An atlas of genetic influences on osteoporosis in humans and mice

doi:10.1038/s41588-018-0302-x

. 2019 Feb;51(2):258-266.

doi: 10.1038/s41588-018-0302-x. Epub 2018 Dec 31.

An atlas of genetic influences on osteoporosis in humans and mice

John A Morris^{1

2}, John P Kemp^{3

4}, Scott E Youlten⁵, Laetitia Laurent², John G Logan⁶, Ryan C Chai⁵, Nicholas A Vulpescu⁷, Vincenzo Forgetta², Aaron Kleinman⁸, Sindhu T Mohanty⁵, C Marcelo Sergio⁵, Julian Quinn⁵, Loan Nguyen-Yamamoto⁹, Aimee-Lee Luco⁹, Jinchu Vijay¹⁰, Marie-Michelle Simon¹⁰, Albena Pramatarova¹⁰, Carolina Medina-Gomez¹¹, Katerina Trajanoska¹¹, Elena J Ghirardello⁶, Natalie C Butterfield⁶, Katharine F Curry⁶, Victoria D Leitch⁶, Penny C Sparkes⁶, Anne-Tounsia Adoum⁶, Naila S Mannan⁶, Davide S K Komla-Ebri⁶, Andrea S Pollard⁶, Hannah F Dewhurst⁶, Thomas A D Hassall³, Michael-John G Beltejar¹²; 23andMe Research Team; Douglas J Adams¹³, Suzanne M Vaillancourt¹⁴, Stephen Kaptoge¹⁵, Paul Baldock⁵, Cyrus Cooper^{16

17

18}, Jonathan Reeve¹⁸, Evangelia E Ntzani^{19

20}, Evangelos Evangelou^{19

21}, Claes Ohlsson²², David Karasik²³, Fernando Rivadeneira¹¹, Douglas P Kiel^{23

24

25

26}, Jonathan H Tobias²⁷, Celia L Gregson²⁷, Nicholas C Harvey^{16

17}, Elin Grundberg^{10

28}, David Goltzman⁹, David J Adams²⁹, Christopher J Lelliott²⁹, David A Hinds⁸, Cheryl L Ackert-Bicknell³⁰, Yi-Hsiang Hsu^{23

24

25

26}, Matthew T Maurano⁷, Peter I Croucher⁵, Graham R Williams⁶, J H Duncan Bassett⁶, David M Evans^{31

32}, J Brent Richards^{33

34

35

36

37}

Collaborators, Affiliations

Affiliations

¹ Department of Human Genetics, McGill University, Montréal, Québec, Canada.
² Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada.
³ University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia.
⁴ MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
⁵ Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
⁶ Molecular Endocrinology Laboratory, Department of Medicine, Imperial College London, London, UK.
⁷ Institute for Systems Genetics, New York University Langone Medical Center, New York, NY, USA.
⁸ Department of Research, 23andMe, Inc., Mountain View, CA, USA.
⁹ Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.
¹⁰ McGill University and Genome Quebec Innovation Centre, Montréal, Québec, Canada.
¹¹ Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.
¹² Department of Biomedical Genetics, University of Rochester, Rochester, NY, USA.
¹³ Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁴ Department of Medicine, McGill University, Montréal, Québec, Canada.
¹⁵ Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁶ MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.
¹⁷ NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.
¹⁸ NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
¹⁹ Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece.
²⁰ Center for Evidence Synthesis in Health, Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI, USA.
²¹ Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
²² Department of Internal Medicine and Clinical Nutrition, University of Gothenburg, Gothenburg, Sweden.
²³ Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA.
²⁴ Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁵ Department of Medicine, Harvard Medical School, Boston, MA, USA.
²⁶ Broad Institute of Harvard and Massachusetts Institute of Technology, Boston, MA, USA.
²⁷ Musculoskeletal Research Unit, Department of Translational Health Sciences, University of Bristol, Bristol, UK.
²⁸ Children's Mercy Hospitals and Clinics, Kansas City, MO, USA.
²⁹ Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
³⁰ Center for Musculoskeletal Research, Department of Orthopaedics, University of Rochester, Rochester, NY, USA.
³¹ University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia. d.evans1@uq.edu.au.
³² MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. d.evans1@uq.edu.au.
³³ Department of Human Genetics, McGill University, Montréal, Québec, Canada. brent.richards@mcgill.ca.
³⁴ Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada. brent.richards@mcgill.ca.
³⁵ Department of Medicine, McGill University, Montréal, Québec, Canada. brent.richards@mcgill.ca.
³⁶ Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada. brent.richards@mcgill.ca.
³⁷ Department of Twin Research and Genetic Epidemiology, King's College London, London, UK. brent.richards@mcgill.ca.

PMID: 30598549
PMCID: PMC6358485
DOI: 10.1038/s41588-018-0302-x

An atlas of genetic influences on osteoporosis in humans and mice

John A Morris et al. Nat Genet. 2019 Feb.

. 2019 Feb;51(2):258-266.

doi: 10.1038/s41588-018-0302-x. Epub 2018 Dec 31.

Authors

Affiliations

¹ Department of Human Genetics, McGill University, Montréal, Québec, Canada.
² Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada.
³ University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia.
⁴ MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
⁵ Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
⁶ Molecular Endocrinology Laboratory, Department of Medicine, Imperial College London, London, UK.
⁷ Institute for Systems Genetics, New York University Langone Medical Center, New York, NY, USA.
⁸ Department of Research, 23andMe, Inc., Mountain View, CA, USA.
⁹ Research Institute of the McGill University Health Centre, Montréal, Québec, Canada.
¹⁰ McGill University and Genome Quebec Innovation Centre, Montréal, Québec, Canada.
¹¹ Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.
¹² Department of Biomedical Genetics, University of Rochester, Rochester, NY, USA.
¹³ Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁴ Department of Medicine, McGill University, Montréal, Québec, Canada.
¹⁵ Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁶ MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.
¹⁷ NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.
¹⁸ NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
¹⁹ Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece.
²⁰ Center for Evidence Synthesis in Health, Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI, USA.
²¹ Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
²² Department of Internal Medicine and Clinical Nutrition, University of Gothenburg, Gothenburg, Sweden.
²³ Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA.
²⁴ Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁵ Department of Medicine, Harvard Medical School, Boston, MA, USA.
²⁶ Broad Institute of Harvard and Massachusetts Institute of Technology, Boston, MA, USA.
²⁷ Musculoskeletal Research Unit, Department of Translational Health Sciences, University of Bristol, Bristol, UK.
²⁸ Children's Mercy Hospitals and Clinics, Kansas City, MO, USA.
²⁹ Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
³⁰ Center for Musculoskeletal Research, Department of Orthopaedics, University of Rochester, Rochester, NY, USA.
³¹ University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia. d.evans1@uq.edu.au.
³² MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. d.evans1@uq.edu.au.
³³ Department of Human Genetics, McGill University, Montréal, Québec, Canada. brent.richards@mcgill.ca.
³⁴ Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada. brent.richards@mcgill.ca.
³⁵ Department of Medicine, McGill University, Montréal, Québec, Canada. brent.richards@mcgill.ca.
³⁶ Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada. brent.richards@mcgill.ca.
³⁷ Department of Twin Research and Genetic Epidemiology, King's College London, London, UK. brent.richards@mcgill.ca.

PMID: 30598549
PMCID: PMC6358485
DOI: 10.1038/s41588-018-0302-x

Erratum in

Author Correction: An atlas of genetic influences on osteoporosis in humans and mice.
Morris JA, Kemp JP, Youlten SE, Laurent L, Logan JG, Chai RC, Vulpescu NA, Forgetta V, Kleinman A, Mohanty ST, Sergio CM, Quinn J, Nguyen-Yamamoto L, Luco AL, Vijay J, Simon MM, Pramatarova A, Medina-Gomez C, Trajanoska K, Ghirardello EJ, Butterfield NC, Curry KF, Leitch VD, Sparkes PC, Adoum AT, Mannan NS, Komla-Ebri DSK, Pollard AS, Dewhurst HF, Hassall TAD, Beltejar MG; 23andMe Research Team; Adams DJ, Vaillancourt SM, Kaptoge S, Baldock P, Cooper C, Reeve J, Ntzani EE, Evangelou E, Ohlsson C, Karasik D, Rivadeneira F, Kiel DP, Tobias JH, Gregson CL, Harvey NC, Grundberg E, Goltzman D, Adams DJ, Lelliott CJ, Hinds DA, Ackert-Bicknell CL, Hsu YH, Maurano MT, Croucher PI, Williams GR, Bassett JHD, Evans DM, Richards JB. Morris JA, et al. Nat Genet. 2019 May;51(5):920. doi: 10.1038/s41588-019-0415-x. Nat Genet. 2019. PMID: 30988516

Abstract

Osteoporosis is a common aging-related disease diagnosed primarily using bone mineral density (BMD). We assessed genetic determinants of BMD as estimated by heel quantitative ultrasound in 426,824 individuals, identifying 518 genome-wide significant loci (301 novel), explaining 20% of its variance. We identified 13 bone fracture loci, all associated with estimated BMD (eBMD), in ~1.2 million individuals. We then identified target genes enriched for genes known to influence bone density and strength (maximum odds ratio (OR) = 58, P = 1 × 10^-75) from cell-specific features, including chromatin conformation and accessible chromatin sites. We next performed rapid-throughput skeletal phenotyping of 126 knockout mice with disruptions in predicted target genes and found an increased abnormal skeletal phenotype frequency compared to 526 unselected lines (P < 0.0001). In-depth analysis of one gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This genetic atlas provides evidence linking associated SNPs to causal genes, offers new insight into osteoporosis pathophysiology, and highlights opportunities for drug development.

PubMed Disclaimer

Conflict of interest statement

Competing Interests Statement

A.K. and D.A.H. are employees of 23andMe, Inc.

Figures

**Figure 1.. Manhattan plot of genome-wide association results for eBMD in the UK Biobank.**
The dashed red line denotes the threshold for declaring genome-wide significance (6.6×10⁻⁹). 1,103 conditionally independent SNPs at 515 loci passed the criteria for genome-wide significance in n=426,824 UK Biobank participants. 301 novel loci (defined as > 1 Mbp from previously reported genome-wide significant BMD variants) 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.

**Figure 2.. Fine-mapping SNPs and target gene selection diagram.**
a) For each 500 Mbp region around a conditionally independent lead SNP (p<6.6×10⁻⁹ after conditional independence testing; n=426,824 UK Biobank participants) we applied statistical fine-mapping to calculate log₁₀ Bayes factors for each SNP as a measure of their posterior probability for causality. Conditional independence testing was implemented using GCTA-COJO^, and log₁₀ Bayes factors were estimated using FINEMAP. SNPs that were conditionally independent lead SNPs or that had log₁₀ Bayes factors > 3 were considered our fine-mapped SNPs that we then used for target gene identification. b) Target Genes were identified if: 1) It was the gene closest to a fine-mapped SNP. 2) A fine-mapped SNP was in its gene body. 3) A fine-mapped SNP was coding. 4) The gene mapped closest to a fine-mapped SNP which resided in an SaOS-2 ATAC-seq peak. 5) A fine-mapped SNP was present in a Hi-C osteoblast or osteocyte promoter interaction peak, therefore being closer to a target gene in three-dimensions than linearly on the genome.

**Figure 3.. SNPs at genome-wide significant loci are enriched for bone-relevant open chromatin sites.**
Comparison of eBMD-associated SNPs in terms of enrichment for DHSs from primary osteoblasts, and ATAC-seq peaks from SaOS-2 osteosarcoma cells. Odds ratios were computed relative to all SNPs at genome-wide significant regions. Enrichments for missense protein coding SNPs are shown as baselines. a) Enrichments for conditionally independent (COJO) or log₁₀ Bayes factor >3 (FINEMAP); note the latter set contains nearly twice the number of SNPs. b) Ranking SNPs by log₁₀ Bayes factor (x-axis) showed increasing enrichment. 95% confidence interval (shaded region) was calculated by a two-sided Fisher’s Exact Test.

**Figure 4.**
Target Gene Identification Workflow.

**Figure 5.. Reduction of DAAM2 protein resulted in reduced mineralization in SaOS-2 cells.**
Mineralization quantification in control cells and *DAAM2* exon 2 double-stranded break (DSB) induced cells in either the presence of osteogenic factors (treated) or absence (untreated). a) Dot plot of n=6 independent experiments ± standard error of the mean (SEM) from Alizarin red staining in (b) to quantify mineralization; Bar=5mm. ***p=1.3×10⁻¹⁵ compared to untreated control cells and ^&&&p=9.3×10⁻¹⁵ (left) and 8.2×10⁻¹³ (right) compared to treated control cells determined by one-way ANOVA (F=49.7, df=5) and Bonferroni post-hoc tests.

**Figure 6.. Biomechanical analyses of mice with Daam2 knockdown.**
a) Femur biomechanical analysis. Destructive 3-point bend testing (Instron 5543 load frame) of femurs from wild-type (WT, n_female=3, n_male=4), *Daam2*^+/tm1a (n_female=6, n_male=4) and *Daam2*^tm1a/tm1a (n_female=5, n_male=9) mice. Graphs show yield load, maximum load, fracture load, stiffness (gradient of the linear elastic phase) and toughness (energy dissipated prior to fracture). Female data are shown on the left and male data on the right. Data are shown as mean ± standard error of the mean (SEM). Female maximum load analyses for WT versus Daam2^tm1a/tm1a (**) and Daam2^+/tm1a versus Daam2^tm1a/tm1a (^#) had statistically significant differences (one-way ANOVA p=3.0×10⁻³, F=10.29, df=13, Tukey’s post-hoc test **p<0.01 and ^#p<0.05). Male maximum load analyses for WT versus *Daam2*^tm1a/tm1a (***) and *Daam2*^+/tm1a versus *Daam2*^tm1a/tm1a had statistically significant differences [one-way ANOVA p<1.0×10⁻⁴ (GraphPad Prism does not report smaller p-values), F=50.11, df=16, Tukey’s post-hoc test ***p<1.0×10⁻³ and ^###p<1.0×10⁻³]. Male fracture load analyses for WT vs *Daam2*^tm1a/tm1a (***) and *Daam2*^+/tm1a vs *Daam2*^tm1a/tm1 (^##) had statistically significant differences (one-way ANOVA p=3.0×10⁻⁴, F=15.49, df=16, Tukey’s post-hoc test ***p<1.0×10⁻³ and ^##p<0.01). b) Vertebra biomechanical analyses. Destructive compression testing (Instron 5543 load frame) of caudal vertebrae from WT (n_female=3, n_male=4), *Daam2*^+/tm1a (n_female=6, n_male=4) and *Daam2*^tm1a/tm1a (n_female=5, n_male=9) mice. Graphs show yield load, maximum load and stiffness. Data are shown as mean ± SEM. Female yield load analysis for WT versus *Daam2*^tm1a/tm1a (**) had a statistically significant difference (one-way ANOVA p=6.5×10⁻³, F=8.26, df=13, Tukey’s post-hoc test **p<0.01). Female maximum load analyses for WT versus *Daam2*^tm1a/tm1a (**) and WT versus *Daam2*^+/tm1a (*) had statistically significant differences (one-way ANOVA p=2.9×10⁻³, F=10.45, df=13, Tukey’s post-hoc test **p<0.01 and *p<0.05). Male maximum load analysis for WT vs *Daam2*^tm1a/tm1a (*) had a statistically significant difference (one-way ANOVA p=0.04, F=4.10, df=16, Tukey’s post-hoc test *p<0.05). c) Bone quality analysis from rapid throughput screening mouse knockouts. The graph demonstrates the physiological relationship between bone mineral content and stiffness in caudal vertebrae from P112 female WT mice (n=320). The blue line shows the linear regression (Pearson’s r=0.21, p=1.2×10⁻⁴) and the grey box indicates ± 2 standard deviations (SD). The mean value for female *Daam2*^tm1a/tm1a [n=2 from initial OBCD screen (Supplementary Note)] mice is shown in orange (−2.14 SD).

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Comment in

Unravelling the genetics of osteoporosis.
Greenhill C. Greenhill C. Nat Rev Endocrinol. 2019 Mar;15(3):129. doi: 10.1038/s41574-019-0158-x. Nat Rev Endocrinol. 2019. PMID: 30647468 No abstract available.
GWAS cracks fracture risk.
Trenkmann M. Trenkmann M. Nat Rev Rheumatol. 2019 Mar;15(3):126. doi: 10.1038/s41584-019-0173-2. Nat Rev Rheumatol. 2019. PMID: 30697000 No abstract available.

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References

1. World Health Organization. Consensus development conference: Prophylaxis and treatment of osteoporosis. Osteoporos. Int. 1, 114–117 (1991). - PubMed
1. Richards JB, Zheng H-F & Spector TD Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat. Rev. Genet. 13, 576–588 (2012). - PubMed
1. Johnell O et al. Predictive value of BMD for hip and other fractures. J. Bone Miner. Res. 20, 1185–1194 (2005). - PubMed
1. Kemp JP 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). - PMC - PubMed
1. Arden NK, Baker J, Hogg C, Baan K & Spector TD 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). - PubMed

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[1] World Health Organization. Consensus development conference: Prophylaxis and treatment of osteoporosis. Osteoporos. Int. 1, 114–117 (1991). - PubMed

[2] World Health Organization. Consensus development conference: Prophylaxis and treatment of osteoporosis. Osteoporos. Int. 1, 114–117 (1991). - PubMed

[3] Richards JB, Zheng H-F & Spector TD Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat. Rev. Genet. 13, 576–588 (2012). - PubMed

[4] Richards JB, Zheng H-F & Spector TD Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat. Rev. Genet. 13, 576–588 (2012). - PubMed

[5] Johnell O et al. Predictive value of BMD for hip and other fractures. J. Bone Miner. Res. 20, 1185–1194 (2005). - PubMed

[6] Johnell O et al. Predictive value of BMD for hip and other fractures. J. Bone Miner. Res. 20, 1185–1194 (2005). - PubMed

[7] Kemp JP 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). - PMC - PubMed

[8] Kemp JP 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). - PMC - PubMed

[9] Arden NK, Baker J, Hogg C, Baan K & Spector TD 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). - PubMed

[10] Arden NK, Baker J, Hogg C, Baan K & Spector TD 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). - PubMed

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An atlas of genetic influences on osteoporosis in humans and mice

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An atlas of genetic influences on osteoporosis in humans and mice

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