Background Chronic pain and depression are common disorders and leading causes of disability worldwide. They frequently co-occur and show substantial genetic correlation, indicating a shared genetic basis. However, the locus-specific architecture of this overlap remains poorly characterised and may yield important insights into the pathophysiology of their comorbidity.

Methods Using the largest currently available European-ancestry genome-wide association studies of major depressive disorder (MDD) (n = 1,639,572) and multisite chronic pain (MCP) (n = 387,649), we estimated the polygenic overlap between traits using the bivariate causal mixture model (MiXeR), identified shared loci via conjunctional false discovery rate (conjFDR), and tested colocalisation with each trait and genetically regulated gene expression in 13 brain tissues.

Results MiXeR analysis demonstrated a high degree of directionally consistent polygenic overlap between MDD and MCP. Subsequent conjFDR analysis identified 375 shared loci, 22 of which showed cross-trait colocalisation between the MDD and MCP signals. Gene mapping and enrichment of shared loci implicated several biological processes, including cadherin-mediated cell-cell adhesion and translational initiation. Gene expression colocalisation in brain tissue highlighted protein phosphatase 6 catalytic subunit (PPP6C) and suppressor of cancer cell invasion (SCAI) in both disorders.

Conclusion Overall, these findings have enhanced our understanding of the complex relationship between chronic pain and depression by identifying shared molecular mechanisms that warrant further study as targets for prevention and treatment.

The authors have declared no competing interest.

HC is funded by the Medical Research Council and the University of Edinburgh through the Precision Medicine Doctoral Training Program.

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All GWAS summary statistics used in this study are publicly available. Specifically, MDD summary statistics can be downloaded from https://pgc.unc.edu/for-researchers/download-results/, and MCP summary statistics are available at https://researchdata.gla.ac.uk/822/.

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