Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data

Abstract

Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we perform a genome-wide association study for osteoarthritis (77,052 cases and 378,169 controls), analysing 4 phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discover 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine map to a single variant. We identify putative effector genes by integrating eQTL colocalization, fine-mapping, human rare disease, animal model, and osteoarthritis tissue expression data. We find enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organisation biological pathways. Ten of the likely effector genes, including TGFB1 , FGF18 , CTSK and IL11 have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis.

Decoding the genomic basis of osteoarthritis

“…We identified 36 genes for which the experimental perturbation induces changes in the opposite direction to molecular differences between high-grade and low-grade cartilage ( Supplementary Table 8), notably including knock-down of IL11. Variation in IL11 is associated with increased risk of hip osteoarthritis 9 , and the gene is up-regulated in osteoarthritis knee tissue 36 , with a similar trend observed here. IL11 is a cytokine with a key role in inflammation, and monoclonal anti-IL11 antibodies have been developed for use in several diseases.…”

“…We used a soft-thresholding centroid-based method, PAMR 13 , to develop a gene expression-based classifier capable of distinguishing between the two cartilage clusters. We identified 7 genes, the expression levels of which could be combined to predict cluster assignment for each patient sample (Figure 4a, Supplementary Figure 9): MMP1, MMP2, and MMP13, known to be involved in cartilage degradation 14 ; IL6, a proinflammatory cytokine; CYTL1, a cytokine-like gene, loss of which has been found to augment cartilage destruction in surgical osteoarthritis mouse models 15 ; APOD, a component of high-density lipoprotein found to be strongly up-regulated by retinoic acid 16 , which is in turn regulated by ALDH1A2 17 , an osteoarthritis risk locus 9,18 ; and C15orf48, with currently unknown function. Notably, the posterior probabilities for cluster assignment output by the classifier captured the main continuous spectrum of variation in this tissue (Figure 4b).…”

“…In a rat model of osteoarthritis, a rho kinase inhibitor was found to reduce knee cartilage damage 34 . Finally, there is a well-established effect of smoking on osteoarthritis 9,35 . Together, these results identify candidate compounds that warrant investigation, and provide evidence for the validity of this approach.…”

“…To examine colocalisation between molQTLs and GWAS associations, we used genome-wide summary statistics from the largest osteoarthritis meta-analysis to date, based on UK Biobank and arcOGEN data 9 . We analysed all 64 genome-wide significant signals using coloc 59 , separately for each tissue and omics level (Supplementary Methods).…”

RNA sequencing reveals interacting key determinants of osteoarthritis acting in subchondral bone and articular cartilage

“…Development of OA is dependent on multiple factors, with both environmental and genetic components [2,3]. To discover genes and underlying disease pathways genetic studies, such as large genome wide association studies (GWAS), have been performed that identified compelling OA risk single nucleotide polymorphisms (SNPs) [4][5][6].…”