ArticleNature communications2026
GWAS of extended prescription analgesic use identifies genetic loci in chronic pain.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- The Pharmacogenomics of Opioid Response in Cancer Pain: From Receptor Polymorphisms to Tumour-Mediated Interference-A Narrative-Critical Review.International journal of molecular sciences · 2026Review
- The cell-type-specific genetic architecture of chronic pain in brain and dorsal root ganglia.The Journal of clinical investigation · 2025Article
- Etiological basis for chronic pain genetic variation in brain and dorsal root ganglia cell types.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pain-related conditions are the leading cause of disability worldwide. Existing GWAS for chronic pain have mainly focused on individual pain-related disorders, which may not optimally capture the phenotype. Here, we define chronic pain based on prescription analgesic use ( ≥ 90 days) in two large biobanks (UK Biobank and FinnGen). GWAS meta-analyses of 11 prescription-based pain phenotypes identify 140 associations with chronic pain, including 78 novel (e.g. ARPP21, CNTNAP2) and 62 previously reported (e.g. SLC39A8, DCC, TRPM8) associations. Integrating these genetic associations with functional data including transcriptome-wide association studies, cell-type and pathway enrichment, and gene enrichment in mouse phenotypes identifies potential mechanisms involved in chronic pain, implicating oligodendrocyte differentiation, neuronal guidance, endolysosomal function and post-synaptic endosome recycling. Our study showcases how the use of prescription data to identify and characterize pain can provide insights into pain genetics and its underlying biology.
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