Chronic musculoskeletal pain affects all aspects of human life. However, mechanisms of its genetic control remain poorly understood. Genetic studies of pain are complicated by the high complexity and heterogeneity of pain phenotypes. Here, we apply principal component analysis to reduce phenotype heterogeneity of chronic musculoskeletal pain at four locations: the back, neck/shoulder, hip, and knee. Using matrices of genetic covariances, we constructed four genetically independent phenotypes (GIPs) with the leading GIP (GIP1) explaining 78.4% of the genetic variance of the analyzed conditions, and GIP2-4 explain progressively less. We identified and replicated five GIP1-associated loci and one GIP2associated locus and prioritized the most likely causal genes. For GIP1, we showed enrichment with multiple nervous system-related terms and genetic correlations with anthropometric, sociodemographic, psychiatric/personality traits and osteoarthritis. We suggest that GIP1 represents a biopsychological component of chronic musculoskeletal pain, related to physiological and psychological aspects and reflecting pain perception and processing.

Purpose Measures of body fat accumulation are associated with back pain, but a causal association is unclear. We hypothesized that BMI would have causal effects on back pain. We conducted a two-sample Mendelian randomization (MR) study to assess the causal effect of body mass index (BMI) on the outcomes of (1) back pain and (2) chronic back pain (duration > 3 months). Methods We identified genetic instrumental variables for BMI (n = 60 variants) from a meta-analysis of genome-wide association studies (GWAS) conducted by the Genetic Investigation of ANthropometric Traits consortium in individuals of European ancestry (n = 322,154). We conducted GWAS of back pain and chronic back pain (n = 453,860) in a non-overlapping sample of individuals of European ancestry. We used inverse-variance weighted (IVW) meta-analysis as the primary method to estimate causal effects. Results The IVW analysis showed evidence supporting a causal association of BMI on back pain, with a 1-standard deviation (4.65 kg/m 2 ) increase in BMI conferring 1.15 times the odds of back pain (95% confidence interval [CI]: 1.06-1.25, p = 0.001]; effects were directionally consistent in secondary analysis and sensitivity analyses. The IVW analysis supported a causal association of BMI on chronic back pain (OR 1.20 per 1 SD deviation increase in BMI [95% CI 1.09-1.32; p = 0.0002]), and effects were directionally consistent in secondary analysis and sensitivity analyses. Conclusion In this first MR study of BMI and back pain, we found a significant causal effect of BMI on both back pain and chronic back pain.

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