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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  1. 1.

    et al. Long-term risk of incident vertebral fractures. J. Am. Med. Assoc. 298, 2761–2767 (2007).

  2. 2.

    et al. Heritability of prevalent vertebral fracture and volumetric bone mineral density and geometry at the lumbar spine in three generations of the Framingham study. J. Bone Miner. Res. 27, 954–958 (2012).

  3. 3.

    et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44, 491–501 (2012).

  4. 4.

    et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526, 112–117 (2015).

  5. 5.

    , , , & The heritability of bone mineral density, ultrasound of the calcaneus and hip axis length: a study of postmenopausal twins. J. Bone Miner. Res. 11, 530–534 (1996).

  6. 6.

    , , , & Genetic and environmental contributions to the association between quantitative ultrasound and bone mineral density measurements: a twin study. J. Bone Miner. Res. 13, 1318–1327 (1998).

  7. 7.

    et al. Genetic variation in bone mineral density and calcaneal ultrasound: a study of the influence of menopause using female twins. Osteoporos. Int. 12, 406–411 (2001).

  8. 8.

    et al. Unique and common genetic effects between bone mineral density and calcaneal quantitative ultrasound measures: the Fels Longitudinal Study. Osteoporos. Int. 17, 865–871 (2006).

  9. 9.

    et al. Broadband ultrasound attenuation predicts fractures strongly and independently of densitometry in older women. A prospective study. Arch. Intern. Med. 157, 629–634 (1997).

  10. 10.

    et al. Quantitative ultrasound predicts hip and non-spine fracture in men: the MrOS study. Osteoporos. Int. 18, 771–777 (2007).

  11. 11.

    et al. Quantitative ultrasound and dual-energy X-ray absorptiometry in the prediction of fragility fracture in men. Osteoporos. Int. 16, 963–968 (2005).

  12. 12.

    et al. Genetic determinants of heel bone properties: genome-wide association meta-analysis and replication in the GEFOS/GENOMOS consortium. Hum. Mol. Genet. 23, 3054–3068 (2014).

  13. 13.

    et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

  14. 14.

    , & Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat. Rev. Genet. 13, 576–588 (2012).

  15. 15.

    et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

  16. 16.

    et al. Genome-wide association study using extreme truncate selection identifies novel genes affecting bone mineral density and fracture risk. PLoS Genet. 7, e1001372 (2011).

  17. 17.

    et al. Genome-wide association study of bone mineral density in premenopausal European-American women and replication in African-American women. J. Clin. Endocrinol. Metab. 95, 1802–1809 (2010).

  18. 18.

    et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 371, 1505–1512 (2008).

  19. 19.

    et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat. Genet. 41, 1199–1206 (2009).

  20. 20.

    et al. Multiple genetic loci for bone mineral density and fractures. N. Engl. J. Med. 358, 2355–2365 (2008).

  21. 21.

    et al. New sequence variants associated with bone mineral density. Nat. Genet. 41, 15–17 (2009).

  22. 22.

    et al. Genome-wide association and follow-up replication studies identified ADAMTS18 and TGFBR3 as bone mass candidate genes in different ethnic groups. Am. J. Hum. Genet. 84, 388–398 (2009).

  23. 23.

    et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

  24. 24.

    et al. High-trauma fractures and low bone mineral density in older women and men. J. Am. Med. Assoc. 298, 2381–2388 (2007).

  25. 25.

    et al. The exclusion of high trauma fractures may underestimate the prevalence of bone fragility fractures in the community: the Geelong Osteoporosis Study. J. Bone Miner. Res. 13, 1337–1342 (1998).

  26. 26.

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

  27. 27.

    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).

  28. 28.

    et al. A Mendelian randomization study of the effect of type-2 diabetes and glycemic traits on bone mineral density. J. Bone Miner. Res. 32, 1072–1081 (2017).

  29. 29.

    , , , & Using Mendelian randomization to investigate a possible causal relationship between adiposity and increased bone mineral density at different skeletal sites in children. Int. J. Epidemiol. 45, 1560–1572 (2016).

  30. 30.

    et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).

  31. 31.

    et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  32. 32.

    et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

  33. 33.

    et al. Population genomics in a disease targeted primary cell model. Genome Res. 19, 1942–1952 (2009).

  34. 34.

    et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

  35. 35.

    et al. A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337–342 (2011).

  36. 36.

    et al. Rapid-throughput skeletal phenotyping of 100 knockout mice identifies 9 new genes that determine bone strength. PLoS Genet. 8, e1002858 (2012).

  37. 37.

    et al. Mutations in the heparan-sulfate proteoglycan glypican 6 (GPC6) impair endochondral ossification and cause recessive omodysplasia. Am. J. Hum. Genet. 84, 760–770 (2009).

  38. 38.

    et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32, 3207–3209 (2016).

  39. 39.

    et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

  40. 40.

    & Extracellular modulators of Wnt signalling. Curr. Opin. Struct. Biol. 29, 77–84 (2014).

  41. 41.

    , , , & Modular mechanism of Wnt signaling inhibition by Wnt inhibitory factor 1. Nat. Struct. Mol. Biol. 18, 886–893 (2011).

  42. 42.

    , , , & Localization of glypican-4 in different membrane microdomains is involved in the regulation of Wnt signaling. J. Cell Sci. 125, 449–460 (2012).

  43. 43.

    et al. Quantitative ultrasound of the heel and fracture risk assessment: an updated meta-analysis. Osteoporos. Int. 23, 143–153 (2012).

  44. 44.

    et al. Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. Nat. Genet. 47, 1449–1456 (2015).

  45. 45.

    et al. Validity of self-report of fractures: results from a prospective study in men and women across Europe. Osteoporos. Int. 11, 248–254 (2000).

  46. 46.

    , & Genotype imputation with thousands of genomes. G3 (Bethesda) 1, 457–470 (2011).

  47. 47.

    et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

  48. 48.

    et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  49. 49.

    et al. EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data. Bioinformatics 31, 259–261 (2015).

  50. 50.

    et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

  51. 51.

    et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

  52. 52.

    & BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  53. 53.

    et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat. Genet. 47, 1385–1392 (2015).

  54. 54.

    et al. A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans. Bioinformatics 32, 2598–2603 (2016).

  55. 55.

    et al. A travel guide to Cytoscape plugins. Nat. Methods 9, 1069–1076 (2012).

  56. 56.

    , , , & Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).

  57. 57.

    et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

  58. 58.

    et al. The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data. Nucleic Acids Res. 42, D802–D809 (2014).

  59. 59.

    International Mouse Knockout Consortium. A mouse for all reasons. Cell 128, 9–13 (2007).

  60. 60.

    et al. Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics. Nat. Genet. 47, 969–978 (2015).

  61. 61.

    et al. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. Nat. Commun. 6, 8111 (2015).

  62. 62.

    & Mendelian randomization: new applications in the coming age of hypothesis-free causality. Annu. Rev. Genomics Hum. Genet. 16, 327–350 (2015).

  63. 63.

    et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

  64. 64.

    , , , & Finding the active genes in deep RNA-seq gene expression studies. BMC Genomics 14, 778 (2013).

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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.


  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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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

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  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.

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