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GWAS of Extended Prescription Analgesic Use Identifies Novel Genetic Loci in Chronic Pain

Harlow, C. E.; Uzochukwu, E.; Fernando, H. A.; Mordaunt, C. E.; Hughey, J. M.; Eicher, J. D.; Robinson, L.; Bowker, N.; Howe, L.; Liu, J.; Cortes, A.; Wilson, P.; Gungabissoon, U.; Tribble, V.; Nash, A.; Young, G.; Addis, L.; Xu, C.-F.; Webber, C.; Davitte, J. M.; Cader, M. Z.

2024-12-03 genetic and genomic medicine
10.1101/2024.12.02.24318312 medRxiv
Show abstract

Pain-related conditions are the leading cause of disability worldwide. Current genome-wide association studies (GWAS) for chronic pain have mainly focused on individual pain-related disorders, which may not optimally capture the phenotype. Here, we applied a novel method of defining 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 identified 140 associations with chronic pain--78 novel (e.g. ARPP21, CNTNAP2) and 62 previously reported (e.g. SLC39A8, DCC, TRPM8). Integrating these genetic associations with functional data including transcriptome-wide association studies, cell-type and pathway enrichment, and gene enrichment in mouse phenotypes identified novel 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 provides new insights into pain genetics and its underlying biology.

Published in Nature Communications (predicted rank #2) · training set

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