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Exome sequencing identifies genes associated with sleep-related traits

Nature Human Behaviour volume 8, pages 576–589 (2024)Cite this article

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Abstract

Sleep is vital for human health and has a moderate heritability. Previous genome-wide association studies have limitations in capturing the role of rare genetic variants in sleep-related traits. Here we conducted a large-scale exome-wide association study of eight sleep-related traits (sleep duration, insomnia symptoms, chronotype, daytime sleepiness, daytime napping, ease of getting up in the morning, snoring and sleep apnoea) among 450,000 participants from UK Biobank. We identified 22 new genes associated with chronotype (ADGRL4, COL6A3, CLK4 and KRTAP3-3), daytime sleepiness (ST3GAL1 and ANKRD12), daytime napping (PLEKHM1, ANKRD12 and ZBTB21), snoring (WDR59) and sleep apnoea (13 genes). Notably, 20 of these genes were confirmed to be significantly associated with sleep disorders in the FinnGen cohort. Enrichment analysis revealed that these discovered genes were enriched in circadian rhythm and central nervous system neurons. Phenotypic association analysis showed that ANKRD12 was associated with cognition and inflammatory traits. Our results demonstrate the value of large-scale whole-exome analysis in understanding the genetic architecture of sleep-related traits and potential biological mechanisms.

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Fig. 1: Design of the study.
Fig. 2: Exome-wide single-variant tests for sleep-related traits.
Fig. 3: Exome-wide gene-based tests for sleep-related traits.
Fig. 4: Burden heritability of sleep-related traits.
Fig. 5: Biological functions of genes associated with sleep-related traits.
Fig. 6: Phenome-wide association of sleep-related genes.

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Data availability

The main data, including the individual-level phenotypic and genetic data used in this study, were accessed from UK Biobank under application number 19542 and are available through UK Biobank (https://www.ukbiobank.ac.uk/). Summary GWAS statistics of FinnGen were obtained through https://r9.finngen.fi/. The single-cell sequencing data from the human brain were obtained from the Gene Expression Omnibus database (GSE173731). The single-cell sequencing data from the mouse central nervous system were obtained from the Mouse Brain Atlas (http://mousebrain.org/adolescent/).

Code availability

The code used for single-variant and gene-based analysis is an adaptation of the R package SAIGE-GENE+ v.1.1.6.2 (https://github.com/saigegit/SAIGE/). Quality control of individual-level data was performed using Hail v.0.2 (https://hail.is) and PLINK v.2.0 (https://www.cog-genomics.org/plink/2.0/). Variant annotation was performed using SnpEff v.5.1 (https://pcingola.github.io/SnpEff/). Burden heritability estimation was performed using BHR v.0.1.0 (https://github.com/ajaynadig/bhr/). The analysis and visualization of scRNA-seq data were performed using Seurat v.4.3.0 (https://github.com/satijalab/seurat/). GO enrichment analysis was performed using clusterProfiler v.4.2.2 (https://github.com/YuLab-SMU/clusterProfiler/). Tissue expression enrichment analysis was performed using FUMA v.1.5.6 (https://fuma.ctglab.nl/). Custom scripts for the analyses in this paper are available at https://github.com/cjfei18/sleep_wes.

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Acknowledgements

We thank all UK Biobank participants for their time and UK Biobank team members for collating the data. We thank the participants and investigators of the FinnGen study. Z.-L.H. was supported by grants from the Science and Technology Innovation 2030 Major Projects (no. 2021ZD0203400). J.-T.Y. was supported by grants from the Science and Technology Innovation 2030 Major Projects (no. 2022ZD0211600), the National Natural Science Foundation of China (nos 82071201, 81971032 and 92249305), the Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01), the Research Start-Up Fund of Huashan Hospital (no. 2022QD002), the Excellence 2025 Talent Cultivation Program at Fudan University (no. 3030277001), Shanghai Talent Development Funding for the Project (no. 2019074), and the Zhangjiang Lab, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University. W.C. was supported by grants from the National Natural Science Foundation of China (no. 82071997) and the Shanghai Rising-Star Program (no. 21QA1408700). J.-F.F. was supported by the National Key R&D Program of China (nos 2018YFC1312904 and 2019YFA0709502), the Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01), the 111 Project (no. B18015), the Shanghai Center for Brain Science and Brain-Inspired Technology and the Zhangjiang Lab. 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: Chen-Jie Fei, Ze-Yu Li, Jing Ning, Liu Yang.

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

    Chen-Jie Fei, Jing Ning, Liu Yang, Bang-Sheng Wu, Wei-Shi Liu, Xiao-Yu He, Shi-Dong Chen, Huan Yu, Jin-Tai Yu & Wei Cheng

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

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

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

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

  4. Department of Pharmacology, School of Basic Medical Sciences, State Key Laboratory of Medical Neurobiology, Institutes of Brain Science and Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China

    Zhi-Li Huang

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

    Jian-Feng Feng

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Contributions

W.C. and J.-T.Y. designed the study. C.-J.F., Z.-Y.L., J.N. and L.Y. conducted the main analysis. C.-J.F., Z.-Y.L., J.N., L.Y., J.Y. and S.-D.C. drafted the manuscript. B.-S.W., J.-J.K., W.-S.L. and X.-Y.H. contributed to the data collection and analysis. W.C., J.-T.Y., J.-F.F., Z.-L.H. and H.Y. critically revised the manuscript. All authors reviewed and approved the final version.

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Correspondence to Jin-Tai Yu or Wei Cheng.

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Fei, CJ., Li, ZY., Ning, J. et al. Exome sequencing identifies genes associated with sleep-related traits. Nat Hum Behav 8, 576–589 (2024). https://doi.org/10.1038/s41562-023-01785-5

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