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Application of High-Throughput Technologies in Personal Genomics: How Is the Progress in Personal Genome Service?

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Clinical Relevance of Genetic Factors in Pulmonary Diseases

Abstract

The advent of high-throughput profiling technologies, in particular next-generation sequencing, has revolutionized our genomic studies and provided unprecedented insights into the human diseases. The use of genetic testing in the clinical settings has grown substantially and has now entered medical practice around the world. Here we provide an overview of recent advances in various high-throughput methods for genomic and functional genomic analyses. Next we review recent findings in genomics, ranging from single nucleotide polymorphisms associated with respiratory diseases and genomic alterations in lung cancers to aberrant gene expressions in lung diseases. Finally, we summarize the current status of clinical sequencing efforts and further describe challenges in the clinical implementation of personal genomic medicine. We anticipate increase in the use of clinical sequencing, which require sufficient resource of computation to interpret large genomic datasets in a clinical laboratory. It is also crucial to extend existing electronic medical record systems so that we can interact with genomic data and make full use of personal genomic information. Furthermore, standardization of genomic data is also necessary for the efficient exchange of patients’ genomes between hospitals.

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References

  1. Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet. 2016;17(6):333–51. https://doi.org/10.1038/nrg.2016.49.

    Article  PubMed  CAS  Google Scholar 

  2. Krueger F, Kreck B, Franke A, Andrews SR. DNA methylome analysis using short bisulfite sequencing data. Nat Methods. 2012;9(2):145–51. https://doi.org/10.1038/nmeth.1828.

    Article  PubMed  CAS  Google Scholar 

  3. Zentner GE, Henikoff S. High-resolution digital profiling of the epigenome. Nat Rev Genet. 2014;15(12):814–27. https://doi.org/10.1038/nrg3798.

    Article  PubMed  CAS  Google Scholar 

  4. Furey TS. ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet. 2012;13(12):840–52. https://doi.org/10.1038/nrg3306.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Gu H, Smith ZD, Bock C, Boyle P, Gnirke A, Meissner A. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat Protoc. 2011;6(4):468–81. https://doi.org/10.1038/nprot.2010.190.

    Article  PubMed  CAS  Google Scholar 

  6. Jin W, Tang Q, Wan M, Cui K, Zhang Y, Ren G, et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature. 2015;528(7580):142–6. https://doi.org/10.1038/nature15740.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Cejas P, Li L, O'Neill NK, Duarte M, Rao P, Bowden M, et al. Chromatin immunoprecipitation from fixed clinical tissues reveals tumor-specific enhancer profiles. Nat Med. 2016;22(6):685–91. https://doi.org/10.1038/nm.4085.

    Article  PubMed  CAS  Google Scholar 

  8. Consortium F, the RP, ClST, Forrest AR, Kawaji H, Rehli M, et al. A promoter-level mammalian expression atlas. Nature. 2014;507(7493):462–70. https://doi.org/10.1038/nature13182.

    Article  Google Scholar 

  9. Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507(7493):455–61. https://doi.org/10.1038/nature12787.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Schmitt AD, Hu M, Ren B. Genome-wide mapping and analysis of chromosome architecture. Nat Rev Mol Cell Biol. 2016;17(12):743–55. https://doi.org/10.1038/nrm.2016.104.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Beagrie RA, Scialdone A, Schueler M, Kraemer DC, Chotalia M, Xie SQ, et al. Complex multi-enhancer contacts captured by genome architecture mapping. Nature. 2017;543(7646):519–24. https://doi.org/10.1038/nature21411.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Prakadan SM, Shalek AK, Weitz DA. Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices. Nat Rev Genet. 2017;18(6):345–61. https://doi.org/10.1038/nrg.2017.15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science. Nat Rev Genet. 2016;17(3):175–88. https://doi.org/10.1038/nrg.2015.16.

    Article  PubMed  CAS  Google Scholar 

  14. Clark SJ, Lee HJ, Smallwood SA, Kelsey G, Reik W. Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol. 2016;17:72. https://doi.org/10.1186/s13059-016-0944-x.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, et al. Comparative analysis of single-cell RNA sequencing methods. Mol Cell. 2017;65(4):631–43. e4. https://doi.org/10.1016/j.molcel.2017.01.023.

    Article  PubMed  CAS  Google Scholar 

  16. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42(Database issue):D1001–6. https://doi.org/10.1093/nar/gkt1229.

    Article  PubMed  CAS  Google Scholar 

  17. Li MJ, Wang P, Liu X, Lim EL, Wang Z, Yeager M, et al. GWASdb: a database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 2012;40(Database issue):D1047–54. https://doi.org/10.1093/nar/gkr1182.

    Article  PubMed  CAS  Google Scholar 

  18. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2016;44(D1):D862–8. https://doi.org/10.1093/nar/gkv1222.

    Article  PubMed  CAS  Google Scholar 

  19. Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, et al. Toward a shared vision for cancer genomic data. N Engl J Med. 2016;375(12):1109–12. https://doi.org/10.1056/NEJMp1607591.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Forbes SA, Beare D, Bindal N, Bamford S, Ward S, Cole CG, et al. COSMIC: high-resolution cancer genetics using the catalogue of somatic mutations in cancer. Curr Protoc Hum Genet. 2016;91:10 1 1–1 37. https://doi.org/10.1002/cphg.21.

    Article  Google Scholar 

  21. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603–7. https://doi.org/10.1038/nature11003.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Genomes Project C, Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, et al. A map of human genome variation from population-scale sequencing. Nature. 2010;467(7319):1061–73. https://doi.org/10.1038/nature09534.

    Article  CAS  Google Scholar 

  23. Gudbjartsson DF, Helgason H, Gudjonsson SA, Zink F, Oddson A, Gylfason A, et al. Large-scale whole-genome sequencing of the Icelandic population. Nat Genet. 2015;47(5):435–44. https://doi.org/10.1038/ng.3247.

    Article  PubMed  CAS  Google Scholar 

  24. Bujold D, Morais DA, Gauthier C, Cote C, Caron M, Kwan T, et al. The international human Epigenome consortium data portal. Cell Syst. 2016;3(5):496–9. e2. https://doi.org/10.1016/j.cels.2016.10.019.

    Article  PubMed  CAS  Google Scholar 

  25. Fernandez JM, de la Torre V, Richardson D, Royo R, Puiggros M, Moncunill V, et al. The BLUEPRINT data analysis portal. Cell Syst. 2016;3(5):491–5. e5. https://doi.org/10.1016/j.cels.2016.10.021.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Roadmap Epigenomics C, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature 2015;518(7539):317–330. doi:https://doi.org/10.1038/nature14248.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Hon CC, Ramilowski JA, Harshbarger J, Bertin N, Rackham OJ, Gough J, et al. An atlas of human long non-coding RNAs with accurate 5′ ends. Nature. 2017;543(7644):199–204. https://doi.org/10.1038/nature21374.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Stoller JK, Aboussouan LS. Alpha1-antitrypsin deficiency. Lancet. 2005;365(9478):2225–36. https://doi.org/10.1016/S0140-6736(05)66781-5.

    Article  CAS  PubMed  Google Scholar 

  29. Cheng SL, Yu CJ, Chen CJ, Yang PC. Genetic polymorphism of epoxide hydrolase and glutathione S-transferase in COPD. Eur Respir J. 2004;23(6):818–24.

    Article  CAS  PubMed  Google Scholar 

  30. Smith CA, Harrison DJ. Association between polymorphism in gene for microsomal epoxide hydrolase and susceptibility to emphysema. Lancet. 1997;350(9078):630–3. https://doi.org/10.1016/S0140-6736(96)08061-0.

    Article  PubMed  CAS  Google Scholar 

  31. Hung RJ, McKay JD, Gaborieau V, Boffetta P, Hashibe M, Zaridze D, et al. A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature. 2008;452(7187):633–7. https://doi.org/10.1038/nature06885.

    Article  PubMed  CAS  Google Scholar 

  32. Pillai SG, Ge D, Zhu G, Kong X, Shianna KV, Need AC, et al. A genome-wide association study in chronic obstructive pulmonary disease (COPD): identification of two major susceptibility loci. PLoS Genet. 2009;5(3):e1000421. https://doi.org/10.1371/journal.pgen.1000421.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Hobbs BD, de Jong K, Lamontagne M, Bosse Y, Shrine N, Artigas MS, et al. Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis. Nat Genet. 2017;49(3):426–32. https://doi.org/10.1038/ng.3752.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Bell DW, Gore I, Okimoto RA, Godin-Heymann N, Sordella R, Mulloy R, et al. Inherited susceptibility to lung cancer may be associated with the T790M drug resistance mutation in EGFR. Nat Genet. 2005;37(12):1315–6. https://doi.org/10.1038/ng1671.

    Article  PubMed  CAS  Google Scholar 

  35. Shiraishi K, Kunitoh H, Daigo Y, Takahashi A, Goto K, Sakamoto H, et al. A genome-wide association study identifies two new susceptibility loci for lung adenocarcinoma in the Japanese population. Nat Genet. 2012;44(8):900–3. https://doi.org/10.1038/ng.2353.

    Article  PubMed  CAS  Google Scholar 

  36. Ober C, Yao TC. The genetics of asthma and allergic disease: a 21st century perspective. Immunol Rev. 2011;242(1):10–30. https://doi.org/10.1111/j.1600-065X.2011.01029.x.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43(9):887–92. https://doi.org/10.1038/ng.888.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Hirota T, Takahashi A, Kubo M, Tsunoda T, Tomita K, Doi S, et al. Genome-wide association study identifies three new susceptibility loci for adult asthma in the Japanese population. Nat Genet. 2011;43(9):893–6. https://doi.org/10.1038/ng.887.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Ferreira MA, Matheson MC, Tang CS, Granell R, Ang W, Hui J, et al. Genome-wide association analysis identifies 11 risk variants associated with the asthma with hay fever phenotype. J Allergy Clin Immunol. 2014;133(6):1564–71. https://doi.org/10.1016/j.jaci.2013.10.030.

    Article  PubMed  CAS  Google Scholar 

  40. Wang Y, Kuan PJ, Xing C, Cronkhite JT, Torres F, Rosenblatt RL, et al. Genetic defects in surfactant protein A2 are associated with pulmonary fibrosis and lung cancer. Am J Hum Genet. 2009;84(1):52–9. https://doi.org/10.1016/j.ajhg.2008.11.010.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Nogee LM, Dunbar AE 3rd, Wert SE, Askin F, Hamvas A, Whitsett JA. A mutation in the surfactant protein C gene associated with familial interstitial lung disease. N Engl J Med. 2001;344(8):573–9. https://doi.org/10.1056/NEJM200102223440805.

    Article  CAS  PubMed  Google Scholar 

  42. Armanios MY, Chen JJ, Cogan JD, Alder JK, Ingersoll RG, Markin C, et al. Telomerase mutations in families with idiopathic pulmonary fibrosis. N Engl J Med. 2007;356(13):1317–26. https://doi.org/10.1056/NEJMoa066157.

    Article  CAS  PubMed  Google Scholar 

  43. Kropski JA, Lawson WE, Young LR, Blackwell TS. Genetic studies provide clues on the pathogenesis of idiopathic pulmonary fibrosis. Dis Model Mech. 2013;6(1):9–17. https://doi.org/10.1242/dmm.010736.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Noth I, Zhang Y, Ma SF, Flores C, Barber M, Huang Y, et al. Genetic variants associated with idiopathic pulmonary fibrosis susceptibility and mortality: a genome-wide association study. Lancet Respir Med. 2013;1(4):309–17. https://doi.org/10.1016/S2213-2600(13)70045-6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Mushiroda T, Wattanapokayakit S, Takahashi A, Nukiwa T, Kudoh S, Ogura T, et al. A genome-wide association study identifies an association of a common variant in TERT with susceptibility to idiopathic pulmonary fibrosis. J Med Genet. 2008;45(10):654–6. https://doi.org/10.1136/jmg.2008.057356.

    Article  CAS  PubMed  Google Scholar 

  46. Govindan R, Ding L, Griffith M, Subramanian J, Dees ND, Kanchi KL, et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell. 2012;150(6):1121–34. https://doi.org/10.1016/j.cell.2012.08.024.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. George J, Lim JS, Jang SJ, Cun Y, Ozretic L, Kong G, et al. Comprehensive genomic profiles of small cell lung cancer. Nature. 2015;524(7563):47–53. https://doi.org/10.1038/nature14664.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Soda M, Choi YL, Enomoto M, Takada S, Yamashita Y, Ishikawa S, et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature. 2007;448(7153):561–6. https://doi.org/10.1038/nature05945.

    Article  PubMed  CAS  Google Scholar 

  49. Awad MM, Shaw ATALK. Inhibitors in non-small cell lung cancer: crizotinib and beyond. Clin Adv Hematol Oncol. 2014;12(7):429–39.

    PubMed  PubMed Central  Google Scholar 

  50. Fernandez-Cuesta L, Sun R, Menon R, George J, Lorenz S, Meza-Zepeda LA, et al. Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data. Genome Biol. 2015;16:7. https://doi.org/10.1186/s13059-014-0558-0.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Kumar S, Vo AD, Qin F, Li H. Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data. Sci Rep. 2016;6:21597. https://doi.org/10.1038/srep21597.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Takamochi K, Ohmiya H, Itoh M, Mogushi K, Saito T, Hara K, et al. Novel biomarkers that assist in accurate discrimination of squamous cell carcinoma from adenocarcinoma of the lung. BMC Cancer. 2016;16(1):760. https://doi.org/10.1186/s12885-016-2792-1.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Bowser MJ, Duffy-Hynes E, Mahanta LM, Rehm HL, Raby BA, Funke BH. Integrating genetics into subspecialty care: The PulmoGene Test - comprehensive testing for hereditary causes of lung disease. Immunologic and genetic biomarkers of inflammatory lung disease American Thoracic Society International Conference, 16–21 May 2014, San Diego, CA, USA, A2175.

    Google Scholar 

  54. McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB, et al. The eMERGE network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genet. 2011;4:13. https://doi.org/10.1186/1755-8794-4-13.

    Article  Google Scholar 

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Mogushi, K., Murakawa, Y., Kawaji, H. (2018). Application of High-Throughput Technologies in Personal Genomics: How Is the Progress in Personal Genome Service?. In: Kaneko, T. (eds) Clinical Relevance of Genetic Factors in Pulmonary Diseases. Respiratory Disease Series: Diagnostic Tools and Disease Managements. Springer, Singapore. https://doi.org/10.1007/978-981-10-8144-6_17

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