Abstract
Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25–50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 125 independent genetic loci, 66 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, β-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.
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Data availability
The cross-ancestry and within-ancestry GWAS and meta-analysis summary-level association data will be available in the database of Genotypes and Phenotypes (dbGaP) (https://www.ncbi.nlm.nih.gov/gap/) under accession phs001672 ‘Veterans Administration (VA) MVP Summary Results from Omics Studies’. Registration and approval are needed following dbGaP’s data access process.
Code availability
Imputation was performed in the MVP using SHAPEIT4 (https://odelaneau.github.io/shapeit4/) and Minimac4 (https://genome.sph.umich.edu/wiki/Minimac4). GWAS was performed using PLINK2 (https://www.cog-genomics.org/plink2). Meta-analyses were performed using METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation). GCTA-COJO (https://cnsgenomics.com/software/gcta/#Overview) was used for the identification of independent loci. FINEMAP (http://www.christianbenner.com/) was used to fine-map genomic risk loci. FUMA (https://fuma.ctglab.nl/) was used for gene association, functional enrichment and gene-set enrichment analyses. Transcriptomic and proteomic analyses were performed using FUSION (https://github.com/gusevlab/fusion_twas). Validation of transcriptomic analyses was performed using SMR (https://yanglab.westlake.edu.cn/software/smr/#Overview). Chromatin accessibility analyses were performed using H-MAGMA (https://github.com/thewonlab/H-MAGMA). LDSC (https://github.com/bulik/ldsc) was used for heritability estimation, genetic correlation analysis (also using the CTG-VL; https://genoma.io) and heritability enrichment analyses. Trans-ancestry genetic correlation was estimated using Popcorn (https://github.com/brielin/Popcorn). Genotyping and sample QC in the PMBB were performed using PLINK 1.9 (https://www.cog-genomics.org/plink/). Genotype phasing and imputation in Yale–Penn and PMBB were performed using Minimac3 (https://genome.sph.umich.edu/wiki/Minimac3). Genetic ancestry in PMBB was estimated using Eigensoft (https://github.com/DReichLab/EIG). PRS analyses were performed using PRS-CS (https://github.com/getian107/PRScs). PheWAS analyses were run using the PheWAS R package (https://github.com/PheWAS/PheWAS). The MendelianRandomization R package (https://cran.r-project.org/web/packages/MendelianRandomization/index.html) was used for MR analyses.
Change history
07 May 2024
A Correction to this paper has been published: https://doi.org/10.1038/s41591-024-03024-4
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Acknowledgements
This work was supported by the US Department of Veterans Affairs (grants I01 BX003341 to H.R.K. and A.C.J., IK2 CX002336 to E.E.H. and the VISN 4 Mental Illness Research, Education and Clinical Center) and NIH (grants K01 AA028292 to R.L.K. and P30 DA046345 to H.R.K.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The views expressed in this article are those of the authors and do not necessarily represent the position or policy of the Department of Veterans Affairs or the US Government. We acknowledge the PMBB for providing data to generate PRSs and conduct PheWAS analyses and thank the patients of Penn Medicine who consented to participate in this research program. We thank the PMBB team and Regeneron Genetics Center for providing genetic variant data for analysis. The PMBB is approved under IRB protocol 813913 and supported by the Perelman School of Medicine at the University of Pennsylvania, a gift from the Smilow family, and the National Center for Advancing Translational Sciences of the National Institutes of Health under CTSA award UL1TR001878. This manuscript has been co-authored by UT-Battelle, LLC under contract DE-AC05-00OR22725 with the US Department of Energy. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript or allow others to do so, for US Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
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S.T. conducted the main analyses and drafted the manuscript. R.V.-S. conducted phenotype-related analyses. Z.J. and H.X. conducted downstream analyses. D.S. annotated gene findings. M.P. and K.A.S. helped in conducting analyses. R.V.-S., Z.J., H.X., D.S., E.E.H., M.P., K.A.S., K.X., J.G., D.A.J., C.T.R., M.C., E.S. and S.G.W. helped in writing the manuscript. A.C.J. obtained funding to support the project and helped in writing the manuscript. R.L.K. supervised the analyses and helped in writing the manuscript. H.R.K. conceived the project, obtained funding to support it and helped in supervising the analyses and writing the manuscript. All authors reviewed and approved the final version of the manuscript
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H.R.K. is a member of advisory boards for Dicerna Pharmaceuticals, Sophrosyne Pharmaceuticals, Enthion Pharmaceuticals and Clearmind Medicine; a consultant to Sobrera Pharmaceuticals; the recipient of research funding and medication supplies from Alkermes for an investigator-initiated study; and a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the last 3 years by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi and Otsuka. H.R.K. and J.G. are named as inventors on PCT patent application 15/878,640 entitled ‘genotype-guided dosing of opioid agonists’, filed on 24 January 2018. E.S. is a full-time employee of Regeneron Pharmaceuticals. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Overview of the study.
Top left: primary GWAS analyses for pain intensity. Within ancestry, GWAS for African American (AA), European American (EA) and Hispanic American (HA) followed by cross-ancestry meta-analysis. These results were used for all downstream analyses. Top right: secondary GWAS analyses for pain intensity. Bottom: downstream analyses were conducted using the cross-ancestry, AA and EA GWAS results as indicated by color shadings: primary GWAS (green) and supplementary GWAS (brown).
Extended Data Fig. 2 Manhattan plot for the pain intensity in European American GWAS analysis.
Identified 87 independent risk loci. Novel loci (n = 52) are annotated in pink. The red line indicates GWS after correction for multiple testing (P < 5 × 10−8).
Extended Data Fig. 3 Effect-effect plot of cross-ancestry meta-analyses lead SNPs in the primary and secondary GWASs.
The magnitude and direction of the effect sizes are plotted for each GWAS. The results show significant (P < 2.2 × 10−16) high correlation (Pearson r test, two-sided) between the effect sizes (β) of pain intensity lead SNPs for primary GWAS and those for non-OUD (r = 1, a), non-zero (r = 0.97, b), males (r = 1, c) and females (r = 0.88, d).
Extended Data Fig. 4 LDSC genetic correlations for pain intensity primary and secondary GWAS.
African American: primary GWAS, n = 112,968; non-OUD GWAS, n = 104,050; non-zero GWAS, n = 61,499; male GWAS, n = 97,343; female GWAS, n = 15,625. European American: primary GWAS, n = 436,683; non-OUD GWAS, n = 416,740; non-zero GWAS, n = 202,784; male GWAS, n = 404,510; female GWAS, N = 32,173. Error bar is presented as 95% confidence interval.
Extended Data Fig. 5 MAGMA tissue enrichment for pain intensity in cross-ancestry and European American GWAS results.
Tissue enrichment analyses were conducted using FUMA. Bonferroni correction threshold (represented by the black dashed line) = 9.25 × 10−4 (0.05/54).
Extended Data Fig. 6 Gene-based Manhattan plots for cross-ancestry, European American and African American GWAS.
Gene-based association analyses were conducted using FUMA and genes that survive multiple correction are annotated (Bonferroni p = 2.67 × 10−6 [0.05/18,702]).
Extended Data Fig. 7 Regional plot for TRAIP*rs2247036 and MST1R*rs9815930 on chromosome 3.
Extended Data Fig. 8 Regional plot for NOP14*rs71597204 and GRK4*rs2798303 on chromosome 4.
Credible locus prioritized by FINEMAP (PP > 0.5) is annotated with red rings. The GRK4*rs2798303 locus is in moderate LD (r2 > 0.4) with the lead variant NOP14*rs71597204.
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List of consortium members and Supplementary Figs. 1–9.
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Toikumo, S., Vickers-Smith, R., Jinwala, Z. et al. A multi-ancestry genetic study of pain intensity in 598,339 veterans. Nat Med 30, 1075–1084 (2024). https://doi.org/10.1038/s41591-024-02839-5
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