legend. Horizontal bars indicate median value and rhombuses indicate mean value of the burden heritability. The lower and upper hinges represent the 25th and 75th percentiles. The upper whisker extends from the hinge to the largest value no further than 1.5 times the inter-quartile range (IQR) from the hinge. The lower whisker extends from the hinge to the smallest value at most 1.5*IQR of the hinge. The data beyond the end of the whisker is plotted separately, in which case the minimum and maximum values are the bottom and top points respectively, while if there are no separate points drawn, the minimum and maximum values are the end of the lower and upper whisker respectively. The sample sizes for each disease were provided in Supplementary Table 1,c. d. The distribution of heritability based on different annotation groups in neuropsychiatric traits (n = 23). Each color represents one annotation group as indicated in the plot legend. Horizontal bars, hinges and whiskers indicate same type of statistics as c. The sample sizes for each disease were provided in Supplementary Table 1,d.

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Deng, YT., Wu, BS., Yang, L. et al. Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01861-4

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