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Meta-Analysis
. 2025 May;641(8065):1217-1224.
doi: 10.1038/s41586-025-08771-z. Epub 2025 Apr 9.

Translational genomics of osteoarthritis in 1,962,069 individuals

Konstantinos Hatzikotoulas #  1 , Lorraine Southam #  1 , Lilja Stefansdottir #  2 , Cindy G Boer #  3 , Merry-Lynn McDonald #  4   5   6   7 , J Patrick Pett  8 , Young-Chan Park  1 , Margo Tuerlings  9 , Rick Mulders  9 , Andrei Barysenka  1 , Ana Luiza Arruda  1 , Vinicius Tragante  2 , Alison Rocco  4   5 , Norbert Bittner  1 , Shibo Chen  1 , Susanne Horn  1 , Vinodh Srinivasasainagendra  4   10 , Ken To  8   11   12 , Georgia Katsoula  1 , Peter Kreitmaier  1 , Amabel M M Tenghe  13 , Arthur Gilly  14 , Liubov Arbeeva  15 , Lane G Chen  16 , Agathe M de Pins  17 , Daniel Dochtermann  4 , Cecilie Henkel  18 , Jonas Höijer  19 , Shuji Ito  20   21   22 , Penelope A Lind  23   24   25 , Bitota Lukusa-Sawalena  4 , Aye Ko Ko Minn  26 , Marina Mola-Caminal  19 , Akira Narita  27 , Chelsea Nguyen  4 , Ene Reimann  28 , Micah D Silberstein  29 , Anne-Heidi Skogholt  30 , Hemant K Tiwari  10 , Michelle S Yau  31 , Ming Yue  32 , Wei Zhao  33   34 , Jin J Zhou  35 , George Alexiadis  36 , Karina Banasik  37 , Søren Brunak  37 , Archie Campbell  38 , Jackson T S Cheung  39 , Joseph Dowsett  40 , Tariq Faquih  41   42 , Jessica D Faul  33 , Lijiang Fei  8   11 , Anne Marie Fenstad  43 , Takamitsu Funayama  27 , Maiken E Gabrielsen  30 , Chinatsu Gocho  27 , Kirill Gromov  18 , Thomas Hansen  44 , Georgi Hudjashov  28 , Thorvaldur Ingvarsson  45   46 , Jessica S Johnson  47   48 , Helgi Jonsson  46   49 , Saori Kakehi  50 , Juha Karjalainen  51   52   53   54 , Elisa Kasbohm  55 , Susanna Lemmelä  51   56 , Kuang Lin  57 , Xiaoxi Liu  22 , Marieke Loef  58 , Massimo Mangino  59 , Daniel McCartney  38 , Iona Y Millwood  57 , Joshua Richman  4   60 , Mary B Roberts  61 , Kathleen A Ryan  62 , Dino Samartzis  63   64 , Manu Shivakumar  65 , Søren T Skou  66   67 , Sachiyo Sugimoto  27 , Ken Suzuki  68   69   70 , Hiroshi Takuwa  20 , Maris Teder-Laving  28 , Laurent Thomas  30   71 , Kohei Tomizuka  22 , Constance Turman  72 , Stefan Weiss  73 , Tian T Wu  16 , Eleni Zengini  74 , Yanfei Zhang  75 ; arcOGEN Consortium; ARGO Consortium; DBDS Genomic Consortium; Estonian Biobank Research Team; FinnGen; Genes & Health Research Team; HUNT All-In Pain; Million Veteran Program; Regeneron Genetics Center; Manuel Allen Revez Ferreira  14 , George Babis  76 , Aris Baras  14 , Tyler Barker  77   78   79 , David J Carey  80 , Kathryn S E Cheah  32 , Zhengming Chen  57 , Jason Pui-Yin Cheung  63 , Mark Daly  51   52   53   54 , Renée de Mutsert  41 , Charles B Eaton  61   81   82 , Christian Erikstrup  83   84 , Ove Nord Furnes  43   85 , Yvonne M Golightly  15   86 , Daniel F Gudbjartsson  2   87 , Nils P Hailer  88 , Caroline Hayward  89 , Marc C Hochberg  90 , Georg Homuth  73 , Laura M Huckins  91 , Kristian Hveem  30   92   93 , Shiro Ikegawa  21   22 , Muneaki Ishijima  94 , Minoru Isomura  95 , Marcus Jones  14 , Jae H Kang  96 , Sharon L R Kardia  34 , Margreet Kloppenburg  41   58 , Peter Kraft  72 , Nobuyuki Kumahashi  97 , Suguru Kuwata  20 , Ming Ta Michael Lee  75 , Phil H Lee  29   98 , Robin Lerner  99 , Liming Li  100   101 , Steve A Lietman  102 , Luca Lotta  14 , Michelle K Lupton  24   103   104 , Reedik Mägi  28 , Nicholas G Martin  105 , Timothy E McAlindon  106 , Sarah E Medland  23   107   108 , Karl Michaëlsson  19 , Braxton D Mitchell  62 , Dennis O Mook-Kanamori  109 , Andrew P Morris  1   68 , Toru Nabika  110 , Fuji Nagami  27 , Amanda E Nelson  15 , Sisse Rye Ostrowski  40   111 , Aarno Palotie  51   52   53   54 , Ole Birger Pedersen  111   112 , Frits R Rosendaal  41 , Mika Sakurai-Yageta  27 , Carsten Oliver Schmidt  55 , Pak Chung Sham  113 , Jasvinder A Singh  4   6   114   115   116 , Diane T Smelser  80 , Jennifer A Smith  33   34 , You-Qiang Song  32 , Erik Sørensen  40 , Gen Tamiya  26   27   117 , Yoshifumi Tamura  118 , Chikashi Terao  22   119   120 , Gudmar Thorleifsson  2 , Anders Troelsen  121 , Aspasia Tsezou  122 , Yuji Uchio  20 , A G Uitterlinden  3 , Henrik Ullum  123 , Ana M Valdes  124 , David A van Heel  99 , Robin G Walters  57 , David R Weir  33 , J Mark Wilkinson  125 , Bendik S Winsvold  30   126   127 , Masayuki Yamamoto  26   27 , John-Anker Zwart  30   126   128 , Kari Stefansson  2   46 , Ingrid Meulenbelt  9 , Sarah A Teichmann  129 , Joyce B J van Meurs  3 , Unnur Styrkarsdottir  2 , Eleftheria Zeggini  130   131
Collaborators, Affiliations
Meta-Analysis

Translational genomics of osteoarthritis in 1,962,069 individuals

Konstantinos Hatzikotoulas et al. Nature. 2025 May.

Abstract

Osteoarthritis is the third most rapidly growing health condition associated with disability, after dementia and diabetes1. By 2050, the total number of patients with osteoarthritis is estimated to reach 1 billion worldwide2. As no disease-modifying treatments exist for osteoarthritis, a better understanding of disease aetiopathology is urgently needed. Here we perform a genome-wide association study meta-analyses across up to 489,975 cases and 1,472,094 controls, establishing 962 independent associations, 513 of which have not been previously reported. Using single-cell multiomics data, we identify signal enrichment in embryonic skeletal development pathways. We integrate orthogonal lines of evidence, including transcriptome, proteome and epigenome profiles of primary joint tissues, and implicate 700 effector genes. Within these, we find rare coding-variant burden associations with effect sizes that are consistently higher than common frequency variant associations. We highlight eight biological processes in which we find convergent involvement of multiple effector genes, including the circadian clock, glial-cell-related processes and pathways with an established role in osteoarthritis (TGFβ, FGF, WNT, BMP and retinoic acid signalling, and extracellular matrix organization). We find that 10% of the effector genes express a protein that is the target of approved drugs, offering repurposing opportunities, which can accelerate translation.

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Conflict of interest statement

Competing interests: U.S., D.F.G., K. Stefansson, L. Stefánsdóttir, V.T. and G. Thorleifsson are employed by deCODE genetics/Amgen. M. Isijima has received research support from Stryker, Zimmer-biomet and Mathys; is a member of the editorial/governing board of the journal of joint surgery and research and osteoarthritis and cartilage; and is a board member and committee appointment for the osteoarthritis research society international. A. Baras, M.A.R.F., L. Lotta, M.J. and A.G.U. are employed at Regeneron Pharmaceuticals. A.M.V. is a consultant for Zoe Global. In the past 3 years, S.A.T. has received remuneration for scientific advisory board membership from Sanofi, GlaxoSmithKline, Foresite Labs and Qiagen. S.A.T. is a co-founder and holds equity in Transition Bio and Ensocell. From 8 January 2024, S.A.T. is a part-time employee of GlaxoSmithKline. O.N.F. has received fees for lecture by Heraeus Medical and Ortomedic AS the past three years. C.E. received unrestricted research grants from Novo Nordisk and Abbott Diagnostics; no personal fees. J.A. Singh has received consultant fees from ROMTech, Atheneum, Clearview healthcare partners, American College of Rheumatology, Yale, Hulio, Horizon Pharmaceuticals/DINORA, Frictionless Solutions, Schipher, Crealta/Horizon, Medisys, Fidia, PK Med, Two labs, Adept Field Solutions, Clinical Care options, Putnam associates, Focus forward, Navigant consulting, Spherix, MedIQ, Jupiter Life Science, UBM, Trio Health, Medscape, WebMD and Practice Point communications; the National Institutes of Health; and the American College of Rheumatology. J.A. Singh has received institutional research support from Zimmer Biomet Holdings. J.A. Singh received food and beverage payments from Intuitive Surgical/Philips Electronics North America. J.A. Singh owns stock options in Atai life sciences, Kintara therapeutics, Intelligent Biosolutions, Acumen pharmaceutical, TPT Global Tech, Vaxart pharmaceuticals, Atyu biopharma, Adaptimmune Therapeutics, GeoVax Labs, Pieris Pharmaceuticals, Enzolytics, Seres Therapeutics, Tonix Pharmaceuticals Holding, Aebona Pharmaceuticals and Charlotte’s Web Holdings. J.A. Singh previously owned stock options in Amarin, Viking and Moderna pharmaceuticals. J.A. Singh is on the speaker’s bureau of Simply Speaking. J.A. Singh was a member of the executive of Outcomes Measures in Rheumatology (OMERACT), an organization that develops outcome measures in rheumatology and receives arms-length funding from eight companies. J.A. Singh serves on the FDA Arthritis Advisory Committee. J.A. Singh is the co-chair of the Veterans Affairs Rheumatology Field Advisory Board (FAB). J.A. Singh is the editor and the Director of the University of Alabama at Birmingham (UAB) Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis. J.A. Singh previously served as a member of the following committees: the American College of Rheumatology’s (ACR) Annual Meeting Planning Committee (AMPC) and Quality of Care Committees, the Chair of the ACR Meet-the-Professor, Workshop and Study Group Subcommittee and the co-chair of the ACR Criteria and Response Criteria subcommittee. N.P.H. has received institutional support, lecturer’s fees or honoraria from Waldemar Link, Zimmer Biomet, DePuy Synthes and Heraeus Medical, and has a license agreement with Waldemar Link. The other authors declare no competing interests.

Figures

Fig. 1
Fig. 1. The genetic architecture of osteoarthritis.
Meta-analysis-based odds ratios of the 962 index variants as a function of their risk-allele frequency, and phenotypic variance explained (VEP) for each variant indicated by the size of each circle. Each colour corresponds to an osteoarthritis phenotype: osteoarthritis at any site (ALLOA), hip osteoarthritis (HIP), knee osteoarthritis (KNEE), hip and/or knee osteoarthritis (HIPKNEE), spine osteoarthritis (SPINE), hand osteoarthritis (HAND), finger osteoarthritis (FINGER), thumb osteoarthritis (THUMB), total hip replacement (THR), total knee replacement (TKR) and total hip and/or knee replacement (total joint replacement, TJR).
Fig. 2
Fig. 2. Signal enrichment in cell types associated with skeletal development.
fGWAS enrichment for osteoarthritis in 30 cell states of the skeletal development atlas. Significance (FDR < 0.1) and effect size (log-transformed OR, log[OR]) are indicated by colour and dot size, respectively. InterzoneChon, interzone chondrocytes; PAX7high Chon, PAX7-expressing chondrocytes; ChondroPro1, chondrocyte progenitors; CyclingChon, chondrocytes with high cell cycle activity; ArticularChon1, articular chondrocytes with high TRPV4 and VEGFA expression; ArticularChon2, articular chondrocytes with high EPYC and low SOX9 expression; DLK1high Chon, DLK1-expressing chondrocytes; HypertrophicChon, hypertrophic chondrocytes; MaturingChon, maturing chondrocytes; LimbMes, early limb mesenchyme cells; Perichondrium, perichondrial osteoblast progenitors; MatureOsteocyte, osteocytes; FibroPRO1/2, fibroblast progenitors; SynFIB, synovial fibroblasts; DermFIB1/2, dermal fibroblasts; TENO, tenocytes; PAX7+ Myo, PAX7-expressing myocytes; MYH3+ Myo, MYH3 expressing myocytes; PERI, pericytes; PerineuralFIB, perineural fibroblasts; HIC1+ Mes, HIC1-expressing mesenchymal cells.
Extended Data Fig. 1
Extended Data Fig. 1. Lines of evidence used to identify effector genes.
Created in BioRender. Southam, L. (2025) https://BioRender.com/d58k400.
Extended Data Fig. 2
Extended Data Fig. 2. Contribution of the 24 lines of evidence to each biological process.
Each bar represents the cumulative absolute number of effector genes identified in the 24 lines of evidence, supporting each of the 8 highlighted biological processes, as indicated by the colour coding. The lines of evidence are ranked, with those contributing the most displayed at the top. MSK: musculoskeletal. MOD: moderate.
Extended Data Fig. 3
Extended Data Fig. 3. Gene Ontology over-represented pathways involved in osteoarthritis pathogenesis.
(a) Top 20 overrepresented pathways enriched by effector genes with scores across 3 to 6 and all over-represented pathways enriched by effector genes with score 7 (P.adjust was calculated by two-tailed hypergeometric test and corrected by multiple testing of Benjamini-Hochberg). The colour scale represents scaled adjusted P value. The exact adjusted P values are provided in Supplementary Table 26. The bubble size was scaled based on the counts of genes enriched for each pathway. Top pathways were ranked based on the gene ratio which was calculated by counts of enriched genes divided by the total number of genes that can be found in the background gene set within each score. (b) Upset plot illustrating the connection of top 20 pathways across gene sets with scores ≥3 to ≥7. Black dots and lines represent inclusion in the top 20 pathways and dark grey dots and lines represent over-represented but not in the top 20 rank, pathways were ranked by gene ratio. The bar plot displays the total number of over-represented pathways and the fraction of top 20 pathways within each gene score set.
Extended Data Fig. 4
Extended Data Fig. 4. Effector genes associated with the retinoic acid pathway.
Effector genes are highlighted in orange with white text, bold indicates a newly-discovered effector gene. Created in BioRender. Southam, L. (2025) https://BioRender.com/z16x054.
Extended Data Fig. 5
Extended Data Fig. 5. Effector genes associated with TGFB signalling.
Effector genes are highlighted in orange with white text, bold indicates a newly-discovered effector gene. A bold outline indicates an effector gene whose protein is the target of an approved drug. Created in BioRender. Southam, L. (2025) https://BioRender.com/x47k064.
Extended Data Fig. 6
Extended Data Fig. 6. Effector genes associated with the circadian clock.
Effector genes are highlighted in orange with white text, bold text indicates a newly-discovered effector gene. A bold outline indicates an effector gene whose protein is the target of an approved drug. Created in BioRender. Southam, L. (2025) https://BioRender.com/h77m147.
Extended Data Fig. 7
Extended Data Fig. 7. Number of risk alleles carried by UK Biobank osteoarthritis patients in each pathway.
Distribution of risk alleles carried by UK Biobank patients with (a) osteoarthritis at any site (n = 82,420), (b) knee osteoarthritis (n = 25,293) and (c) hip osteoarthritis (n = 16,876). The pathways are represented by: RA, retinoic acid signalling; TGFB, TGFB signalling; BMP, BMP signalling; WNT, Wnt signalling; FGF, FGF signalling; ECM, ECM assembly and organization; CIRC, circadian rhythm and GLIAL, glial cell related. On the x-axis the maximum number of variants included in the analysis is provided.
Extended Data Fig. 8
Extended Data Fig. 8. Number of risk alleles carried by Million Veteran Program osteoarthritis patients in each pathway.
Distribution of risk alleles carried by the Million Veteran Program patients with (a) osteoarthritis at any site (n = 56,848), (b) knee osteoarthritis (n = 37,814) and (c) hip osteoarthritis (n = 11,873). The pathways are represented by: RA, retinoic acid signalling; TGFB, TGFB signalling; BMP, BMP signalling; WNT, Wnt signalling; FGF, FGF signalling; ECM, ECM assembly and organization; CIRC, circadian rhythm and GLIAL, glial cell related. On the x-axis the maximum number of variants included in the analysis is provided.
See this image and copyright information in PMC

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