Abstract
Objective
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.
Methods
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.
Results
Multiple SNPs were found to be associated with the glycan clusters near genes known to be involved in glycosylation and the inflammatory response (e.g. ST6GAL1, B4GALT1, FUT8). A total of 175 SNPs was used to calculate weighted PRS. In UK Biobank the PRS was a statically significant, but poor, predictor of the risk of back pain (β = 0.126±0.050, p = 0.015, R2 = 2.6e-5). The SNPs on chromosome 14 in regulatory regions of FUT8 gene, one of the key governors of core fucosylation, were found to be significantly associated with back pain in UK Biobank (FDR-adjusted p-value < 0.05).
Conclusions
These pilot data suggest that genetic component of glycosylation may be associated with the risk of LBP; however, its predictive ability is poor.
Conflict of Interest: YSA is a co-owner of Maatschap PolyOmica. GL is a founder and CEO of Genos Glycoscience Research Laboratory. MBF and FMKW declare no conflict of interests.
Sources of Funding: The research has been supported by the EC FP7 project PainOmics (grant agreement #602736) and conducted using the UK Biobank Resource (project # 18219).
