Low back pain (LBP) is a common debilitating condition with great socioeconomic impact. Identifying individuals at risk of LBP is challenging. We have shown IgG N-glycans are associated with LBP. Herewith, we used polygenic risk scores (PRS) from IgG-glycome to test predictability for LBP. Clusters of IgG-glycans were identified using weighted correlation network approach in TwinsUK (n = 4246). Genome-wide association studies were carried out for the clusters and top associated SNPs (p<5e-8) were extracted. Weighted PRS was calculated as the sum of the number of copies of effect allele from GWAS multiplied by their effect size using the UK Biobank data (n = 350000). The predictive capacity of the PRS for back pain in UK Biobank was estimated using logistic regression.Objective
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Back pain is the primary cause of disability worldwide yet surprisingly little is known of the underlying pathobiology. We conducted a genome-wide association study (GWAS) meta-analysis of chronic back pain (CBP). Adults of European ancestry from 15 cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and UK Biobank were studied. CBP cases were defined as reporting back pain present for ≥3–6 months; non-cases were included as comparisons (“controls”). Each cohort conducted genotyping followed by imputation. GWAS used logistic regression with additive genetic effects adjusting for age, sex, study-specific covariates, and population substructure. Suggestive (p<5×10–7) & genome-wide significant (p<5×10–8) variants were carried forward for replication in an independent sample of UK Biobank participants. Discovery sample n = 158,025 individuals, including 29,531 CBP cases.Purpose
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