Skip to main content
SpringerLink
Log in

Omics: Potential Role in Early Phase Drug Development

  • Chapter
  • First Online:
Translational Research Methods in Diabetes, Obesity, and Nonalcoholic Fatty Liver Disease

  • 816 Accesses

Abstract

The development of high-throughput omics technologies has nourished the hope to improve our understanding and treatment of the pathophysiology of globally increasing diseases such as type 2 diabetes, obesity and nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. These technologies provide innovative tools that have the potential to truly revolutionize patient care. Technologies continue to propel the omics fields forward. However, translating research discovery into routine clinical applications use is a complex process not only from scientific prospective but also from ethical, political, and logistic points of view. Particularly the implementation of omics-based tests requires changes in fundamental processes of regulation, reimbursement, and clinical practice. Altogether, developments in the field of omics technologies hold great promise to optimize patient care and improve outcomes and eventually lead to new tests that are well integrated in routine medical care.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Federation ID. IDF diabetes atlas. Brussels, Belgium: International Diabetes Federation; 2013. Available from: http://www.idf.org/diabetesatlas.

    Google Scholar 

  2. Lund E, Dumeaux V. Systems epidemiology in cancer. Cancer Epidemiol Biomarkers Prev: A Publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2008; 17(11):2954–7. Epub 2008/11/08.

    Google Scholar 

  3. Hu FB. Metabolic profiling of diabetes: from black-box epidemiology to systems epidemiology. Clin Chem. 2011;57(9):1224–6. Epub 2011/06/22.

    Article  CAS  PubMed  Google Scholar 

  4. McCarthy MI. Painting a new picture of personalised medicine for diabetes. Diabetologia. 2017;60(5):793–9. Epub 2017/02/09.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012;8:615. Epub 2012/09/27.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17(4):448–53. Epub 2011/03/23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Mannino GC, Sesti G. Individualized therapy for type 2 diabetes: clinical implications of pharmacogenetic data. Mol Diagn Ther. 2012;16(5):285–302. Epub 2012/09/29.

    Article  CAS  PubMed  Google Scholar 

  8. McCarthy MI. Genomics, type 2 diabetes, and obesity. N Engl J Med. 2010;363(24):2339–50. Epub 2010/12/15.

    Article  CAS  PubMed  Google Scholar 

  9. Scott RA, Scott LJ, Magi R, Marullo L, Gaulton KJ, Kaakinen M, et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes. 2017;66(11):2888–902. Epub 2017/06/02.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wheeler E, Barroso I. Genome-wide association studies and type 2 diabetes. Brief Funct Genomics. 2011;10(2):52–60.

    Article  CAS  PubMed  Google Scholar 

  11. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46(3):234–44. Epub 2014/02/11.

    Google Scholar 

  12. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44(9):981–90. Epub 2012/08/14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Locke AE, Kahali B, Berndt S, Justice AE, Pers TH, Day FR, et al. Large-scale genetic studies of body mass index provide insight into the biological basis of obesity. in press. 2014.

    Google Scholar 

  14. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R, et al. New genetic loci link adipocyte and insulin biology to body 1 fat distribution. in press. 2014.

    Google Scholar 

  15. Recommendations from the EGAPP working group: does genomic profiling to assess type 2 diabetes risk improve health outcomes? Genet Meds. 2013;15(8):612–7. Epub 2013/03/16.

    Google Scholar 

  16. Burke W, Psaty BM. Personalized medicine in the era of genomics. JAMA. 2007;298(14):1682–4. Epub 2007/10/11.

    Article  CAS  PubMed  Google Scholar 

  17. Maxam AM, Gilbert W. A new method for sequencing DNA. Proc Natl Acad Sci U S A. 1977;74(2):560–4. Epub 1977/02/01.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A. 1977;74(12):5463–7. Epub 1977/12/01.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308(5720):385–9. Epub 2005/03/12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. McCarthy JJ, McLeod HL, Ginsburg GS. Genomic medicine: a decade of successes, challenges, and opportunities. Sci Transl Med. 2013;5(189):189sr4. Epub 2013/06/14.

    Article  PubMed  CAS  Google Scholar 

  21. Lander ES. Initial impact of the sequencing of the human genome. Nature. 2011;470(7333):187–97. Epub 2011/02/11.

    Article  CAS  PubMed  Google Scholar 

  22. Boyd SD. Diagnostic applications of high-throughput DNA sequencing. Annu Rev Pathol. 2013;8:381–410. Epub 2012/11/06.

    Article  CAS  PubMed  Google Scholar 

  23. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491(7422):56–65. Epub 2012/11/07.

    Article  PubMed  CAS  Google Scholar 

  24. Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med. 2011;3(96):96ra76. Epub 2011/08/19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sanseau P, Agarwal P, Barnes MR, Pastinen T, Richards JB, Cardon LR, et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 2012;30(4):317–20. Epub 2012/04/12

    Article  CAS  PubMed  Google Scholar 

  26. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med. 2011;3(96):96ra77. Epub 2011/08/19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Portela A, Esteller M. Epigenetic modifications and human disease. Nat Biotechnol. 2010;28(10):1057–68. Epub 2010/10/15.

    Article  CAS  PubMed  Google Scholar 

  28. Feil R, Fraga MF. Epigenetics and the environment: emerging patterns and implications. Nat Rev Genet. 2011;13(2):97–109. Epub 2012/01/05.

    Article  CAS  Google Scholar 

  29. Herceg Z, Vaissiere T. Epigenetic mechanisms and cancer: an interface between the environment and the genome. Epigenetics. 2011;6(7):804–19. Epub 2011/07/16.

    Article  CAS  PubMed  Google Scholar 

  30. Sandoval J, Heyn H, Moran S, Serra-Musach J, Pujana MA, Bibikova M, et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics. 2011;6(6):692–702. Epub 2011/05/20.

    Article  CAS  PubMed  Google Scholar 

  31. Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011;98(4):288–95. Epub 2011/08/16.

    Article  CAS  PubMed  Google Scholar 

  32. Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey SL, et al. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol. 2010;28(10):1097–105. Epub 2010/09/21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Baylin SB, Jones PA. A decade of exploring the cancer epigenome - biological and translational implications. Nat Rev Cancer. 2011;11(10):726–34. Epub 2011/09/24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD, et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature. 2008;452(7184):215–9. Epub 2008/02/19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lister R, O’Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell. 2008;133(3):523–36. Epub 2008/04/22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462(7271):315–22. Epub 2009/10/16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Schweighoffer F, Ait-Ikhlef A, Resink AL, Brinkman B, Melle-Milovanovic D, Laurent-Puig P, et al. Qualitative gene profiling: a novel tool in genomics and in pharmacogenomics that deciphers messenger RNA isoforms diversity. Pharmacogenomics. 2000;1(2):187–97. Epub 2001/03/21.

    Article  CAS  PubMed  Google Scholar 

  38. Yamada K, Lim J, Dale JM, Chen H, Shinn P, Palm CJ, et al. Empirical analysis of transcriptional activity in the Arabidopsis genome. Science. 2003;302(5646):842–6. Epub 2003/11/01.

    Article  CAS  PubMed  Google Scholar 

  39. Cheng J, Kapranov P, Drenkow J, Dike S, Brubaker S, Patel S, et al. Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution. Science. 2005;308(5725):1149–54. Epub 2005/03/26.

    Article  CAS  PubMed  Google Scholar 

  40. Bertone P, Stolc V, Royce TE, Rozowsky JS, Urban AE, Zhu X, et al. Global identification of human transcribed sequences with genome tiling arrays. Science. 2004;306(5705):2242–6. Epub 2004/11/13.

    Article  CAS  PubMed  Google Scholar 

  41. David L, Huber W, Granovskaia M, Toedling J, Palm CJ, Bofkin L, et al. A high-resolution map of transcription in the yeast genome. Proc Natl Acad Sci U S A. 2006;103(14):5320–5. Epub 2006/03/30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Okoniewski MJ, Miller CJ. Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations. BMC Bioinformatics. 2006;7:276. Epub 2006/06/06.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Royce TE, Rozowsky JS, Gerstein MB. Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification. Nucleic Acids Res. 2007;35(15):e99. Epub 2007/08/10.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Wolf-Yadlin A, Sevecka M, MacBeath G. Dissecting protein function and signaling using protein microarrays. Curr Opin Chem Biol. 2009;13(4):398–405. Epub 2009/08/08.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ahrens CH, Brunner E, Qeli E, Basler K, Aebersold R. Generating and navigating proteome maps using mass spectrometry. Nat Rev Mol Cell Biol. 2010;11(11):789–801. Epub 2010/10/15.

    Article  CAS  PubMed  Google Scholar 

  46. Picotti P, Rinner O, Stallmach R, Dautel F, Farrah T, Domon B, et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat Methods. 2010;7(1):43–6. Epub 2009/12/08.

    Article  CAS  PubMed  Google Scholar 

  47. Omenn GS, Baker MS, Aebersold R. Recent Workshops of the HUPO Human Plasma Proteome Project (HPPP): a bridge with the HUPO CardioVascular Initiative and the emergence of SRM targeted proteomics. Proteomics. 2011;11(17):3439–43. Epub 2011/08/19.

    Article  CAS  PubMed  Google Scholar 

  48. Farrah T, Deutsch EW, Omenn GS, Campbell DS, Sun Z, Bletz JA, et al. A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas. Mol Cell Proteomics: MCP. 2011;10(9):M110 006353. Epub 2011/06/03.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  49. Fagerberg L, Stromberg S, El-Obeid A, Gry M, Nilsson K, Uhlen M, et al. Large-scale protein profiling in human cell lines using antibody-based proteomics. J Proteome Res. 2011;10(9):4066–75. Epub 2011/07/06.

    Article  CAS  PubMed  Google Scholar 

  50. Ayoglu B, Haggmark A, Neiman M, Igel U, Uhlen M, Schwenk JM, et al. Systematic antibody and antigen-based proteomic profiling with microarrays. Expert Rev Mol Diagn. 2011;11(2):219–34. Epub 2011/03/17.

    Article  CAS  PubMed  Google Scholar 

  51. Legrain P, Aebersold R, Archakov A, Bairoch A, Bala K, Beretta L, et al. The human proteome project: current state and future direction. Mol Cell Proteomics: MCP. 2011;10(7):M111 009993. Epub 2011/07/12.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  52. Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One. 2010;5(12):e15004. Epub 2010/12/18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Service RF. Chemistry. Click chemistry clicks along. Science. 2008;320(5878):868–9. Epub 2008/05/20.

    Article  PubMed  Google Scholar 

  54. Weckwerth W. Metabolomics in systems biology. Annu Rev Plant Biol. 2003;54:669–89. Epub 2003/09/25.

    Article  CAS  PubMed  Google Scholar 

  55. Zhang GF, Sadhukhan S, Tochtrop GP, Brunengraber H. Metabolomics, pathway regulation, and pathway discovery. J Biol Chem. 2011;286(27):23631–5. Epub 2011/05/14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Seppanen-Laakso T, Oresic M. How to study lipidomes. J Mol Endocrinol. 2009;42(3):185–90. Epub 2008/12/09.

    Article  CAS  PubMed  Google Scholar 

  57. Masoodi M, Eiden M, Koulman A, Spaner D, Volmer DA. Comprehensive lipidomics analysis of bioactive lipids in complex regulatory networks. Anal Chem. 2010;82(19):8176–85. Epub 2010/09/11.

    Article  CAS  PubMed  Google Scholar 

  58. Drexler DM, Reily MD, Shipkova PA. Advances in mass spectrometry applied to pharmaceutical metabolomics. Anal Bioanal Chem. 2011;399(8):2645–53. Epub 2010/11/26.

    Article  CAS  PubMed  Google Scholar 

  59. Lewis GD, Gerszten RE. Toward metabolomic signatures of cardiovascular disease. Circ Cardiovasc Genet. 2010;3(2):119–21. Epub 2010/04/22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Arrell DK, Zlatkovic Lindor J, Yamada S, Terzic A. K(ATP) channel-dependent metaboproteome decoded: systems approaches to heart failure prediction, diagnosis, and therapy. Cardiovasc Res. 2011;90(2):258–66. Epub 2011/02/16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Millis MP. Medium-throughput SNP genotyping using mass spectrometry: multiplex SNP genotyping using the iPLEX(R) Gold assay. Methods Mol Biol. 2011;700:61–76. Epub 2011/01/05.

    Article  CAS  PubMed  Google Scholar 

  62. Johnson JA, Burkley BM, Langaee TY, Clare-Salzler MJ, Klein TE, Altman RB. Implementing personalized medicine: development of a cost-effective customized pharmacogenetics genotyping array. Clin Pharmacol Ther. 2012;92(4):437–9. Epub 2012/08/23.

    Article  CAS  PubMed  Google Scholar 

  63. Kim KK, Won HH, Cho SS, Park JH, Kim MJ, Kim S, et al. Comparison of identical single nucleotide polymorphisms genotyped by the GeneChip Targeted Genotyping 25K, Affymetrix 500K and Illumina 550K platforms. Genomics. 2009;94(2):89–93. Epub 2009/04/28.

    Article  CAS  PubMed  Google Scholar 

  64. Zagursky RJ, McCormick RM. DNA sequencing separations in capillary gels on a modified commercial DNA sequencing instrument. BioTechniques. 1990;9(1):74–9. Epub 1990/07/01.

    CAS  PubMed  Google Scholar 

  65. Kircher M, Kelso J. High-throughput DNA sequencing--concepts and limitations. Bioessays. 2010;32(6):524–36. Epub 2010/05/21.

    Article  CAS  PubMed  Google Scholar 

  66. Kircher M, Stenzel U, Kelso J. Improved base calling for the Illumina Genome Analyzer using machine learning strategies. Genome Biol. 2009;10(8):R83. Epub 2009/08/18.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Emond MJ, Louie T, Emerson J, Zhao W, Mathias RA, Knowles MR, et al. Exome sequencing of extreme phenotypes identifies DCTN4 as a modifier of chronic Pseudomonas aeruginosa infection in cystic fibrosis. Nat Genet. 2012;44(8):886–9. Epub 2012/07/10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Schnabel RB, Baccarelli A, Lin H, Ellinor PT, Benjamin EJ. Next steps in cardiovascular disease genomic research--sequencing, epigenetics, and transcriptomics. Clin Chem. 2012;58(1):113–26. Epub 2011/11/22.

    Article  CAS  PubMed  Google Scholar 

  69. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41–7. Epub 2016/07/12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Umer M, Herceg Z. Deciphering the epigenetic code: an overview of DNA methylation analysis methods. Antioxid Redox Signal. 2013;18(15):1972–86. Epub 2012/11/06.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Herman JG, Graff JR, Myohanen S, Nelkin BD, Baylin SB. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci U S A. 1996;93(18):9821–6. Epub 1996/09/03.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Eads CA, Danenberg KD, Kawakami K, Saltz LB, Blake C, Shibata D, et al. MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res. 2000;28(8):E32. Epub 2000/03/29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Warnecke PM, Stirzaker C, Melki JR, Millar DS, Paul CL, Clark SJ. Detection and measurement of PCR bias in quantitative methylation analysis of bisulphite-treated DNA. Nucleic Acids Res. 1997;25(21):4422–6. Epub 1997/10/23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Uhlmann K, Brinckmann A, Toliat MR, Ritter H, Nurnberg P. Evaluation of a potential epigenetic biomarker by quantitative methyl-single nucleotide polymorphism analysis. Electrophoresis. 2002;23(24):4072–9. Epub 2002/12/14.

    Article  CAS  PubMed  Google Scholar 

  75. Dejeux E, El abdalaoui H, Gut IG, Tost J. Identification and quantification of differentially methylated loci by the pyrosequencing technology. Methods Mol Biol. 2009;507:189–205. Epub 2008/11/07.

    Article  CAS  PubMed  Google Scholar 

  76. Tost J, El abdalaoui H, Gut IG. Serial pyrosequencing for quantitative DNA methylation analysis. BioTechniques. 2006;40(6):721–2, 4, 6. Epub 2006/06/16.

    Article  CAS  PubMed  Google Scholar 

  77. Ehrich M, Nelson MR, Stanssens P, Zabeau M, Liloglou T, Xinarianos G, et al. Quantitative high-throughput analysis of DNA methylation patterns by base-specific cleavage and mass spectrometry. Proc Natl Acad Sci U S A. 2005;102(44):15785–90. Epub 2005/10/26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet. 2005;37(8):853–62. Epub 2005/07/12.

    Article  CAS  PubMed  Google Scholar 

  79. Rauch TA, Wu X, Zhong X, Riggs AD, Pfeifer GP. A human B cell methylome at 100-base pair resolution. Proc Natl Acad Sci U S A. 2009;106(3):671–8. Epub 2009/01/14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Laird PW. Principles and challenges of genomewide DNA methylation analysis. Nat Rev Genet. 2010;11(3):191–203. Epub 2010/02/04.

    Article  CAS  PubMed  Google Scholar 

  81. Bibikova M, Fan JB. Golden Gate assay for DNA methylation profiling. Methods Mol Biol. 2009;507:149–63. Epub 2008/11/07.

    Article  CAS  PubMed  Google Scholar 

  82. Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R, et al. Genome-wide DNA methylation profiling using Infinium(R) assay. Epigenomics. 2009;1(1):177–200. Epub 2009/10/01.

    Article  CAS  PubMed  Google Scholar 

  83. Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17(1):208. Epub 2016/10/09.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Frommer M, McDonald LE, Millar DS, Collis CM, Watt F, Grigg GW, et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A. 1992;89(5):1827–31. Epub 1992/03/01.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Lister R, Ecker JR. Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res. 2009;19(6):959–66. Epub 2009/03/11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Oda M, Glass JL, Thompson RF, Mo Y, Olivier EN, Figueroa ME, et al. High-resolution genome-wide cytosine methylation profiling with simultaneous copy number analysis and optimization for limited cell numbers. Nucleic Acids Res. 2009;37(12):3829–39. Epub 2009/04/24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Brunner AL, Johnson DS, Kim SW, Valouev A, Reddy TE, Neff NF, et al. Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res. 2009;19(6):1044–56. Epub 2009/03/11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Meissner A, Mikkelsen TS, Gu H, Wernig M, Hanna J, Sivachenko A, et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature. 2008;454(7205):766–70. Epub 2008/07/05.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Park JH, Park J, Choi JK, Lyu J, Bae MG, Lee YG, et al. Identification of DNA methylation changes associated with human gastric cancer. BMC Med Genet. 2011;4:82. Epub 2011/12/03.

    CAS  Google Scholar 

  90. Down TA, Rakyan VK, Turner DJ, Flicek P, Li H, Kulesha E, et al. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat Biotechnol. 2008;26(7):779–85. Epub 2008/07/10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Brinkman AB, Simmer F, Ma K, Kaan A, Zhu J, Stunnenberg HG. Whole-genome DNA methylation profiling using MethylCap-seq. Methods. 2010;52(3):232–6. Epub 2010/06/15.

    Article  CAS  PubMed  Google Scholar 

  92. Serre D, Lee BH, Ting AH. MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome. Nucleic Acids Res. 2010;38(2):391–9. Epub 2009/11/13.

    Article  CAS  PubMed  Google Scholar 

  93. Heyn H, Esteller M. DNA methylation profiling in the clinic: applications and challenges. Nat Rev Genet. 2012;13(10):679–92. Epub 2012/09/05.

    Article  CAS  PubMed  Google Scholar 

  94. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH roadmap epigenomics mapping consortium. Nat Biotechnol. 2010;28(10):1045–8. Epub 2010/10/15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Adams D, Altucci L, Antonarakis SE, Ballesteros J, Beck S, Bird A, et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat Biotechnol. 2012;30(3):224–6. Epub 2012/03/09.

    Article  CAS  PubMed  Google Scholar 

  96. Zhou X, Maricque B, Xie M, Li D, Sundaram V, Martin EA, et al. The human epigenome browser at Washington University. Nat Methods. 2011;8(12):989–90. Epub 2011/12/01.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Holloway AJ, van Laar RK, Tothill RW, Bowtell DD. Options available--from start to finish--for obtaining data from DNA microarrays II. Nat Genet. 2002;32(Suppl):481–9. Epub 2002/11/28.

    Article  CAS  PubMed  Google Scholar 

  98. Schulze A, Downward J. Navigating gene expression using microarrays--a technology review. Nat Cell Biol. 2001;3(8):E190–5. Epub 2001/08/03.

    Article  CAS  PubMed  Google Scholar 

  99. Kane MD, Jatkoe TA, Stumpf CR, Lu J, Thomas JD, Madore SJ. Assessment of the sensitivity and specificity of oligonucleotide (50mer) microarrays. Nucleic Acids Res. 2000;28(22):4552–7. Epub 2000/11/10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Gerhard DS, Wagner L, Feingold EA, Shenmen CM, Grouse LH, Schuler G, et al. The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC). Genome Res. 2004;14(10B):2121–7. Epub 2004/10/19.

    Article  PubMed  Google Scholar 

  101. Boguski MS, Tolstoshev CM, Bassett DE Jr. Gene discovery in dbEST. Science. 1994;265(5181):1993–4. Epub 1994/09/30.

    Article  CAS  PubMed  Google Scholar 

  102. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression. Science. 1995;270(5235):484–7. Epub 1995/10/20.

    Article  CAS  PubMed  Google Scholar 

  103. Harbers M, Carninci P. Tag-based approaches for transcriptome research and genome annotation. Nat Methods. 2005;2(7):495–502. Epub 2005/06/24.

    Article  CAS  PubMed  Google Scholar 

  104. Shiraki T, Kondo S, Katayama S, Waki K, Kasukawa T, Kawaji H, et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc Natl Acad Sci U S A. 2003;100(26):15776–81. Epub 2003/12/10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Kodzius R, Kojima M, Nishiyori H, Nakamura M, Fukuda S, Tagami M, et al. CAGE: cap analysis of gene expression. Nat Methods. 2006;3(3):211–22. Epub 2006/02/21.

    Article  CAS  PubMed  Google Scholar 

  106. Reinartz J, Bruyns E, Lin JZ, Burcham T, Brenner S, Bowen B, et al. Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms. Brief Funct Genomic Proteomic. 2002;1(1):95–104. Epub 2004/07/15.

    Article  CAS  PubMed  Google Scholar 

  107. Peiffer JA, Kaushik S, Sakai H, Arteaga-Vazquez M, Sanchez-Leon N, Ghazal H, et al. A spatial dissection of the Arabidopsis floral transcriptome by MPSS. BMC Plant Biol. 2008;8:43. Epub 2008/04/23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, Johnson D, et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat Biotechnol. 2000;18(6):630–4. Epub 2000/06/03.

    Article  CAS  PubMed  Google Scholar 

  109. Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V, Goodhead I, et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature. 2008;453(7199):1239–43. Epub 2008/05/20.

    Article  CAS  PubMed  Google Scholar 

  110. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320(5881):1344–9. Epub 2008/05/03.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8. Epub 2008/06/03.

    Article  CAS  PubMed  Google Scholar 

  112. Morin R, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh T, et al. Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. BioTechniques. 2008;45(1):81–94. Epub 2008/07/10.

    Article  CAS  PubMed  Google Scholar 

  113. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008;18(9):1509–17. Epub 2008/06/14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods. 2008;5(7):613–9. Epub 2008/06/03.

    Article  CAS  PubMed  Google Scholar 

  115. Holt RA, Jones SJ. The new paradigm of flow cell sequencing. Genome Res. 2008;18(6):839–46. Epub 2008/06/04.

    Article  CAS  PubMed  Google Scholar 

  116. Vera JC, Wheat CW, Fescemyer HW, Frilander MJ, Crawford DL, Hanski I, et al. Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing. Mol Ecol. 2008;17(7):1636–47. Epub 2008/02/13.

    Article  CAS  PubMed  Google Scholar 

  117. Emrich SJ, Barbazuk WB, Li L, Schnable PS. Gene discovery and annotation using LCM-454 transcriptome sequencing. Genome Res. 2007;17(1):69–73. Epub 2006/11/11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Dutrow N, Nix DA, Holt D, Milash B, Dalley B, Westbroek E, et al. Dynamic transcriptome of Schizosaccharomyces pombe shown by RNA-DNA hybrid mapping. Nat Genet. 2008;40(8):977–86. Epub 2008/07/22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Wu JQ, Du J, Rozowsky J, Zhang Z, Urban AE, Euskirchen G, et al. Systematic analysis of transcribed loci in ENCODE regions using RACE sequencing reveals extensive transcription in the human genome. Genome Biol. 2008;9(1):R3. Epub 2008/01/05.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Scott EM, Carter AM, Findlay JB. The application of proteomics to diabetes. Diabetes & Vascular Disease Research: Official Journal of the International Society of Diabetes and Vascular Disease. 2005;2(2):54–60. Epub 2005/11/25.

    Article  Google Scholar 

  121. Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B. Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem. 2007;389(4):1017–31. Epub 2007/08/02.

    Article  CAS  PubMed  Google Scholar 

  122. Bantscheff M, Lemeer S, Savitski MM, Kuster B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal Bioanal Chem. 2012;404(4):939–65. Epub 2012/07/10.

    Article  CAS  PubMed  Google Scholar 

  123. Paul FE, Hosp F, Selbach M. Analyzing protein-protein interactions by quantitative mass spectrometry. Methods. 2011;54(4):387–95. Epub 2011/03/09.

    Article  CAS  PubMed  Google Scholar 

  124. Picotti P, Bodenmiller B, Mueller LN, Domon B, Aebersold R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell. 2009;138(4):795–806. Epub 2009/08/12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Schafer A, von Toerne C, Becker S, Sarioglu H, Neschen S, Kahle M, et al. Two-dimensional peptide separation improving sensitivity of selected reaction monitoring-based quantitative proteomics in mouse liver tissue: comparing off-gel electrophoresis and strong cation exchange chromatography. Anal Chem. 2012;84(20):8853–62. Epub 2012/09/22.

    Article  PubMed  CAS  Google Scholar 

  126. Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol. 2007;25(1):125–31. Epub 2007/01/02.

    Article  CAS  PubMed  Google Scholar 

  127. Venable JD, Dong MQ, Wohlschlegel J, Dillin A, Yates JR. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods. 2004;1(1):39–45. Epub 2005/03/23.

    Article  CAS  PubMed  Google Scholar 

  128. Silva JC, Gorenstein MV, Li GZ, Vissers JP, Geromanos SJ. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol Cell Proteomics: MCP. 2006;5(1):144–56. Epub 2005/10/13.

    Article  CAS  PubMed  Google Scholar 

  129. Levin Y, Bahn S. Quantification of proteins by label-free LC-MS/MS. Methods Mol Biol. 2010;658:217–31. Epub 2010/09/15.

    Article  CAS  PubMed  Google Scholar 

  130. Gillet LC, Navarro P, Tate S, Rost H, Selevsek N, Reiter L, et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics: MCP. 2012;11(6):O111 016717. Epub 2012/01/21.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  131. Aebersold R, Bensimon A, Collins BC, Ludwig C, Sabido E. Applications and developments in targeted proteomics: from SRM to DIA/SWATH. Proteomics. 2016;16(15–16):2065–7. Epub 2016/08/21.

    Article  CAS  PubMed  Google Scholar 

  132. Bruderer R, Bernhardt OM, Gandhi T, Miladinovic SM, Cheng LY, Messner S, et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol Cell Proteomics: MCP. 2015;14(5):1400–10. Epub 2015/03/01.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Navarro P, Kuharev J, Gillet LC, Bernhardt OM, MacLean B, Rost HL, et al. A multicenter study benchmarks software tools for label-free proteome quantification. Nat Biotechnol. 2016;34(11):1130–6. Epub 2016/11/01.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Huttenhain R, Malmstrom J, Picotti P, Aebersold R. Perspectives of targeted mass spectrometry for protein biomarker verification. Curr Opin Chem Biol. 2009;13(5–6):518–25. Epub 2009/10/13.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  135. Lepper MF, Ohmayer U, von Toerne C, Maison N, Ziegler AG, Hauck SM. Proteomic landscape of patient-derived CD4+ T cells in recent-onset type 1 diabetes. J Proteome Res. 2017;17(1):618–34. Epub 2017/11/29.

    Article  PubMed  CAS  Google Scholar 

  136. Geyer PE, Wewer Albrechtsen NJ, Tyanova S, Grassl N, Iepsen EW, Lundgren J, et al. Proteomics reveals the effects of sustained weight loss on the human plasma proteome. Mol Syst Biol. 2016;12(12):901. Epub 2016/12/23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  137. Bjelosevic S, Pascovici D, Ping H, Karlaftis V, Zaw T, Song X, et al. Quantitative age-specific variability of plasma proteins in healthy neonates, children and adults. Mol Cell Proteomics: MCP. 2017;16(5):924–35. Epub 2017/03/25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics: MCP. 2002;1(11):845–67. Epub 2002/12/19.

    Article  CAS  PubMed  Google Scholar 

  139. Hathout Y, Brody E, Clemens PR, Cripe L, DeLisle RK, Furlong P, et al. Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy. Proc Natl Acad Sci U S A. 2015;112(23):7153–8. Epub 2015/06/04.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Suhre K, Arnold M, Bhagwat AM, Cotton RJ, Engelke R, Raffler J, et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun. 2017;8:14357. Epub 2017/02/28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Assarsson E, Lundberg M, Holmquist G, Bjorkesten J, Thorsen SB, Ekman D, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One. 2014;9(4):e95192. Epub 2014/04/24.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  142. Backryd E, Tanum L, Lind AL, Larsson A, Gordh T. Evidence of both systemic inflammation and neuroinflammation in fibromyalgia patients, as assessed by a multiplex protein panel applied to the cerebrospinal fluid and to plasma. J Pain Res. 2017;10:515–25. Epub 2017/04/21.

    Article  PubMed  PubMed Central  Google Scholar 

  143. Edvardsson U, von Lowenhielm HB, Panfilov O, Nystrom AC, Nilsson F, Dahllof B. Hepatic protein expression of lean mice and obese diabetic mice treated with peroxisome proliferator-activated receptor activators. Proteomics. 2003;3(4):468–78. Epub 2003/04/11.

    Article  CAS  PubMed  Google Scholar 

  144. Sanchez JC, Converset V, Nolan A, Schmid G, Wang S, Heller M, et al. Effect of rosiglitazone on the differential expression of obesity and insulin resistance associated proteins in lep/lep mice. Proteomics. 2003;3(8):1500–20. Epub 2003/08/19.

    Article  CAS  PubMed  Google Scholar 

  145. Kim GH, Park EC, Yun SH, Hong Y, Lee DG, Shin EY, et al. Proteomic and bioinformatic analysis of membrane proteome in type 2 diabetic mouse liver. Proteomics. 2013;13(7):1164–79. Epub 2013/01/26.

    Article  CAS  PubMed  Google Scholar 

  146. von Toerne C, Kahle M, Schafer A, Ispiryan R, Blindert M, Hrabe De Angelis M, et al. Apoe, Mbl2, and Psp plasma protein levels correlate with diabetic phenotype in NZO mice--an optimized rapid workflow for SRM-based quantification. J Proteome Res. 2013;12(3):1331–43. Epub 2013/01/29.

    Article  CAS  Google Scholar 

  147. Schmid GM, Converset V, Walter N, Sennitt MV, Leung KY, Byers H, et al. Effect of high-fat diet on the expression of proteins in muscle, adipose tissues, and liver of C57BL/6 mice. Proteomics. 2004;4(8):2270–82. Epub 2004/07/27.

    Article  CAS  PubMed  Google Scholar 

  148. Sabido E, Wu Y, Bautista L, Porstmann T, Chang CY, Vitek O, et al. Targeted proteomics reveals strain-specific changes in the mouse insulin and central metabolic pathways after a sustained high-fat diet. Mol Syst Biol. 2013;9:681. Epub 2013/07/19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Qiu L, List EO, Kopchick JJ. Differentially expressed proteins in the pancreas of diet-induced diabetic mice. Mol Cell Proteomics: MCP. 2005;4(9):1311–8. Epub 2005/06/18.

    Article  CAS  PubMed  Google Scholar 

  150. Kirpich IA, Gobejishvili LN, Bon Homme M, Waigel S, Cave M, Arteel G, et al. Integrated hepatic transcriptome and proteome analysis of mice with high-fat diet-induced nonalcoholic fatty liver disease. J Nutr Biochem. 2011;22(1):38–45. Epub 2010/03/23.

    Article  CAS  PubMed  Google Scholar 

  151. Guo Y, Darshi M, Ma Y, Perkins GA, Shen Z, Haushalter KJ, et al. Quantitative proteomic and functional analysis of liver mitochondria from high fat diet (HFD) diabetic mice. Mol Cell Proteomics: MCP. 2013;12(12):3744–58. Epub 2013/09/14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Hwang H, Bowen BP, Lefort N, Flynn CR, De Filippis EA, Roberts C, et al. Proteomics analysis of human skeletal muscle reveals novel abnormalities in obesity and type 2 diabetes. Diabetes. 2010;59(1):33–42. Epub 2009/10/17.

    Article  CAS  PubMed  Google Scholar 

  153. Thingholm TE, Bak S, Beck-Nielsen H, Jensen ON, Gaster M. Characterization of human myotubes from type 2 diabetic and nondiabetic subjects using complementary quantitative mass spectrometric methods. Mol Cell Proteomics: MCP. 2011;10(9):M110 006650. Epub 2011/06/24.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  154. Hojlund K, Wrzesinski K, Larsen PM, Fey SJ, Roepstorff P, Handberg A, et al. Proteome analysis reveals phosphorylation of ATP synthase beta -subunit in human skeletal muscle and proteins with potential roles in type 2 diabetes. J Biol Chem. 2003;278(12):10436–42. Epub 2003/01/18.

    Article  CAS  PubMed  Google Scholar 

  155. Giebelstein J, Poschmann G, Hojlund K, Schechinger W, Dietrich JW, Levin K, et al. The proteomic signature of insulin-resistant human skeletal muscle reveals increased glycolytic and decreased mitochondrial enzymes. Diabetologia. 2012;55(4):1114–27. Epub 2012/01/28.

    Article  CAS  PubMed  Google Scholar 

  156. Topf F, Schvartz D, Gaudet P, Priego-Capote F, Zufferey A, Turck N, et al. The Human Diabetes Proteome Project (HDPP): from network biology to targets for therapies and prevention. Translational Proteomics. 2013;1(1):3–11.

    Article  Google Scholar 

  157. Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, et al. The human serum metabolome. PLoS One. 2011;6(2):e16957. Epub 2011/03/02.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Suhre K, Gieger C. Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet. 2012;13(11):759–69. Epub 2012/10/04.

    Article  CAS  PubMed  Google Scholar 

  159. Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010;5(11):e13953. Epub 2010/11/19.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  160. Gieger C, Geistlinger L, Altmaier E, Hrabe de Angelis M, Kronenberg F, Meitinger T, et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 2008;4(11):e1000282. Epub 2008/12/02.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  161. Suhre K, Shin SY, Petersen AK, Mohney RP, Meredith D, Wagele B, et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature. 2011;477(7362):54–60. Epub 2011/09/03.

    Article  CAS  PubMed  Google Scholar 

  162. Link E, Parish S, Armitage J, Bowman L, Heath S, Matsuda F, et al. SLCO1B1 variants and statin-induced myopathy--a genomewide study. N Engl J Med. 2008;359(8):789–99. Epub 2008/07/25.

    Article  CAS  PubMed  Google Scholar 

  163. Lu Y, Feskens EJ, Dolle ME, Imholz S, Verschuren WM, Muller M, et al. Dietary n-3 and n-6 polyunsaturated fatty acid intake interacts with FADS1 genetic variation to affect total and HDL-cholesterol concentrations in the Doetinchem Cohort Study. Am J Clin Nutr. 2010;92(1):258–65. Epub 2010/05/21.

    Article  CAS  PubMed  Google Scholar 

  164. Dumont J, Huybrechts I, Spinneker A, Gottrand F, Grammatikaki E, Bevilacqua N, et al. FADS1 genetic variability interacts with dietary alpha-linolenic acid intake to affect serum non-HDL-cholesterol concentrations in European adolescents. J Nutr. 2011;141(7):1247–53. Epub 2011/05/20.

    Article  CAS  PubMed  Google Scholar 

  165. Kettunen J, Demirkan A, Wurtz P, Draisma HH, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7:11122. Epub 2016/03/24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50. Epub 2014/05/13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Demirkan A, van Duijn CM, Ugocsai P, Isaacs A, Pramstaller PP, Liebisch G, et al. Genome-wide association study identifies novel loci associated with circulating phospho- and sphingolipid concentrations. PLoS Genet. 2012;8(2):e1002490. Epub 2012/02/24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Draisma HHM, Pool R, Kobl M, Jansen R, Petersen AK, Vaarhorst AAM, et al. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels. Nat Commun. 2015;6:7208. Epub 2015/06/13.

    Article  CAS  PubMed  Google Scholar 

  169. Inouye M, Ripatti S, Kettunen J, Lyytikainen LP, Oksala N, Laurila PP, et al. Novel Loci for metabolic networks and multi-tissue expression studies reveal genes for atherosclerosis. PLoS Genet. 2012;8(8):e1002907. Epub 2012/08/24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  170. Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytikainen LP, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet. 2012;44(3):269–76. Epub 2012/01/31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Krumsiek J, Suhre K, Evans AM, Mitchell MW, Mohney RP, Milburn MV, et al. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 2012;8(10):e1003005. Epub 2012/10/25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. Nicholson G, Rantalainen M, Li JV, Maher AD, Malmodin D, Ahmadi KR, et al. A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. PLoS Genet. 2011;7(9):e1002270. Epub 2011/09/21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Raffler J, Romisch-Margl W, Petersen AK, Pagel P, Blochl F, Hengstenberg C, et al. Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma. Genome Med. 2013;5(2):13. Epub 2013/02/19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Rhee EP, Ho JE, Chen MH, Shen D, Cheng S, Larson MG, et al. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab. 2013;18(1):130–43. Epub 2013/07/05.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Ried JS, Doring A, Oexle K, Meisinger C, Winkelmann J, Klopp N, et al. PSEA: Phenotype Set Enrichment Analysis--a new method for analysis of multiple phenotypes. Genet Epidemiol. 2012;36(3):244–52. Epub 2012/06/21.

    Article  PubMed  Google Scholar 

  176. Tukiainen T, Kettunen J, Kangas AJ, Lyytikainen LP, Soininen P, Sarin AP, et al. Detailed metabolic and genetic characterization reveals new associations for 30 known lipid loci. Hum Mol Genet. 2012;21(6):1444–55. Epub 2011/12/14.

    Article  CAS  PubMed  Google Scholar 

  177. Yu B, Zheng Y, Alexander D, Morrison AC, Coresh J, Boerwinkle E. Genetic determinants influencing human serum metabolome among African Americans. PLoS Genet. 2014;10(3):e1004212. Epub 2014/03/15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  178. Long T, Hicks M, Yu HC, Biggs WH, Kirkness EF, Menni C, et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet. 2017;49(4):568–78. Epub 2017/03/07.

    Article  CAS  PubMed  Google Scholar 

  179. Raffler J, Friedrich N, Arnold M, Kacprowski T, Rueedi R, Altmaier E, et al. Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality. PLoS Genet. 2015;11(9):e1005487. Epub 2015/09/10.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  180. Rueedi R, Ledda M, Nicholls AW, Salek RM, Marques-Vidal P, Morya E, et al. Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links. PLoS Genet. 2014;10(2):e1004132. Epub 2014/03/04.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  181. Suhre K, Wallaschofski H, Raffler J, Friedrich N, Haring R, Michael K, et al. A genome-wide association study of metabolic traits in human urine. Nat Genet. 2011;43(6):565–9. Epub 2011/05/17.

    Article  CAS  PubMed  Google Scholar 

  182. Yu B, Li AH, Metcalf GA, Muzny DM, Morrison AC, White S, et al. Loss-of-function variants influence the human serum metabolome. Sci Adv. 2016;2(8):e1600800. Epub 2016/09/08.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  183. Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 2009;37(Database issue):D603–10. Epub 2008/10/28.

    Article  CAS  PubMed  Google Scholar 

  184. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109–14. Epub 2011/11/15.

    Article  CAS  PubMed  Google Scholar 

  185. Krug S, Kastenmuller G, Stuckler F, Rist MJ, Skurk T, Sailer M, et al. The dynamic range of the human metabolome revealed by challenges. FASEB J. 2012;26(6):2607–19. Epub 2012/03/20.

    Article  CAS  PubMed  Google Scholar 

  186. Zhang A, Sun H, Wang P, Han Y, Wang X. Recent and potential developments of biofluid analyses in metabolomics. J Proteome. 2012;75(4):1079–88. Epub 2011/11/15.

    Article  CAS  Google Scholar 

  187. Fall T, Salihovic S, Brandmaier S, Nowak C, Ganna A, Gustafsson S, et al. Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes. Diabetologia. 2016;59(10):2114–24. Epub 2016/07/14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Nowak C, Salihovic S, Ganna A, Brandmaier S, Tukiainen T, Broeckling CD, et al. Effect of insulin resistance on monounsaturated fatty acid levels: a multi-cohort non-targeted metabolomics and Mendelian randomization study. PLoS Genet. 2016;12(10):e1006379. Epub 2016/10/22.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  189. Ganna A, Salihovic S, Sundstrom J, Broeckling CD, Hedman AK, Magnusson PK, et al. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. PLoS Genet. 2014;10(12):e1004801. Epub 2014/12/17.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  190. Goek ON, Prehn C, Sekula P, Romisch-Margl W, Doring A, Gieger C, et al. Metabolites associate with kidney function decline and incident chronic kidney disease in the general population. Nephrol Dial Transplant. 2013;28(8):2131–8. Epub 2013/06/07.

    Article  CAS  PubMed  Google Scholar 

  191. Sekula P, Goek ON, Quaye L, Barrios C, Levey AS, Romisch-Margl W, et al. A metabolome-wide association study of kidney function and disease in the general population. J Am Soc Nephrol. 2016;27(4):1175–88. Epub 2015/10/10.

    Article  CAS  PubMed  Google Scholar 

  192. Inouye M, Kettunen J, Soininen P, Silander K, Ripatti S, Kumpula LS, et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol Syst Biol. 2010;6:441. Epub 2010/12/24.

    Article  PubMed  PubMed Central  Google Scholar 

  193. Petersen AK, Zeilinger S, Kastenmuller G, Romisch-Margl W, Brugger M, Peters A, et al. Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. Hum Mol Genet. 2014;23(2):534–45. Epub 2013/09/10.

    Article  CAS  PubMed  Google Scholar 

  194. Matafora V, Bachi A, Capasso G. Genomics and proteomics: how long do we need to reach clinical results? Blood Purif. 2013;36(1):7–11. Epub 2013/06/06.

    Article  CAS  PubMed  Google Scholar 

  195. Vassy JL, Meigs JB. Is genetic testing useful to predict type 2 diabetes? Best Pract Res Clin Endocrinol Metab. 2012;26(2):189–201. Epub 2012/04/14.

    Article  PubMed  PubMed Central  Google Scholar 

  196. Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med. 2008;359(21):2208–19. Epub 2008/11/21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. Lyssenko V, Jonsson A, Almgren P, Pulizzi N, Isomaa B, Tuomi T, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008;359(21):2220–32. Epub 2008/11/21.

    Article  CAS  PubMed  Google Scholar 

  198. de Miguel-Yanes JM, Shrader P, Pencina MJ, Fox CS, Manning AK, Grant RW, et al. Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care. 2011;34(1):121–5. Epub 2010/10/05.

    Article  PubMed  Google Scholar 

  199. Muhlenbruch K, Jeppesen C, Joost HG, Boeing H, Schulze MB. The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity. PLoS One. 2013;8(5):e64307. Epub 2013/05/24.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  200. Chambers JC, Loh M, Lehne B, Drong A, Kriebel J, Motta V, et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol. 2015;3(7):526–34. Epub 2015/06/23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Kriebel J, Herder C, Rathmann W, Wahl S, Kunze S, Molnos S, et al. Association between DNA methylation in whole blood and measures of glucose metabolism: KORA F4 Study. PLoS One. 2016;11(3):e0152314. Epub 2016/03/29.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  202. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017;541(7635):81–6. Epub 2016/12/22.

    Article  CAS  PubMed  Google Scholar 

  203. Roberts LD, Koulman A, Griffin JL. Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol. 2014;2(1):65–75.

    Article  CAS  PubMed  Google Scholar 

  204. Nelson MR, Johnson T, Warren L, Hughes AR, Chissoe SL, Xu CF, et al. The genetics of drug efficacy: opportunities and challenges. Nat Rev Genet. 2016;17(4):197–206. Epub 2016/03/15.

    Article  CAS  PubMed  Google Scholar 

  205. Bonnefond A, Clement N, Fawcett K, Yengo L, Vaillant E, Guillaume JL, et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat Genet. 2012;44(3):297–301. Epub 2012/01/31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  206. Hardeland R. Antioxidative protection by melatonin: multiplicity of mechanisms from radical detoxification to radical avoidance. Endocrine. 2005;27(2):119–30. Epub 2005/10/12.

    Article  CAS  PubMed  Google Scholar 

  207. Korkmaz A, Ma S, Topal T, Rosales-Corral S, Tan DX, Reiter RJ. Glucose: a vital toxin and potential utility of melatonin in protecting against the diabetic state. Mol Cell Endocrinol. 2012;349(2):128–37. Epub 2011/11/15.

    Article  CAS  PubMed  Google Scholar 

  208. Bonnefond A, Froguel P. Disentangling the role of melatonin and its receptor MTNR1B in type 2 diabetes: still a long way to go? Curr Diab Rep. 2017;17(12):122. Epub 2017/10/25.

    Article  PubMed  CAS  Google Scholar 

  209. Romeo S, Kozlitina J, Xing C, Pertsemlidis A, Cox D, Pennacchio LA, et al. Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nat Genet. 2008;40(12):1461–5. Epub 2008/09/30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  210. Dongiovanni P, Valenti L. Genetics of nonalcoholic fatty liver disease. Metab Clin Exp. 2016;65(8):1026–37. Epub 2015/09/28.

    Article  CAS  PubMed  Google Scholar 

  211. Dongiovanni P, Anstee QM, Valenti L. Genetic predisposition in NAFLD and NASH: impact on severity of liver disease and response to treatment. Curr Pharm Des. 2013;19(29):5219–38. Epub 2013/02/12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  212. Kirchheiner J, Brockmoller J, Meineke I, Bauer S, Rohde W, Meisel C, et al. Impact of CYP2C9 amino acid polymorphisms on glyburide kinetics and on the insulin and glucose response in healthy volunteers. Clin Pharmacol Ther. 2002;71(4):286–96. Epub 2002/04/17.

    Article  CAS  PubMed  Google Scholar 

  213. Zhou K, Donnelly L, Burch L, Tavendale R, Doney AS, Leese G, et al. Loss-of-function CYP2C9 variants improve therapeutic response to sulfonylureas in type 2 diabetes: a Go-DARTS study. Clin Pharmacol Ther. 2010;87(1):52–6. Epub 2009/10/02.

    Article  CAS  PubMed  Google Scholar 

  214. Sesti G, Laratta E, Cardellini M, Andreozzi F, Del Guerra S, Irace C, et al. The E23K variant of KCNJ11 encoding the pancreatic beta-cell adenosine 5′-triphosphate-sensitive potassium channel subunit Kir6.2 is associated with an increased risk of secondary failure to sulfonylurea in patients with type 2 diabetes. J Clin Endocrinol Metab. 2006;91(6):2334–9. Epub 2006/04/06.

    Article  CAS  PubMed  Google Scholar 

  215. Feng Y, Mao G, Ren X, Xing H, Tang G, Li Q, et al. Ser1369Ala variant in sulfonylurea receptor gene ABCC8 is associated with antidiabetic efficacy of gliclazide in Chinese type 2 diabetic patients. Diabetes Care. 2008;31(10):1939–44. Epub 2008/07/05.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  216. Wang DS, Jonker JW, Kato Y, Kusuhara H, Schinkel AH, Sugiyama Y. Involvement of organic cation transporter 1 in hepatic and intestinal distribution of metformin. J Pharmacol Exp Ther. 2002;302(2):510–5. Epub 2002/07/20.

    Article  CAS  PubMed  Google Scholar 

  217. Tanihara Y, Masuda S, Sato T, Katsura T, Ogawa O, Inui K. Substrate specificity of MATE1 and MATE2-K, human multidrug and toxin extrusions/H(+)-organic cation antiporters. Biochem Pharmacol. 2007;74(2):359–71. Epub 2007/05/19.

    Article  CAS  PubMed  Google Scholar 

  218. Shu Y, Sheardown SA, Brown C, Owen RP, Zhang S, Castro RA, et al. Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J Clin Invest. 2007;117(5):1422–31. Epub 2007/05/04.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  219. Becker ML, Visser LE, van Schaik RH, Hofman A, Uitterlinden AG, Stricker BH. Genetic variation in the multidrug and toxin extrusion 1 transporter protein influences the glucose-lowering effect of metformin in patients with diabetes: a preliminary study. Diabetes. 2009;58(3):745–9. Epub 2009/02/21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  220. Zhou K, Bellenguez C, Sutherland C, Hardie G, Palmer C, Donnelly P, et al. The role of ATM in response to metformin treatment and activation of AMPK. Nat Genet. 2012;44(4):361–2. Epub 2012/03/30.

    Article  CAS  PubMed  Google Scholar 

  221. Pawlyk AC, Giacomini KM, McKeon C, Shuldiner AR, Florez JC. Metformin pharmacogenomics: current status and future directions. Diabetes. 2014;63(8):2590–9. Epub 2014/07/26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  222. Zhou K, Yee SW, Seiser EL, van Leeuwen N, Tavendale R, Bennett AJ, et al. Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat Genet. 2016;48(9):1055–9. Epub 2016/08/09.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  223. Ly A, Scheerer MF, Zukunft S, Muschet C, Merl J, Adamski J, et al. Retinal proteome alterations in a mouse model of type 2 diabetes. Diabetologia. 2014;57(1):192–203. Epub 2013/10/01.

    Article  CAS  PubMed  Google Scholar 

  224. Kim SJ, Nian C, McIntosh CH. Glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 modulate beta-cell chromatin structure. J Biol Chem. 2009;284(19):12896–904. Epub 2009/03/13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  225. Pinney SE, Simmons RA. Epigenetic mechanisms in the development of type 2 diabetes. Trends Endocrinol Metab. 2010;21(4):223–9. Epub 2009/10/30.

    Article  CAS  PubMed  Google Scholar 

  226. Christensen DP, Dahllof M, Lundh M, Rasmussen DN, Nielsen MD, Billestrup N, et al. Histone deacetylase (HDAC) inhibition as a novel treatment for diabetes mellitus. Mol Med. 2011;17(5–6):378–90. Epub 2011/01/29.

    CAS  PubMed  PubMed Central  Google Scholar 

  227. Dongiovanni P, Petta S, Mannisto V, Mancina RM, Pipitone R, Karja V, et al. Statin use and non-alcoholic steatohepatitis in at risk individuals. J Hepatol. 2015;63(3):705–12. Epub 2015/05/20.

    Article  CAS  PubMed  Google Scholar 

  228. Ashley EA, Butte AJ, Wheeler MT, Chen R, Klein TE, Dewey FE, et al. Clinical assessment incorporating a personal genome. Lancet. 2010;375(9725):1525–35. Epub 2010/05/04.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  229. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Miriami E, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148(6):1293–307. Epub 2012/03/20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  230. Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92(4):414–7. Epub 2012/09/21.

    Article  CAS  PubMed  Google Scholar 

  231. Collino S, Martin FP, Rezzi S. Clinical metabolomics paves the way towards future healthcare strategies. Br J Clin Pharmacol. 2013;75(3):619–29. Epub 2012/02/22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  232. Trupp M, Zhu H, Wikoff WR, Baillie RA, Zeng ZB, Karp PD, et al. Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment. PLoS One. 2012;7(7):e38386. Epub 2012/07/19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  233. Ji Y, Hebbring S, Zhu H, Jenkins GD, Biernacka J, Snyder K, et al. Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics. Clin Pharmacol Ther. 2011;89(1):97–104. Epub 2010/11/26.

    Article  CAS  PubMed  Google Scholar 

  234. Koulman A, Lane GA, Harrison SJ, Volmer DA. From differentiating metabolites to biomarkers. Anal Bioanal Chem. 2009;394(3):663–70. Epub 2009/03/12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  235. Teague B, Waterman MS, Goldstein S, Potamousis K, Zhou S, Reslewic S, et al. High-resolution human genome structure by single-molecule analysis. Proc Natl Acad Sci U S A. 2010;107(24):10848–53. Epub 2010/06/11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  236. Tang F, Lao K, Surani MA. Development and applications of single-cell transcriptome analysis. Nat Methods. 2011;8(4 Suppl):S6–11. Epub 2011/04/01.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  237. Ma C, Fan R, Ahmad H, Shi Q, Comin-Anduix B, Chodon T, et al. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat Med. 2011;17(6):738–43. Epub 2011/05/24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  238. Ideker T, Dutkowski J, Hood L. Boosting signal-to-noise in complex biology: prior knowledge is power. Cell. 2011;144(6):860–3. Epub 2011/03/19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  239. Chang RL, Xie L, Bourne PE, Palsson BO. Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput Biol. 2010;6(9):e1000938. Epub 2010/10/20.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  240. Bailey RC, Kwong GA, Radu CG, Witte ON, Heath JR. DNA-encoded antibody libraries: a unified platform for multiplexed cell sorting and detection of genes and proteins. J Am Chem Soc. 2007;129(7):1959–67. Epub 2007/01/31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  241. Roach JC, Glusman G, Smit AF, Huff CD, Hubley R, Shannon PT, et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science. 2010;328(5978):636–9. Epub 2010/03/12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  242. Ng SB, Bigham AW, Buckingham KJ, Hannibal MC, McMillin MJ, Gildersleeve HI, et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat Genet. 2010;42(9):790–3. Epub 2010/08/17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany

    Harald Grallert, Carola S. Marzi & Christian Gieger

  2. Research Unit Protein Science, Helmholtz Zentrum München – German Research Center for Environmental Health (GmbH), Neuherberg, Germany

    Stefanie M. Hauck

Authors
  1. Harald Grallert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carola S. Marzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefanie M. Hauck
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christian Gieger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harald Grallert .

Editor information

Editors and Affiliations

  1. ProSciento, Chula Vista, CA, USA

    Andrew J. Krentz

  2. ProSciento, Chula Vista, CA, USA

    Christian Weyer

  3. ProSciento, Chula Vista, CA, USA

    Marcus Hompesch

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Grallert, H., Marzi, C.S., Hauck, S.M., Gieger, C. (2019). Omics: Potential Role in Early Phase Drug Development. In: Krentz, A., Weyer, C., Hompesch, M. (eds) Translational Research Methods in Diabetes, Obesity, and Nonalcoholic Fatty Liver Disease. Springer, Cham. https://doi.org/10.1007/978-3-030-11748-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-11748-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11747-4

  • Online ISBN: 978-3-030-11748-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation