medRxivGenetic & Genomic MedicinePreprint
Investigating the shared genetic architecture between chronic pain and depression
Hannah CaseyiD, Xueyi SheniD, Laurence NisbetiD, Marie FalloniD, Daniel J. SmithiD, Rona J. StrawbridgeiD, Heather Clare WhalleyiD
Abstract
BackgroundChronic 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. MethodsUsing 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. ResultsMiXeR 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. ConclusionOverall, these findings have enhanced our understanding of the complex relationship between chronic pain and depression by identifying potential shared molecular mechanisms that warrant further study as targets for prevention and treatment.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 0
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