A novel variant in GLIS3 is associated with osteoarthritis

Objectives Osteoarthritis (OA) is a complex disease, but its genetic aetiology remains poorly characterised. To identify novel susceptibility loci for OA, we carried out a genome-wide association study (GWAS) in individuals from the largest UK-based OA collections to date. Methods We carried out a discovery GWAS in 5414 OA individuals with knee and/or hip total joint replacement (TJR) and 9939 population-based controls. We followed-up prioritised variants in OA subjects from the interim release of the UK Biobank resource (up to 12 658 cases and 50 898 controls) and our lead finding in operated OA subjects from the full release of UK Biobank (17 894 cases and 89 470 controls). We investigated its functional implications in methylation, gene expression and proteomics data in primary chondrocytes from 12 pairs of intact and degraded cartilage samples from patients undergoing TJR. Results We detect a genome-wide significant association at rs10116772 with TJR (P=3.7×10−8; for allele A: OR (95% CI) 0.97 (0.96 to 0.98)), an intronic variant in GLIS3, which is expressed in cartilage. Variants in strong correlation with rs10116772 have been associated with elevated plasma glucose levels and diabetes. Conclusions We identify a novel susceptibility locus for OA that has been previously implicated in diabetes and glycaemic traits

Genome-wide association study of developmental dysplasia of the hip identifies an association with GDF5

Developmental dysplasia of the hip (DDH) is the most common skeletal developmental disease. However, its genetic architecture is poorly understood. We conduct the largest DDH genome-wide association study to date and replicate our findings in independent cohorts. We find the heritable component of DDH attributable to common genetic variants to be 55% and distributed equally across the autosomal and X-chromosomes. We identify replicating evidence for association between GDF5 promoter variation and DDH (rs143384, effect allele A, odds ratio 1.44, 95% confidence interval 1.34–1.56, P = 3.55 × 10−22). Gene-based analysis implicates GDF5 (P = 9.24 × 10−12), UQCC1 (P = 1.86 × 10−10), MMP24 (P = 3.18 × 10−9), RETSAT (P = 3.70 × 10−8) and PDRG1 (P = 1.06 × 10−7) in DDH susceptibility. We find shared genetic architecture between DDH and hip osteoarthritis, but no predictive power of osteoarthritis polygenic risk score on DDH status, underscoring the complex nature of the two traits. We report a scalable, time-efficient recruitment strategy and establish for the first time to our knowledge a robust DDH genetic association locus at GDF5

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

Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we performed a genome-wide association study for osteoarthritis (77,052 cases and 378,169 controls), analyzing four phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discovered 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine-mapped to a single variant. We identified putative effector genes by integrating expression quantitative trait loci (eQTL) colocalization, fine-mapping, and human rare-disease, animal-model, and osteoarthritis tissue expression data. We found enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organization biological pathways. Ten of the likely effector genes, including TGFB1 (transforming growth factor beta 1), FGF18 (fibroblast growth factor 18), CTSK (cathepsin K), and IL11 (interleukin 11), have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis

Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology

Publisher Copyright: © 2022, The Author(s).Back pain is a common and debilitating disorder with largely unknown underlying biology. Here we report a genome-wide association study of back pain using diagnoses assigned in clinical practice; dorsalgia (119,100 cases, 909,847 controls) and intervertebral disc disorder (IDD) (58,854 cases, 922,958 controls). We identify 41 variants at 33 loci. The most significant association (ORIDD = 0.92, P = 1.6 × 10−39; ORdorsalgia = 0.92, P = 7.2 × 10−15) is with a 3’UTR variant (rs1871452-T) in CHST3, encoding a sulfotransferase enzyme expressed in intervertebral discs. The largest effects on IDD are conferred by rare (MAF = 0.07 − 0.32%) loss-of-function (LoF) variants in SLC13A1, encoding a sodium-sulfate co-transporter (LoF burden OR = 1.44, P = 3.1 × 10−11); variants that also associate with reduced serum sulfate. Genes implicated by this study are involved in cartilage and bone biology, as well as neurological and inflammatory processes.Peer reviewe

The genetic epidemiology of joint shape and the development of osteoarthritis

Congruent, low-friction relative movement between the articulating elements of a synovial joint is an essential pre-requisite for sustained, efficient, function. Where disorders of joint formation or maintenance exist, mechanical overloading and osteoarthritis (OA) follow. The heritable component of OA accounts for ~ 50% of susceptible risk. Although almost 100 genetic risk loci for OA have now been identified, and the epidemiological relationship between joint development, joint shape and osteoarthritis is well established, we still have only a limited understanding of the contribution that genetic variation makes to joint shape and how this modulates OA risk. In this article, a brief overview of synovial joint development and its genetic regulation is followed by a review of current knowledge on the genetic epidemiology of established joint shape disorders and common shape variation. A summary of current genetic epidemiology of OA is also given, together with current evidence on the genetic overlap between shape variation and OA. Finally, the established genetic risk loci for both joint shape and osteoarthritis are discussed

Identification of novel loci associated with hip shape:a meta-analysis of genome-wide association studies

This study was funded by Arthritis Research UK project grant 20244, which also provided salary funding for DB and CVG. LP works in the MRC Integrative Epidemiology Unit, a UK MRC‐funded unit (MC_ UU_ 12013/4 & MC_UU_12013/5). ALSPAC: We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. ALSPAC data collection was supported by the Wellcome Trust (grants WT092830M; WT088806; WT102215/2/13/2), UK Medical Research Council (G1001357), and University of Bristol. The UK Medical Research Council and the Wellcome Trust (102215/2/13/2) and the University of Bristol provide core support for ALSPAC. Framingham Heart Study: The Framingham Osteoporosis Study is supported by grants from the National Institute of Arthritis, Musculoskeletal, and Skin Diseases and the National Institute on Aging (R01 AR41398, R01 AR 061162, R01 AR050066, and R01 AR061445). The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource project. The Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health and Boston University School of Medicine were supported by the National Heart, Lung, and Blood Institute's Framingham Heart Study (N01‐HC‐25195) and its contract with Affymetrix, Inc., for genotyping services (N02‐HL‐6‐4278). Analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. A portion of this research was conducted using the Linux Cluster for Genetic Analysis (LinGA‐II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. DK was also supported by Israel Science Foundation grant #1283/14. TDC and DR thank Dr Claire Reardon and the entire Harvard University Bauer Core facility for assistance with ATAC‐seq next generation sequencing. This work was funded in part by the Harvard University Milton Fund, NSF (BCS‐1518596), and NIH NIAMS (1R01AR070139‐01A1) to TDC. MrOS: The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) provides funding for the MrOS ancillary study “Replication of candidate gene associations and bone strength phenotype in MrOS” under the grant number R01 AR051124. The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) provides funding for the MrOS ancillary study “GWAS in MrOS and SOF” under the grant number RC2 AR058973. SOF: The Study of Osteoporotic Fractures (SOF) is supported by National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: R01 AG005407, R01 AR35582, R01 AR35583, R01 AR35584, R01 AG005394, R01 AG027574, and R01 AG027576. TwinsUK: The study was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007‐2013). The study also receives support from the National Institute for Health Research (NIHR)‐funded BioResource, Clinical Research Facility, and Biomedical Research Centre based at Guy's and St Thomas’ NHS Foundation Trust in partnership with King's College London. SNP genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via NIH/CIDR. This study was also supported by the Australian National Health and Medical Research Council (project grants 1048216 and 1127156), the Sir Charles Gairdner Hospital RAC (SGW), and the iVEC/Pawsey Supercomputing Centre (project grants Pawsey0162 and Director2025 [SGW]). The salary of BHM was supported by a Raine Medical Research Foundation Priming Grant. The Umeå Fracture and Osteoporosis Study (UFO) is supported by the Swedish Research Council (K20006‐72X‐20155013), the Swedish Sports Research Council (87/06), the Swedish Society of Medicine, the Kempe‐Foundation (JCK‐1021), and by grants from the Medical Faculty of Umeå Unviersity (ALFVLL:968:22‐2005, ALFVL:‐937‐2006, ALFVLL:223:11‐2007, and ALFVLL:78151‐2009) and from the county council of Västerbotten (Spjutspetsanslag VLL:159:33‐2007). This publication is the work of the authors and does not necessarily reflect the views of any funders. None of the funders had any influence on data collection, analysis, interpretation of the results, or writing of the paper. DB will serve as the guarantor of the paper. Authors’ roles: Study conception and design: DAB, JSG, RMA, LP, DK, and JHT. Data collection: DJ, DPK, ESO, SRC, NEL, BHM, FMKW, JBR, SGW, TDC, BGF, DAL, CO, and UP‐L. Data analysis: DAB, DSE, FKK, JSG, FRS, CVG, RJB, RMA, SGW, EG, TDC, DR, and TB. Data interpretation: JSG, RMA, TDC, DR, DME, LP, DK, and JHT. Drafting manuscript: DAB and JHT. Revising manuscript content: JHT. All authors approved the final version of manuscript. DAB takes responsibility for the integrity of the data analysis.Peer reviewedPublisher PD

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation

Using multivariable Mendelian randomization to estimate the causal effect of bone mineral density on osteoarthritis risk, independently of body mass index

Objectives Observational analyses suggest that high bone mineral density (BMD) is a risk factor for osteoarthritis (OA); it is unclear whether this represents a causal effect or shared aetiology and whether these relationships are body mass index (BMI)-independent. We performed bidirectional Mendelian randomization (MR) to uncover the causal pathways between BMD, BMI and OA. Methods One-sample (1S)MR estimates were generated by two-stage least-squares regression. Unweighted allele scores instrumented each exposure. Two-sample (2S)MR estimates were generated using inverse-variance weighted random-effects meta-analysis. Multivariable MR (MVMR), including BMD and BMI instruments in the same model, determined the BMI-independent causal pathway from BMD to OA. Latent causal variable (LCV) analysis, using weight-adjusted femoral neck (FN)–BMD and hip/knee OA summary statistics, determined whether genetic correlation explained the causal effect of BMD on OA. Results 1SMR provided strong evidence for a causal effect of BMD estimated from heel ultrasound (eBMD) on hip and knee OA {odds ratio [OR]hip = 1.28 [95% confidence interval (CI) = 1.05, 1.57], p = 0.02, ORknee = 1.40 [95% CI = 1.20, 1.63], p = 3 × 10–5, OR per standard deviation [SD] increase}. 2SMR effect sizes were consistent in direction. Results suggested that the causal pathways between eBMD and OA were bidirectional (βhip = 1.10 [95% CI = 0.36, 1.84], p = 0.003, βknee = 4.16 [95% CI = 2.74, 5.57], p = 8 × 10–9, β = SD increase per doubling in risk). MVMR identified a BMI-independent causal pathway between eBMD and hip/knee OA. LCV suggested that genetic correlation (i.e. shared genetic aetiology) did not fully explain the causal effects of BMD on hip/knee OA. Conclusions These results provide evidence for a BMI-independent causal effect of eBMD on OA. Despite evidence of bidirectional effects, the effect of BMD on OA did not appear to be fully explained by shared genetic aetiology, suggesting a direct action of bone on joint deterioration

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation