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Cross-ancestry genome-wide association study and systems-level integrative analyses implicate new risk genes and therapeutic targets for depression

Nature Human Behaviour volume 9, pages 806–823 (2025)Cite this article

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Abstract

Deciphering the genetic architecture of depression is pivotal for characterizing the associated pathophysiological processes and development of new therapeutics. Here we conducted a cross-ancestry genome-wide meta-analysis on depression (416,437 cases and 1,308,758 controls) and identified 287 risk loci, of which 49 are new. Variant-level fine mapping prioritized potential causal variants and functional genomic analysis identified variants that regulate the binding of transcription factors. We validated that 80% of the identified functional variants are regulatory variants, and expression quantitative trait loci analysis uncovered the potential target genes regulated by the prioritized risk variants. Gene-level analysis, including transcriptome and proteome-wide association studies, colocalization and Mendelian randomization-based analyses, prioritized potential causal genes and drug targets. Gene prioritization analyses highlighted likely causal genes, including TMEM106B, CTNND1, AREL1 and so on. Pathway analysis indicated significant enrichment of depression risk genes in synapse-related pathways. Finally, knockdown of Tmem106b in mice resulted in depression-like behaviours, supporting the involvement of Tmem106b in depression. Our study identified new risk loci, likely causal variants and genes for depression, providing important insights into the genetic architecture of depression and potential therapeutic targets.

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Fig. 1: Manhattan plot of the GWAS meta-analysis.
Fig. 2: Novel risk loci identified in the cross-ancestry GWAS meta-analysis.
Fig. 3: SNPs that affect the binding of TFs.
Fig. 4: Reporter gene assays validated the regulatory effect of the identified TF binding-affecting SNPs.
Fig. 5: Transcriptome-wide and proteome-wide association results.
Fig. 6: Drug target gene MR analysis.
Fig. 7: Gene prioritization results.
Fig. 8: Tmem106b downregulation in ventral hippocampus causes anxious and depressive behaviours in mice.

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Data availability

Genome-wide summary statistics of MVP were obtained from dbGaP via application (accession no. phs001672.v1.p.1), summary statistics of FinnGen (publicly available) were downloaded from FinnGen website (https://www.finngen.fi/en), summary statistics of Howard et al. (UKB + PGC) were publicly available and downloaded from https://doi.org/10.7488/ds/2458, summary statistics of the AGDS were from B.L.M., summary statistics of Giannakopoulou et al. were downloaded from PGC website (https://pgc.unc.edu/) and summary statistics of Sakaue et al. were publicly available and downloaded from BioBank Japan (BBJ) (https://pheweb.jp/downloads). The genome-wide summary statistics of 23andMe were obtained under a data transfer agreement. The genome-wide summary statistics (not including 23andMe and AGDS) will be made publicly available (at https://doi.org/10.6084/m9.figshare.24521968.v1 (ref. 97)) once the article has been published. 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. GWAS summary statistics for the AGDS dataset will be available upon reasonable request (please contact B.L.M. at Brittany.mitchell@qimrberghofer). The SNP–expression weights of PsychENCODE used in this study were downloaded from http://resource.psychencode.org/. The processed protein weight files were downloaded from https://www.synapse.org/ (Synapse ID: syn9884314 and syn23245237). The PsychENCODE cis-eQTL data were downloaded from the SMR website (https://yanglab.westlake.edu.cn/data/SMR/PsychENCODE_cis_eqtl_HCP100_summary.tar.gz). The gene expression data used for MAGMA were from the GTEx consortium, GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9. All GO terms (including cellular components, biological processes and molecular functions) and Kyoto Encyclopedia of Genes and Genomes pathway gene sets were downloaded from the MSigDB database (https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp#C5, v2022.1.Hs). The gene list and feature files were downloaded from the PoPS GitHub page (https://github.com/FinucaneLab/pops) to calculate the PoPS score of all candidate genes. Source data are provided with this paper.

Code availability

GWAS meta-analysis was used an inverse-variance-based fixed-effects meta-analysis implemented in PLINK (v1.90, https://www.cog-genomics.org/plink/). FUMA v1.3.7 (https://fuma.ctglab.nl/home) was used to define the risk loci, with the default parameters. LDSC (https://github.com/bulik/ldsc) was used to estimate the SNP-based heritability and pairwise genetic correlations between the GWASs. FIMO was used to compare the derived binding motifs with the publicly available PWM and search the best-matched motifs. MESuSiE (https://github.com/borangao/meSuSie) was used for statistical fine mapping. The TwoSampleMR R package was used to perform two-sample MR analysis (v0.5.6, https://mrcieu.github.io/TwoSampleMR/). Other custom codes is available via Zenodo at https://doi.org/10.5281/zenodo.13856052 (ref. 98).

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Acknowledgements

This study was equally supported by the startup funds from Southeast University (RF1028623032 to X.-J.L.) and the Special Project for Social Development of Yunnan Province (nos. 202203AC100007 and 202305AH340006). This study was also supported by the National Natural Science Foundation of China (nos. U2102205, 82271570, 82260276, 82301690, 82130042, 82471552, 82230046 and 81920108018), the Talent and Fundamental Research Project of Yunnan Province (nos. 202301AY070001-299, 202105AC160004 and 202301AT070037), the China Science and Technology Innovation 2030 – Major Project (nos. 2022ZD0211701 and 2021ZD0200700), and the SEU Innovation Capability Enhancement Plan for Doctoral Students (CXJH_SEU 25233). We thank Q. Li and Z. Ding for their technical assistance.

Author information

Author notes

  1. These authors contributed equally: Yifan Li, Xinglun Dang, Rui Chen, Zhaowei Teng, Junyang Wang, Shiwu Li.

Authors and Affiliations

  1. Department of Psychiatry and Psychosomatics, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing, China

    Yifan Li, Xinglun Dang, Yingying Yue, Yonggui Yuan, Zhijun Zhang & Xiong-Jian Luo

  2. Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China

    Rui Chen, Shiwu Li, Yong-Gang Yao & Ming Li

  3. Key Laboratory of Neurological and Psychiatric Disease Research of Yunnan Province, The Second Affiliated Hospital of Kunming Medical University, Yunnan Provincial Department of Education Gut Microbiota Transplantation Engineering Research Center, Kunming, China

    Zhaowei Teng & Yong Zeng

  4. Department of Human Anatomy, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, China

    Junyang Wang

  5. Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Brittany L. Mitchell

  6. Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China

    Zhongchun Liu

  7. Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, China

    Zhongchun Liu

  8. Affiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Tao Li

  9. Department of Mental Health and Public Health, Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhijun Zhang

Authors
  1. Yifan Li
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  2. Xinglun Dang
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  3. Rui Chen
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  4. Zhaowei Teng
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  5. Junyang Wang
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  6. Shiwu Li
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  7. Yingying Yue
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  8. Brittany L. Mitchell
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  9. Yong Zeng
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  10. Yong-Gang Yao
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  11. Ming Li
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  12. Zhongchun Liu
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  13. Yonggui Yuan
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  14. Tao Li
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  15. Zhijun Zhang
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  16. Xiong-Jian Luo
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Contributions

X.-J.L. conceived, designed and supervised the whole study. X.D. performed all the analyses, including meta-analysis, TWAS, PWAS, colocalization, fine mapping, SMR and so on. Y.L., J.W. and S.L. conducted the behavioural tests. R.C. performed reporter gene assays. X.-J.L., Z.T., Y.Z., Y. Yue, B.L.M., Y.-G.Y., M.L., Z.L., Y. Yuan, T.L. and Z.Z. contributed to this work in study design, data interpretation, paper writing and revision. X.D. drafted the paper. X.-J.L. oversaw the project and finalized the paper. All authors revised the paper critically and approved the final version.

Corresponding authors

Correspondence to Yonggui Yuan, Tao Li, Zhijun Zhang or Xiong-Jian Luo.

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Competing interests

The authors declare no competing interests.

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Nature Human Behaviour thanks Jonathan Coleman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary methods and Figs. 1–18.

Reporting Summary

Supplementary Tables 1–23

Supplementary Tables 1–23.

Supplementary Data 1

Statistical source data for Supplementary Fig. 6.

Source data

Source Data Figs. 3, 4 and 8

Statistical source data for Figs. 3, 4 and 8.

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Li, Y., Dang, X., Chen, R. et al. Cross-ancestry genome-wide association study and systems-level integrative analyses implicate new risk genes and therapeutic targets for depression. Nat Hum Behav 9, 806–823 (2025). https://doi.org/10.1038/s41562-024-02073-6

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