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

Abstract

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.

Familial effects account for association between chronic pain and past month smoking

“…A longitudinal, psychometric analysis has supported the validity of a common component underlying pain across body sites (Battaglia et al, 2022). Consistent with this psychometric evidence for a general pain factor, twin and genome-wide association studies have identified a common genetic factor that contributes to many chronic pain conditions (Vehof et al, 2014;Zorina-Lichtenwalter et al, 2023). Of the people who reported pain in our sample, 97.2% reported pain lasting more than 3 months, characterizing the measure as chronic.…”

Identification of ethanol analgesia quantitative trait loci and candidate genes in BXD recombinant inbred mouse lines

“…Variants associated with drinks per week, schizophrenia, smoking initiation, and bipolar disorder were mapped to synaptosome associated protein 91 (SNAP91). The chromosome 17 Pehlq1 interval maps to regions on human chromosomes 5 (109.5-110.7Mb) and 18 (2.57-8.83; 9.10-9.96Mb), and it features variants associated with opioid analgesic sensitivity (DLGAP1)[41], schizophrenia (MAN2A1)[42], chronic pain (TMEM200C)[43], and post-traumatic stress disorder symptomatology (DLGAP1)[44].…”

A genome-wide association study identifies genetic variants associated with hip pain in the UK Biobank cohort (N=221,127)

“…Our study demonstrates an interplay of genetic determinants in the manifestation of pain, corroborating previous research. Specifically, a prior network analysis has identified a substantial cluster of conditions and pinpointed arthropathic, back, and neck pain as potential hubs for cross-condition chronic pain (Zorina-Lichtenwalter et al 2023).…”