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Systems Genetics: A Novel Approach to Dissect the Genetic Basis of Osteoporosis

  • Bone Genetics (S Ferrari, Section Editor)
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Abstract

From the early 1990s to the middle of the last decade, the search for genes influencing osteoporosis proved difficult with few successes. However, over the last 5 years this has begun to change with the introduction of genome-wide association (GWA) studies. In this short period of time, GWA studies have significantly accelerated the pace of gene discovery, leading to the identification of nearly 100 independent associations for osteoporosis-related traits. However, GWA does not specifically pinpoint causal genes or provide functional context for associations. Thus, there is a need for approaches that provide systems-level insight on how associated variants influence cellular function, downstream gene networks, and ultimately disease. In this review we discuss the emerging field of “systems genetics” and how it is being used in combination with and independent of GWA to improve our understanding of the molecular mechanisms involved in bone fragility.

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References

Papers of particular interest, published recently, have been highlighted as: •• Of major importance

  1. Reginster J-Y, Burlet N. Osteoporosis: a still increasing prevalence. Bone. 2006;38:S4–9.

    Article  PubMed  Google Scholar 

  2. Anonymous. Consensus development conference: diagnosis, prophylaxis, and treatment of osteoporosis. Am J Med. 1993;94:646–50.

    Article  Google Scholar 

  3. Gass M, Dawson-Hughes B. Preventing osteoporosis-related fractures: an overview. Am J Med. 2006;119:S3–S11.

    Article  PubMed  Google Scholar 

  4. Seeman E, Delmas PD. Bone quality–the material and structural basis of bone strength and fragility. N Engl J Med. 2006;354:2250–61.

    Article  PubMed  CAS  Google Scholar 

  5. Peacock M, Turner CH, Econs MJ, et al. Genetics of osteoporosis. Endocr Rev. 2002;23:303–26.

    Article  PubMed  CAS  Google Scholar 

  6. Ralston SH, Uitterlinden AG. Genetics of osteoporosis. Endocr Rev. 2010;31:629–62.

    Article  PubMed  CAS  Google Scholar 

  7. •• Kiel DP, Demissie S, Dupuis J, et al. Genome-wide association with bone mass and geometry in the Framingham Heart Study. BMC Med Genet. 2007;8 Suppl 1:S14. This was the first GWAS for a complex osteoporosis-related phenotype.

    Article  PubMed  Google Scholar 

  8. Altshuler D, Daly MJ, Lander ES. Genetic mapping in human disease. Science. 2008;322:881–8.

    Article  PubMed  CAS  Google Scholar 

  9. Rivadeneira F, Styrkársdottir U, Estrada K, et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet. 2009;41:1199–206.

    Article  PubMed  CAS  Google Scholar 

  10. Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, et al. Multiple genetic loci for bone mineral density and fractures. N Engl J Med. 2008;358:2355–65.

    Article  PubMed  CAS  Google Scholar 

  11. Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, et al. New sequence variants associated with bone mineral density. Nat Genet. 2008;41:15–7.

    Article  PubMed  Google Scholar 

  12. Richards JB, Rivadeneira F, Inouye M, et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet. 2008;371:1505–12.

    Article  PubMed  CAS  Google Scholar 

  13. Zheng H-F, Spector TD, Richards JB. Insights into the genetics of osteoporosis from recent genome-wide association studies. Expert Rev Mol Med. 2011;13:e28.

    Article  PubMed  Google Scholar 

  14. Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461:218–23.

    Article  PubMed  CAS  Google Scholar 

  15. Nadeau J, Dudley AM. Genetics. Systems genetics. Science. 2011;331:1015–6.

    Article  PubMed  Google Scholar 

  16. Farber CR, Lusis AJ. Future of osteoporosis genetics: enhancing genome-wide association studies. J Bone Miner Res. 2009;24:1937–42.

    Article  PubMed  CAS  Google Scholar 

  17. Farber CR, Lusis AJ. Integrating global gene expression analysis and genetics. Adv Genet. 2008;60:571–601.

    Article  PubMed  CAS  Google Scholar 

  18. Zeller T, Wild P, Szymczak S, et al. Genetics and beyond–the transcriptome of human monocytes and disease susceptibility. PLoS One. 2010;5:e10693.

    Article  PubMed  Google Scholar 

  19. Pickrell JK, Marioni JC, Pai AA, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010;464:768–72.

    Article  PubMed  CAS  Google Scholar 

  20. Montgomery SB, Sammeth M, Gutierrez Arcelus M, et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature. 2010;464:773–7.

    Article  PubMed  CAS  Google Scholar 

  21. Alberts R, Terpstra P, Li Y, et al. Sequence polymorphisms cause many false cis eQTLs. PLoS One. 2007;2:e622.

    Article  PubMed  Google Scholar 

  22. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63.

    Article  PubMed  CAS  Google Scholar 

  23. Rockman MV, Kruglyak L. Genetics of global gene expression. Nat Rev Genet. 2006;7:862–72.

    Article  PubMed  CAS  Google Scholar 

  24. Schadtt EE, Monks SA, Drake TA, et al. Genetics of gene expression surveyed in maize, mouse and man. Nature. 2003;422:297–302.

    Article  Google Scholar 

  25. •• Grundberg E, Kwan T, Ge B, et al. Population genomics in a disease targeted primary cell model. Genome Res. 2009;19:1942–52. Excellent example of using eQTL analysis in primary bone cell cultures to identify novel genes influencing BMD.

    Article  PubMed  CAS  Google Scholar 

  26. Kwan T, Grundberg E, Koka V, et al. Tissue effect on genetic control of transcript isoform variation. PLoS Genet. 2009;5:e1000608.

    Article  PubMed  Google Scholar 

  27. Gardina PJ, Clark TA, Shimada B, et al. Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array. BMC Genomics. 2006;7:325.

    Article  PubMed  Google Scholar 

  28. Simonet WS, Lacey DL, Dunstan CR, et al. Osteoprotegerin: a novel secreted protein involved in the regulation of bone density. Cell. 1997;89:309–19.

    Article  PubMed  CAS  Google Scholar 

  29. Kong YY, Yoshida H, Sarosi I, et al. OPGL is a key regulator of osteoclastogenesis, lymphocyte development and lymph-node organogenesis. Nature. 1997;397:315–23.

    Google Scholar 

  30. Kung AWC, Xiao S-M, Cherny S, et al. Association of JAG1 with bone mineral density and osteoporotic fractures: a genome-wide association study and follow-up replication studies. Am J Hum Genet. 2010;86:229–39.

    Article  PubMed  CAS  Google Scholar 

  31. •• Hsu Y-H, Zillikens MC, Wilson SG, Farber CR, et al. An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility Loci for osteoporosis-related traits. PLoS Genet. 2010;6:e1000977. Provides one of the first examples of combining mouse systems genetics and human BMD GWA.

    Article  PubMed  Google Scholar 

  32. Beamer WG, Donahue LR, Rosen CJ, et al. Genetic variability in adult bone density among inbred strains of mice. Bone. 1996;18:397–403.

    Article  PubMed  CAS  Google Scholar 

  33. Ackert-Bicknell CL, Karasik D, Li Q, et al. Mouse BMD quantitative trait loci show improved concordance with human genome-wide association loci when recalculated on a new, common mouse genetic map. J Bone Miner Res. 2010;25:1808–20.

    Article  PubMed  CAS  Google Scholar 

  34. Flint J, Valdar W, Shifman S, et al. Strategies for mapping and cloning quantitative trait genes in rodents. Nat Rev Genet. 2005;6:271–86.

    Article  PubMed  CAS  Google Scholar 

  35. Bennett BJ, Farber CR, Orozco L, et al. A high-resolution association mapping panel for the dissection of complex traits in mice. Genome Res. 2010;20:281–90.

    Article  PubMed  CAS  Google Scholar 

  36. •• Farber CR, Bennett BJ, Orozco L, et al. Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis. PLoS Genet. 2011;7:e1002038. An example of a comprehensive systems genetics study in the mouse that combines GWA for gene discovery and network analysis for the functional characterization of novel genes.

    Article  PubMed  CAS  Google Scholar 

  37. Valdar W, Solberg LC, Gauguier D, et al. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet. 2006;38:879–87.

    Article  PubMed  CAS  Google Scholar 

  38. •• Farber CR, van Nas A, Ghazalpour A, et al. An integrative genetics approach to identify candidate genes regulating BMD: combining linkage, gene expression, and association. J Bone Miner Res. 2009;24:105–16. Demonstrates the use of causality modeling to identify candidate genes for BMD.

    Article  PubMed  CAS  Google Scholar 

  39. Mehrabian M, Allayee H, Stockton J, et al. Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nat Genet. 2005;37:1224–33.

    Article  PubMed  CAS  Google Scholar 

  40. Le P, Kawai M, Bornstein S, et al. A high-fat diet induces bone loss in mice lacking the Alox5 gene. Endocrinology. 2012;153:6–16.

    Article  PubMed  CAS  Google Scholar 

  41. van Nas A, Ingram-Drake L, Sinsheimer JS, et al. Expression quantitative trait loci: replication, tissue- and sex-specificity in mice. Genetics. 2010;185:1059–68.

    Article  PubMed  Google Scholar 

  42. Schadt EE, Lamb J, Yang X, et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet. 2005;37:710–7.

    Article  PubMed  CAS  Google Scholar 

  43. Aten JE, Fuller TF, Lusis AJ, et al. Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Syst Biol. 2008;2:34.

    Article  PubMed  Google Scholar 

  44. Später D, Hill TP, O’sullivan RJ, et al. Wnt9a signaling is required for joint integrity and regulation of Ihh during chondrogenesis. Development. 2006;133:3039–49.

    Article  PubMed  Google Scholar 

  45. Holmbeck K, Bianco P, Caterina J, et al. MT1-MMP-deficient mice develop dwarfism, osteopenia, arthritis, and connective tissue disease due to inadequate collagen turnover. Cell. 1999;99:81–92.

    Article  PubMed  CAS  Google Scholar 

  46. Hikita A, Yana I, Wakeyama H, et al. Negative regulation of osteoclastogenesis by ectodomain shedding of receptor activator of NF-kappaB ligand. J Biol Chem. 2006;281:36846–55.

    Article  PubMed  CAS  Google Scholar 

  47. Ideno H, Takanabe R, Shimada A, et al. Protein related to DAN and cerberus (PRDC) inhibits osteoblastic differentiation and its suppression promotes osteogenesis in vitro. Exp Cell Res. 2009;315:474–84.

    Article  PubMed  CAS  Google Scholar 

  48. Bialek P, Kern B, Yang X, et al. A twist code determines the onset of osteoblast differentiation. Dev Cell. 2004;6:423–35.

    Article  PubMed  CAS  Google Scholar 

  49. •• Chen Y, Zhu J, Lum PY, et al. Variations in DNA elucidate molecular networks that cause disease. Nature. 2008;452:429–35. One of the first papers to demonstrate that genetic variation induces changes in “network states” and these states drive quantitative phenotypic variation. It outlines the use of systems genetics to identify network states and then uses this information for novel gene discovery.

    Article  PubMed  CAS  Google Scholar 

  50. Zhu X, Gerstein M, Snyder M. Getting connected: analysis and principles of biological networks. Genes Dev. 2007;21:1010–24. doi:10.1101/gad.1528707.

    Article  PubMed  CAS  Google Scholar 

  51. Zhao W, Langfelder P, Fuller T, et al. Weighted gene coexpression network analysis: state of the art. J Biopharm Stat. 2010;20:281–300. doi:10.1080/10543400903572753.

    Article  PubMed  Google Scholar 

  52. Vidal M, Cusick ME, Barabasi A-L. Interactome networks and human disease. Cell. 2011;144:986–98.

    Article  PubMed  CAS  Google Scholar 

  53. Ghazalpour A, Doss S, Zhang B, et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet. 2006;2:e130.

    Article  PubMed  Google Scholar 

  54. Plaisier CL, Horvath S, Huertas-Vazquez A, et al. A systems genetics approach implicates USF1, FADS3, and other causal candidate genes for familial combined hyperlipidemia. PLoS Genet. 2009;5:e1000642.

    Article  PubMed  Google Scholar 

  55. Farber CR. Identification of a gene module associated with BMD through the integration of network analysis and genome-wide association data. J Bone Miner Res. 2009;25:2359–67.

    Article  Google Scholar 

  56. Presson AP, Sobel EM, Papp JC, et al. Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome. BMC Syst Biol. 2008;2:95.

    Article  PubMed  Google Scholar 

  57. Miller JA, Oldham MC, Geschwind DH. A systems level analysis of transcriptional changes in Alzheimer’s disease and normal aging. J Neurosci. 2008;28:1410–20.

    Article  PubMed  CAS  Google Scholar 

  58. Gong KW, Zhao W, Li N, et al. Air-pollutant chemicals and oxidized lipids exhibit genome-wide synergistic effects on endothelial cells. Genome Biol. 2007;8:R149.

    Article  PubMed  Google Scholar 

  59. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17.

    PubMed  Google Scholar 

  60. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 2008;9:559.

    Article  Google Scholar 

  61. •• Suwanwela J, Farber CR, Haung B-L, et al. Systems genetics analysis of mouse chondrocyte differentiation. J Bone Miner Res. 2011;26:747–60. The first systems genetics analysis of chondrogenesis.

    Article  PubMed  CAS  Google Scholar 

  62. Gao Y, Grassi F, Ryan MR, et al. IFN-gamma stimulates osteoclast formation and bone loss in vivo via antigen-driven T cell activation. J Clin Invest. 2007;117:122–32.

    Article  PubMed  CAS  Google Scholar 

  63. Teitelbaum SL, Ross FP. Genetic regulation of osteoclast development and function. Nat Rev Genet. 2003;4:638–49.

    Article  PubMed  CAS  Google Scholar 

Download references

Disclosure

Conflicts of interest: C.R. Farber: has received grant support from NIAMS/NIH (R01 grant support).

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  1. Department of Medicine and Biochemistry & Molecular Genetics, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA

    Charles R. Farber

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  1. Charles R. Farber
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Correspondence to Charles R. Farber.

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Farber, C.R. Systems Genetics: A Novel Approach to Dissect the Genetic Basis of Osteoporosis. Curr Osteoporos Rep 10, 228–235 (2012). https://doi.org/10.1007/s11914-012-0112-5

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