1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Genomics and Human Genetics
  4. Volume 25, 2024
  5. Article

Annual Review of Genomics and Human Genetics

Volume 25, 2024

The Genetics and Functional Genomics of Osteoarthritis

Abstract

Osteoarthritis is the most prevalent whole-joint degenerative disorder, and is characterized by the degradation of articular cartilage and the underlying bone structures. Almost 600 million people are affected by osteoarthritis worldwide. No curative treatments are available, and management strategies focus mostly on pain relief. Here, we provide a comprehensive overview of the available human genetic and functional genomics studies for osteoarthritis to date and delineate how these studies have helped shed light on disease etiopathology. We highlight genetic discoveries from genome-wide association studies and provide a detailed overview of molecular-level investigations in osteoarthritis tissues, including methylation-, transcriptomics-, and proteomics-level analyses. We review how functional genomics data from different molecular levels have helped to prioritize effector genes that can be used as drug targets or drug-repurposing opportunities. Finally, we discuss future directions with the potential to drive a step change in osteoarthritis research.

Keyword(s): genomics, osteoarthritis, proteomics, transcriptomics
Loading

Article metrics loading...

/content/journals/10.1146/annurev-genom-010423-095636
2024-08-27
2024-09-09
Loading full text...

Full text loading...

/deliver/fulltext/genom/25/1/annurev-genom-010423-095636.html?itemId=/content/journals/10.1146/annurev-genom-010423-095636&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Aguet F, Alasoo K, Li YI, Battle A, Im HK, et al. 2023.. Molecular quantitative trait loci. . Nat. Rev. Methods Primers 3:(1):4
     [Crossref] [Google Scholar]
  2. 2.
    Ajekigbe B, Cheung K, Xu Y, Skelton AJ, Panagiotopoulos A, et al. 2019.. Identification of long non-coding RNAs expressed in knee and hip osteoarthritic cartilage. . Osteoarthr. Cartil. 27:(4):694–702
     [Crossref] [Google Scholar]
  3. 3.
    Ali N, Turkiewicz A, Hughes V, Folkesson E, Tjörnstand J, et al. 2022.. Proteomics profiling of human synovial fluid suggests increased protein interplay in early-osteoarthritis (OA) that is lost in late-stage OA. . Mol. Cell. Proteom. 21:(3):100200
     [Crossref] [Google Scholar]
  4. 4.
    Aubourg G, Rice SJ, Bruce-Wootton P, Loughlin J. 2022.. Genetics of osteoarthritis. . Osteoarthr. Cartil. 30:(5):636–49
     [Crossref] [Google Scholar]
  5. 5.
    Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, et al. 2011.. High density DNA methylation array with single CpG site resolution. . Genomics 98:(4):288–95
     [Crossref] [Google Scholar]
  6. 6.
    Boer CG, Hatzikotoulas K, Southam L, Stefánsdóttir L, Zhang Y, et al. 2021.. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. . Cell 184:(18):4784–818.e17
     [Crossref] [Google Scholar]
  7. 7.
    Boer CG, Yau MS, Rice SJ, Coutinho de Almeida R, Cheung K, et al. 2021.. Genome-wide association of phenotypes based on clustering patterns of hand osteoarthritis identify WNT9A as novel osteoarthritis gene. . Ann. Rheum. Dis. 80:(3):367–75
     [Crossref] [Google Scholar]
  8. 8.
    Bonin CA, Lewallen EA, Baheti S, Bradley EW, Stuart MJ, et al. 2016.. Identification of differentially methylated regions in new genes associated with knee osteoarthritis. . Gene 576:(1, Part 2):312–18
     [Crossref] [Google Scholar]
  9. 9.
    Brena RM, Huang TH-M, Plass C. 2006.. Toward a human epigenome. . Nat. Genet. 38:(12):1359–60
     [Crossref] [Google Scholar]
  10. 10.
    Brophy RH, Zhang B, Cai L, Wright RW, Sandell LJ, Rai MF. 2018.. Transcriptome comparison of meniscus from patients with and without osteoarthritis. . Osteoarthr. Cartil. 26:(3):422–32
     [Crossref] [Google Scholar]
  11. 11.
    Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, et al. 2018.. The UK Biobank resource with deep phenotyping and genomic data. . Nature 562:(7726):203–9
     [Crossref] [Google Scholar]
  12. 12.
    Casalone E, Tachmazidou I, Zengini E, Hatzikotoulas K, Hackinger S, et al. 2018.. A novel variant in GLIS3 is associated with osteoarthritis. . Ann. Rheum. Dis. 77:(4):620–23
     [Crossref] [Google Scholar]
  13. 13.
    Castaño Betancourt MC, Cailotto F, Kerkhof HJ, Cornelis FMF, Doherty SA, et al. 2012.. Genome-wide association and functional studies identify the DOT1L gene to be involved in cartilage thickness and hip osteoarthritis. . PNAS 109:(21):8218–23
     [Crossref] [Google Scholar]
  14. 14.
    Chang J, Liao Z, Lu M, Meng T, Han W, Ding C. 2018.. Systemic and local adipose tissue in knee osteoarthritis. . Osteoarthr. Cartil. 26:(7):864–71
     [Crossref] [Google Scholar]
  15. 15.
    Chou C-H, Jain V, Gibson J, Attarian DE, Haraden CA, et al. 2020.. Synovial cell cross-talk with cartilage plays a major role in the pathogenesis of osteoarthritis. . Sci. Rep. 10::10868
     [Crossref] [Google Scholar]
  16. 16.
    Coutinho de Almeida R, Ramos YFM, Mahfouz A, den Hollander W, Lakenberg N, et al. 2019.. RNA sequencing data integration reveals an miRNA interactome of osteoarthritis cartilage. . Ann. Rheum. Dis. 78:(2):270–77
     [Crossref] [Google Scholar]
  17. 17.
    Damerau A, Kirchner M, Pfeiffenberger M, Ehlers L, Do Nguyen DH, et al. 2022.. Metabolic reprogramming of synovial fibroblasts in osteoarthritis by inhibition of pathologically overexpressed pyruvate dehydrogenase kinases. . Metab. Eng. 72::116–32
     [Crossref] [Google Scholar]
  18. 18.
    de Seny D, Baiwir D, Bianchi E, Cobraiville G, Deroyer C, et al. 2021.. New proteins contributing to immune cell infiltration and pannus formation of synovial membrane from arthritis diseases. . Int. J. Mol. Sci. 23:(1):434
     [Crossref] [Google Scholar]
  19. 19.
    den Hollander W, Boer CG, Hart DJ, Yau MS, Ramos YFM, et al. 2017.. Genome-wide association and functional studies identify a role for matrix Gla protein in osteoarthritis of the hand. . Ann. Rheum. Dis. 76:(12):2046–53
     [Crossref] [Google Scholar]
  20. 20.
    den Hollander W, Ramos YFM, Bomer N, Elzinga S, van der Breggen R, et al. 2015.. Transcriptional associations of osteoarthritis-mediated loss of epigenetic control in articular cartilage. . Arthritis Rheumatol. 67:(8):2108–16
     [Crossref] [Google Scholar]
  21. 21.
    den Hollander W, Ramos YFM, Bos SD, Bomer N, van der Breggen R, et al. 2014.. Knee and hip articular cartilage have distinct epigenomic landscapes: implications for future cartilage regeneration approaches. . Ann. Rheum. Dis. 73:(12):2208–12
     [Crossref] [Google Scholar]
  22. 22.
    Di J, Chen Z, Wang Z, He T, Wu D, et al. 2023.. Cartilage tissue from sites of weight bearing in patients with osteoarthritis exhibits a differential phenotype with distinct chondrocytes subsets. . RMD Open 9:(4):e003255
     [Crossref] [Google Scholar]
  23. 23.
    Dong Y, Wang P, Zhang M, Xiao L, Yang Y, et al. 2022.. Phosphoproteomics reveals the BRAF-ERK1/2 axis as an important pathogenic signaling node in cartilage degeneration. . Osteoarthr. Cartil. 30:(11):1443–54
     [Crossref] [Google Scholar]
  24. 24.
    ENCODE Proj. Consort., Moore JE, Purcaro MJ, Pratt HE, Epstein CB, et al. 2020.. Expanded encyclopaedias of DNA elements in the human and mouse genomes. . Nature 583:(7818):699–710
     [Crossref] [Google Scholar]
  25. 25.
    Englund M. 2023.. Osteoarthritis, part of life or a curable disease? A bird's-eye view. . J. Intern. Med. 293:(6):681–93
     [Crossref] [Google Scholar]
  26. 26.
    Evangelou E, Valdes AM, Kerkhof HJM, Styrkarsdottir U, Zhu Y, et al. 2011.. Meta-analysis of genome-wide association studies confirms a susceptibility locus for knee osteoarthritis on chromosome 7q22. . Ann. Rheum. Dis. 70:(2):349–55
     [Crossref] [Google Scholar]
  27. 27.
    Faber BG, Frysz M, Boer CG, Evans DS, Ebsim R, et al. 2023.. The identification of distinct protective and susceptibility mechanisms for hip osteoarthritis: findings from a genome-wide association study meta-analysis of minimum joint space width and Mendelian randomisation cluster analyses. . eBioMedicine 95::104759
     [Crossref] [Google Scholar]
  28. 28.
    Faber BG, Frysz M, Hartley AE, Ebsim R, Boer CG, et al. 2023.. A genome-wide association study meta-analysis of alpha angle suggests cam-type morphology may be a specific feature of hip osteoarthritis in older adults. . Arthritis Rheumatol. 75:(6):900–9. Erratum . 2023.. Arthritis Rheumatol. 75:(9):1585
     [Google Scholar]
  29. 29.
    Fan X, Wu X, Trevisan Franca De Lima L, Stehbens S, Punyadeera C, et al. 2022.. The deterioration of calcified cartilage integrity reflects the severity of osteoarthritis—a structural, molecular, and biochemical analysis. . FASEB J. 36:(2):e22142
     [Crossref] [Google Scholar]
  30. 30.
    Feng G, Zhou Y, Yan J, Wang Z, Yang Y, et al. 2023.. Proteomic and N-glycoproteomic analyses of total subchondral bone protein in patients with primary knee osteoarthritis. . J. Proteom. 280::104896
     [Crossref] [Google Scholar]
  31. 31.
    Fisch KM, Gamini R, Alvarez-Garcia O, Akagi R, Saito M, et al. 2018.. Identification of transcription factors responsible for dysregulated networks in human osteoarthritis cartilage by global gene expression analysis. . Osteoarthr. Cartil. 26:(11):1531–38
     [Crossref] [Google Scholar]
  32. 32.
    Fu W, Chen S, Yang R, Li C, Gao H, et al. 2022.. Cellular features of localized microenvironments in human meniscal degeneration: a single-cell transcriptomic study. . eLife 11::e79585
     [Crossref] [Google Scholar]
  33. 33.
    Fuentes M, Ruiz-Romero C, Misiego S, Juanes-Velasco P, Landeira-Viñuela A, et al. 2023.. Exploring high-throughput immunoassays for biomarker validation in rheumatic diseases in the context of the Human Proteome Project. . J. Proteome Res. 22:(4):1105–15
     [Crossref] [Google Scholar]
  34. 34.
    Gandhi R, Takahashi M, Virtanen C, Syed K, Davey JR, Mahomed NN. 2011.. Microarray analysis of the infrapatellar fat pad in knee osteoarthritis: relationship with joint inflammation. . J. Rheumatol. 38:(9):1966–72
     [Crossref] [Google Scholar]
  35. 35.
    Gaziano JM, Concato J, Brophy M, Fiore L, Pyarajan S, et al. 2016.. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. . J. Clin. Epidemiol. 70::214–23
     [Crossref] [Google Scholar]
  36. 36.
    GBD 2021 Osteoarthr. Collab. 2023.. Global, regional, and national burden of osteoarthritis, 1990–2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. . Lancet Rheumatol. 5:(9):e508–22
     [Crossref] [Google Scholar]
  37. 37.
    Han P, Hou C, Zheng X, Cao L, Shi X, et al. 2022.. Serum antigenome profiling reveals diagnostic models for rheumatoid arthritis. . Front. Immunol. 13::884462
     [Crossref] [Google Scholar]
  38. 38.
    Henkel C, Styrkarsdottir U, Thorleifsson G, Stefansdottir L, Bjornsdottir G, et al. 2023.. Genome-wide association meta-analysis of knee and hip osteoarthritis uncovers genetic differences between patients treated with joint replacement and patients without joint replacement. . Ann. Rheum. Dis. 82:(3):384–92
     [Crossref] [Google Scholar]
  39. 39.
    Hervouet E, Peixoto P, Delage-Mourroux R, Boyer-Guittaut M, Cartron P-F. 2018.. Specific or not specific recruitment of DNMTs for DNA methylation, an epigenetic dilemma. . Clin. Epigenet. 10::17
     [Crossref] [Google Scholar]
  40. 40.
    Hu W, Chen Y, Dou C, Dong S. 2021.. Microenvironment in subchondral bone: predominant regulator for the treatment of osteoarthritis. . Ann. Rheum. Dis. 80:(4):413–22
     [Crossref] [Google Scholar]
  41. 41.
    Hu Y, Cui J, Liu H, Wang S, Zhou Q, et al. 2022.. Single-cell RNA-sequencing analysis reveals the molecular mechanism of subchondral bone cell heterogeneity in the development of osteoarthritis. . RMD Open 8:(2):e002314
     [Crossref] [Google Scholar]
  42. 42.
    Huang Y, Liu Y, Huang Q, Sun S, Ji Z, et al. 2022.. TMT-based quantitative proteomics analysis of synovial fluid-derived exosomes in inflammatory arthritis. . Front. Immunol. 13::800902
     [Crossref] [Google Scholar]
  43. 43.
    Huang ZY, Luo ZY, Cai YR, Chou C-H, Yao ML, et al. 2022.. Single cell transcriptomics in human osteoarthritis synovium and in silico deconvoluted bulk RNA sequencing. . Osteoarthr. Cartil. 30:(3):475–80
     [Crossref] [Google Scholar]
  44. 44.
    Hunter DJ, Bierma-Zeinstra S. 2019.. Osteoarthritis. . Lancet 393:(10182):1745–59
     [Crossref] [Google Scholar]
  45. 45.
    Ikram MA, Brusselle GGO, Murad SD, van Duijn CM, Franco OH, et al. 2017.. The Rotterdam Study: 2018 update on objectives, design and main results. . Eur. J. Epidemiol. 32:(9):807–50
     [Crossref] [Google Scholar]
  46. 46.
    Jeffries MA, Donica M, Baker LW, Stevenson ME, Annan AC, et al. 2016.. Genome-wide DNA methylation study identifies significant epigenomic changes in osteoarthritic subchondral bone and similarity to overlying cartilage. . Arthritis Rheumatol. 68:(6):1403–14
     [Crossref] [Google Scholar]
  47. 47.
    Ji Q, Zheng Y, Zhang G, Hu Y, Fan X, et al. 2019.. Single-cell RNA-seq analysis reveals the progression of human osteoarthritis. . Ann. Rheum. Dis. 78:(1):100–10
     [Crossref] [Google Scholar]
  48. 48.
    Kamps A, Runhaar J, de Ridder MAJ, de Wilde M, van der Lei J, et al. 2023.. Occurrence of comorbidity following osteoarthritis diagnosis: a cohort study in the Netherlands. . Osteoarthr. Cartil. 31:(4):519–28
     [Crossref] [Google Scholar]
  49. 49.
    Katsoula G, Kreitmaier P, Zeggini E. 2022.. Insights into the molecular landscape of osteoarthritis in human tissues. . Curr. Opin. Rheumatol. 34::79
     [Crossref] [Google Scholar]
  50. 50.
    Katsoula G, Steinberg J, Tuerlings M, Coutinho de Almeida R, Southam L, et al. 2022.. A molecular map of long non-coding RNA expression, isoform switching and alternative splicing in osteoarthritis. . Hum. Mol. Genet. 31:(12):2090–105
     [Crossref] [Google Scholar]
  51. 51.
    Kehayova YS, Watson E, Wilkinson JM, Loughlin J, Rice SJ. 2021.. Genetic and epigenetic interplay within a COLGALT2 enhancer associated with osteoarthritis. . Arthritis Rheumatol. 73:(10):1856–65
     [Crossref] [Google Scholar]
  52. 52.
    Klinedinst NJ, Huang W, Nelson AK, Resnick B, Renn C, et al. 2023.. Protein changes after 6 weeks of walking and the relationship to pain in adults with knee osteoarthritis. . Biol. Res. Nurs. 25:(1):65–75
     [Crossref] [Google Scholar]
  53. 53.
    Kreitmaier P, Katsoula G, Zeggini E. 2023.. Insights from multi-omics integration in complex disease primary tissues. . Trends Genet. 39:(1):46–58
     [Crossref] [Google Scholar]
  54. 54.
    Kreitmaier P, Park Y-C, Swift D, Gilly A, Wilkinson JM, Zeggini E. 2024.. Epigenomic profiling of the infrapatellar fat pad in osteoarthritis. . Hum. Mol. Genet. 33::5019
     [Crossref] [Google Scholar]
  55. 55.
    Kreitmaier P, Suderman M, Southam L, Coutinho de Almeida R, Hatzikotoulas K, et al. 2022.. An epigenome-wide view of osteoarthritis in primary tissues. . Am. J. Hum. Genet. 109:(7):1255–71
     [Crossref] [Google Scholar]
  56. 56.
    Kulkarni P, Harsulkar A, Märtson A-G, Suutre S, Märtson A, Koks S. 2022.. Mast cells differentiated in synovial fluid and resident in osteophytes exalt the inflammatory pathology of osteoarthritis. . Int. J. Mol. Sci. 23:(1):541
     [Crossref] [Google Scholar]
  57. 57.
    Kulm S, Kaidi AC, Kolin D, Langhans MT, Bostrom MP, et al. 2023.. Genetic risk factors for end-stage hip osteoarthritis treated with total hip arthroplasty: a genome-wide association study. . J. Arthroplasty 38:(10):2149–53.e1
     [Crossref] [Google Scholar]
  58. 58.
    Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. 2015.. Integrative analysis of 111 reference human epigenomes. . Nature 518:(7539):317–30
     [Crossref] [Google Scholar]
  59. 59.
    Li H, Jiang X, Xiao Y, Zhang Y, Zhang W, et al. 2023.. Combining single-cell RNA sequencing and population-based studies reveals hand osteoarthritis-associated chondrocyte subpopulations and pathways. . Bone Res. 11:(1):58
     [Crossref] [Google Scholar]
  60. 60.
    Liu Y, Yau MS, Yerges-Armstrong LM, Duggan DJ, Renner JB, et al. 2017.. Genetic determinants of radiographic knee osteoarthritis in African Americans. . J. Rheumatol. 44:(11):1652–58
     [Crossref] [Google Scholar]
  61. 61.
    Lv Z, Han J, Li J, Guo H, Fei Y, et al. 2022.. Single cell RNA-seq analysis identifies ferroptotic chondrocyte cluster and reveals TRPV1 as an anti-ferroptotic target in osteoarthritis. . eBioMedicine 84::104258
     [Crossref] [Google Scholar]
  62. 62.
    Lyu Y, Deng H, Qu C, Qiao L, Liu X, et al. 2022.. Identification of proteins and N-glycosylation sites of knee cartilage in Kashin-Beck disease compared with osteoarthritis. . Int. J. Biol. Macromol. 210::128–38
     [Crossref] [Google Scholar]
  63. 63.
    McDonald M-LN, Lakshman Kumar P, Srinivasasainagendra V, Nair A, Rocco AP, et al. 2022.. Novel genetic loci associated with osteoarthritis in multi-ancestry analyses in the Million Veteran Program and UK Biobank. . Nat. Genet. 54:(12):1816–26
     [Crossref] [Google Scholar]
  64. 64.
    Miyamoto Y, Mabuchi A, Shi D, Kubo T, Takatori Y, et al. 2007.. A functional polymorphism in the 5′ UTR of GDF5 is associated with susceptibility to osteoarthritis. . Nat. Genet. 39:(4):529–33
     [Crossref] [Google Scholar]
  65. 65.
    Moazedi-Fuerst FC, Hofner M, Gruber G, Weinhaeusel A, Stradner MH, et al. 2014.. Epigenetic differences in human cartilage between mild and severe OA. . J. Orthop. Res. 32:(12):1636–45
     [Crossref] [Google Scholar]
  66. 66.
    Moran S, Arribas C, Esteller M. 2016.. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. . Epigenomics 8:(3):389–99
     [Crossref] [Google Scholar]
  67. 67.
    Nakajima M, Takahashi A, Kou I, Rodriguez-Fontenla C, Gomez-Reino JJ, et al. 2010.. New sequence variants in HLA class II/III region associated with susceptibility to knee osteoarthritis identified by genome-wide association study. . PLOS ONE 5:(3):e9723
     [Crossref] [Google Scholar]
  68. 68.
    Nanus DE, Badoume A, Wijesinghe SN, Halsey AM, Hurley P, et al. 2021.. Synovial tissue from sites of joint pain in knee osteoarthritis patients exhibits a differential phenotype with distinct fibroblast subsets. . eBioMedicine 72::103618
     [Crossref] [Google Scholar]
  69. 69.
    Oliva M, Demanelis K, Lu Y, Chernoff M, Jasmine F, et al. 2023.. DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits. . Nat. Genet. 55:(1):112–22
     [Crossref] [Google Scholar]
  70. 70.
    Panoutsopoulou K, Southam L, Elliott KS, Wrayner N, Zhai G, et al. 2011.. Insights into the genetic architecture of osteoarthritis from stage 1 of the arcOGEN study. . Ann. Rheum. Dis. 70:(5):864–67
     [Crossref] [Google Scholar]
  71. 71.
    Park J, Lee H-S, Go E-B, Lee JY, Kim JY, et al. 2021.. Proteomic analysis of the meniscus cartilage in osteoarthritis. . Int. J. Mol. Sci. 22:(15):8181
     [Crossref] [Google Scholar]
  72. 72.
    Parker E, Hofer IMJ, Rice SJ, Earl L, Anjum SA, et al. 2021.. Multi-tissue epigenetic and gene expression analysis combined with epigenome modulation identifies RWDD2B as a target of osteoarthritis susceptibility. . Arthritis Rheumatol. 73:(1):100–9
     [Crossref] [Google Scholar]
  73. 73.
    Rai MF, Cai L, Zhang Q, Townsend RR, Brophy RH. 2023.. Synovial fluid proteomics from serial aspirations of ACL-injured knees identifies candidate biomarkers. . Am. J. Sports Med. 51:(7):1733–42
     [Crossref] [Google Scholar]
  74. 74.
    Ren X, Geng M, Xu K, Lu C, Cheng Y, et al. 2021.. Quantitative proteomic analysis of synovial tissue reveals that upregulated OLFM4 aggravates inflammation in rheumatoid arthritis. . J. Proteome Res. 20:(10):4746–57
     [Crossref] [Google Scholar]
  75. 75.
    Rice SJ, Aubourg G, Sorial AK, Almarza D, Tselepi M, et al. 2018.. Identification of a novel, methylation-dependent, RUNX2 regulatory region associated with osteoarthritis risk. . Hum. Mol. Genet. 27:(19):3464–74
     [Crossref] [Google Scholar]
  76. 76.
    Rice SJ, Brumwell A, Falk J, Kehayova YS, Casement J, et al. 2023.. Genetic risk of osteoarthritis operates during human skeletogenesis. . Hum. Mol. Genet. 32:(13):2124–38
     [Crossref] [Google Scholar]
  77. 77.
    Rice SJ, Cheung K, Reynard LN, Loughlin J. 2019.. Discovery and analysis of methylation quantitative trait loci (mQTLs) mapping to novel osteoarthritis genetic risk signals. . Osteoarthr. Cartil. 27:(10):1545–56
     [Crossref] [Google Scholar]
  78. 78.
    Rice SJ, Roberts JB, Tselepi M, Brumwell A, Falk J, et al. 2021.. Genetic and epigenetic fine-tuning of TGFB1 expression within the human osteoarthritic joint. . Arthritis Rheumatol. 73:(10):1866–77
     [Crossref] [Google Scholar]
  79. 79.
    Rice SJ, Tselepi M, Sorial AK, Aubourg G, Shepherd C, et al. 2019.. Prioritization of PLEC and GRINA as osteoarthritis risk genes through the identification and characterization of novel methylation quantitative trait loci. . Arthritis Rheumatol. 71:(8):1285–96
     [Crossref] [Google Scholar]
  80. 80.
    Sarkar A, Chakraborty D, Kumar V, Malhotra R, Biswas S. 2022.. Upregulation of leucine-rich alpha-2 glycoprotein: a key regulator of inflammation and joint fibrosis in patients with severe knee osteoarthritis. . Front. Immunol. 13::1028994
     [Crossref] [Google Scholar]
  81. 81.
    Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M, et al. 2023.. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. . Nucleic Acids Res. 51:(D1):D977–85
     [Crossref] [Google Scholar]
  82. 82.
    Song J, Wu J, Poulet B, Liang J, Bai C, et al. 2021.. Proteomics analysis of hip articular cartilage identifies differentially expressed proteins associated with osteonecrosis of the femoral head. . Osteoarthr. Cartil. 29:(7):1081–92
     [Crossref] [Google Scholar]
  83. 83.
    Soul J, Dunn SL, Anand S, Serracino-Inglott F, Schwartz J-M, et al. 2018.. Stratification of knee osteoarthritis: two major patient subgroups identified by genome-wide expression analysis of articular cartilage. . Ann. Rheum. Dis. 77:(3):423–30. Erratum . 2018.. Ann. Rheum. Dis. 77::960
     [Google Scholar]
  84. 84.
    Spector TD, MacGregor AJ. 2004.. Risk factors for osteoarthritis: genetics. . Osteoarthr. Cartil. 12:(Suppl. A):S39–44
     [Crossref] [Google Scholar]
  85. 85.
    Steinberg J, Ritchie GRS, Roumeliotis TI, Jayasuriya RL, Clark MJ, et al. 2017.. Integrative epigenomics, transcriptomics and proteomics of patient chondrocytes reveal genes and pathways involved in osteoarthritis. . Sci. Rep. 7::8935
     [Crossref] [Google Scholar]
  86. 86.
    Steinberg J, Southam L, Fontalis A, Clark MJ, Jayasuriya RL, et al. 2021.. Linking chondrocyte and synovial transcriptional profile to clinical phenotype in osteoarthritis. . Ann. Rheum. Dis. 80:(8):1070–74
     [Crossref] [Google Scholar]
  87. 87.
    Steinberg J, Southam L, Roumeliotis TI, Clark MJ, Jayasuriya RL, et al. 2021.. A molecular quantitative trait locus map for osteoarthritis. . Nat. Commun. 12::1309
     [Crossref] [Google Scholar]
  88. 88.
    Styrkarsdottir U, Helgason H, Sigurdsson A, Norddahl GL, Agustsdottir AB, et al. 2017.. Whole-genome sequencing identifies rare genotypes in COMP and CHADL associated with high risk of hip osteoarthritis. . Nat. Genet. 49:(5):801–5
     [Crossref] [Google Scholar]
  89. 89.
    Styrkarsdottir U, Lund SH, Saevarsdottir S, Magnusson MI, Gunnarsdottir K, et al. 2021.. The CRTAC1 protein in plasma is associated with osteoarthritis and predicts progression to joint replacement: a large-scale proteomics scan in Iceland. . Arthritis Rheumatol. 73:(11):2025–34
     [Crossref] [Google Scholar]
  90. 90.
    Styrkarsdottir U, Lund SH, Thorleifsson G, Zink F, Stefansson OA, et al. 2018.. Meta-analysis of Icelandic and UK data sets identifies missense variants in SMO, IL11, COL11A1 and 13 more new loci associated with osteoarthritis. . Nat. Genet. 50:(12):1681–87
     [Crossref] [Google Scholar]
  91. 91.
    Styrkarsdottir U, Stefansdottir L, Thorleifsson G, Stefansson OA, Saevarsdottir S, et al. 2023.. Meta-analysis of erosive hand osteoarthritis identifies four common variants that associate with relatively large effect. . Ann. Rheum. Dis. 82:(6):873–80
     [Crossref] [Google Scholar]
  92. 92.
    Styrkarsdottir U, Thorleifsson G, Helgadottir HT, Bomer N, Metrustry S, et al. 2014.. Severe osteoarthritis of the hand associates with common variants within the ALDH1A2 gene and with rare variants at 1p31. . Nat. Genet. 46:(5):498–502
     [Crossref] [Google Scholar]
  93. 93.
    Sudlow C, Gallacher J, Allen N, Beral V, Burton P, et al. 2015.. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. . PLOS Med. 12:(3):e1001779
     [Crossref] [Google Scholar]
  94. 94.
    Sun H, Wen X, Li H, Wu P, Gu M, et al. 2020.. Single-cell RNA-seq analysis identifies meniscus progenitors and reveals the progression of meniscus degeneration. . Ann. Rheum. Dis. 79:(3):408–17
     [Crossref] [Google Scholar]
  95. 95.
    Swahn H, Li K, Duffy T, Olmer M, D'Lima DD, et al. 2023.. Senescent cell population with ZEB1 transcription factor as its main regulator promotes osteoarthritis in cartilage and meniscus. . Ann. Rheum. Dis. 82:(3):403–15
     [Crossref] [Google Scholar]
  96. 96.
    Szilagyi IA, Vallerga CL, Boer CG, Schiphof D, Ikram MA, et al. 2023.. Plasma proteomics identifies CRTAC1 as a biomarker for osteoarthritis severity and progression. . Rheumatology 62:(3):1286–95
     [Crossref] [Google Scholar]
  97. 97.
    Tachmazidou I, Hatzikotoulas K, Southam L, Esparza-Gordillo J, Haberland V, et al. 2019.. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. . Nat. Genet. 51:(2):230–36
     [Crossref] [Google Scholar]
  98. 98.
    Tardif G, Paré F, Gotti C, Roux-Dalvai F, Droit A, et al. 2022.. Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers. . Arthritis Res. Ther. 24:(1):120
     [Crossref] [Google Scholar]
  99. 99.
    Tuerlings M, van Hoolwerff M, Houtman E, Suchiman EHED, Lakenberg N, et al. 2021.. RNA sequencing reveals interacting key determinants of osteoarthritis acting in subchondral bone and articular cartilage: identification of IL11 and CHADL as attractive treatment targets. . Arthritis Rheumatol. 73:(5):789–99
     [Crossref] [Google Scholar]
  100. 100.
    Valdes AM, Evangelou E, Kerkhof HJM, Tamm A, Doherty SA, et al. 2011.. The GDF5 rs143383 polymorphism is associated with osteoarthritis of the knee with genome-wide statistical significance. . Ann. Rheum. Dis. 70:(5):873–75
     [Crossref] [Google Scholar]
  101. 101.
    Valdes AM, Spector TD, Tamm A, Kisand K, Doherty SA, et al. 2010.. Genetic variation in the SMAD3 gene is associated with hip and knee osteoarthritis. . Arthritis Rheum. 62:(8):2347–52
     [Crossref] [Google Scholar]
  102. 102.
    Wang Z, Gerstein M, Snyder M. 2009.. RNA-Seq: a revolutionary tool for transcriptomics. . Nat. Rev. Genet. 10:(1):57–63
     [Crossref] [Google Scholar]
  103. 103.
    Wijesinghe SN, Badoume A, Nanus DE, Sharma-Oates A, Farah H, et al. 2023.. Obesity defined molecular endotypes in the synovium of patients with osteoarthritis provides a rationale for therapeutic targeting of fibroblast subsets. . Clin. Transl. Med. 13:(4):e1232
     [Crossref] [Google Scholar]
  104. 104.
    Yang G, Mishra M, Perera MA. 2023.. Multi-omics studies in historically excluded populations: the road to equity. . Clin. Pharmacol. Ther. 113:(3):541–56
     [Crossref] [Google Scholar]
  105. 105.
    Yang L, Chen Z, Guo H, Wang Z, Sun K, et al. 2021.. Extensive cytokine analysis in synovial fluid of osteoarthritis patients. . Cytokine 143::155546
     [Crossref] [Google Scholar]
  106. 106.
    Zengini E, Hatzikotoulas K, Tachmazidou I, Steinberg J, Hartwig FP, et al. 2018.. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. . Nat. Genet. 50:(4):549–58
     [Crossref] [Google Scholar]
  107. 107.
    Zhang Y, Fukui N, Yahata M, Katsuragawa Y, Tashiro T, et al. 2016.. Genome-wide DNA methylation profile implicates potential cartilage regeneration at the late stage of knee osteoarthritis. . Osteoarthr. Cartil. 24:(5):835–43
     [Crossref] [Google Scholar]
  108. 108.
    Zhang Y, Fukui N, Yahata M, Katsuragawa Y, Tashiro T, et al. 2016.. Identification of DNA methylation changes associated with disease progression in subchondral bone with site-matched cartilage in knee osteoarthritis. . Sci. Rep. 6::34460
     [Crossref] [Google Scholar]
  109. 109.
    Zhang Y, Wang Q, Liang J, Liu L, Liu P, Zhao H. 2022.. Serum proteomic analysis of differentially expressed proteins and pathways involved in the mechanism of endemic osteoarthritis. . Mol. Omics 18:(8):745–53
     [Crossref] [Google Scholar]
  110. 110.
    Zhao L, Wu T, Li J, Cai C, Yao Q, Zhu Y-S. 2022.. Data-independent acquisition-based proteomics analysis correlating type 2 diabetes mellitus with osteoarthritis in total knee arthroplasty patients. . Medicine 101:(5):e28738
     [Crossref] [Google Scholar]
  111. 111.
    Zhu T, Tan Q, Xin X, Li F, Zhang K, et al. 2022.. Proteomic analysis of human articular cartilage unravels the dyscoagulation in osteoarthritis and the potential value of serpinA5 as a biomarker for osteoarthritis. . Proteom. Clin. Appl. 16:(3):e2100117
     [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-genom-010423-095636
Loading
The Genetics and Functional Genomics of Osteoarthritis
Annual Review of Genomics and Human Genetics 25, 239 (2024); https://doi.org/10.1146/annurev-genom-010423-095636
/content/journals/10.1146/annurev-genom-010423-095636
/content/journals/10.1146/annurev-genom-010423-095636
Loading

Data & Media loading...

Supplemental Materials

  • Supplemental Table 1

file format download
  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error