Skip to main content

Advertisement

Log in

Genomic medicine on the frontier of precision medicine

  • Review article
  • Published:
Journal of Diabetes & Metabolic Disorders Aims and scope Submit manuscript

Abstract

Genomic medicine has created a great deal of hope since the completion of the Human Genome Project (HGP). Genomic medicine promises disease prevention and early diagnosis in the context of precision medicine. Precision medicine as a scientific discipline has introduced as an evolution in medicine. The rapid growth of high-development technologies permits the assessment of biological systems. Study of the integrated profiles of omics, such as genome, transcriptome, proteome and other omics information lead to significant advances in personalized and precision medicine. In the context of precision medicine, pharmacogenomics can play an important role in order to discriminate responders and non-responders to medications and avoiding toxicity and achieving the optimum dose. So precision medicine in accordance with genomic medicine will transform medicine from conventional evidence-based medicine in the diagnosis and treatment towards precision based-medicine. In this review, we have summarized the related issues for genomic medicine and precision medicine.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Abbreviations

PGx:

Pharmacogenomics

NHGRI:

National Human Genome Research Institute

NGS:

Next-generation sequencing

HGP:

Human Genome Project

WES:

Whole Exome Sequencing

GWAS:

Genome-Wide Association Studies

LD:

Linkage disequilibrium

indels:

Insertions and deletions

CNVs:

Copy number variations

DOE:

Department of Energy

lncRNAs:

Long non-coding RNAs

SNPs:

Single nucleotide polymorphisms

TFs:

Transcription factors

EWAS:

Epigenome-wide association studies

PRS:

Polygenic risk scores

PGS:

Polygenic scores

References

  1. National Cancer Institute (NIH). NCI is the nation’s leader in cancer research. Available at: https://www.cancer.gov/.

  2. Narad P, Kirthanashri S. Introduction to omics. Omics approaches, technologies and applications. Springer; 2018. p. 1–10.

    Book  Google Scholar 

  3. Food and Drug Adminstration (FDA). Department of Health and Human Services. Table of pharmacogenomic biomarkers in drug labeling. Available at: https://www.fda.gov/drugs/scienceresearch/ucm572698.htm.

  4. Roth SC. What is genomic medicine? J Med Libr Assoc. 2019;107(3):442–8.

  5. Davies K. The era of genomic medicine. Clin Med (Lond). 2013;13(6):594–601.

    Article  Google Scholar 

  6. Rubanovich CK, Cheung C, Mandel J, Bloss CS. Physician preparedness for big genomic data: a review of genomic medicine education initiatives in the United States. Hum Mol Genet. 2018;27(R2):R250–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Roden D, Tyndale R. Genomic medicine, precision medicine, personalized medicine: what’s in a name? Clin Pharmacol Ther. 2013;94(2):169–72.

    Article  CAS  PubMed  Google Scholar 

  8. Sykiotis GP, Kalliolias GD, Papavassiliou AG. Hippocrates and genomic medicine. Arch Med Res. 2006;37(1):181–3.

    Article  PubMed  Google Scholar 

  9. Raza S, Hall A. Genomic medicine and data sharing. Br Med Bull. 2017;123(1):35–45.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Manolio TA, Green ED. Leading the way to genomic medicine. Am J Med Genet C Semin Med Genet. 2014;166c(1):1–7.

    Article  PubMed  Google Scholar 

  11. Semiz S, Dujic T, Causevic A. Pharmacogenetics and personalized treatment of type 2 diabetes. Biochem Med (Zagreb). 2013;23(2):154–71.

    Article  CAS  Google Scholar 

  12. Council NR. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. National Academies Press; 2011.

    Google Scholar 

  13. Ginsburg GS, Phillips KA. Precision medicine: From science to value. Health Aff. 2018;37(5):694–701.

    Article  Google Scholar 

  14. Vailati-Riboni M, Palombo V, Loor JJ. What are omics sciences? Periparturient diseases of dairy cows. Springer; 2017. p. 1–7.

    Google Scholar 

  15. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):1–15.

    Article  CAS  Google Scholar 

  16. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights. 2020;14:1177932219899051.

    Article  PubMed  PubMed Central  Google Scholar 

  17. OMIM Gene Map Statistics 2019, Last updated February 1st. Available at: https://www.omim.org/statistics/geneMap.

  18. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860–922.

    Article  CAS  PubMed  Google Scholar 

  19. 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. 2013;42(D1):D1001–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Edwards SL, Beesley J, French JD, Dunning AM. Beyond GWASs: illuminating the dark road from association to function. Am J Hum Genet. 2013;93(5):779–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Flister MJ, Tsaih S-W, O’Meara CC, Endres B, Hoffman MJ, Geurts AM, et al. Identifying multiple causative genes at a single GWAS locus. Genome Res. 2013;23:1996–2002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Witte JS. Genome-wide association studies and beyond. Annu Rev Public Health. 2010;31:9–20.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ku CS, Loy EY, Salim A, Pawitan Y, Chia KS. The discovery of human genetic variations and their use as disease markers: Past, present and future. J Hum Genet. 2010;55(7):403–15.

    Article  PubMed  Google Scholar 

  24. Krulwich R, Lander E. Cracking the code of life. Public Broadcasting Service; 2001.

    Google Scholar 

  25. Collins FS, Patrinos A, Jordan E, Chakravarti A, Gesteland R, Walters L. New goals for the US human genome project: 1998–2003. Science. 1998;282(5389):682–9.

    Article  CAS  PubMed  Google Scholar 

  26. Collins F, Galas D. A new five-year plan for the US Human Genome Project. Science. 1993;262(5130):43–6.

    Article  CAS  PubMed  Google Scholar 

  27. Collins FS, Morgan M, Patrinos A. The Human Genome Project: lessons from large-scale biology. Science. 2003;300(5617):286–90.

    Article  CAS  PubMed  Google Scholar 

  28. Consortium IHGS. Finishing the euchromatic sequence of the human genome. Nature. 2004;431(7011):931.

    Article  CAS  Google Scholar 

  29. Osoegawa K, Mammoser AG, Wu C, Frengen E, Zeng C, Catanese JJ, et al. A bacterial artificial chromosome library for sequencing the complete human genome. Genome Res. 2001;11(3):483–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Asakawa S, Abe I, Kudoh Y, Kishi N, Wang Y, Kubota R, et al. Human BAC library: construction and rapid screening. Gene. 1997;191(1):69–79.

    Article  CAS  PubMed  Google Scholar 

  31. Lodish H, Berk A, Zipursky SL, Matsudaira P, Baltimore D, Darnell J. Molecular cell biology. WH Freeman; 1995.

    Google Scholar 

  32. Pines M. The genes we share with yeast, flies, worms, and mice: new clues to human health and disease. Howard Hughes Medical Institute; 2001.

    Google Scholar 

  33. Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet. 2010;11(6):415.

    Article  CAS  PubMed  Google Scholar 

  34. Consortium IH. A haplotype map of the human genome. Nature. 2005;437(7063):1299.

    Article  CAS  Google Scholar 

  35. Kennedy GC, Matsuzaki H, Dong S, Liu W-M, Huang J, Liu G, et al. Large-scale genotyping of complex DNA. Nat Biotechnol. 2003;21(10):1233.

    Article  CAS  PubMed  Google Scholar 

  36. Cargill M, Altshuler D, Ireland J, Sklar P, Ardlie K, Patil N, et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet. 1999;22(3):231.

    Article  CAS  PubMed  Google Scholar 

  37. Ke X, Cardon LR. Efficient selective screening of haplotype tag SNPs. Bioinformatics. 2003;19(2):287–8.

    Article  CAS  PubMed  Google Scholar 

  38. †The International HapMap C, Gibbs RA, Belmont JW, Hardenbol P, Willis TD, Yu F, et al. The International HapMap Project. Nature. 2003;426:789.

  39. Consortium IH. The international HapMap project. Nature. 2003;426(6968):789.

    Article  CAS  Google Scholar 

  40. Thorisson GA, Smith AV, Krishnan L, Stein LD. The international HapMap project web site. Genome Res. 2005;15(11):1592–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Haralambieva IH, Ovsyannikova IG, Pankratz VS, Kennedy RB, Jacobson RM, Poland GA. The genetic basis for interindividual immune response variation to measles vaccine: new understanding and new vaccine approaches. Expert Rev Vaccines. 2013;12(1):57–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Elmas A, Yang T-HO, Wang X, Anastassiou D. Discovering Genome-Wide Tag SNPs Based on the Mutual Information of the Variants. PloS One. 2016;11(12):e0167994.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Evans WE, McLeod HL. Pharmacogenomics—drug disposition, drug targets, and side effects. N Engl J Med. 2003;348(6):538–49.

    Article  CAS  PubMed  Google Scholar 

  44. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science. 1999;286(5439):487–91.

    Article  CAS  PubMed  Google Scholar 

  45. Hartl D, de Luca V, Kostikova A, Laramie J, Kennedy S, Ferrero E, et al. Translational precision medicine: an industry perspective. J Transl Med. 2021;19(1):1–14.

    Article  Google Scholar 

  46. Yong SY, Raben TG, Lello L, Hsu SD. Genetic architecture of complex traits and disease risk predictors. Sci Rep. 2020;10(1):1–14.

    Article  CAS  Google Scholar 

  47. Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nat Revi Genet. 2020;21(8):493–502.

    Article  CAS  Google Scholar 

  48. Zeggini E, Gloyn AL, Barton AC, Wain LV. Translational genomics and precision medicine: Moving from the lab to the clinic. Science. 2019;365(6460):1409–13.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

MH: Design the study and write the manuscript; NS, FKH, and EGH: Search and find the relevant articles and help to draft; AN and SE: Provide guidance to the research; HRAM: Revise the manuscript; BL: Develop the project. All authors will read and approved the final manuscript before submission.

Corresponding author

Correspondence to Hamid Reza Aghaei Meybodi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hasanzad, M., Sarhangi, N., Naghavi, A. et al. Genomic medicine on the frontier of precision medicine. J Diabetes Metab Disord 21, 853–861 (2022). https://doi.org/10.1007/s40200-021-00880-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40200-021-00880-6

Keywords

Navigation