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

Nature Genetics volume 51, pages 230–236 (2019)Cite this article

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Abstract

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.

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Fig. 1: Genetic correlations between osteoarthritis and other traits and diseases.
Fig. 2: Allelic architecture of index variants.

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Data availability

All RNA sequencing data have been deposited in the European Genome-Phenome Archive (cohort 1, EGAD00001001331; cohort 2, EGAD00001003355; and cohort 3, EGAD00001003354). Genotype data of the arcOGEN cases and UKHLS controls have been deposited at the European Genome-Phenome Archive under accession numbers EGAS00001001017 and EGAS00001001232, respectively.

References

  1. Vos, T. et al. Years lived with disability (ylds) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the global burden of disease study 2010. Lancet 380, 2163–2196 (2012).

    Article  Google Scholar 

  2. Hiligsmann, M. et al. Health economics in the field of osteoarthritis: an expert’s consensus paper from the european society for clinical and economic aspects of osteoporosis and osteoarthritis (ESCEO). Semin. Arthritis Rheum. 43, 303–313 (2013).

    Article  Google Scholar 

  3. Baker, P. N. et al. The effect of surgical factors on early patient-reported outcome measures (PROMS) following total knee replacement. J. Bone Joint Surg. Br. 94, 1058–1066 (2012).

    Article  CAS  Google Scholar 

  4. Zengini, E. et al. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat. Genet. 50, 549–558 (2018).

    Article  CAS  Google Scholar 

  5. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  Google Scholar 

  6. Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    Article  CAS  Google Scholar 

  7. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  Google Scholar 

  8. Wakefield, J. Bayes factors for genome-wide association studies: comparison with P-values. Genet. Epidemiol. 33, 79–86 (2009).

    Article  Google Scholar 

  9. arcOGEN Consortium et al. Identification of new susceptibility loci for osteoarthritis (arcOGEN): a genome-wide association study. Lancet 380, 815–823 (2012).

    Article  Google Scholar 

  10. Takeuchi, Y. et al. Interleukin-11 as a stimulatory factor for bone formation prevents bone loss with advancing age in mice. J. Biol. Chem. 277, 49011–49018 (2002).

    Article  CAS  Google Scholar 

  11. Sims, N. A. et al. Interleukin-11 receptor signaling is required for normal bone remodeling. J. Bone Miner. Res. 20, 1093–1102 (2005).

    Article  CAS  Google Scholar 

  12. Chou, C. H. et al. Insights into osteoarthritis progression revealed by analyses of both knee tibiofemoral compartments. Osteoarthritis Cartilage 23, 571–580 (2015).

    Article  Google Scholar 

  13. Lanktree, M. B. et al. Meta-analysis of dense genecentric association studies reveals common and uncommon variants associated with height. Am. J. Hum. Genet. 88, 6–18 (2011).

    Article  CAS  Google Scholar 

  14. Harmegnies, D. et al. Characterization of a potent human interleukin-11 agonist. Biochem. J. 375, 23–32 (2003).

    Article  CAS  Google Scholar 

  15. Keynan, S., Hooper, N. M., Felici, A., Amicosante, G. & Turner, A. J. The renal membrane dipeptidase (dehydropeptidase I) inhibitor, cilastatin, inhibits the bacterial metallo-beta-lactamase enzyme CphA. Antimicrob. Agents Chemother. 39, 1629–1631 (1995).

    Article  CAS  Google Scholar 

  16. Janssens, K. et al. Camurati-Engelmann disease: review of the clinical, radiological, and molecular data of 24 families and implications for diagnosis and treatment. J. Med. Genet. 43, 1–11 (2006).

    Article  CAS  Google Scholar 

  17. Yuldashev, A. J. et al. Orthopedic manifestations of type i camurati-engelmann disease. Clin. Orthop. Surg. 9, 109–115 (2017).

    Article  Google Scholar 

  18. Wu, M., Chen, G. & Li, Y. P. TGF-β and BMP signaling in osteoblast, skeletal development, and bone formation, homeostasis and disease. Bone Res. 4, 16009 (2016).

    Article  Google Scholar 

  19. Tang, Y. et al. TGF-β1-induced migration of bone mesenchymal stem cells couples bone resorption with formation. Nat. Med. 15, 757–765 (2009).

    Article  CAS  Google Scholar 

  20. Zhao, H. et al. Transforming growth factor β1/smad4 signaling affects osteoclast differentiation via regulation of mir-155 expression. Mol. Cell 40, 211–221 (2017).

    CAS  Google Scholar 

  21. Zhou, S. TGF-β regulates β-catenin signaling and osteoblast differentiation in human mesenchymal stem cells. J. Cell Biochem. 112, 1651–1660 (2011).

    Article  CAS  Google Scholar 

  22. Zhou, S., Eid, K. & Glowacki, J. Cooperation between TGF-β and Wnt pathways during chondrocyte and adipocyte differentiation of human marrow stromal cells. J. Bone Miner. Res. 19, 463–470 (2004).

    Article  CAS  Google Scholar 

  23. Kim, M. K. et al. A multicenter, double-blind, phase iii clinical trial to evaluate the efficacy and safety of a cell and gene therapy in knee osteoarthritis patients. Hum. Gene Ther. Clin. Dev. 29, 48–59 (2018).

    Article  CAS  Google Scholar 

  24. Nuchel, J. et al. TGFB1 is secreted through an unconventional pathway dependent on the autophagic machinery and cytoskeletal regulators. Autophagy 14, 465–486 (2018).

    Article  Google Scholar 

  25. Koli, K., Ryynanen, M. J. & Keski-Oja, J. Latent TGF-beta binding proteins (LTBPs)-1 and -3 coordinate proliferation and osteogenic differentiation of human mesenchymal stem cells. Bone 43, 679–688 (2008).

    Article  CAS  Google Scholar 

  26. Cheung, K. S. et al. MicroRNA-146a regulates human foetal femur derived skeletal stem cell differentiation by down-regulating SMAD2 and SMAD3. PLoS ONE 9, e98063 (2014).

    Article  Google Scholar 

  27. Tardif, G. et al. NFAT3 and TGF-β/SMAD3 regulate the expression of miR-140 in osteoarthritis. Arthritis. Res. Ther. 15, R197 (2013).

    Article  Google Scholar 

  28. Nishimura, R., Hata, K., Nakamura, E., Murakami, T. & Takahata, Y. Transcriptional network systems in cartilage development and disease. Histochem. Cell Biol. 149, 353–363 (2018).

    Article  CAS  Google Scholar 

  29. Kanaan, R. A. & Kanaan, L. A. Transforming growth factor-β1, bone connection. Med. Sci. Moni. 12, RA164–RA169 (2006).

    CAS  Google Scholar 

  30. Song, J. et al. MicroRNA-488 regulates zinc transporter SLC39A8/ZIP8 during pathogenesis of osteoarthritis. J. Biomed. Sci. 20, 31 (2013).

    Article  CAS  Google Scholar 

  31. Kim, J. H. et al. Regulation of the catabolic cascade in osteoarthritis by the zinc-ZIP8-MTF1 axis. Cell 156, 730–743 (2014).

    Article  CAS  Google Scholar 

  32. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    Article  CAS  Google Scholar 

  33. Zhang, R. et al. A blood pressure-associated variant of the SLC39A8 gene influences cellular cadmium accumulation and toxicity. Hum. Mol. Genet. 25, 4117–4126 (2016).

    Article  CAS  Google Scholar 

  34. Li, D. et al. A pleiotropic missense variant in SLC39A8 is associated with Crohn’s disease and human gut microbiome composition. Gastroenterology 151, 724–732 (2016).

    Article  CAS  Google Scholar 

  35. Sudlow, C. et al. 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, e1001779 (2015).

    Article  Google Scholar 

  36. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    Article  CAS  Google Scholar 

  37. 1000 Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  38. Panoutsopoulou, K. et al. Insights into the genetic architecture of osteoarthritis from stage 1 of the arcOGEN study. Ann. Rheum. Dis. 70, 864–867 (2011).

    Article  CAS  Google Scholar 

  39. Evangelou, E. et al. A meta-analysis of genome-wide association studies identifies novel variants associated with osteoarthritis of the hip. Ann. Rheum. Dis. 73, 2130–2136 (2014).

    Article  CAS  Google Scholar 

  40. Prins, B. P. et al. Genome-wide analysis of health-related biomarkers in the UK Household Longitudinal Study reveals novel associations. Sci. Rep. 7, 11008 (2017).

    Article  Google Scholar 

  41. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  Google Scholar 

  42. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  Google Scholar 

  43. Li, M. X., Yeung, J. M., Cherny, S. S. & Sham, P. C. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum. Genet. 131, 747–756 (2012).

    Article  CAS  Google Scholar 

  44. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  Google Scholar 

  45. Barbeira, A. et al. MetaXcan: summary statistics based gene-level association method infers accurate predixcan results. Preprint at bioRxiv https://doi.org/10.1101/045260 (2016).

  46. Carithers, L. J. & Moore, H. M. The genotype-tissue expression (gtex) project. Biopreserv. Biobank 13, 307–308 (2015).

    Article  Google Scholar 

  47. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  Google Scholar 

  48. Farh, K. K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    Article  CAS  Google Scholar 

  49. Guo, C. et al. A little data goes a long way: finding target genes across the GWAS Catalog by colocalizing GWAS and eQTL top hits. in Am. Soc. Hum. Genet., abstr. PgmNr 220 (American Society of Human Genetics, San Diego, 2018).

  50. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  Google Scholar 

  51. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

  52. Shen, J., Song, K., Slater, A. J., Ferrero, E. & Nelson, M. R. STOPGAP: a database for systematic target opportunity assessment by genetic association predictions. Bioinformatics 33, 2784–2786 (2017).

    Article  CAS  Google Scholar 

  53. Aury-Landas, J., Marcelli, C., Leclercq, S., Boumediene, K. & Bauge, C. Genetic determinism of primary early-onset osteoarthritis. Trends Mol. Med. 22, 38–52 (2016).

    Article  Google Scholar 

  54. Steinberg, J. et al. Integrative epigenomics, transcriptomics and proteomics of patient chondrocytes reveal genes and pathways involved in osteoarthritis. Sci. Rep. 7, 8935 (2017).

    Article  Google Scholar 

  55. Koscielny, G. et al. Open Targets: a platform for therapeutic target identification and validation. Nucleic Acids Res. 45, D985–D994 (2017).

    Article  CAS  Google Scholar 

  56. Yang, X. et al. TGF-β/Smad3 signals repress chondrocyte hypertrophic differentiation and are required for maintaining articular cartilage. J. Cell Biol. 153, 35–46 (2001).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research was conducted by using the UK Biobank Resource under application numbers 26041 and 9979. This work was funded by the Wellcome Trust (206194). We are grateful to R. Brooks, A. McCaskie, J. Choudhary, and T. Roumeliotis for their contributions to the transcriptomic and proteomic data collection, and to A. Gilly for help with figures. The Human Research Tissue Bank is supported by the National Institute for Health Researh (NIHR) Cambridge Biomedical Research Centre. arcOGEN was funded by a special-purpose grant from Arthritis Research UK (grant 18030). The UKHLS was funded by grants from the Economic and Social Research Council (ES/H029745/1) and the Wellcome Trust (WT098051). UKHLS is led by the Institute for Social and Economic Research at the University of Essex. The survey was conducted by NatCen, and the genome-wide scan data were analysed and deposited by the Wellcome Sanger Institute. Information on how to access the data can be found on the Understanding Society website https://www.understandingsociety.ac.uk/. PICCOLO was developed by K. Sieber and K.Guo. GDS and TRG receive funding from the UK Medical Research Council (MC_UU_00011/1 and MC_UU_00011/4). The authors would like to acknowledge Open Targets for enabling the collaboration on this work.

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Author notes

  1. These authors contributed equally: Ioanna Tachmazidou, Konstantinos Hatzikotoulas, Lorraine Southam.

  2. A list of members and affiliations appears in the Supplementary Note.

Authors and Affiliations

  1. Target Sciences–R&D, GSK Medicines Research Centre, Stevenage, UK

    Ioanna Tachmazidou, Jorge Esparza-Gordillo, Toby Johnson, Natalie Buchan, Robert A. Scott & Linda C. McCarthy

  2. Human Genetics, Wellcome Genome Campus, Wellcome Sanger Institute, Cambridge, UK

    Konstantinos Hatzikotoulas, Lorraine Southam, Mine Koprulu, Julia Steinberg & Eleftheria Zeggini

  3. Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Konstantinos Hatzikotoulas & Eleftheria Zeggini

  4. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

    Lorraine Southam

  5. MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK

    Valeriia Haberland, Jie Zheng, George Davey Smith & Tom R. Gaunt

  6. Department of Medical Genetics, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK

    Mine Koprulu

  7. Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK

    Eleni Zengini & Jeremy M. Wilkinson

  8. 5th Psychiatric Department, Dromokaiteio Psychiatric Hospital, Haidari, Athens, Greece

    Eleni Zengini

  9. Cancer Research Division, Cancer Council NSW, Woolloomooloo, New South Wales, Australia

    Julia Steinberg

  10. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada

    Sahir Bhatnagar

  11. Target Sciences–R&D, GSK, King of Prussia, PA, USA

    Joshua D. Hoffman & Laura Yerges-Armstrong

  12. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK

    Dániel Süveges

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  1. Ioanna Tachmazidou
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arcOGEN Consortium

Contributions

I.T., L.Y.A., R.S., T.J., J.H., E. Zengini, J.E.G., K.H., and M.K. contributed to UK Biobank association analyses. arcOGEN and L.S. contributed to arcOGEN analyses. V.H., J.Z., R.S., T.G., and G.D.S. contributed to work on Mendelian randomization. J.M.W., J.E.G., L.M.C., J.S., L.S., S.B., D.S., and E. Zeggini contributed to functional genomics work. L.M.C., J.E.G., N.B., and E. Zeggini contributed to translation work. I.T., K.H., L.S., J.E.G., L.M.C., R.S., and E. Zeggini wrote the manuscript.

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Correspondence to Eleftheria Zeggini.

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Competing interests

I.T., J.E.G., T.J., L.Y.A., J.D.H., N.B., R.S., and L.M.C. are employees of GlaxoSmithKline and may own company stock. T.R.G. receives research funding from GlaxoSmithKline and Biogen. V.H. is funded by a research grant from GlaxoSmithKline.

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Tachmazidou, I., Hatzikotoulas, K., Southam, L. et al. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat Genet 51, 230–236 (2019). https://doi.org/10.1038/s41588-018-0327-1

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