A Shared Genetic Signature for Common Chronic Pain Conditions and its Impact on Biopsychosocial Traits

• DOI: 10.1016/j.jpain.2022.10.005
• Publication Date: 2022-10-14T11:37:59Z
• Authors: Scott F. Farrell; Pik-Fang Kho; Mischa Lundberg; Adrián I. Campos; Miguel E. Rentería; Rutger M.J. de Zoete; Michele Sterling; Trung Thanh Ngo; Gabriel Cuéllar-P...
• Keywords: 0301 basic medicine; Musculoskeletal pain; Pain; Genetic predisposition to disease; 610; Chronic pain; Comorbidity; Phenome-wide association analysis; Genome-wide association studies; Polymorphism, Single Nucleotide; 3. Good health; 03 medical and health sciences; Phenotype; Medical genetics (excl. cancer genetics); Chronic Disease; Humans; Genetic Predisposition to Disease; Chronic Pain; Genome-Wide Association Study

AbstractThe multifactorial nature of chronic pain with its numerous comorbidities presents a formidable challenge in disentangling their aetiology. Here, we performed genome-wide association studies of eight regional chronic pain types using UK Biobank data (N=4,037–79,089 cases; N=239,125 controls), followed by bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine (respectively) their genetic correlations and genetic causal proportion (GCP) parameters with 1,492 other complex traits. We report evidence of a shared genetic signature across common chronic pain types as their genetic correlations and GCP parameter directions were broadly consistent across a wide array of biopsychosocial traits. Across 5,942 significant genetic correlations, 570 trait pairs could be explained by a causal association (|GCP| > 0.6; 5% false discovery rate), including 82 traits affected by pain while 488 contributed to an increased risk of chronic pain such as certain somatic pathologies (e.g., musculoskeletal), psychiatric traits (e.g., depression), socioeconomic factors (e.g., occupation) and medical comorbidities (e.g., cardiovascular disease). This data-driven study has demonstrated a novel & efficient strategy for identifying genetically supported risk & protective traits to enhance the design of interventional trials targeting underlying causal factors and help accelerate the development of more effective treatments with broader clinical utility.