Human Genetics

, Volume 136, Issue 8, pp 963–974 | Cite as

Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis

  • Shi Yao
  • Yan Guo
  • Shan-Shan Dong
  • Ruo-Han Hao
  • Xiao-Feng Chen
  • Yi-Xiao Chen
  • Jia-Bin Chen
  • Qing Tian
  • Hong-Wen Deng
  • Tie-Lin YangEmail author
Original Investigation


Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants. We further utilized Genetic Factors for Osteoporosis Consortium (GEFOS) and three in-house GWASs samples to validate the associations for predicted positive SNPs. The random forest classifier performed best among all machine learning methods with the F1 score of 0.8871. Using the optimized model, we predicted 37,584 candidate SNPs for osteoporosis. According to the meta-analysis results, a list of regulatory variants was significantly associated with osteoporosis after multiple testing corrections and contributed to the expression of known osteoporosis-associated protein-coding genes. In summary, combining GWASs and regulatory elements through machine learning could provide additional information for understanding the mechanism of osteoporosis. The regulatory variants we predicted will provide novel targets for etiology research and treatment of osteoporosis.



This work was supported by the National Natural Science Foundation of China (31471188, 81573241, and 31511140285); China Postdoctoral Science Foundation (2016M602797, 2016T90902); Natural Science Basic Research Program Shaanxi Province (2016JQ3026); and the Fundamental Research Funds for the Central Universities. The study was also funded by the Grants from National Institutes of Health (P50AR055081, R01AG026564, R01AR050496, and R01AR057049).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

For this type of study formal consent is not required. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

439_2017_1825_MOESM1_ESM.pdf (1.3 mb)
Supplementary material 1 (PDF 1375 kb)


  1. Bernstein BE et al (2010) The NIH roadmap epigenomics mapping consortium. Nat Biotechnol 28:1045–1048. doi: 10.1038/nbt1010-1045 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Byers PH, Pyott SM (2012) Recessively inherited forms of osteogenesis imperfecta. Annu Rev Genet 46:475–497. doi: 10.1146/annurev-genet-110711-155608 CrossRefPubMedGoogle Scholar
  3. Chesi A et al (2015) A trans-ethnic genome-wide association study identifies gender-specific loci influencing pediatric aBMD and BMC at the distal radius. Hum Mol Genet 24:5053–5059. doi: 10.1093/hmg/ddv210 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Compston JE et al (2014) Relationship of weight, height, and body mass index with fracture risk at different sites in postmenopausal women: the Global Longitudinal study of Osteoporosis in Women (GLOW). J Bone Miner Res 29:487–493. doi: 10.1002/jbmr.2051 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Consortium EP (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74. doi: 10.1038/nature11247 CrossRefGoogle Scholar
  6. Consortium GT (2015) Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348:648–660. doi: 10.1126/science.1262110 CrossRefGoogle Scholar
  7. Coughlin C et al (2016) The genotypic spectrum of classic nonketotic hyperglycinemia Due to mutations in Gldc and Amt molecular genetics and metabolism, vol 117, pp 236–236Google Scholar
  8. Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S (2010) Identifying a high fraction of the human genome to be under selective constraint using GERP ++. PLoS Comput Biol 6:e1001025. doi: 10.1371/journal.pcbi.1001025 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Estrada K et al (2012) Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet 44:491–501. doi: 10.1038/ng.2249 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Fink HA, Kuskowski MA, Orwoll ES, Cauley JA, Ensrud KE, Osteoporotic Fractures in Men Study G (2005) Association between Parkinson’s disease and low bone density and falls in older men: the osteoporotic fractures in men study. J Am Geriatr Soc 53:1559–1564. doi: 10.1111/j.1532-5415.2005.53464.x CrossRefPubMedGoogle Scholar
  11. Grant SF, Hakonarson H (2008) Microarray technology and applications in the arena of genome-wide association. Clin Chem 54:1116–1124. doi: 10.1373/clinchem.2008.105395 CrossRefPubMedGoogle Scholar
  12. Guo Y et al (2010) Genome-wide association study identifies ALDH7A1 as a novel susceptibility gene for osteoporosis. PLoS Genet 6:e1000806. doi: 10.1371/journal.pgen.1000806 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Guo Y et al (2016) Integrating epigenomic elements and GWASs identifies BDNF gene affecting bone mineral density and osteoporotic fracture ris. Sci Rep 6:30558. doi: 10.1038/srep30558 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Ham S, Roh TY (2014) A follow-up association study of genetic variants for bone mineral density in a Korean Population. Genom Inf 12:114–120. doi: 10.5808/GI.2014.12.3.114 CrossRefGoogle Scholar
  15. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 106:9362–9367. doi: 10.1073/pnas.0903103106 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Hofbauer LC, Brueck CC, Singh SK, Dobnig H (2007) Osteoporosis in patients with diabetes mellitus. J Bone Miner Res 22:1317–1328. doi: 10.1359/jbmr.070510 CrossRefPubMedGoogle Scholar
  17. Howie B, Marchini J, Stephens M (2011) Genotype imputation with thousands of genomes. G3 1:457–470. doi: 10.1534/g3.111.001198 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Ionita-Laza I, McCallum K, Xu B, Buxbaum JD (2016) A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48:214–220. doi: 10.1038/ng.3477 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Jing H et al (2016) Suppression of EZH2 prevents the shift of osteoporotic MSC fate to adipocyte and enhances bone formation during osteoporosis. Mol Ther 24:217–229. doi: 10.1038/mt.2015.152 CrossRefPubMedGoogle Scholar
  20. Khan TS, Fraser LA (2015) Type 1 diabetes and osteoporosis: from molecular pathways to bone phenotype. J Osteoporosis 2015:174186. doi: 10.1155/2015/174186 CrossRefGoogle Scholar
  21. Kim MH, Kim HM, Jeong HJ (2016) Estrogen-like osteoprotective effects of glycine in in vitro and in vivo models of menopause. Amino Acids 48:791–800. doi: 10.1007/s00726-015-2127-6 CrossRefPubMedGoogle Scholar
  22. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46:310–315. doi: 10.1038/ng.2892 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Kotake S et al (2001) Activated human T cells directly induce osteoclastogenesis from human monocytes - Possible role of T cells in bone destruction in rheumatoid arthritis patients. Arthritis Rheum 44:1003–1012. doi: 10.1002/1529-0131(200105)44:5<1003 (:Aid-Anr179>3.0.Co;2-#) CrossRefPubMedGoogle Scholar
  24. Kung AW et al (2010) Association of JAG1 with bone mineral density and osteoporotic fractures: a genome-wide association study and follow-up replication studies. Am J Hum Genet 86:229–239. doi: 10.1016/j.ajhg.2009.12.014 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Libbrecht MW, Noble WS (2015) Machine learning applications in genetics and genomics. Nat Rev Genet 16:321–332. doi: 10.1038/nrg3920 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Manilay JO, Zouali M (2014) Tight relationships between B lymphocytes and the skeletal system. Trends Mol Med 20:405–412. doi: 10.1016/j.molmed.2014.03.003 CrossRefPubMedGoogle Scholar
  27. McClellan J, King MC (2010) Genetic heterogeneity in human disease. Cell 141:210–217. doi: 10.1016/j.cell.2010.03.032 CrossRefPubMedGoogle Scholar
  28. McDonald AC, Schuijers JA, Gundlach AL, Grills BL (2007) Galanin treatment offsets the inhibition of bone formation and downregulates the increase in mouse calvarial expression of TNFalpha and GalR2 mRNA induced by chronic daily injections of an injurious vehicle. Bone 40:895–903. doi: 10.1016/j.bone.2006.10.018 CrossRefPubMedGoogle Scholar
  29. Mitchell SA et al (2010) Determinants of functional performance in long-term survivors of allogeneic hematopoietic stem cell transplantation with chronic graft-versus-host disease (cGVHD). Bone Marrow Transpl 45:762–769. doi: 10.1038/bmt.2009.238 CrossRefGoogle Scholar
  30. Musunuru K et al (2010) From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466:714–719. doi: 10.1038/nature09266 CrossRefPubMedPubMedCentralGoogle Scholar
  31. Purcell S et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. doi: 10.1086/519795 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841–842. doi: 10.1093/bioinformatics/btq033 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Ralston SH, Uitterlinden AG (2010) Genetics of osteoporosis. Endocr Rev 31:629–662. doi: 10.1210/er.2009-0044 CrossRefPubMedGoogle Scholar
  34. Ramos EM et al (2014) Phenotype-Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources. Europ J Hum Genet EJHG 22:144–147. doi: 10.1038/ejhg.2013.96 CrossRefPubMedGoogle Scholar
  35. Reinholt FP, Hultenby K, Oldberg A, Heinegard D (1990) Osteopontin—a possible anchor of osteoclasts to bone. Proc Natl Acad Sci USA 87:4473–4475. doi: 10.1073/pnas.87.12.4473 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Ritchie GRS, Dunham I, Zeggini E, Flicek P (2014) Functional annotation of noncoding sequence variants. Nat Methods 11:294–U351. doi: 10.1038/nmeth.2832 CrossRefPubMedPubMedCentralGoogle Scholar
  37. Samuel L (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3:210–229CrossRefGoogle Scholar
  38. Sellmeyer DE, Stone KL, Sebastian A, Cummings SR, Study of Osteoporotic Fractures Research Group (2001) A high ratio of dietary animal to vegetable protein increases the rate of bone loss and the risk of fracture in postmenopausal women. Am J Clin Nutr 73:118–122PubMedGoogle Scholar
  39. Slatkin M (2009) Epigenetic inheritance and the missing heritability problem. Genetics 182:845–850. doi: 10.1534/genetics.109.102798 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Styrkarsdottir U et al (2016) Sequence variants in the PTCH1 gene associate with spine bone mineral density and osteoporotic fractures. Nat Commun 7:10129. doi: 10.1038/ncomms10129 CrossRefPubMedPubMedCentralGoogle Scholar
  41. Tat SK, Padrines M, Theoleyre S, Couillaud-Battaglia S, Heymann D, Redini F, Fortun Y (2006) OPG/membranous-RANKL complex is internalized via the clathrin pathway before a lysosomal and a proteasomal degradation. Bone 39:706–715. doi: 10.1016/j.bone.2006.03.016 CrossRefPubMedGoogle Scholar
  42. Timpson NJ et al (2009) Common variants in the region around Osterix are associated with bone mineral density and growth in childhood. Hum Mol Genet 18:1510–1517. doi: 10.1093/hmg/ddp052 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Udagawa N et al (1990) Origin of osteoclasts—mature monocytes and macrophages are capable of differentiating into osteoclasts under a suitable microenvironment prepared by bone marrow-derived stromal cells. Proc Natl Acad Sci USA 87:7260–7264. doi: 10.1073/pnas.87.18.7260 CrossRefPubMedPubMedCentralGoogle Scholar
  44. Wang L, Jin Q, Lee JE, Su IH, Ge K (2010) Histone H3K27 methyltransferase Ezh2 represses Wnt genes to facilitate adipogenesis. Proc Natl Acad Sci USA 107:7317–7322. doi: 10.1073/pnas.1000031107 CrossRefPubMedPubMedCentralGoogle Scholar
  45. Welter D et al (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42:D1001–1006. doi: 10.1093/nar/gkt1229 CrossRefPubMedGoogle Scholar
  46. Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26:2190–2191. doi: 10.1093/bioinformatics/btq340 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Xiong DH et al (2009) 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. doi: 10.1016/j.ajhg.2009.01.025 CrossRefPubMedPubMedCentralGoogle Scholar
  48. Yang H, Wang K (2015) Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat Protoc 10:1556–1566. doi: 10.1038/nprot.2015.105 CrossRefPubMedPubMedCentralGoogle Scholar
  49. Yang TL et al (2008) Genome-wide copy-number-variation study identified a susceptibility gene, UGT2B17, for osteoporosis. Am J Hum Genet 83:663–674. doi: 10.1016/j.ajhg.2008.10.006 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Yang J et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569. doi: 10.1038/ng.608 CrossRefPubMedPubMedCentralGoogle Scholar
  51. Yang TL et al (2012) Genetic variants in the SOX6 gene are associated with bone mineral density in both Caucasian and Chinese populations. Osteoporos Int 23:781–787. doi: 10.1007/s00198-011-1626-x CrossRefPubMedGoogle Scholar
  52. Zheng HF et al (2015) Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526:112–117. doi: 10.1038/nature14878 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12:931–934. doi: 10.1038/nmeth.3547 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Shi Yao
    • 1
  • Yan Guo
    • 1
  • Shan-Shan Dong
    • 1
  • Ruo-Han Hao
    • 1
  • Xiao-Feng Chen
    • 1
  • Yi-Xiao Chen
    • 1
  • Jia-Bin Chen
    • 1
  • Qing Tian
    • 2
  • Hong-Wen Deng
    • 2
  • Tie-Lin Yang
    • 1
    Email author
  1. 1.Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and TechnologyXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  2. 2.School of Public Health and Tropical MedicineTulane UniversityNew OrleansUSA

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