Advertisement

Using GWAS to identify novel therapeutic targets for osteoporosis

  • Olivia L. Sabik
    Affiliations
    Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, Va

    Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, Va
    Search for articles by this author
  • Charles R. Farber
    Correspondence
    Reprint requests: Dr. Charles R. Farber, Center for Public Health Genomics at UVA, PO Box 800717, Charlottesville, VA 22908
    Affiliations
    Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, Va

    Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, Va

    Department of Public Health Science, School of Medicine, University of Virginia, Charlottesville, Va
    Search for articles by this author
Published:October 27, 2016DOI:https://doi.org/10.1016/j.trsl.2016.10.009
      Osteoporosis is a common, increasingly prevalent, global health burden characterized by low bone mineral density (BMD) and increased risk of fracture. Despite its significant impact on human health, there is currently a lack of highly effective treatments free of side effects for osteoporosis. Therefore, a major goal in the field is to identify new drug targets. Genetic discovery has been shown to be effective in the unbiased identification of novel drug targets and genome-wide association studies (GWASs) have begun to provide insight into genetic basis of osteoporosis. Over the last decade, GWASs have led to the identification of ∼100 loci associated with BMD and other bone traits related to risk of fracture. However, there have been limited efforts to identify the causal genes underlying the GWAS loci or the mechanisms by which GWAS loci alter bone physiology. In this review, we summarize the current state of the field and discuss strategies for causal gene discovery and the evidence that the novel genes underlying GWAS loci are likely to be a new source of drug targets.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Translational Research
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Kanis J.A.
        Diagnosis of osteoporosis.
        Osteoporos Int. 1997; 7: 108-116
        • Cauley J.A.
        Public health impact of osteoporosis.
        Journals Gerontol Ser A: Biol Sci Med Sci. 2013; 68: 1243-1251
        • Office of the Surgeon General (US)
        Bone Health and Osteoporosis: A Report of the Surgeon General.
        Office of the Surgeon General (US), Rockville (MD)2004 (Available at:)
        • Burge R.
        • Dawson-Hughes B.
        • Solomon D.H.
        • et al.
        Incidence and economic burden of osteoporosis-related fractures in the United States, 2005-2025.
        J Bone Mineral Res. 2006; 22: 465-475
        • Maraka S.
        • Kennel K.A.
        Bisphosphonates for the prevention and treatment of osteoporosis.
        BMJ. 2015; 351: h3783
        • Carano A.
        • Teitelbaum S.L.
        • Konsek J.D.
        • et al.
        Bisphosphonates directly inhibit the bone resorption activity of isolated avian osteoclasts in vitro.
        J Clin Invest. 1990; 85: 456-461
        • Black D.M.
        • Rosen C.J.
        Clinical practice. postmenopausal osteoporosis.
        N Engl J Med. 2016; 374: 254-262
        • Neer R.M.
        • Arnaud C.D.
        • Zanchetta J.R.
        • et al.
        Effect of parathyroid hormone (1-34) on fractures and bone mineral density in postmenopausal women with osteoporosis.
        N Engl J Med. 2001; 344: 1434-1441
        • Brommage R.
        Genetic approaches to identifying novel osteoporosis drug targets.
        J Cell Biochem. 2015; 116: 2139-2145
        • Durie B.G.M.
        • Katz M.
        • Crowley J.
        Osteonecrosis of the jaw and bisphosphonates.
        N Engl J Med. 2005; 353 (discussion 99–102): 99-102
        • Schilcher J.
        • Koeppen V.
        • Aspenberg P.
        • et al.
        Risk of atypical femoral fracture during and after bisphosphonate use.
        N Engl J Med. 2014; 371: 974-976
        • Kolata G.
        Fearing drugs' rare side effects, millions take their chances with osteoporosis.
        New York Times, New York2016
        • Cummings S.R.
        • Martin J.S.
        • McClung M.R.
        • et al.
        Denosumab for prevention of fractures in postmenopausal women with osteoporosis.
        N Engl J Med. 2009; 361: 756-765
        • Lewiecki E.M.
        Sclerostin: a novel target for intervention in the treatment of osteoporosis.
        Discov Med. 2011; 12: 263-273
        • Ettinger B.
        • Black D.M.
        • Mitlak B.H.
        • et al.
        Reduction of vertebral fracture risk in postmenopausal women with osteoporosis treated with raloxifene: results from a 3-year randomized clinical trial.
        JAMA. 1999; 282: 637-645
        • Greenspan S.L.
        • Bone H.G.
        • Ettinger M.P.
        • et al.
        Effect of recombinant human parathyroid hormone (1-84) on Vertebral fracture and bone mineral density in postmenopausal women with osteoporosis: a randomized trial.
        Ann Intern Med. 2007; 146: 326-339
        • Liberman U.A.
        • Weiss S.R.
        • Bröll J.
        • et al.
        Effect of oral alendronate on bone mineral density and the incidence of fractures in postmenopausal osteoporosis.
        N Engl J Med. 1995; 333: 1437-1444
        • Rossouw J.E.
        • Anderson G.L.
        • Prentice R.L.
        • et al.
        Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women's Health Initiative randomized controlled trial.
        JAMA. 2002; 288: 321-333
        • Gauthier J.Y.
        • Chauret N.
        • Cromlish W.
        • et al.
        The discovery of odanacatib (MK-0822), a selective inhibitor of cathepsin K.
        Bioorg Med Chem Lett. 2008; 18: 923-928
        • Canalis E.
        Update in new anabolic therapies for osteoporosis.
        J Clin Endocrinol Metab. 2010; 95: 1496-1504
        • Richards J.B.
        • Zheng H.-F.
        • Spector T.D.
        Genetics of osteoporosis from genome-wide association studies: advances and challenges.
        Nat Rev Gen. 2012; 13: 576-588
        • Rissanen J.P.
        • Halleen J.M.
        Models and screening assays for drug discovery in osteoporosis.
        Expert Opin Drug Discov. 2010; 5: 1163-1174
        • Lacey D.L.
        • Boyle W.J.
        • Simonet W.S.
        • et al.
        Bench to bedside: elucidation of the OPG-RANK-RANKL pathway and the development of denosumab.
        Nat Rev Drug Discov. 2012; 11: 401-419
        • Nelson M.R.
        • Tipney H.
        • Painter J.L.
        • et al.
        The support of human genetic evidence for approved drug indications.
        Nat Genet. 2015; 47: 856-860
        • Ralston S.H.
        • Uitterlinden A.G.
        Genetics of osteoporosis.
        Endocr Rev. 2010; 31: 629-662
        • Auton A.
        • Altshuler D.M.
        • Durbin R.M.
        • et al.
        A global reference for human genetic variation.
        Nature. 2015; 526: 68-74
        • The International HapMap Consortium
        The International HapMap project.
        Nature. 2003; 426: 789-796
        • Risch N.
        • Merikangas K.
        The future of genetic studies of complex human diseases.
        Science. 1996; 273: 1516-1517
        • Shalon D.
        • Smith S.J.
        • Brown P.O.
        A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization.
        Genome Res. 1996; 6: 639-645
        • Altshuler D.
        • Daly M.J.
        • Lander E.S.
        Genetic mapping in human disease.
        Science. 2008; 322: 881-888
        • Porcu E.
        • Sanna S.
        • Fuchsberger C.
        • et al.
        Genotype imputation in genome-wide association studies.
        Curr Protoc Hum Genet. 2013; Chapter 1: Unit 1.25
        • Altshuler D.M.
        • Durbin R.M.
        • Bentley D.R.
        • et al.
        An integrated map of genetic variation from 1,092 human genomes.
        Nature. 2012; 491: 56-65
        • LaFramboise T.
        Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances.
        Nucleic Acids Res. 2009; 37: 4181-4193
        • Pearson T.A.
        • Manolio T.A.
        How to interpret a genome-wide association study.
        JAMA. 2008; 299: 1335-1344
        • Welter D.
        • MacArthur J.
        • Morales J.
        • et al.
        The NHGRI GWAS Catalog, a curated resource of SNP-trait associations.
        Nucleic Acids Res. 2013; 42: D1001-D1006
        • Maurano M.T.
        • Humbert R.
        • Rynes E.
        • et al.
        Systematic localization of common disease-associated variation in regulatory DNA.
        Science. 2012; 337: 1190-1195
        • Freedman M.L.
        • Monteiro A.N.A.
        • Gayther S.A.
        • et al.
        Principles for the post-GWAS functional characterization of cancer risk loci.
        Nat Genet. 2011; 43: 513-518
        • Hindorff L.A.
        • Sethupathy P.
        • Junkins H.A.
        • et al.
        Potential etiologic and functional implications of genome-wide association loci for human diseases and traits.
        Proc Natl Acad Sci USA. 2009; 106: 9362-9367
        • Kilpinen H.
        • Waszak S.M.
        • Gschwind A.R.
        • et al.
        Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription.
        Science. 2013; 342: 744-747
        • Darrow E.M.
        • Huntley M.H.
        • Dudchenko O.
        • et al.
        Deletion of DXZ4 on the human inactive X chromosome alters higher-order genome architecture.
        Proc Natl Acad Sci U S A. 2016; 113: E4504-E4512
        • Jakubczik F.
        • Jones K.
        • Nichols J.
        • et al.
        A SNP in the immunoregulatory molecule CTLA-4 controls mRNA splicing in vivo but does not alter diabetes susceptibility in the NOD mouse.
        Diabetes. 2016; 65: 120-128
        • Arnold C.D.
        • Gerlach D.
        • Stelzer C.
        • et al.
        Genome-wide quantitative enhancer activity maps identified by STARR-seq.
        Science. 2013; 339: 1074-1077
        • Peterson T.A.
        • Mort M.
        • Cooper D.N.
        • et al.
        Regulatory single nucleotide variant predictor (RSVP) increases predictive performance of functional regulatory variants.
        Hum Mutat. 2016; 37: 1137-1143
        • Chen J.
        • Tian W.
        Explaining the disease phenotype of intergenic SNP through predicted long range regulation.
        Nucleic Acids Res. 2016; 44: 8641-8654
        • Pocock N.A.
        • Eisman J.A.
        • Hopper J.L.
        • et al.
        Genetic determinants of bone mass in adults. A twin study.
        J Clin Invest. 1987; 80: 706-710
        • Krall E.A.
        • Dawson-Hughes B.
        Heritable and life-style determinants of bone mineral density.
        J Bone Mineral Res. 1993; 8: 1-9
        • Trémollieres F.A.
        • Pouillès J.-M.
        • Drewniak N.
        • et al.
        Fracture risk prediction using BMD and clinical risk factors in early postmenopausal women: Sensitivity of the WHO FRAX tool.
        J Bone Mineral Res. 2010; 25: 1002-1009
        • Rivadeneira F.
        • Styrkarsdottir U.
        • Estrada K.
        • et al.
        Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies.
        Nat Genet. 2009; 41: 1199-1206
        • Estrada K.
        • Styrkarsdottir U.
        • Evangelou E.
        • et al.
        Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture.
        Nat Genet. 2012; 44: 491-501
        • Brommage R.
        • Liu J.
        • Hansen G.M.
        • et al.
        High-throughput screening of mouse gene knockouts identifies established and novel skeletal phenotypes.
        Bone Res. 2014; 2: 14034
        • Wood A.R.
        • Esko T.
        • Yang J.
        • et al.
        Defining the role of common variation in the genomic and biological architecture of adult human height.
        Nat Genet. 2014; 46: 1173-1186
        • Krishnan V.
        • Bryant H.U.
        • Macdougald O.A.
        Regulation of bone mass by Wnt signaling.
        J Clin Invest. 2006; 116: 1202-1209
        • Baron R.
        • Kneissel M.
        WNT signaling in bone homeostasis and disease: from human mutations to treatments.
        Nat Med. 2013; 19: 179-192
        • Boyce B.F.
        • Xing L.
        Functions of RANKL/RANK/OPG in bone modeling and remodeling.
        Arch Biochem Biophys. 2008; 473: 139-146
        • Wittrant Y.
        • Théoleyre S.
        • Chipoy C.
        • et al.
        RANKL/RANK/OPG: new therapeutic targets in bone tumours and associated osteolysis.
        Biochim Biophys Acta. 2004; 1704: 49-57
        • Nishimura R.
        • Hata K.
        • Ono K.
        • et al.
        Regulation of endochondral ossification by transcription factors.
        Front Biosci (Landmark Ed). 2012; 17: 2657-2666
        • Jepsen K.J.
        Functional interactions among morphologic and tissue quality traits define bone quality.
        Clin Orthop Relat Res. 2010; 469: 2150-2159
        • Paternoster L.
        • Lorentzon M.
        • Lehtimäki T.
        • et al.
        Genetic determinants of trabecular and cortical volumetric bone mineral densities and bone microstructure.
        PLoS Genet. 2013; 9: e1003247
        • Levy R.
        • Mott R.F.
        • Iraqi F.A.
        • et al.
        Collaborative cross mice in a genetic association study reveal new candidate genes for bone microarchitecture.
        BMC Genomics. 2015; 16: 465
        • Guo Y.
        • Tan L.-J.
        • Lei S.-F.
        • et al.
        Genome-wide association study identifies ALDH7A1 as a novel susceptibility gene for osteoporosis.
        PLoS Genet. 2010; 6: e1000806
        • Hwang J.-Y.
        • Lee S.H.
        • Go M.J.
        • et al.
        Meta-analysis identifies a MECOM gene as a novel predisposing factor of osteoporotic fracture.
        J Med Genet. 2013; 50: 212-219
        • Van Dijk F.S.
        • Sillence D.O.
        Osteogenesis imperfecta: clinical diagnosis, nomenclature and severity assessment.
        Am J Med Genet A. 2014; 164: 1470-1481
        • Laine C.M.
        • Joeng K.S.
        • Campeau P.M.
        • et al.
        WNT1 mutations in early-onset osteoporosis and osteogenesis imperfecta.
        N Engl J Med. 2013; 368: 1809-1816
        • Styrkarsdottir U.
        • Thorleifsson G.
        • Sulem P.
        • et al.
        Nonsense mutation in the LGR4 gene is associated with several human diseases and other traits.
        Nature. 2013; 497: 517-520
        • Styrkarsdottir U.
        • Thorleifsson G.
        • Eiriksdottir B.
        • et al.
        Two rare mutations in the COL1A2Gene associate with low bone mineral density and fractures in Iceland.
        J Bone Mineral Res. 2015; 31: 173-179
        • Zheng H.-F.
        • Forgetta V.
        • Hsu Y.-H.
        • et al.
        Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture.
        Nature. 2015; 526: 112-117
        • McClellan J.
        • King M.-C.
        Genetic heterogeneity in human disease.
        Cell. 2010; 141: 210-217
        • Visscher P.M.
        • Brown M.A.
        • McCarthy M.I.
        • et al.
        Five years of GWAS discovery.
        Am J Hum Genet. 2012; 90: 7-24
        • Törn C.
        • Liu X.
        • Hagopian W.
        • et al.
        Complement gene variants in relation to autoantibodies to beta cell specific antigens and type 1 diabetes in the TEDDY Study.
        Scientific Rep. 2016; 6: 27887
        • Kumar P.
        • Henikoff S.
        • Ng P.C.
        Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.
        Nat Protoc. 2009; 4: 1073-1081
        • Adzhubei I.
        • Jordan D.M.
        • Sunyaev S.R.
        Predicting functional effect of human missense mutations using PolyPhen-2.
        John Wiley & Sons, Inc, Hoboken, NJ, USA2001: 7.20.1-7.20.41
        • Sloan C.A.
        • Chan E.T.
        • Davidson J.M.
        • et al.
        ENCODE data at the ENCODE portal.
        Nucleic Acids Res. 2016; 44: D726-D732
        • Chadwick L.H.
        The NIH Roadmap Epigenomics Program data resource.
        Epigenomics. 2012; 4: 317-324
        • Chung D.
        • Yang C.
        • Li C.
        • et al.
        GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.
        PLoS Genet. 2014; 10: e1004787
        • Schaub M.A.
        • Boyle A.P.
        • Kundaje A.
        • et al.
        Linking disease associations with regulatory information in the human genome.
        Genome Res. 2012; 22: 1748-1759
        • Hardison R.C.
        Genome-wide epigenetic data facilitate understanding of disease susceptibility association studies.
        J Biol Chem. 2012; 287: 30932-30940
        • Spain S.L.
        • Barrett J.C.
        Strategies for fine-mapping complex traits.
        Hum Mol Genet. 2015; 24: R111-R119
        • Stephens M.
        • Balding D.J.
        Bayesian statistical methods for genetic association studies.
        Nat Rev Genet. 2009; 10: 681-690
        • Maller J.B.
        • McVean G.
        • Byrnes J.
        • Wellcome Trust Case Control Consortium
        Bayesian refinement of association signals for 14 loci in 3 common diseases.
        Nat Genet. 2012; 44: 1294-1301
        • Carninci P.
        • Sandelin A.
        • Lenhard B.
        • et al.
        Genome-wide analysis of mammalian promoter architecture and evolution.
        Nat Genet. 2006; 38: 626-635
        • Farber C.R.
        • Lusis A.J.
        Integrating global gene expression analysis and genetics.
        in: Genetic Dissection of Complex Traits. Vol 60. Advances in Genetics. Elsevier, New York2008: 571-601
        • Rockman M.V.
        • Kruglyak L.
        Genetics of global gene expression.
        Nat Rev Gen. 2006; 7: 862-872
        • Albert F.W.
        • Kruglyak L.
        The role of regulatory variation in complex traits and disease.
        Nat Rev Gen. 2015; 16: 197-212
        • Melé M.
        • Ferreira P.G.
        • Reverter F.
        • et al.
        The human transcriptome across tissues and individuals.
        Science. 2015; 348: 660-665
        • Jia P.
        • Zhao Z.
        Network-assisted analysis to prioritize GWAS results: principles, methods and perspectives.
        Hum Genet. 2013; 133: 125-138
        • Leiserson M.D.M.
        • Eldridge J.V.
        • Ramachandran S.
        • et al.
        Network analysis of GWAS data.
        Curr Opin Genet Development. 2013; 23: 602-610
        • Califano A.
        • Butte A.J.
        • Friend S.
        • et al.
        Leveraging models of cell regulation and GWAS data in integrative network-based association studies.
        Nat Genet. 2012; 44: 841-847
        • Farber C.R.
        Systems-level analysis of genome-wide association data.
        G3 (Bethesda). 2013; 3: 119-129
        • Farber C.R.
        Identification of a gene module associated with BMD through the integration of network analysis and genome-wide association data.
        J Bone Mineral Res. 2010; 25: 2359-2367
        • Gustafsson M.
        • Gawel D.R.
        • Alfredsson L.
        • et al.
        A validated gene regulatory network and GWAS identifies early regulators of T cell–associated diseases.
        Sci Translational Med. 2015; 7: 313ra178
        • Huan T.
        • Meng Q.
        • Saleh M.A.
        • et al.
        Integrative network analysis reveals molecular mechanisms of blood pressure regulation.
        Mol Syst Biol. 2015; 11: 799
        • Mäkinen V.-P.
        • Civelek M.
        • Meng Q.
        • et al.
        Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease.
        PLoS Genet. 2014; 10: e1004502
        • Horvath S.
        • Dong J.
        Geometric interpretation of gene coexpression network analysis.
        PLOS Comput Biol. 2008; 4: e1000117
        • Goh K.-I.
        • Cusick M.E.
        • Valle D.
        • et al.
        The human disease network.
        PNAS. 2007; 104: 8685-8690
        • Horvath S.
        Weighted Network Analysis.
        Springer New York, New York, NY2011
        • Horvath S.
        • Zhang B.
        • Carlson M.
        • et al.
        Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target.
        PNAS. 2006; 103: 17402-17407
        • Fabregat A.
        • Sidiropoulos K.
        • Garapati P.
        • et al.
        The reactome pathway knowledgebase.
        Nucleic Acids Res. 2016; 44: D481-D487
        • Nishimura D.
        BioCarta.
        Biotech Softw Internet Rep. 2001; 2: 117-120
        • Kanehisa M.
        • Goto S.
        KEGG: kyoto encyclopedia of genes and genomes.
        Nucleic Acids Res. 2000; 28: 27-30
        • Preuss M.
        • Konig I.R.
        • Thompson J.R.
        • et al.
        Design of the Coronary ARtery DIsease Genome-Wide replication and meta-analysis (CARDIoGRAM) study: a genome-wide association meta-analysis involving more than 22 000 cases and 60 000 controls.
        Circ Cardiovasc Genet. 2010; 3: 475-483
        • Zhu J.
        • Wiener M.C.
        • Zhang C.
        • et al.
        Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations.
        PLoS Comput Biol. 2007; 3: e69
        • Zhu J.
        • Zhang B.
        • Smith E.N.
        • et al.
        Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks.
        Nat Genet. 2008; 40: 854-861
        • Karlebach G.
        • Shamir R.
        Modelling and analysis of gene regulatory networks.
        Nat Rev Mol Cell Biol. 2008; 9: 770-780
        • Chen C.-Y.
        • Chang I.-S.
        • Hsiung C.A.
        • Wasserman W.W.
        On the identification of potential regulatory variants within genome wide association candidate SNP sets.
        BMC Med Genomics. 2014; 7: 34
        • Grundberg E.
        • Kwan T.
        • Ge B.
        • et al.
        Population genomics in a disease targeted primary cell model.
        Genome Res. 2009; 19: 1942-1952
        • Grundberg E.
        • Adoue V.
        • Kwan T.
        • et al.
        Global analysis of the impact of environmental perturbation on cis-regulation of gene expression.
        PLoS Genet. 2011; 7: e1001279
        • Nielson C.M.
        • Liu C.-T.
        • Smith A.V.
        • et al.
        Novel genetic variants are associated with increased vertebral volumetric BMD, reduced vertebral fracture risk, and increased expression of SCL1A3 and EPHB2.
        J Bone Mineral Res. 2016; https://doi.org/10.1002/jbmr.2913
      1. Shaffer J, Kammerer C, Dressen A, et al. Different genes contribute to variation in peak bone density and bone loss. Annu Meet Am Soc Bone Mineral Res. Published 2014.http://www.asbmr.org/Itinerary/PresentationDetail.aspx?id=68c5c92f-1060-4d1b-a177-88b7126301e1. Accessed August 16, 2016.

        • Kemp J.P.
        • Medina-Gomez C.
        • Tobias J.H.
        • et al.
        The case for genome-wide association studies of bone acquisition in paediatric and adolescent populations.
        Bonekey Rep. 2016; 5: 796
        • Chesi A.
        • Mitchell J.A.
        • Kalkwarf H.J.
        • et al.
        A trans-ethnic genome-wide association study identifies gender-specific loci influencing pediatric aBMD and BMC at the distal radius.
        Hum Mol Genet. 2015; 24: 5053-5059
        • Cho Y.S.
        • Go M.J.
        • Kim Y.J.
        • et al.
        A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits.
        Nat Genet. 2009; 41: 527-534
        • Choi H.J.
        • Park H.
        • Zhang L.
        • et al.
        Genome-wide association study in East Asians suggests UHMK1 as a novel bone mineral density susceptibility gene.
        Bone. 2016; 91: 113-121
        • Knight J.C.
        Approaches for establishing the function of regulatory genetic variants involved in disease.
        Genome Med. 2014; 6: 92
        • Dailey L.
        High throughput technologies for the functional discovery of mammalian enhancers: new approaches for understanding transcriptional regulatory network dynamics.
        Genomics. 2015; 106: 151-158
        • Inoue F.
        • Kircher M.
        • Martin B.
        • et al.
        A systematic comparison reveals substantial differences in chromosomal versus episomal encoding of enhancer activity.
        bioRxiv. 2016; (061606)https://doi.org/10.1101/061606
        • Soldner F.
        • Stelzer Y.
        • Shivalila C.S.
        • et al.
        Parkinson-associated risk variant in distal enhancer of α-synuclein modulates target gene expression.
        Nature. 2016; 533: 95-99
        • Raghavan A.
        • Wang X.
        • Rogov P.
        • et al.
        High-throughput screening and CRISPR-Cas9 modeling of causal lipid-associated expression quantitative trait locus variants.
        Cold Spring Harb Lab J. 2016; (056820)https://doi.org/10.1101/056820
        • Bilousova G.
        • Hyun J.D.
        • King K.B.
        • et al.
        Osteoblasts derived from induced pluripotent stem cells form calcified structures in scaffolds both in vitro and in vivo.
        Stem Cells. 2011; 29: 206-216
        • Jeon O.H.
        • Panicker L.M.
        • Lu Q.
        • et al.
        Human iPSC-derived osteoblasts and osteoclasts together promote bone regeneration in 3D biomaterials.
        Scientific Rep. 2016; 6: 26761