Abstract
Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in ‘systems genetics and genomics’. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans.
Key points
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Osteoporosis, which is the most common bone disorder worldwide, and its related traits (low bone mineral density and osteoporotic fracture) are highly heritable.
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Multiple omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have been applied to identify the molecular factors contributing to the pathogenesis of osteoporosis.
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Building upon the success in single-omics discovery research, studies have integrated data from different omics levels to better elucidate the molecular and functional mechanisms for osteoporosis.
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Integration of omics approaches can provide a holistic road map to comprehensively illuminate the complex pathogenesis of osteoporosis and fulfil the potential of personalized disease risk prediction, intervention and treatment as well as drug development or re-purposing.
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Acknowledgements
T.L.Y. and S.S.D. acknowledge the support of the National Natural Science Foundation of China (31771399, 31970569 and 81573241), the Innovative Talent Promotion Plan of Shaanxi Province for Young Sci-Tech New Star (2018KJXX-010) and the special guidance funds for the construction of world-class universities (disciplines) and characteristic development in central universities. H.S. and H.W.D. acknowledge the support of grants from the National Institutes of Health (R01AR059781, P20GM109036, R01MH107354, R01MH104680, R01GM109068, R01AR069055, U19AG055373, R01DK115679), the Edward G. Schlieder Endowment and the Drs. W. C. Tsai and P. T. Kung Professorship in Biostatistics from Tulane University.
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H-W.D., T-L.Y., H.S., S-S.D., L.Z., F-Y.D and Q.Z. researched data for the article, made substantial contributions to the discussion of content, and contributed to the writing and review/editing of the manuscript before submission. A.L. researched data for the article and contributed to the writing and review/editing of the manuscript before submission.
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Nature Reviews Endocrinology thanks D. Karasik, J. Tobias and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Related links
FINEMAP: http://www.christianbenner.com/
GCTA-COJO: https://cnsgenomics.com/software/gcta/#COJO
International Mouse Phenotyping Consortium: http://www.mousephenotype.org/
Mouse Genome Informatics: http://www.informatics.jax.org/
PAINTOR: https://github.com/gkichaev/PAINTOR_V3.0
The Origins of Bone and Cartilage Disease project: http://www.boneandcartilage.com/
UK Biobank: https://www.ukbiobank.ac.uk/
Supplementary information
41574_2019_282_MOESM2_ESM.xlsx
Supplementary Table 2. Drug target information for GWAS genes (drugs for osteoporosis, other diseases or disease pathways).
41574_2019_282_MOESM5_ESM.xlsx
Supplementary Table 5 and 6. Summary of potential diagnostic protein biomarkers of osteoporotic fracture predictive protein biomarkers of osteoporotic fracture.
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Yang, TL., Shen, H., Liu, A. et al. A road map for understanding molecular and genetic determinants of osteoporosis. Nat Rev Endocrinol 16, 91–103 (2020). https://doi.org/10.1038/s41574-019-0282-7
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DOI: https://doi.org/10.1038/s41574-019-0282-7
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