Specific isoforms of the ubiquitin ligase gene WWP2 are targets of osteoarthritis genetic risk via a differentially methylated DNA sequence.

Background: Transitioning from a genetic association signal to an effector gene and a targetable molecular mechanism requires the application of functional fine-mapping tools such as reporter assays and genome editing. In this report, we undertook such studies on the osteoarthritis (OA) risk that is marked by single nucleotide polymorphism (SNP) rs34195470 (A > G). The OA risk-conferring G allele of this SNP associates with increased DNA methylation (DNAm) at two CpG dinucleotides within WWP2.

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study.

Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study.

Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression.

Osteoarthritis genetic risk acting on the galactosyltransferase gene COLGALT2 has opposing functional effects in articulating joint tissues.

Background: Investigation of cartilage and chondrocytes has revealed that the osteoarthritis risk marked by the independent DNA variants rs11583641 and rs1046934 mediate  their effects by decreasing the methylation status of CpG dinucleotides in enhancers and increasing the expression of shared target gene COLGALT2. We set out to investigate if these functional effects operate in a non-cartilaginous joint tissue.

Methods: Nucleic acids were extracted from the synovium of osteoarthritis patients.

Mediation of the Same Epigenetic and Transcriptional Effect by Independent Osteoarthritis Risk-Conferring Alleles on a Shared Target Gene, COLGALT2.

Objective: Over 100 DNA variants have been associated with osteoarthritis (OA), including rs1046934, located within a linkage disequilibrium block encompassing part of COLGALT2 and TSEN15. The present study was undertaken to determine the target gene(s) and the mechanism of action of the OA locus using human fetal cartilage, cartilage from OA and femoral neck fracture arthroplasty patients, and a chondrocyte cell model.

Methods: Genotyping and methylation array data of DNA from human OA cartilage samples (n = 87) were used to determine whether the rs1046934 genotype is associated with differential DNA methylation at proximal CpGs.

Genetic risk of osteoarthritis operates during human skeletogenesis.

Osteoarthritis (OA) is a polygenic disease of older people resulting in the breakdown of cartilage within articular joints. Although it is a leading cause of disability, there are no disease-modifying therapies. Evidence is emerging to support the origins of OA in skeletogenesis.

Identification of TMEM129, encoding a ubiquitin-protein ligase, as an effector gene of osteoarthritis genetic risk.

Background: Osteoarthritis is highly heritable and genome-wide studies have identified single nucleotide polymorphisms (SNPs) associated with the disease. One such locus is marked by SNP rs11732213 (T > C). Genotype at rs11732213 correlates with the methylation levels of nearby CpG dinucleotides (CpGs), forming a methylation quantitative trait locus (mQTL).

Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort.

Objectives: The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores.

Methods: Actual structural progression was measured using minimum joint space width (minJSW).

Translating osteoarthritis genetics research: challenging times ahead.

The ultimate goal of molecular genetic studies of human diseases is to translate the discoveries for patient benefit. For diseases that lack licensed disease-modifying therapeutics, such as osteoarthritis (OA), the need is acute. OA is polygenic and affects older individuals, with a recent genome-wide study of over 800 000 individuals adding 52 novel association signals to those already reported on for this common arthritis.

Neuropathic pain in the IMI-APPROACH knee osteoarthritis cohort: prevalence and phenotyping.

Objectives: Osteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component.

Methods: Baseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used.

Genetic and Epigenetic Interplay Within a COLGALT2 Enhancer Associated With Osteoarthritis.

Objective: The osteoarthritis (OA)-associated single-nucleotide polymorphism (SNP) rs11583641 is located in COLGALT2, encoding a posttranslational modifier of collagen. In cartilage, the SNP genotype correlates with DNA methylation in a putative enhancer. This study was undertaken to characterize the mechanistic relationship between rs11583641, the putative enhancer, and COLGALT2 expression using cartilage samples from human patients and a chondrocyte cell model.

Genetic and Epigenetic Fine-Tuning of TGFB1 Expression Within the Human Osteoarthritic Joint.

Objective: Osteoarthritis (OA) is an age-related disease characterized by articular cartilage degeneration. It is largely heritable, and genetic screening has identified single-nucleotide polymorphisms (SNPs) marking genomic risk loci. One such locus is marked by the G>A SNP rs75621460, downstream of TGFB1.

Osteoarthritis Research Society International (OARSI): Past, present and future.

We provide a detailed account of the origin and establishment of the Osteoarthritis Research Society International (OARSI) and celebrate its history from inception to the current day. We discuss the mission, vision and strategic objectives of OARSI and how these have developed and evolved over the last 3 decades. We celebrate the achievements of the society as we approach its 30th birthday, honor the entire presidential line and respectfully pay tribute to the past presidents who are no longer with us.

Multi-Tissue Epigenetic and Gene Expression Analysis Combined With Epigenome Modulation Identifies RWDD2B as a Target of Osteoarthritis Susceptibility.

Objective: Osteoarthritis (OA) is polygenic, with more than 90 risk loci currently mapped, including at the single-nucleotide polymorphism rs6516886. Previous analysis of OA cartilage DNA identified 6 CpG dinucleotides whose methylation levels correlated with the rs6516886 genotype, forming methylation quantitative trait loci (mQTLs). We undertook this study to investigate these mQTLs and to map expression quantitative trait loci (eQTLs) across joint tissues in order to prioritize a particular gene as a target of the rs6516886 association effect.

The use of technology in the subcategorisation of osteoarthritis: a Delphi study approach.

Objective: This UK-wide OATech Network + consensus study utilised a Delphi approach to discern levels of awareness across an expert panel regarding the role of existing and novel technologies in osteoarthritis research. To direct future cross-disciplinary research it aimed to identify which could be adopted to subcategorise patients with osteoarthritis (OA).

Design: An online questionnaire was formulated based on technologies which might aid OA research and subcategorisation.

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded.

Interplay between genetics and epigenetics in osteoarthritis.

Research into the molecular genetics of osteoarthritis (OA) has been substantially bolstered in the past few years by the implementation of powerful genome-wide scans that have revealed a large number of novel risk loci associated with the disease. This refreshing wave of discovery has occurred concurrently with epigenetic studies of joint tissues that have examined DNA methylation, histone modifications and regulatory RNAs. These epigenetic analyses have involved investigations of joint development, homeostasis and disease and have used both human samples and animal models.

Expression analysis of the osteoarthritis genetic susceptibility mapping to the matrix Gla protein gene MGP.

Background: Osteoarthritis (OA) is a common disease of older individuals that impacts detrimentally on the quality and the length of life. It is characterised by the painful loss of articular cartilage and is polygenic and multifactorial. Genome-wide association scans have highlighted over 90 osteoarthritis genetic signals, some of which reside within or close to highly plausible candidate genes.

Functional testing of thousands of osteoarthritis-associated variants for regulatory activity.

To date, genome-wide association studies have implicated at least 35 loci in osteoarthritis but, due to linkage disequilibrium, the specific variants underlying these associations and the mechanisms by which they contribute to disease risk have yet to be pinpointed. Here, we functionally test 1,605 single nucleotide variants associated with osteoarthritis for regulatory activity using a massively parallel reporter assay. We identify six single nucleotide polymorphisms (SNPs) with differential regulatory activity between the major and minor alleles.

Publisher Correction: GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures.

The original HTML version of this Article was updated shortly after publication to add links to the Peer Review file.In addition, affiliations 16 and 17 incorrectly read 'School of Medicine Sydney, University of Notre Dame Australia, Sydney, WA, 6160, Australia' and 'St Vincent's Clinical School, University of New South Wales Medicine, University of New South Wales, Sydney, NSW, 2052, Australia.' This has now been corrected in both the PDF and HTML versions of the Article.

GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures.

Bone area is one measure of bone size that is easily derived from dual-energy X-ray absorptiometry (DXA) scans. In a GWA study of DXA bone area of the hip and lumbar spine (N ≥ 28,954), we find thirteen independent association signals at twelve loci that replicate in samples of European and East Asian descent (N = 13,608 - 21,277). Eight DXA area loci associate with osteoarthritis, including rs143384 in GDF5 and a missense variant in COL11A1 (rs3753841).

Prioritization of PLEC and GRINA as Osteoarthritis Risk Genes Through the Identification and Characterization of Novel Methylation Quantitative Trait Loci.

Objective: To identify methylation quantitative trait loci (mQTLs) correlating with osteoarthritis (OA) risk alleles and to undertake mechanistic characterization as a means of target gene prioritization.

Methods: We used genome-wide genotyping and cartilage DNA methylation array data in a discovery screen of novel OA risk loci. This was followed by methylation, gene expression analysis, and genotyping studies in additional cartilage samples, accompanied by in silico analyses.