Genetic risk shared across 24 chronic pain conditions: identification and characterization with genomic structural equation modeling : PAIN

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Genetic risk shared across 24 chronic pain conditions: identification and characterization with genomic structural equation modeling

Zorina-Lichtenwalter, Katerinaa,*; Bango, Carmen I.b; Van Oudenhove, Lukasc; Čeko, Martad; Lindquist, Martin A.e; Grotzinger, Andrew D.f; Keller, Matthew C.f; Friedman, Naomi P.f; Wager, Tor D.b

Author Information

aInstitute of Cognitive Science and Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States

bDepartment of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States

cDepartment of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium

dDepartment of Psychology and Neuroscience and Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States

eDepartment of Biostatistics, Johns Hopkins University, Baltimore, MD, United States

fDepartment of Psychology and Neuroscience and Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States

*Corresponding author. Address: Institute for Behavioral Genetics, University of Colorado, Room 213, 1480 30th St, Boulder, CO 80303, United States. Tel.: +1 720-277-4465. E-mail address: [email protected] (K. Zorina-Lichtenwalter).

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.painjournalonline.com).

PAIN 164(10):p 2239-2252, October 2023. | DOI: 10.1097/j.pain.0000000000002922

Chronic pain conditions frequently co-occur, suggesting common risks and paths to prevention and treatment. Previous studies have reported genetic correlations among specific groups of pain conditions and reported genetic risk for within-individual multisite pain counts (≤7). Here, we identified genetic risk for multiple distinct pain disorders across individuals using 24 chronic pain conditions and genomic structural equation modeling (Genomic SEM). First, we ran individual genome-wide association studies (GWASs) on all 24 conditions in the UK Biobank (N ≤ 436,000) and estimated their pairwise genetic correlations. Then we used these correlations to model their genetic factor structure in Genomic SEM, using both hypothesis- and data-driven exploratory approaches. A complementary network analysis enabled us to visualize these genetic relationships in an unstructured manner. Genomic SEM analysis revealed a general factor explaining most of the shared genetic variance across all pain conditions and a second, more specific factor explaining genetic covariance across musculoskeletal pain conditions. Network analysis revealed a large cluster of conditions and identified arthropathic, back, and neck pain as potential hubs for cross-condition chronic pain. Additionally, we ran GWASs on both factors extracted in Genomic SEM and annotated them functionally. Annotation identified pathways associated with organogenesis, metabolism, transcription, and DNA repair, with overrepresentation of strongly associated genes exclusively in brain tissues. Cross-reference with previous GWASs showed genetic overlap with cognition, mood, and brain structure. These results identify common genetic risks and suggest neurobiological and psychosocial mechanisms that should be targeted to prevent and treat cross-condition chronic pain.

© 2023 International Association for the Study of Pain

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