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Genetic risk of chronic pain conditions associated with risk of suicide death through an integrative analysis of EHR and genomics data
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  • Published: 16 February 2026

Genetic risk of chronic pain conditions associated with risk of suicide death through an integrative analysis of EHR and genomics data

  • Seonggyun Han  ORCID: orcid.org/0000-0003-2227-49531,
  • Emily DiBlasi1,
  • Eric T. Monson  ORCID: orcid.org/0000-0002-8552-83001,
  • Andrey A. Shabalin  ORCID: orcid.org/0000-0003-0309-68211,
  • Lisa Baird1,
  • Danli Chen1,
  • Dirga Lamichhane1,
  • Doug Tharp2,
  • Elliott Ferris3,
  • Zhe Yu4,
  • W. Brandon Callor5,
  • Michael J. Staley5,
  • Qingqin S. Li  ORCID: orcid.org/0000-0003-4182-45356,
  • Virginia Willour7,8,
  • David K. Crockett9,
  • Karen Eilbeck10,
  • Amanda V. Bakian  ORCID: orcid.org/0000-0001-6805-11601,
  • Brooks R. Keeshin1,11,12,
  • Akiko Okifuji13,
  • Hilary Coon  ORCID: orcid.org/0000-0002-8877-54461 &
  • …
  • Anna R. Docherty  ORCID: orcid.org/0000-0001-7139-70071 

Translational Psychiatry , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Comparative genomics
  • Genomics
  • Psychiatric disorders

Abstract

Chronic pain represents heritable conditions linked to suicide death. It has been suggested that a shared genetic predisposition may contribute to this relationship, but there has not yet been a comprehensive assessment of genetic and clinical overlaps of different types of chronic pain with suicide death. Here, we integrated whole-genome sequencing and electronic health records from 986 unrelated individuals of European ancestry who died by suicide in the Utah Suicide Mortality Research Study and 415 ancestrally-matched population controls selected for absence of disease. Polygenic scores (PGSs) for seven distinct types of chronic pain were calculated and tested in the suicide cohort. We observed significant positive associations of PGSs for multisite chronic pain (PGSMCP) and chronic widespread pain (PGSCWP) with suicide mortality. Sex-stratified analyses showed elevations in both males and females. Pain diagnosis-stratified analyses revealed associations with suicide death regardless of chronic pain diagnoses. Follow-up tests of PGSs for more specific pain conditions showed additional associations with suicide death for: 1) monoarticular arthritis, 2) back pain, and 3) chronic inflammatory demyelinating polyneuropathy across all suicide death individuals, and 4) irritable bowel syndrome within males only. In a multiple logistic regression test of all chronic pain PGSs associating suicide death status, four types of pain remained uniquely associated with suicide death, highlighting distinct subgroups within suicide death: some attributed to MCP and CWP, and others associated with monoarticular arthritis or chronic inflammatory demyelinating polyneuropathy. This cohort study reports associations between suicide death and PGSs from various pain conditions, regardless of sex or chronic pain diagnosis, suggesting that combining genetic and clinical risk factors may better identify genetic overlap, causal directions, and/or specific gene pathways.

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Data availability

Publicly available GWAS datasets investigated in this study are available from the following sources. Multisite chronic pain: https://www.ebi.ac.uk/gwas/publications/31194737. Chronic widespread pain: https://zenodo.org/records/4459546. Monoarticular arthritis: https://www.ebi.ac.uk/gwas/publications/34737426. Back pain: https://zenodo.org/records/1319332, Chronic inflammatory demyelinating polyneuropathy: https://www.ebi.ac.uk/gwas/publications/34737426. Irritable bowel syndrome: https://www.ebi.ac.uk/gwas/publications/34741163, and Knee pain: https://www.ebi.ac.uk/gwas/publications/31482140. Additional data from this study is available from the authors upon request.

Code availability

PRSice-2: https://choishingwan.github.io/PRSice/; PRS-CS: https://github.com/getian107/PRScs; LDSC: https://github.com/bulik/ldsc; PLINK v1.9: https://www.cog-genomics.org/plink/1.9.

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Funding

This work was supported by the National Institutes of Health (R01MH122412, R01MH123489, R01MH123619, R01ES032028); the American Foundation for Suicide Prevention (VW, HC: BSG-1-005-18), the Brain & Behavior Research Foundation-NARSAD (ED, grant number 28132; AS grant number 28686; EM grant number 31248); and the Clark Tanner Foundation (HC, AS, EM, AB). Cellular Translational Research Core Services at the University of Utah are supported by NIH CTSA UM1 TR004409. Partial support for all datasets housed within the Utah Population Data Base is provided by the Huntsman Cancer Institute (HCI), http://www.huntsmancancer.org/, and the HCI Cancer Center Support grant, P30CA42014 from the National Cancer Institute. Whole-genome sequencing of suicide deaths was supported in part by a donation from the Huntsman Mental Health Institute. Research was supported by NCRR grant “Sharing statewide health data for genetic research” R01RR021746 with additional support from the Utah Department of Health and Human Services and the University of Utah. We thank the University of Utah Pedigree and Population Resource and the University of Utah Health Enterprise Data Warehouse for establishing the Master Subject Index between the Utah Population Database and the University of Utah Health Sciences Center. We additionally thank our colleagues at Intermountain Health for working with Utah Population Database staff in linkage and subsequent de-identification of IH health records data.

Author information

Authors and Affiliations

  1. Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA

    Seonggyun Han, Emily DiBlasi, Eric T. Monson, Andrey A. Shabalin, Lisa Baird, Danli Chen, Dirga Lamichhane, Amanda V. Bakian, Brooks R. Keeshin, Hilary Coon & Anna R. Docherty

  2. Department of Geography, University of Utah, Salt Lake City, UT, USA

    Doug Tharp

  3. Department of Neurobiology, University of Utah School of Medicine, Salt Lake City, UT, USA

    Elliott Ferris

  4. Pedigree & Population Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA

    Zhe Yu

  5. Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA

    W. Brandon Callor & Michael J. Staley

  6. Neuroscience Therapeutic Area, Janssen Research & Development LLC, Titusville, NJ, USA

    Qingqin S. Li

  7. Department of Psychiatry, University of Iowa, Iowa City, IA, USA

    Virginia Willour

  8. Department of Veterans Affairs, Iowa City Health Care System, Iowa City, IA, USA

    Virginia Willour

  9. Clinical Analytics, Intermountain Health, Salt Lake City, UT, USA

    David K. Crockett

  10. Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA

    Karen Eilbeck

  11. Department of Pediatrics, University of Utah, Salt Lake City, UT, USA

    Brooks R. Keeshin

  12. Department of Public Health and Caring Science, Child Health and Parenting (CHAP), Uppsala University, Uppsala, Sweden

    Brooks R. Keeshin

  13. Departments of Anesthesiology and Psychology, University of Utah School of Medicine, Salt Lake City, UT, USA

    Akiko Okifuji

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  1. Seonggyun Han
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  2. Emily DiBlasi
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Contributions

SH, AD, and HC conceptualized and designed this study. SH, ED, EM, AS, DC, KE, AB, BK, HC, and AD contributed to analysis and interpretation of EHR and genomics data. LB, DC, DL, DT, EF were involved in data preparation. QL, VW, HC, and AD generated whole-genome sequencing data. All of the funding and work needed to generate whole-genome sequencing data includes only QL, FW, and HC. ZY and DC contributed to integration of clinical and demographic data with de-identification. WC, MS, and HC collected biosamples for suicide death. ED and AO were involved in defining suicide cases with chronic pain conditions and interpreting results in the context of chronic pain. SH, ED, EM, HC, and AD prepared the first draft of the manuscript. HC and AD supervised this study. All authors contributed to completing the manuscript by reading and revising it. All authors approved the final manuscript.

Corresponding author

Correspondence to Seonggyun Han.

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Competing interests

Qingqin S. Li is an employee of Janssen Research and Development. All other authors declare no conflicts of interest.

Ethics approval and consent to participate

All methods were performed in accordance with the relevant guidelines and regulations. Ethical approval for this study is received annually from Institutional Review Boards of the University of Utah, Intermountain Health, and the Utah Department of Health and Human Services, and informed consent was obtained from all subjects prior to study participation.

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Han, S., DiBlasi, E., Monson, E.T. et al. Genetic risk of chronic pain conditions associated with risk of suicide death through an integrative analysis of EHR and genomics data. Transl Psychiatry (2026). https://doi.org/10.1038/s41398-026-03861-6

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  • Received: 06 January 2025

  • Revised: 11 December 2025

  • Accepted: 28 January 2026

  • Published: 16 February 2026

  • DOI: https://doi.org/10.1038/s41398-026-03861-6

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