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 GIP2-associated 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.
Genetic and epidemiological analyses of low back pain
We wish to perform genetic analysis and meta-analysis to identify markers associated with low back pain as part of the FP7 Pain_omics study. In addition, we would like to examine environmental risk factors for low back pain. Using the phenotypes reported in the UK biobank database we wish to study the detailed low back pain phenotype and associated genotype of all volunteers. We will classify subjects as cases who report low back pain and controls who don't. From GWAS analysis we hope to improve the knowledge of this common health condition and ultimately improve treatment of low back pain. We will perform epidemiological and genetic epidemiological studies of low back pain (LBP) by comparing profiles of individuals presenting with back pain to those who do not present with back pain. We note that many more people report LBP than don't. As such, it might be appropriate to
a. consider a combined phenotype with other chronic pain such as leg pain
b. consider the 'controls' as cases and regard the GWAS as a search for variants which protect against LBP.
We will also examine variables influencing LBP such as sex, age, BMI alcohol consumption, socioeconomic status, smoking, exercise, occupation. The full cohort (>500,000)and more as available
|Lead investigator:||Professor Frances Williams|
|Lead institution:||King's College London|
5 related Returns
|Return ID||App ID||Description||Archive Date|
|3206||18219||Genome-wide meta-analysis identifies genetic locus on chromosome 9 associated with Modic changes||11 Mar 2021|
|3210||18219||Genome-wide meta-analysis of 158,000 individuals of European ancestry identifies three loci associated with chronic back pain||11 Mar 2021|
|3208||18219||ISSLS Prize in Clinical Science 2020. Examining causal effects of body mass index on back pain: a Mendelian randomization study||11 Mar 2021|
|3209||18219||Insight into the genetic architecture of back pain and its risk factors from a study of 509,000 individuals||11 Mar 2021|
|3247||18219||Sequence variation at 8q24.21 and risk of back pain||19 Mar 2021|
|3208||Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions||Tsepilov et al||2014||Communications Biology (2020)|