The association between lumbar intervertebral disc degeneration (LDD) and low back pain (LBP) is modest. We have recently shown that genetic propensity to pain is an effect modifier of the LDD-LBP relationship when LDD is defined as a summary score of LDD (LSUM), suggesting the association may be driven by individuals with the greatest genetic predisposition to pain. This study examined the association between individual spine magnetic resonance imaging (MRI)-determined LDD features and LBP in subgroups defined by genetic predisposition to pain. We developed a polygenic risk score (PRS) for “genetic propensity to pain” defined as the number of non-back pain locations (head, face, neck/shoulder, stomach/abdomen, hip, and knee) with duration ≥3 months in 377,538 UK Biobank participants of European ancestry. This PRS was used to stratify TwinsUK MRI samples (n=645) into four strata of genetic propensity to pain. We examined the association between LBP and MRI features of lumbar disc height, disc signal intensity, disc bulge, and osteophytes with adjustments for age, sex, PRS strata, interaction terms for each MRI feature x PRS strata, and twin status.Background
Method
Intervertebral disc degeneration (DD) is a complex age-related condition that constitutes the main risk factor for disabling back pain. DD is assessed using different traits extracted from MR imaging (MRI), normally combined to give summary measures (e.g. Pfirmann score). The aetiology of DD is poorly understood and despite its high heritability (75%), the precise genetic predisposition is yet to be defined. Genome wide association study (GWAS) is used to discover genetic variants associated with a disease or phenotype. It tests variants across the whole genome. It requires large samples to provide adequate but unfortunately there is poor availability of spine imaging data due to the high cost of MRI. We have adopted new methods to examine different MRI traits independently and use the information of those traits to boost GWAS power using specialized statistical software for jointly analyse correlated traits. We examined DD MRI features disc narrowing, disc bulge, disc signal intensity and osteophyte formation in the TwinsUK cohort who had undergone T2-weighted sagittal spine MRI. GWAS were performed on the four traits. MTAG software was used to boost single trait GWAS power using the information in the other trait GWAS. 9 different loci were identified.Background
Methods/Results
