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

Yifan Li, Xinglun Dang, Rui Chen, Zhaowei Teng, Junyang Wang, Shiwu Li, Yingying Yue, Brittany L. Mitchell, Yong Zeng, Yong-Gang Yao, Ming Li, Zhongchun Liu, Yonggui Yuan (), Tao Li (), Zhijun Zhang () and Xiong-Jian Luo ()
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Yifan Li: Southeast University
Xinglun Dang: Southeast University
Rui Chen: Chinese Academy of Sciences
Zhaowei Teng: The Second Affiliated Hospital of Kunming Medical University, Yunnan Provincial Department of Education Gut Microbiota Transplantation Engineering Research Center
Junyang Wang: Zhengzhou University
Shiwu Li: Chinese Academy of Sciences
Yingying Yue: Southeast University
Brittany L. Mitchell: QIMR Berghofer Medical Research Institute
Yong Zeng: The Second Affiliated Hospital of Kunming Medical University, Yunnan Provincial Department of Education Gut Microbiota Transplantation Engineering Research Center
Yong-Gang Yao: Chinese Academy of Sciences
Ming Li: Chinese Academy of Sciences
Zhongchun Liu: Renmin Hospital of Wuhan University
Yonggui Yuan: Southeast University
Tao Li: Zhejiang University School of Medicine
Zhijun Zhang: Southeast University
Xiong-Jian Luo: Southeast University

Nature Human Behaviour, 2025, vol. 9, issue 4, 806-823

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41562-024-02073-6

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