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

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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Figure 1: eBMD effect size compared with the effect size from a previous GEFOS meta-analysis of DXA-derived BMD for eBMD-associated SNPs.
Figure 2: The relationship between absolute conditional and joint-analysis effect size (y-axis) and minor allele frequency (x-axis) for 307 conditionally independent SNPs.
Figure 3: Genetic correlations between eBMD as measured in the UK Biobank study (y-axis) and other traits and diseases (x-axis) estimated by LD score regression implemented in LDHub.
Figure 4: Increased bone mass and strength in adult Gpc6−/− mice.

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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.).

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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.

Corresponding authors

Correspondence to J Brent Richards or David M Evans.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Flowchart illustrating the three complementary strategies for gene prioritization used in this study.

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)

Life Sciences Reporting Summary (PDF 158 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)

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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

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