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

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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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.

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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.

Author information

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.

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.

Correspondence to Eleftheria Zeggini.

Supplementary information

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Further reading

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