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. 2021 Sep 2;184(18):4784-4818.e17.
doi: 10.1016/j.cell.2021.07.038. Epub 2021 Aug 26.

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Cindy G Boer  1 , Konstantinos Hatzikotoulas  2 , Lorraine Southam  2 , Lilja Stefánsdóttir  3 , Yanfei Zhang  4 , Rodrigo Coutinho de Almeida  5 , Tian T Wu  6 , Jie Zheng  7 , April Hartley  8 , Maris Teder-Laving  9 , Anne Heidi Skogholt  10 , Chikashi Terao  11 , Eleni Zengini  12 , George Alexiadis  13 , Andrei Barysenka  2 , Gyda Bjornsdottir  3 , Maiken E Gabrielsen  10 , Arthur Gilly  2 , Thorvaldur Ingvarsson  14 , Marianne B Johnsen  15 , Helgi Jonsson  16 , Margreet Kloppenburg  17 , Almut Luetge  10 , Sigrun H Lund  3 , Reedik Mägi  9 , Massimo Mangino  18 , Rob R G H H Nelissen  19 , Manu Shivakumar  20 , Julia Steinberg  21 , Hiroshi Takuwa  22 , Laurent F Thomas  23 , Margo Tuerlings  5 ; arcOGEN Consortium; HUNT All-In Pain; ARGO Consortium; Regeneron Genetics Center; George C Babis  24 , Jason Pui Yin Cheung  25 , Jae Hee Kang  26 , Peter Kraft  27 , Steven A Lietman  28 , Dino Samartzis  29 , P Eline Slagboom  5 , Kari Stefansson  30 , Unnur Thorsteinsdottir  30 , Jonathan H Tobias  31 , André G Uitterlinden  1 , Bendik Winsvold  32 , John-Anker Zwart  33 , George Davey Smith  34 , Pak Chung Sham  35 , Gudmar Thorleifsson  3 , Tom R Gaunt  7 , Andrew P Morris  36 , Ana M Valdes  37 , Aspasia Tsezou  38 , Kathryn S E Cheah  39 , Shiro Ikegawa  40 , Kristian Hveem  41 , Tõnu Esko  9 , J Mark Wilkinson  42 , Ingrid Meulenbelt  5 , Ming Ta Michael Lee  43 , Joyce B J van Meurs  1 , Unnur Styrkársdóttir  3 , Eleftheria Zeggini  44
Collaborators, Affiliations

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Cindy G Boer et al. Cell. .

Erratum in

  • Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.
    Boer CG, Hatzikotoulas K, Southam L, Stefánsdóttir L, Zhang Y, Coutinho de Almeida R, Wu TT, Zheng J, Hartley A, Teder-Laving M, Skogholt AH, Terao C, Zengini E, Alexiadis G, Barysenka A, Bjornsdottir G, Gabrielsen ME, Gilly A, Ingvarsson T, Johnsen MB, Jonsson H, Kloppenburg M, Luetge A, Lund SH, Mägi R, Mangino M, Nelissen RRGHH, Shivakumar M, Steinberg J, Takuwa H, Thomas LF, Tuerlings M; arcOGEN Consortium; HUNT All-In Pain; ARGO Consortium; Regeneron Genetics Center; Babis GC, Cheung JPY, Kang JH, Kraft P, Lietman SA, Samartzis D, Slagboom PE, Stefansson K, Thorsteinsdottir U, Tobias JH, Uitterlinden AG, Winsvold B, Zwart JA, Smith GD, Sham PC, Thorleifsson G, Gaunt TR, Morris AP, Valdes AM, Tsezou A, Cheah KSE, Ikegawa S, Hveem K, Esko T, Wilkinson JM, Meulenbelt I, Michael Lee MT, van Meurs JBJ, Styrkársdóttir U, Zeggini E. Boer CG, et al. Cell. 2021 Nov 24;184(24):6003-6005. doi: 10.1016/j.cell.2021.11.003. Cell. 2021. PMID: 34822786 Free PMC article. No abstract available.

Abstract

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.

Keywords: drug targets; effector genes; functional genomics; genetic architecture; genome-wide association meta-analysis; osteoarthritis.

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Conflict of interest statement

Declaration of interests T.R.G. and J.Z. receive research funding from GlaxoSmithKline. T.R.G. receives research funding from Biogen. U.S., K.S., L. Stefánsdóttir, G.B., S.H.L., U.T., and G. T. are employed by deCODE genetics/Amgen Inc. A.M.V. is a consultant for Zoe Global Ltd. All other authors report no competing interests. All Regeneron Genetics Center banner authors are current employees and/or stockholders of Regeneron Pharmaceuticals.

Figures

None
Graphical abstract
Figure 1
Figure 1
Genetic architecture Graphical summary of the Genetics of Osteoarthritis Consortium workflow and results. (A) Overview of the 11 defined osteoarthritis phenotypes, sex specific analysis, their relationship with each other and their sample sizes (cases/controls). TKR, total knee replacement; THR, total hip replacement. (B) Merged Manhattan-plot of all individual meta-analysis results of all 11 examined osteoarthritis phenotypes. The dashed line represents the genome-wide significance threshold p = 1.3 × 10−8. (C) Graphical overview of all lead genome-wide significant independent osteoarthritis associated single nucleotide variants (SNVs) and the osteoarthritis phenotypes with which they are associated. See also Table S1.
Figure 2
Figure 2
Similarities and differences of signals across phenotypes Correlation and overlap between osteoarthritis genetics (A–D) Heatmap plots of osteoarthritis associated single nucleotide variants (SNVs). Effect sizes (OR, odds ratio) and p values are displayed for each lead SNV for each osteoarthritis phenotype GWAS results. OR are plotted as color, and p values are represented as symbols in the box. (A) Weight bearing joints only (hip, knee, and spine). (B) Both weight and non-weight bearing joints (hip, knee, spine, hand, finger, and thumb). (C) Non-weight bearing joints (hand, finger, and thumb). (D) Any-site osteoarthritis SNVs. (E) Heatmap plot of the genetic correlation (R2) between the examined osteoarthritis phenotypes. (F) Venn diagram depicting the number and overlap of SNVs associated with weight bearing and non-weight bearing joints. (G) Circos plot depicting the overlap in osteoarthritis associations of the 100 lead variants. See also Table S6.
Figure S1
Figure S1
Identification of involved tissues, related to Effector genes and biological pathways and STAR Methods Heatmap depicting tissue-specific gene-regulatory region enrichment significance (-log10 P value) for all osteoarthritis GWAS phenotypes. Tissue/cell type (full name, E-identifier, group name) and P value (-log10) of all significant enrichments (p < 1.3x10−8) are shown. Enrichment was calculated using all osteoarthritis associated lead SNVs and the fine-mapped variants, per osteoarthritis phenotype and all together. Only rows and columns containing a significant enrichment (p < 1.3x10−8) for all osteoarthritis phenotypes (Total) are shown. OA: osteoarthritis.
Figure 3
Figure 3
High-confidence osteoarthritis effector genes (A) Overview of the 77 high-confidence osteoarthritis effector genes and their broad biological classifications, as depicted in Tables 3 and S12. The lead SNV for each is given in brackets. (B) Schematic representation of a chondrocyte and its extracellular matrix, highlighting exemplary osteoarthritis-implicated biological pathways (TGF-β signaling, FGFR3 signaling, and part of the fibrosis pathway) and the high-confidence effector genes (in red boxes), both established and newly identified (in red boxes with a black outline) that have been found to play a role.
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References

    1. Abdollahi-Roodsaz S., Joosten L.A., Roelofs M.F., Radstake T.R., Matera G., Popa C., van der Meer J.W., Netea M.G., van den Berg W.B. Inhibition of Toll-like receptor 4 breaks the inflammatory loop in autoimmune destructive arthritis. Arthritis Rheum. 2007;56:2957–2967. - PubMed
    1. Aguet F., Barbeira A.N., Bonazzola R., Brown A., Castel S.E., Jo B., Kasela S., Kim-Hellmuth S., Liang Y., Oliva M. The GTEx Consortium atlas of genetic regulatory effects across human tissues. bioRxiv. 2019 doi: 10.1101/787903. - DOI
    1. Alman B.A. The role of hedgehog signalling in skeletal health and disease. Nat. Rev. Rheumatol. 2015;11:552–560. - PubMed
    1. Amberger J.S., Bocchini C.A., Schiettecatte F., Scott A.F., Hamosh A. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015;43:D789–D798. - PMC - PubMed
    1. Anderson C.A., Pettersson F.H., Clarke G.M., Cardon L.R., Morris A.P., Zondervan K.T. Data quality control in genetic case-control association studies. Nat. Protoc. 2010;5:1564–1573. - PMC - PubMed

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