Chronic pain is attributable to both local and systemic pathology. To investigate the latter, we focused on genetic risk shared among 24 chronic pain conditions in the UK Biobank. We conducted genome-wide association studies (GWAS) on all conditions and estimated genetic correlations among them, using these to model a factor structure in Genomic SEM. This revealed a general factor explaining most of the shared genetic variance in all conditions and an additional musculoskeletal pain-selective factor. Network analyses revealed a large cluster of highly genetically inter-connected conditions, with arthropathic, back, and neck pain showing the highest centrality. Functional annotation (FUMA) showed organogenesis, metabolism, transcription, and DNA repair as associated pathways, with enrichment for associated genes exclusively in brain tissues. Cross-reference with previous GWAS showed genetic overlap with cognition, mood, and brain structure. In sum, our results identify common genetic risks and suggest neurobiological and psychosocial mechanisms of vulnerability to chronic pain.
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.
Chronic pain and psychiatric conditions have consistently demonstrated substantial overlap in risk factors, epidemiology, and effective treatments. Previous work has identified cross-condition latent factors underlying shared genetic risk for several distinct psychiatric conditions and pain conditions. Here, we sought to examine the relationships between these latent genetic factors to determine biological mechanisms common to both pain and psychiatric conditions. We combined two previously published genetic structural equation models. The first model consisted of 24 pain conditions and their two latent factors: General and Musculoskeletal pain-specific. The second model consisted of 11 psychiatric conditions and their four latent factors: Externalizing, Internalizing, Compulsive Thought, and Psychotic Thought. The combined model of six factors and 35 conditions allowed us to estimate correlations between all factors and between conditions of one domain (pain) and factors of the other (psychiatric). We then added three measures of neuroticism (depressive affect subscale, worrying subscale, and total neuroticism score) to this model to examine correlations with all conditions and factors and test for possible explanation of pain-mental disorder relationships by neuroticism. We found that genetic associations between pain and psychiatric conditions were selective to the General Pain factor (and not Musculoskeletal) and Internalizing and Externalizing, but not Thought disorder factors. Neuroticism was associated with pain conditions to the extent that they loaded onto the General Pain factor (i.e., were associated with other pain conditions). Neuroticism also explained a substantial proportion of shared genetic variance between General Pain and Externalizing and between General Pain and Internalizing factors. Overall, the genetic risks shared among chronic pain and psychiatric conditions and neuroticism suggest shared biological mechanisms and underscore the importance of clinical assessment and treatment programs that leverage these commonalities.
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