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A multi-ancestry genetic study of pain intensity in 598,339 veterans

Nature Medicine volume 30, pages 1075–1084 (2024)Cite this article

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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 126 independent genetic loci, 69 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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Fig. 1: Manhattan plot for the pain intensity cross-ancestry GWAS meta-analysis (n = 598,339).
Fig. 2: Enrichment of pain intensity in the brain.
Fig. 3: Gene prioritization for pain intensity.
Fig. 4: Genetic correlation.
Fig. 5: Drug repurposing.

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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.

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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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Author notes

  1. These authors jointly supervised this work: Rachel L. Kember, Henry R. Kranzler.

Authors and Affiliations

  1. Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA

    Sylvanus Toikumo, Rachel Vickers-Smith, Emily E. Hartwell, Rachel L. Kember & Henry R. Kranzler

  2. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Sylvanus Toikumo, Zeal Jinwala, Heng Xu, Divya Saini, Emily E. Hartwell, Martin Cheatle, Rachel L. Kember & Henry R. Kranzler

  3. Department of Epidemiology and Environmental Health, University of Kentucky College of Public Health, Lexington, KY, USA

    Rachel Vickers-Smith

  4. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Mirko Pavicic, Kyle A. Sullivan, Daniel A. Jacobson & Mirko Pavicic

  5. Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA

    Ke Xu, Joel Gelernter, Christopher T. Rentsch, Hang Zhou, Stephen G. Waxman & Amy C. Justice

  6. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

    Ke Xu, Joel Gelernter & Hang Zhou

  7. Department of Medicine, Yale University School of Medicine, New Haven, CT, USA

    Christopher T. Rentsch & Amy C. Justice

  8. London School of Hygiene & Tropical Medicine, London, UK

    Christopher T. Rentsch

  9. Regeneron Genetics Center, Tarrytown, NY, USA

    Eli Stahl

  10. Section of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA

    Hang Zhou

  11. Department of Neurology, Yale University School of Medicine, New Haven, CT, USA

    Stephen G. Waxman

  12. Yale University School of Public Health, New Haven, CT, USA

    Amy C. Justice

Authors
  1. Sylvanus Toikumo
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  19. Henry R. Kranzler
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Consortia

Million Veteran Program

  • Sylvanus Toikumo
  • , Rachel Vickers-Smith
  • , Zeal Jinwala
  • , Heng Xu
  • , Emily E. Hartwell
  • , Mirko Pavicic
  • , Kyle A. Sullivan
  • , Ke Xu
  • , Daniel A. Jacobson
  • , Joel Gelernter
  • , Christopher T. Rentsch
  • , Hang Zhou
  • , Stephen G. Waxman
  • , Amy C. Justice
  • , Rachel L. Kember
  •  & Henry R. Kranzler

Contributions

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

Corresponding author

Correspondence to Henry R. Kranzler.

Ethics declarations

Competing interests

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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Peer review information

Nature Medicine thanks Seth Ament, Weihua Meng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team.

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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.

Credible locus prioritized by FINEMAP (PP > 0.5) is annotated with red rings. The MST1R*rs9815930 locus is in high LD (r2 > 0.8) with the lead variant TRAIP*rs2247036.

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.

Supplementary information

Supplementary Information

List of consortium members and Supplementary Figs. 1–9.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–37.

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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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