Meta-analysis identifies loci affecting levels of the potential osteoarthritis biomarkers sCOMP and uCTX-II with genome wide significance. Issue 9 (23rd July 2014) - Ramos, Yolande F M; Metrustry, Sarah; Arden, Nigel; Bay-Jensen, Anne C; Beekman, Marian; de Craen, Anton J M; Cupples, L Adrienne; Esko, Tõnu; Evangelou, Evangelos; Felson, David T; Hart, Deborah J; Ioannidis, John P A; et al.
Abstract : Background: Research for the use of biomarkers in osteoarthritis (OA) is promising, however, adequate discrimination between patients and controls may be hampered due to innate differences. We set out to identify loci influencing levels of serum cartilage oligomeric protein (sCOMP) and urinary C-telopeptide of type II collagen (uCTX-II). Methods: Meta-analysis of genome-wide association studies was applied to standardised residuals of sCOMP (N=3316) and uCTX-II (N=4654) levels available in 6 and 7 studies, respectively, from TreatOA. Effects were estimated using a fixed-effects model. Six promising signals were followed up by de novo genotyping in the Cohort Hip and Cohort Knee study (N=964). Subsequently, their role in OA susceptibility was investigated in large-scale genome-wide association studies meta-analyses for OA. Differential expression of annotated genes was assessed in cartilage. Results: Genome-wide significant association with sCOMP levels was found for a SNP within MRC1 (rs691461, p=1.7×10 −12 ) and a SNP within CSMD1 associated with variation in uCTX-II levels with borderline genome-wide significance (rs1983474, p=8.5×10 −8 ). Indication for association with sCOMP levels was also found for a locus close to the COMP gene itself (rs10038, p=7.1×10 −6 ). The latter SNP was subsequently found to be associated with hip OA whereas COMP expression appeared responsive to the OA pathophysiology in cartilage. Conclusions: We have identified genetic loci affecting either uCTX-II or sCOMP levels. The genome wide significant association of MRC1 with sCOMP levels was found likely to act independent of OA subtypes. Increased sensitivity of biomarkers with OA may be accomplished by taking genetic variation into account.