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.
Similar content being viewed by others
Accession codes
References
Cauley, J.A. et al. Long-term risk of incident vertebral fractures. J. Am. Med. Assoc. 298, 2761–2767 (2007).
Liu, C.T. 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).
Estrada, K. 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).
Zheng, H.F. et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526, 112–117 (2015).
Arden, N.K., Baker, J., Hogg, C., Baan, K. & Spector, T.D. 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).
Howard, G.M., Nguyen, T.V., Harris, M., Kelly, P.J. & Eisman, J.A. 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).
Hunter, D.J. 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).
Lee, M. 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).
Bauer, D.C. 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).
Bauer, D.C. et al. Quantitative ultrasound predicts hip and non-spine fracture in men: the MrOS study. Osteoporos. Int. 18, 771–777 (2007).
Gonnelli, S. et al. Quantitative ultrasound and dual-energy X-ray absorptiometry in the prediction of fragility fracture in men. Osteoporos. Int. 16, 963–968 (2005).
Moayyeri, A. 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).
Nelson, M.R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).
Richards, J.B., Zheng, H.F. & Spector, T.D. Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat. Rev. Genet. 13, 576–588 (2012).
Bulik-Sullivan, B.K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Duncan, E.L. 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).
Koller, D.L. 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).
Richards, J.B. et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 371, 1505–1512 (2008).
Rivadeneira, F. et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat. Genet. 41, 1199–1206 (2009).
Styrkarsdottir, U. et al. Multiple genetic loci for bone mineral density and fractures. N. Engl. J. Med. 358, 2355–2365 (2008).
Styrkarsdottir, U. et al. New sequence variants associated with bone mineral density. Nat. Genet. 41, 15–17 (2009).
Xiong, D.H. 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).
Wood, A.R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).
Mackey, D.C. et al. High-trauma fractures and low bone mineral density in older women and men. J. Am. Med. Assoc. 298, 2381–2388 (2007).
Sanders, K.M. 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).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Zheng, J. 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).
Ahmad, O.S. 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).
Kemp, J.P., Sayers, A., Smith, G.D., Tobias, J.H. & Evans, D.M. 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).
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).
Grundberg, E. et al. Population genomics in a disease targeted primary cell model. Genome Res. 19, 1942–1952 (2009).
Pers, T.H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
Skarnes, W.C. et al. A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337–342 (2011).
Bassett, J.H. et al. Rapid-throughput skeletal phenotyping of 100 knockout mice identifies 9 new genes that determine bone strength. PLoS Genet. 8, e1002858 (2012).
Campos-Xavier, A.B. 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).
Staley, J.R. et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32, 3207–3209 (2016).
Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).
Malinauskas, T. & Jones, E.Y. Extracellular modulators of Wnt signalling. Curr. Opin. Struct. Biol. 29, 77–84 (2014).
Malinauskas, T., Aricescu, A.R., Lu, W., Siebold, C. & Jones, E.Y. Modular mechanism of Wnt signaling inhibition by Wnt inhibitory factor 1. Nat. Struct. Mol. Biol. 18, 886–893 (2011).
Sakane, H., Yamamoto, H., Matsumoto, S., Sato, A. & Kikuchi, A. Localization of glypican-4 in different membrane microdomains is involved in the regulation of Wnt signaling. J. Cell Sci. 125, 449–460 (2012).
Moayyeri, A. et al. Quantitative ultrasound of the heel and fracture risk assessment: an updated meta-analysis. Osteoporos. Int. 23, 143–153 (2012).
Paternoster, L. 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).
Ismail, A.A. 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).
Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 (Bethesda) 1, 457–470 (2011).
Loh, P.R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
Chang, C.C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Winkler, T.W. et al. EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data. Bioinformatics 31, 259–261 (2015).
Pruim, R.J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Loh, P.R. et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat. Genet. 47, 1385–1392 (2015).
Bigdeli, T.B. 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).
Saito, R. et al. A travel guide to Cytoscape plugins. Nat. Methods 9, 1069–1076 (2012).
Segrè, A.V., Groop, L., Mootha, V.K., Daly, M.J. & Altshuler, D. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
Koscielny, G. 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).
International Mouse Knockout Consortium. A mouse for all reasons. Cell 128, 9–13 (2007).
de Angelis, M.H. et al. Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics. Nat. Genet. 47, 969–978 (2015).
Huang, J. et al. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. Nat. Commun. 6, 8111 (2015).
Evans, D.M. & Davey Smith, G. Mendelian randomization: new applications in the coming age of hypothesis-free causality. Annu. Rev. Genomics Hum. Genet. 16, 327–350 (2015).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Hart, T., Komori, H.K., LaMere, S., Podshivalova, K. & Salomon, D.R. Finding the active genes in deep RNA-seq gene expression studies. BMC Genomics 14, 778 (2013).
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
Authors and Affiliations
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.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 2 Flow diagram illustrating calcaneal quantitative ultrasound (QUS) data collection by the UK Biobank.
QUS data was collected at three time points: Baseline (2007 - 2010), Follow-up 1 (2012 - 2013) and Follow-up 2 (2014 - 2016). At baseline, QUS was performed using two protocols (denoted protocol 1 and 2). Protocol 1 was implemented from 2007 to mid-2009 and involved measuring the left calcaneus. Only in cases where the left was missing or deemed unsuitable was the right calcaneus measured. Protocol 2 was introduced from mid-2009, (replacing protocol 1) and differed only in that it involved measuring both the left and right calcanei. Protocol 2 was further used for both follow up assessments. For all three time points, calcaneal QUS was performed with the Sahara Clinical Bone Sonometer [Hologic Corporation (Bedford, Massachusetts, USA)]. Vox software was used to automatically collect data from the sonometer (denoted direct input). In cases where direct input failed, QUS outcomes were manually keyed into Vox by the attending healthcare technician or nurse (i.e. manual input). The number of individuals with non-missing measures for speed of sound (SOS) and broadband ultrasound attenuation (BUA) recorded at each assessment period are indicated in light grey. Further details on these methods are publicly available on the UK Biobank website (UK Biobank document #100248 https://biobank.ctsu.ox.ac.uk/crystal/docs/Ultrasoundbonedensitometry.pdf). To reduce the impact of outlying measurements, quality control was applied to male and female subjects separately using the following exclusion thresholds: SOS [Male: (≤ 1,450 and ≥ 1,700 m/s), Female (≤ 1,455 and ≥ 1,700 m/s)] and BUA [Male: (≤ 27 and ≥ 138 dB/MHz), Female (≤ 22 and ≥ 138 dB/MHz)]. Individuals exceeding the threshold for SOS or BUA or both were removed from the analysis. Estimated bone mineral density [eBMD, (g/cm2)] was derived as a linear combination of SOS and BUA (i.e. eBMD = 0.002592 * (BUA + SOS) − 3.687). Individuals exceeding the following thresholds for eBMD were further excluded: [Male: (≤ 0.18 and ≥ 1.06 g/cm2), Female (≤ 0.12 and ≥ 1.025 g/cm2)]. The number of individuals with non-missing measures for SOS, BUA and eBMD after QC are indicated in black. A unique list of individuals with a valid measure for the left calcaneus (N=477,380) and/or right (N=183,824) were identified separately across all three time points. Individuals with a valid right calcaneus measure were included in the final data set when no left measures were available, giving a preliminary working dataset of N=483,992 unique individuals. Bivariate scatter plots of eBMD, BUA and SOS were visually inspected and 762 additional outliers were removed, leaving a total of 483,230 valid QUS measures (left=476,618 and right=6,612) for SOS, BUA and BMD (265,057 females and 218,173 males).
Supplementary Figure 3 Manhattan plot and phenogram showing genome-wide association study results for eBMD in the UK Biobank study.
The dashed red line denotes the threshold for declaring genome-wide significance (α = 6.6 x10-9). In total, 307 conditionally independent SNPs at 203 loci passed the criteria for genome-wide significance. 153 novel loci (i.e. defined as >1MB from previously reported genome-wide significant BMD variant) reaching genome-wide significance are displayed in blue. Previously reported loci that reached genome-wide significance are displayed in red, and previously reported loci failing to reach genome-wide significance in our study are shown in black. Loci that contain more than one conditionally independent signal are marked with an asterisk. Each locus was annotated using the gene contained within the closest gene region identified by DEPICT. In situations where multiple genes were present in a single DEPICT region, priority was given to the gene that displayed a bone phenotype in knockout mouse model, followed by the gene expressed in the most murine bone cell types (3>2>1), followed by the gene with the lowest depict gene p-value. Asterisks denote multiple conditionally independent variants present at the locus, and the “~” symbol denotes the gene closest to the locus (in the case of no genes prioritized by DEPICT at that locus). The FAM9B locus was not genome-wide significant in the analysis of all individuals, but was significant in the analysis of males only.
Supplementary Figure 4 Analysis of sex heterogeneity in eBMD loci.
The top graph is a Miami plot of genome-wide association results for males (top panel) and females (bottom panel). The bottom graph is a Manhattan plot for the test for sex heterogeneity in eBMD regression coefficients between males and females. Previously reported loci that reached genome-wide significance are displayed in red, and previously reported loci failing to reach genome-wide significance in our study are shown in black.
Supplementary Figure 5 The relationship between estimated conditional effect sizes (in s.d.) for eBMD (x-axis) and odds of fracture (y-axis) for genome-wide significant eBMD variants.
The plot on the left is for any fracture, and the plot on the right is for fracture from a simple fall. The shading of the data points represents the P-value for the test of association with fracture (black for robust evidence of association with fracture and white for poor evidence of an association). Variants that meet Bonferroni significance (P < 1.6 x 10-4) are labelled in the plots.
Supplementary Figure 6 ‘Meta gene sets’ enriched for genes in eBMD-associated loci.
35 meta gene-sets were defined from similarity clustering of significantly enriched gene sets (FDR<1%). Each Meta gene-set was named after one of its member gene sets. The color of the Meta gene-sets represents the P value of the member set. Interconnection line width represents the Pearson correlation ρ between the gene membership scores for each Meta gene-set (ρ < 0.3, no line; 0.3 ≤ ρ < 0.5,narrow width; 0.5 ≤ ρ < 0.7, medium width; ρ ≥ 0.7, thick width).
Supplementary Figure 7 Tissue/cell-type enrichment analysis for genes in eBMD-associated loci.
Columns represent the level of evidence for genes in the associated loci to be highly expressed in any of the 209 Medical Subject Heading (MeSH) tissue and cell type annotations. Highlighted in orange are these tissue/cell types significantly (FDR<5%) enriched for the expression of genes in the associated loci. Results are summarized in Supplementary Table 12.
Supplementary Figure 8 Osteocyte enrichment of DEPICT genes with skeletal phenotypes in knockout mice.
A density plot of the log2 fold-change of gene expression in osteocyte-isolated bone samples relative to marrow containing bone samples, highlighting all genes expressed in osteocytes that produce a skeletal phenotype when knocked out in mice.
Supplementary Figure 9 Calculation of genome-wide significance threshold.
After permuting phenotypes and reanalysing the associations against genetic variation on chromosome 9, empirical significance thresholds required to control the family-wise error rate at 0.05 are plotted against Bonferroni thresholds, both on the -log10 scale, for subregions of the chromosome of varying size (see also Online Methods).
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–9 and Supplementary Note. (PDF 2239 kb)
Supplementary Table 1
UK Biobank study descriptives. (XLSX 13 kb)
Supplementary Table 2
Association results for 307 conditionally independent SNPs that reached genome-wide significance in the UK Biobank eBMD GWAS. (XLSX 114 kb)
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. (XLSX 173 kb)
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). (XLSX 93 kb)
Supplementary Table 5
Results of GWAS for genome-wide significant variants corrected for weight. (XLSX 106 kb)
Supplementary Table 6
Results of the test for sex heterogeneity at 307 genome-wide significant SNPs. (XLSX 109 kb)
Supplementary Table 7
Genetic correlation analyses using LD Hub. (XLSX 46 kb)
Supplementary Table 8
Variant Effect Predictor annotations for predicted deleterious genome-wide significant coding SNPs. (XLSX 36 kb)
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. (XLSX 48 kb)
Supplementary Table 10
Results from cis-eQTL analyses in 95 primary human osteoblasts. (XLSX 151 kb)
Supplementary Table 11
DEPICT gene prioritization (FDR < 5%). (XLSX 45 kb)
Supplementary Table 12
DEPICT MeSH tissue and cell-type annotation enrichment (FDR < 0.05). (XLSX 13 kb)
Supplementary Table 13
MAGENTA gene set enrichment analysis. (XLSX 11 kb)
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. (XLSX 38 kb)
Supplementary Table 15
Mouse knockouts from the OBDC study and their mean scores on a variety of bone-related phenotypes. (XLSX 89 kb)
Supplementary Table 16
Summary of the evidence implicating GPC6 in the pathophysiology of osteoporosis. (XLSX 11 kb)
Rights and permissions
About this article
Cite this article
Kemp, J., Morris, J., Medina-Gomez, C. et al. Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat Genet 49, 1468–1475 (2017). https://doi.org/10.1038/ng.3949
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/ng.3949
- Springer Nature America, Inc.