Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Converging evidence from exome sequencing and common variants implicates target genes for osteoporosis

Nature Genetics volume 55, pages 1277–1287 (2023)Cite this article

Subjects

Abstract

In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10–7). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10–5). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10–10). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR–Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of this study, describing methods and results for each major aim.
Fig. 2: Association of rare coding variant burden with eBMD in the exome-wide gene burden analysis.
Fig. 3: Convergent evidence from common and rare genetic variants to identify high-confidence target genes.
Fig. 4: Gene burden associations and Ei scores for exome-wide significant genes.
Fig. 5: CD109 knockdown in SaOS-2 cells impacts mineralization.

Similar content being viewed by others

Data availability

Please address general correspondence to J.B.R. (brent.richards@mcgill.ca); for enquiries about exome sequencing and analysis, please contact L.A.L. (luca.lotta@regeneron.com). Individual-level exome sequencing, genotype and phenotype data is available to approved researchers via UKB at: https://www.ukbiobank.ac.uk/enable-your-research. Summary statistics of the following dataset are publicly available and can be accessed at INTERVAL: http://www.phpc.cam.ac.uk/ceu/proteins and eBMD GWAS: http://www.gefos.org/?q=content/data-release-2018 pQTL-only summary statistics of the AGES data is available at: https://www.science.org/doi/suppl/10.1126/science.aaq1327/suppl_file/aaq1327_excel_tables.xlsx.Source data are provided with this paper.

Code availability

REGENIE can be found at https://github.com/rgcgithub/regenie. UKB exome data was analyzed using REGENIE v.1.0.6.8 (Methods). All other data analysis was performed using R (v.3.6.3), RStudio (v.1.4.1717) and eCAVIAR. R packages including twoSampleMR (v.0.4.26), coloc (v.3.2.1) nlme (v.3.1-144), tidyverse (v.1.3.0), ggpubr (v.0.2.5) and ggplot2 (v.3.3.3) were used for analysis and plotting.

References

  1. Compston, J. E., McClung, M. R. & Leslie, W. D. Osteoporosis. Lancet 393, 364–376 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Jha, S., Wang, Z., Laucis, N. & Bhattacharyya, T. Trends in media reports, oral bisphosphonate prescriptions, and hip fractures 1996–2012: an ecological analysis. J. Bone Miner. Res. 30, 2179–2187 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Noble, J. A., McKenna, M. J. & Crowley, R. K. Should denosumab treatment for osteoporosis be continued indefinitely? Ther. Adv. Endocrinol. Metab. 12, 20420188211010052 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kerschan-Schindl, K. Romosozumab: a novel bone anabolic treatment option for osteoporosis? Wien. Med. Wochenschr. 170, 124–131 (2020).

    Article  PubMed  Google Scholar 

  5. King, E. A., Wade Davis, J. & Degner, J. F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 15, e1008489 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Cook, D. et al. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat. Rev. Drug Discov. 13, 419–431 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  8. Mills, M. C. & Rahal, C. A scientometric review of genome-wide association studies. Commun. Biol. 2, 9 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. MacArthur, J. et al. The new NHGRI-EBI catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).

    Article  CAS  PubMed  Google Scholar 

  10. Akbari, P. et al. Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity. Science 373, eabf8683 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Backman, J. D. et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628–634 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang, Q. et al. Rare variant contribution to human disease in 281,104 UK Biobank exomes. Nature 597, 527–532 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Verweij, N. et al. Germline mutations in CIDEB and protection against liver disease. N. Engl. J. Med. 387, 332–344 (2022).

    Article  CAS  PubMed  Google Scholar 

  14. Akbari, P. et al. Multiancestry exome sequencing reveals INHBE mutations associated with favorable fat distribution and protection from diabetes. Nat. Commun. 13, 4844 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Njeh, C. F., Boivin, C. M. & Langton, C. M. The role of ultrasound in the assessment of osteoporosis: a review. Osteoporos. Int. 7, 7–22 (1997).

    Article  CAS  PubMed  Google Scholar 

  16. Rivadeneira, F. & Makitie, O. Osteoporosis and bone mass disorders: from gene pathways to treatments. Trends Endocrinol. Metab. 27, 262–281 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Morris, J. A. et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat. Genet. 51, 258–266 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Surakka, I. et al. MEPE loss-of-function variant associates with decreased bone mineral density and increased fracture risk. Nat. Commun. 11, 4093 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Dong, J. et al. DLX3 mutation associated with autosomal dominant amelogenesis imperfecta with taurodontism. Am. J. Med. Genet. A 133A, 138–141 (2005).

    Article  PubMed  Google Scholar 

  20. Bilezikian, J. P., Morishima, A., Bell, J. & Grumbach, M. M. Increased bone mass as a result of estrogen therapy in a man with aromatase deficiency. N. Engl. J. Med. 339, 599–603 (1998).

    Article  CAS  PubMed  Google Scholar 

  21. Forgetta, V. et al. An effector index to predict target genes at GWAS loci. Hum. Genet. 141, 1431–1447 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Weeks, E. M. et al. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nat. Genet. https://doi.org/10.1038/s41588-023-01443-6 (2023).

  23. Davey Smith, G., Ebrahim, S., Smith, G. D. & Ebrahim, S. ‘Mendelian randomization’: Can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).

    Article  Google Scholar 

  24. Dastani, Z. et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet. 8, e1002607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zheng, J. et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat. Genet. 52, 1122–1131 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Maik, P. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021).

    Article  Google Scholar 

  31. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Firth, H. V. et al. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am. J. Hum. Genet. 84, 524–533 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Inoue, T. et al. Cell adhesion molecule 1 is a new osteoblastic cell adhesion molecule and a diagnostic marker for osteosarcoma. Life Sci. 92, 91–99 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Van Hout, C. V. et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Dewey, F. E. et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354, aaf6814 (2016).

    Article  PubMed  Google Scholar 

  36. Wang, Y., Inger, M., Jiang, H., Tenenbaum, H. & Glogauer, M. CD109 plays a role in osteoclastogenesis. PLoS ONE 8, e61213 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mii, S. et al. CD109 deficiency induces osteopenia with an osteoporosis-like phenotype in vivo. Genes Cells 23, 590–598 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Dickinson, M. E. et al. High-throughput discovery of novel developmental phenotypes. Nature 537, 508–514 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Simon, M. M. et al. A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol. 14, R82 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Lee, J. W., Min, C., Bang, C. H., Kwon, B. C. & Choi, H. G. Psoriasis is associated with an increased risk of osteoporosis: follow-up and nested case–control studies using a national sample cohort. Osteoporos. Int. 32, 529–538 (2021).

    Article  CAS  PubMed  Google Scholar 

  41. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Freudenthal, B. et al. Rapid phenotyping of knockout mice to identify genetic determinants of bone strength. J. Endocrinol. 231, R31–R46 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nakamura, S. et al. Negative feedback loop of bone resorption by NFATc1-dependent induction of Cadm1. PLoS ONE 12, e0175632 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Yan, X., Kononenko, N. L., Bruel, A., Thomsen, J. S. & Poy, M. N. Neuronal cell adhesion molecule 1 regulates leptin sensitivity and bone mass. Calcif. Tissue Int. 102, 329–336 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. Komori, T. et al. Targeted disruption of Cbfa1 results in a complete lack of bone formation owing to maturational arrest of osteoblasts. Cell 89, 755–764 (1997).

    Article  CAS  PubMed  Google Scholar 

  46. Kim, Y. J., Lee, M. H., Wozney, J. M., Cho, J. Y. & Ryoo, H. M. Bone morphogenetic protein-2-induced alkaline phosphatase expression is stimulated by Dlx5 and repressed by Msx2. J. Biol. Chem. 279, 50773–50780 (2004).

    Article  CAS  PubMed  Google Scholar 

  47. Sevetson, B., Taylor, S. & Pan, Y. Cbfa1/RUNX2 directs specific expression of the sclerosteosis gene (SOST). J. Biol. Chem. 279, 13849–13858 (2004).

    Article  CAS  PubMed  Google Scholar 

  48. Kumar, P., Henikoff, S. & Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    Article  CAS  PubMed  Google Scholar 

  49. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. https://doi.org/10.1002/0471142905.hg0720s76 (2013).

  51. Chun, S. & Fay, J. C. Identification of deleterious mutations within three human genomes. Genome Res. 19, 1553–1561 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Schwarz, J. M., Rödelsperger, C., Schuelke, M. & Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575–576 (2010).

    Article  CAS  PubMed  Google Scholar 

  53. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Kemp, J. P. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  56. Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 1097–1103 (2021).

    Article  CAS  PubMed  Google Scholar 

  57. Loh, P. R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Huang, J. et al. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. Nat. Commun. 14, 8111 (2015).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Jung, S. H. Stratified Fisher’s exact test and its sample size calculation. Biometrical J. 56, 129–140 (2014).

    Article  Google Scholar 

  61. Zhou, S. et al. A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat. Med. 27, 659–667 (2021).

    Article  CAS  PubMed  Google Scholar 

  62. Swerdlow, D. I. et al. Selecting instruments for Mendelian randomization in the wake of genome-wide association studies. Int. J. Epidemiol. 45, 1600–1616 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N. & Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133–1163 (2008).

    Article  PubMed  Google Scholar 

  65. Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Pinheiro, J., Bates, D. & R Core Team nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-144 https://CRAN.R-project.org/package=nlme (2020).

  67. Haeussler, M. et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 17, 148 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This project used UKB exome sequencing data under the project number 26041 and 24268. The Richards research group is supported by the Canadian Institutes of Health Research (CIHR: 365825; 409511, 100558), the McGill Interdisciplinary Initiative in Infection and Immunity (MI4), the Lady Davis Institute of the Jewish General Hospital, the Jewish General Hospital Foundation, the Canadian Foundation for Innovation, the NIH Foundation, Genome Québec, the Public Health Agency of Canada, McGill University, and the Fonds de Recherche Québec Santé (FRQS). J.B.R. is supported by a FRQS Mérite Clinical Research Scholarship. S.Z. is supported by a CIHR fellowship and a FRQS postdoctoral scholarship. Support from Calcul Québec and Compute Canada is acknowledged. TwinsUK is funded by the Welcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. These funding agencies had no role in the design, implementation or interpretation of this study.

Author information

Author notes

  1. These authors contributed equally: Sirui Zhou, Olukayode A. Sosina, Jonas Bovijn, Laetitia Laurent.

  2. These authors jointly supervised this work: J. Brent Richards, Luca A. Lotta.

  3. A full list of members appears in the Supplementary Information.

Authors and Affiliations

  1. Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada

    Sirui Zhou, Laetitia Laurent, Vasundhara Sharma, Vincenzo Forgetta, Lai Jiang, Celia M. T. Greenwood & J. Brent Richards

  2. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada

    Sirui Zhou, Celia M. T. Greenwood & J. Brent Richards

  3. Department of Human Genetics, McGill University, Montréal, Quebec, Canada

    Sirui Zhou & J. Brent Richards

  4. Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA

    Olukayode A. Sosina, Jonas Bovijn, Parsa Akbari, Jack A. Kosmicki, Nilanjana Banerjee, Marcus Jones, Michelle G. LeBlanc, John D. Overton, Jeffrey G. Reid, Michael Cantor, Goncalo R. Abecasis, Aris Baras, Aris N. Economides, Manuel A. R. Ferreira & Luca A. Lotta

  5. Five Prime Sciences Inc, Montréal, Québec, Canada

    Vincenzo Forgetta & J. Brent Richards

  6. New York Genome Center, New York, NY, USA

    John A. Morris

  7. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK

    Erin Oerton & Claudia Langenberg

  8. Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA

    Vincent Idone & Sarah Hatsell

  9. Research Institute of the McGill University Health Centre, Montréal, Québec, Canada

    David Goltzman

  10. Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada

    Celia M. T. Greenwood

  11. Computational Medicine, Berlin Institute of Health, Charité University Medicine Berlin, Berlin, Germany

    Claudia Langenberg

  12. Centre of Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Claes Ohlsson

  13. Department of Drug Treatment, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden

    Claes Ohlsson

  14. Department of Twin Research, King’s College London, London, UK

    J. Brent Richards

Authors
  1. Sirui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Olukayode A. Sosina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonas Bovijn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laetitia Laurent
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vasundhara Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Parsa Akbari
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vincenzo Forgetta
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lai Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jack A. Kosmicki
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nilanjana Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. John A. Morris
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Erin Oerton
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Marcus Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Michelle G. LeBlanc
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Vincent Idone
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. John D. Overton
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jeffrey G. Reid
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Michael Cantor
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Goncalo R. Abecasis
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. David Goltzman
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Celia M. T. Greenwood
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Claudia Langenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Aris Baras
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Aris N. Economides
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Manuel A. R. Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Sarah Hatsell
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Claes Ohlsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. J. Brent Richards
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Luca A. Lotta
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

Regeneron Genetics Center

  • Olukayode A. Sosina
  • , Jonas Bovijn
  • , Parsa Akbari
  • , Jack A. Kosmicki
  • , Nilanjana Banerjee
  • , Marcus Jones
  • , Michelle G. LeBlanc
  • , John D. Overton
  • , Jeffrey G. Reid
  • , Michael Cantor
  • , Goncalo R. Abecasis
  • , Aris Baras
  • , Aris N. Economides
  • , Manuel A. R. Ferreira
  •  & Luca A. Lotta

Contributions

S.Z., O.A.S., J.B., J.B.R. and L.A.L. designed the study, participated in the acquisition, analysis and interpretation of data, and drafted the manuscript. L.L., V.S., P.A., V.F., L.J., J.A.K., N.B., J.A.M., E.O., M.J., M.G.L., V.I., J.D.O., J.G.R., M.C., G.R.A., D.G., C.M.T.G., C.L., A.B., A.N.E., M.A.R.F., S.H. and C.O. participated in the acquisition, analysis or interpretation of the data, and reviewed the manuscript for important intellectual content. All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to J. Brent Richards.

Ethics declarations

Competing interests

J.B.R. has served as an advisor to GlaxoSmithKline and Deerfield Capital. He is the founder of 5 Prime Sciences (5prime.com). Regeneron Genetics Center and Regeneron co-authors (O.A.S., J.B., P.A., J.A.K., N.B., M.J., M.G.L., V.I., J.D.O., J.G.R., M.C., G.R.A., A.B., A.N.E., M.A.R.F., S.H., L.A.L.) receive salary and own stocks or stock-options from Regeneron Pharmaceuticals Inc. This research received funding from Regeneron Pharmaceuticals Inc. E.O. is currently an employee at AstraZeneca. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Jonathan Tobias, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Associations with fracture and osteoporosis for rare coding variants associated with eBMD.

Genes were included in this analysis if they were associated with eBMD at exome-wide significance. For each gene, we used the gene burden exposure with the strongest association (lowest P-value) with eBMD. Estimates (point estimates in blue, with 95% confidence intervals as gray lines) for the association with eBMD are shown on the x-axis, and estimates for the association with fracture (upper panel) or osteoporosis (lower panel) are on the y-axis. The Spearman’s rank correlation coefficient of effect sizes was −0.70 (P = 0.001; eBMD vs. fracture) and −0.49 (P = 0.035; eBMD vs. osteoporosis). SD, standard deviation; CI, confidence interval.

Extended Data Fig. 2 PoP scores of genes in 857 eBMD GWAS loci.

Plots display 17 multi-ancestry exome-wide significant genes, 8 genes with a burden test FDR P < 1% but P ≥ 3.6 × 10−7, and 4,899 genes not in the two previous categories. Box plots show IQR and median; whisker shows 1.5 IQR of the upper quartile/lower quartile.

Extended Data Fig. 3 Gene burden associations and Ei scores for genes with FDR P < 0.01 but not reaching exome-wide significance.

Loci are shown if they were identified in eBMD GWAS and included a gene that was identified in our cross-ancestry exome-wide rare-variant burden analysis (P < 1.49 × 10−5 but ≥ 3.6 × 10−7). Each dot represents a gene in a particular locus. The y-axis indicates the exome burden test –log10 P-value, scaled between 0–1; the x-axis indicates the Ei score. Genes highlighted in red are genes with FDR P < 0.01, and genes highlighted in blue are other genes with Ei > 0.75.

Extended Data Fig. 4 Distribution of rare nonsynonymous variants in CD109 with evidence of association with eBMD in exome-wide association analysis.

Shown from top to bottom are the CD109 protein (with N and C-terminals indicated), a diagrammatic representation of the CD109 exons (shown as alternating blue and purple blocks), and the distribution of rare (alternative allele frequency <1%, minor allele count > 25) nonsynonymous variants with evidence of association (P < 0.05) with eBMD. c.4163-2 A > G is a splice acceptor variant.

Extended Data Fig. 5 CD109-edited SaOS-2 cells.

a, Sanger sequence of the five edited cells. Indel scores (obtained with Synthego Performance Analysis, ICE Analysis. 2019 v.3.0. Synthego) of the five clones were: 70A146 (96), 72A123 (100), 72A144 (98), 72A124 (97) and 70A116 (84). Two different sgRNAs were used to induce double strand breaks in exon 5 of CD109 as shown above. Deletion of 8, 5 and 19 nucleotides were obtained in clones 70A146, 72A123 and 72A144, respectively, whereas a single nucleotide insertion was observed in clone 72A124. Clone 70A116 had three different deletions type (1, 10 and 17 nucleotides). b, Bands from representative western blots of CD109 (190 kDa; upper panel) and total protein (lower panel) of three independent experiments from wild-type control and CD109-edited cells. Full-length blots are provided as Source Data. c, A mineralization staining example of the five edited cells from one of the six experiments, where darker red indicates a higher mineral content.

Source data

Extended Data Fig. 6 Targeting CADM1 exon 1 with CRISPR/Cas9-induced double stranded breaks decreased CADM1 protein level in SaOS-2 cells.

a, CADM1 protein level quantification in control cells and CADM1-edited cells (gRNA1, gRNA2 and gRNA3). Data are presented as mean values +/− standard error of the mean (s.e.m.) of n = 6 independent experiments. ***P = 4.6 × 10−5, P = 5.1 × 10−5 and P = 4.9 × 10−5, respectively, compared to control cells determined by one-way ANOVA and Bonferroni post-hoc tests. b, Bands from representative western blots of CADM1 (upper panel) and total protein (lower panel) of at least six independent experiments from different cell line passages. Full-length blots are provided as Source Data. c, Staining of CADM1 protein using anti-CADM1 monoclonal antibody at the cell surface, showing almost complete knockout of CADM1 using three gRNAs. d, Staining of the intracellular CADM1 protein, showing partial knockout of CADM1 using three gRNAs (8.8-, 11- and 10.7-fold decrease compared to controls).

Source data

Extended Data Fig. 7 CADM1 is expressed in early SaOS-2 cell differentiation and influences alkaline phosphatase activity.

a, Relative expression of CADM1 (mRNA level) to two reference genes PPIA and HPRT1 at day 0 and day 14 in SaOS-2 CADM1 wild-type cells. Data are presented as mean values +/− s.e.m. of n = 3 independent experiments. b, In CADM1-edited cells, the absence of CADM1 at the cell surface increases the activity of alkaline phosphatase after osteogenic treatment. Data are presented as mean values +/− s.e.m. of n = 8 independent experiments. Significant changes were shown between treated and untreated edited cells by gRNA1 (***P = 3 × 10−5) and between treated and untreated edited cells by gRNA2, gRNA3, respectively (*P = 0.0176 and P = 0.0161), determined by one-way ANOVA and Bonferroni post-hoc tests.

Extended Data Fig. 8 Expression of early bone markers in CADM1-edited SaOS-2 cells.

a, b, Expression of RUNX2 mRNA on day 3 (P = 0.027 and P = 0.0096) and day 14 in CADM1-edited SaOS-2 cells. c, d, Ratio of COL1A1/COL1A2 expression on day 3 (P = 0.0229 and P = 0.0494) and day 14 in CADM1-edited SaOS-2 cells. e, f, Expression of ALPL on day 3 (P = 0.0006) and day 14 in CADM1-edited SaOS-2 cells. Data are presented as mean values +/− s.e.m. of n = 6 independent experiments. Statistical differences compared to control cells were determined by one-way ANOVA and Bonferroni post-hoc tests.

Extended Data Fig. 9 Expression levels of late bone markers in control and CADM1-edited cells.

a, b, Expression of BGLAP mRNA on day 3 and day 14 in CADM1-edited SaOS-2 cells. c, d, Expression of SOST mRNA on day 3 (P = 0.0212 and P = 0.0480) and day 14 (P = 0.0356) in CADM1-edited SaOS-2 cells. Data are presented as mean values +/− s.e.m. of n = 6 independent experiments. Statistical differences compared to control cells were determined by one-way ANOVA and Bonferroni post-hoc tests or Kruskal-Wallis and Dunn’s multiple comparison test.

Extended Data Fig. 10 Mineralization of CADM1-edited cells after 14 days of treatment with an osteogenic medium.

The y-axis shows the mineralization levels of three edited cells, normalized against total proteins expressed in the edited cells. Data are presented as mean values +/− s.e.m. of n = 2 independent experiments.

Supplementary information

Supplementary Information

Supplementary Notes 1–10.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–19.

Source data

Source Data Extended Data Fig. 5

Unprocessed western blots.

Source Data Extended Data Fig. 6

Unprocessed western blots.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, S., Sosina, O.A., Bovijn, J. et al. Converging evidence from exome sequencing and common variants implicates target genes for osteoporosis. Nat Genet 55, 1277–1287 (2023). https://doi.org/10.1038/s41588-023-01444-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-023-01444-5

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing