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Whole exome sequencing identified six novel genes for depressive symptoms

Molecular Psychiatry volume 30, pages 1925–1936 (2025)Cite this article

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Abstract

Previous genome-wide association studies of depression have primarily focused on common variants, limiting our comprehensive understanding of the genetic architecture. In contrast, whole–exome sequencing can capture rare coding variants, helping to explore the phenotypic consequences of altering protein-coding genes. Here, we conducted a large-scale exome-wide association study on 296,199 participants from the UK Biobank, assessing their depressive symptom scores through the Patient Health Questionnaire-4. We identified 22 genes associated with depressive symptoms, including 6 newly discovered genes (TRIM27, UBD, SVOP, ADGRB2, IRF2BPL, and ANKRD12). Both ontology enrichment analysis and plasma proteomics association analysis consistently revealed that the identified genes were associated with immune responses. Furthermore, we identified associations between these genes and brain regions related to depression, such as anterior cingulate cortex and orbitofrontal cortex. Additionally, phenome-wide association analysis demonstrated that TRIM27 and UBD were associated with neuropsychiatric, cognitive, biochemistry, and inflammatory traits. Our findings offer new insights into the potential mechanisms and genetic architecture of depressive symptoms.

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Fig. 1: Design of the study.
Fig. 2: Exome-wide tests for depressive symptoms.
Fig. 3: Biological functions of genes associated with depressive symptoms.
Fig. 4: Associations of identified genes with neuroimaging.
Fig. 5: Associations of identified genes with proteomics.
Fig. 6: Phenome-wide association analysis.

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Data availability

The main data used in this study, including individual-level phenotypic and genetic data, were accessed from UK Biobank under application number 19,542 and were available through UK Biobank (https://www.ukbiobank.ac.uk/) by application. GWAS summary results of depressive symptoms were obtained from the previous study (https://doi.org/10.1038/ng.3552). GWAS summary results of depression in FinnGen were obtained through https://r11.finngen.fi/. The single-cell sequencing data of human brain were obtained from GEO database (GSE173731).

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/). Analysis and visualization of single-cell RNA sequencing data was performed using Seurat v.4.3.0 (https://github.com/satijalab/seurat/). Ontology enrichment analysis was performed using Enrichr (https://maayanlab.cloud/Enrichr/). Tissue expression enrichment analysis was performed using FUMA v.1.5.6 (https://fuma.ctglab.nl/).

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Acknowledgements

This study used the UK Biobank Resource under application number 19542. We gratefully thank all UK Biobank participants for their time and UK Biobank team members for collating the data. We gratefully thank the participants and investigators of the FinnGen study. This work was supported by grants from the National Natural Sciences Foundation of China (82472055, 82071997) [to WC], the National Key Research and Development Program of China (2023YFC3605400) [to WC], and the Shanghai Rising-Star Program (21QA1408700) [to WC]; the Science and Technology Innovation 2030 Major Projects (2022ZD0211600) [to JTY], National Natural Science Foundation of China (82071201, 81971032, 92249305) [to JTY], Shanghai Municipal Science and Technology Major Project (2018SHZDZX01) [to JTY], Research Start-up Fund of Huashan Hospital (2022QD002) [to JTY], Excellence 2025 Talent Cultivation Program at Fudan University (3030277001) [to JTY], Shanghai Talent Development Funding for The Project (2019074) [to JTY], and 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; National Key R&D Program of China (2018YFC1312904, 2019YFA0709502) [to JFF], the Shanghai Municipal Science and Technology Major Project (2018SHZDZX01) [to JFF], and the 111 Project (B18015) [to JFF], Shanghai Center for Brain Science and Brain-Inspired Technology and Zhangjiang Lab.

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  1. These authors contributed equally: Ze-Yu Li, Chen-Jie Fei, Rui-Ying Yin, Ju-Jiao Kang

Authors and Affiliations

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

    Ze-Yu Li, Chen-Jie Fei, Rui-Ying Yin, Ju-Jiao Kang, Qing Ma, Xiao-Yu He, Xin-Rui Wu, Yu-Jie Zhao, Wei Zhang, Wei-Shi Liu, Bang-Sheng Wu, Liu Yang, Jian-Feng Feng, Jin-Tai Yu & Wei Cheng

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

    Ze-Yu Li, Rui-Ying Yin, Ju-Jiao Kang, Qing Ma, Yu-Jie Zhao, Wei Zhang, Jian-Feng Feng & Wei Cheng

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

    Ying Zhu

  4. Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK

    Jian-Feng Feng

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

    Jian-Feng Feng & Wei Cheng

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  9. Wei Zhang
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  12. Liu Yang
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  15. Jin-Tai Yu
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  16. Wei Cheng
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Contributions

WC and JTY designed the study. ZYL, CJF, RYY, and JJK conducted main analyses. ZYL and CJF drafted the manuscript. YJZ, BSW, and LY contributed to the data collection. QM, XYH, XRW, WZ, and WSL contributed to the data analyses. WC, JTY, JFF, and YZ critically revised the manuscript. All authors reviewed and approved the final version.

Corresponding authors

Correspondence to Jin-Tai Yu or Wei Cheng.

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Li, ZY., Fei, CJ., Yin, RY. et al. Whole exome sequencing identified six novel genes for depressive symptoms. Mol Psychiatry 30, 1925–1936 (2025). https://doi.org/10.1038/s41380-024-02804-1

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