Article

Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis

  • Nature Genetics volume 49, pages 14681475 (2017)
  • doi:10.1038/ng.3949
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Abstract

Osteoporosis is a common disease diagnosed primarily by measurement of bone mineral density (BMD). We undertook a genome-wide association study (GWAS) in 142,487 individuals from the UK Biobank to identify loci associated with BMD as estimated by quantitative ultrasound of the heel. We identified 307 conditionally independent single-nucleotide polymorphisms (SNPs) that attained genome-wide significance at 203 loci, explaining approximately 12% of the phenotypic variance. These included 153 previously unreported loci, and several rare variants with large effect sizes. To investigate the underlying mechanisms, we undertook (1) bioinformatic, functional genomic annotation and human osteoblast expression studies; (2) gene-function prediction; (3) skeletal phenotyping of 120 knockout mice with deletions of genes adjacent to lead independent SNPs; and (4) analysis of gene expression in mouse osteoblasts, osteocytes and osteoclasts. The results implicate GPC6 as a novel determinant of BMD, and also identify abnormal skeletal phenotypes in knockout mice associated with a further 100 prioritized genes.

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Acknowledgements

We thank P. Sham for helpful discussions and M. Schull for assistance with high-performance computing. We thank research nurses and assistants at the Departments of Surgical and Medical Sciences, Uppsala University, Uppsala, Sweden, for large-scale collection of bone samples and culture of primary osteoblasts. This part of the work was supported by Genome Quebec, Genome Canada and the Canadian Institutes of Health Research (CIHR). We thank T. Winkler for invaluable technical support for the EasyStrata Software used in this study.

This work was supported by the Medical Research Council (Programme Grant MC_UU_12013/4 to D.M.E.), the Wellcome Trust (Strategic Award grant number 101123; project grant 094134; to G.R.W., J.H.D.B. and P.I.C.), the Netherlands Organization for Health Research and Development ZonMw VIDI 016.136.367 (funding to F.R., C.M.-G. and K.T.), the mobility stimuli plan of the European Union Erasmus Mundus Action 2: ERAWEB (programme funding to K.T.), NIAMS, NIH (AR060981 and AR060234 to C.L.A.-B.), the National Health and Medical Research Council (Early Career Fellowship APP1104818 to N.M.W.), the Swedish Research Council (funding to E.G.), the Réseau de Médecine Génétique Appliquée (RMGA; J.A.M.), the Fonds de Recherche du Québec–Santé (FRQS; J.A.M. and J.B.R.), the Natural Sciences and Engineering Research Council of Canada (C.M.T.G.), the J. Gibson and the Ernest Heine Family Foundation (P.I.C.), Arthritis Research UK (ref. 20000; to C.L.G.), the Canadian Institutes of Health Research (J.B.R.), the Jewish General Hospital (J.B.R.), and the Australian Research Council (Future Fellowship FT130101709 to D.M.E.).

This research was conducted using the UK Biobank Resource (application number 12703). Access to the UK Biobank study data was funded by the University of Queensland (Early Career Researcher Grant 2014002959 to N.M.W.).

Author information

Author notes

    • John P Kemp
    • , John A Morris
    •  & Carolina Medina-Gomez

    These authors contributed equally to this work.

    • J Brent Richards
    •  & David M Evans

    These authors jointly supervised this work.

Affiliations

  1. University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia.

    • John P Kemp
    • , Nicole M Warrington
    •  & David M Evans
  2. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

    • John P Kemp
    • , Jie Zheng
    •  & David M Evans
  3. Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada.

    • John A Morris
    • , Vincenzo Forgetta
    • , Keelin M Greenlaw
    • , Celia M T Greenwood
    •  & J Brent Richards
  4. Department of Human Genetics, McGill University, Montréal, Québec, Canada.

    • John A Morris
    • , Elin Grundberg
    • , Celia M T Greenwood
    •  & J Brent Richards
  5. Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.

    • Carolina Medina-Gomez
    • , Katerina Trajanoska
    •  & Fernando Rivadeneira
  6. Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.

    • Carolina Medina-Gomez
    • , Katerina Trajanoska
    •  & Fernando Rivadeneira
  7. Division of Obstetrics and Gynaecology, The University of Western Australia, Perth, Western Australia, Australia.

    • Nicole M Warrington
  8. Garvan Institute of Medical Research, Sydney, New South Wales, Australia.

    • Scott E Youlten
    •  & Peter I Croucher
  9. St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia.

    • Scott E Youlten
    •  & Peter I Croucher
  10. Musculoskeletal Research Unit, Department of Translational Health Sciences, University of Bristol, Bristol, UK.

    • Celia L Gregson
    •  & Jonathan H Tobias
  11. Molecular Endocrinology Laboratory, Department of Medicine, Imperial College London, London, UK.

    • John G Logan
    • , Andrea S Pollard
    • , Penny C Sparkes
    • , Elena J Ghirardello
    • , Rebecca Allen
    • , Victoria D Leitch
    • , Natalie C Butterfield
    • , Davide Komla-Ebri
    • , Anne-Tounsia Adoum
    • , Katharine F Curry
    • , J H Duncan Bassett
    •  & Graham R Williams
  12. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK.

    • Jacqueline K White
    • , Fiona Kussy
    •  & David J Adams
  13. Donnelly Center for Cellular and Biomedical Research, University of Toronto, Toronto, Ontario, Canada.

    • Changjiang Xu
  14. MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.

    • Nicholas C Harvey
    •  & Cyrus Cooper
  15. NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.

    • Nicholas C Harvey
    •  & Cyrus Cooper
  16. NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.

    • Cyrus Cooper
  17. Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada.

    • Celia M T Greenwood
  18. Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada.

    • Celia M T Greenwood
  19. Department of Pathology and Institute for Systems Genetics, New York University Langone Medical Center, New York, New York, USA.

    • Matthew T Maurano
  20. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

    • Stephen Kaptoge
  21. Strangeways Research Laboratory, Cambridge, UK.

    • Stephen Kaptoge
  22. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia.

    • Peter I Croucher
  23. Center for Musculoskeletal Research, Department of Orthopaedics, University of Rochester, Rochester, New York, USA.

    • Cheryl L Ackert-Bicknell
  24. Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.

    • J Brent Richards

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Contributions

S.K., F.R., J.H.T., P.I.C., C.L.A.-B., J.H.D.B., G.R.W., J.B.R. and D.M.E. conceived and designed experiments. J.P.K., J.A.M., C.M.-G., V.F., N.M.W., S.E.Y., J.Z., K.T., E.G., K.M.G., C.X., C.M.T.G., C.L.A.-B., J.H.D.B. and G.R.W. performed statistical analysis. J.P.K., J.A.M., C.M.-G., V.F., N.M.W., S.E.Y., C.L.G., K.T., C.M.T.G., M.T.M., S.K., F.R., J.H.T., P.I.C., C.L.A.-B., J.H.D.B., G.R.W., J.B.R. and D.M.E. wrote the paper. S.E.Y., E.J.G., J.G.L., A.S.P., P.C.S., R.A., V.D.L., N.C.B., D.K.-E., A.-T.A., K.F.C., J.K.W., F.K., D.J.A., P.I.C., C.L.A.-B., J.H.D.B. and G.R.W. generated mouse models and/or functional experiments. N.C.H. and C.C. generated heel eBMD data. J.P.K., J.A.M. and C.M.-G. were the lead analysts. All authors revised and reviewed the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to J Brent Richards or David M Evans.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–9 and Supplementary Note.

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    UK Biobank study descriptives.

  2. 2.

    Supplementary Table 2

    Association results for 307 conditionally independent SNPs that reached genome-wide significance in the UK Biobank eBMD GWAS.

  3. 3.

    Supplementary Table 3

    Look-up of 307 conditionally independent genome-wide significant SNPs for eBMD in the previous GEFOS-seq study and association results for fracture in the UK Biobank study.

  4. 4.

    Supplementary Table 4

    Look up of published genome-wide significant BMD variants in the UK Biobank GWAS (eBMD, fracture) and the GEFOS-seq study (FN-BMD, LS-BMD, FA-BMD).

  5. 5.

    Supplementary Table 5

    Results of GWAS for genome-wide significant variants corrected for weight.

  6. 6.

    Supplementary Table 6

    Results of the test for sex heterogeneity at 307 genome-wide significant SNPs.

  7. 7.

    Supplementary Table 7

    Genetic correlation analyses using LD Hub.

  8. 8.

    Supplementary Table 8

    Variant Effect Predictor annotations for predicted deleterious genome-wide significant coding SNPs.

  9. 9.

    Supplementary Table 9

    Results from statistical fine-mapping of autosomal loci using FINEMAP, functional annotation using DNase I hypersensitivity site data from 115 cell types, CATO score annotation, and possible target genes identified from cis-eQTL analyses in 95 primary human osteoblasts.

  10. 10.

    Supplementary Table 10

    Results from cis-eQTL analyses in 95 primary human osteoblasts.

  11. 11.

    Supplementary Table 11

    DEPICT gene prioritization (FDR < 5%).

  12. 12.

    Supplementary Table 12

    DEPICT MeSH tissue and cell-type annotation enrichment (FDR < 0.05).

  13. 13.

    Supplementary Table 13

    MAGENTA gene set enrichment analysis.

  14. 14.

    Supplementary Table 14

    Skeletal phenotype data from the International Mouse Phenotyping Consortium and Mouse Genome Informatics databases, and expression data from mouse osteoblasts, osteocytes and osteoclasts.

  15. 15.

    Supplementary Table 15

    Mouse knockouts from the OBDC study and their mean scores on a variety of bone-related phenotypes.

  16. 16.

    Supplementary Table 16

    Summary of the evidence implicating GPC6 in the pathophysiology of osteoporosis.