Leveraging osteoclast genetic regulatory data to identify genes with a role in osteoarthritis.

Abstract

There has been growing interest in the role of the subchondral bone and its resident osteoclasts in the progression of osteoarthritis (OA). A recent genome-wide association study (GWAS) identified 100 independent association signals for OA traits. Most of these signals are led by non-coding variants, suggesting genetic regulatory effects may drive many of the associations. We have generated a unique human osteoclast-like cell-specific expression quantitative trait locus (eQTL) resource for study of the genetics of bone disease. Considering the potential role of osteoclasts in the pathogenesis of OA, we performed an integrative analysis of this dataset with the recently published OA GWAS results. Summary-data-based Mendelian Randomisation (SMR) and co-localisation analyses identified 38 genes with a potential role in OA, including some that have been implicated in Mendelian diseases with joint/skeletal abnormalities, such as BICRA, EIF6, CHST3 and FBN2. Several OA GWAS signals demonstrated co-localisation with more than one eQTL peak, including at 19q13.32 (hip OA with BCAM, PRKD2 and BICRA eQTL). We also identified a number of eQTL signals co-localising with more than one OA trait, including FAM53A, GCAT, HMGN1, MGAT4A, RRP7BP and TRIOBP. SMR analysis identified 3 loci with evidence of pleiotropic effects on OA-risk and gene expression: LINC01481, CPNE1 and EIF6. Both CPNE1 and EIF6 are located at 20q11.22, a locus harbouring two other strong OA candidate genes, GDF5 and UQCC1, suggesting the presence of an OA-risk gene cluster. In summary, we have used our osteoclast-specific eQTL dataset to identify genes potentially involved with the pathogenesis of OA.