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<p>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.</p>

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GWAS of extended prescription analgesic use identifies genetic loci in chronic pain

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月28日

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  • Charli E. Harlow,
  • Emeka Uzochukwu,
  • Hazel A. Fernando,
  • Charles E. Mordaunt,
  • Jordan M. Hughey,
  • John D. Eicher,
  • Lara Robinson,
  • Nicholas Bowker,
  • Laurence Howe,
  • Jimmy Liu,
  • Adrian Cortes,
  • Paul Wilson,
  • Usha Gungabissoon,
  • Victoria S. Benson,
  • Anthony Nash,
  • Gareth Young,
  • Laura Addis,
  • Chun-Fang Xu,
  • Caleb Webber,
  • Jonathan Davitte,
  • M. Zameel Cader

摘要

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.