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An Overview of Personalized Medicine Development Through Recent Advances in Genome-Wide Association Studies

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MEDICON’23 and CMBEBIH’23 (MEDICON 2023, CMBEBIH 2023)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 94))

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Abstract

Genome-wide association studies, a term often in use following completion of The Human Genome Project, refers to the analysis and comprehensive understanding of human genetic landscape for practical purposes. An example of such application would be the development of personalized medicine approach and subsequent abandonment of the “one-size-fits all” model. In personalized medicine, a patient’s therapy is tailored according to their indication, clinical parameters, genotype and environmental effects in order to enable the best possible therapeutic outcome with as few adverse side effects, as possible. This review is about a series of exciting events that were important for the development of personalized medicine and to introduce the most important genome-wide association studies analysis approaches that made this possible, such as next-generation sequencing platforms and biomarker identification. In addition, we are presenting several examples of how personalized medicine improved our understanding of adverse drug reactions and ways to optimize patient’s therapy to their best interest.

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References

  1. Nash, D.B.: Personalized medicine: are we there yet? Am. Health Drug Benefits 7(7), 371–372 (2014)

    Google Scholar 

  2. Kichko, K., Marschall, P., Flessa, S.: Personalized medicine in the US and Germany: awareness, acceptance, use and preconditions for the wide implementation into the medical standard. J. Pers. Med. 6(2), 15 (2016)

    Article  Google Scholar 

  3. Hess, G.P., Fonseca, E., Scott, R., Fagerness, J.: Pharmacogenomic and pharmacogenetic-guided therapy as a tool in precision medicine: current state and factors impacting acceptance by stakeholders. Genet. Res. 97 (2015)

    Google Scholar 

  4. Vellekoop, H., et al.: The net benefit of personalized medicine: a systematic literature review and regression analysis. Value Health: J. Int. Soc. Pharmacoeconomics Outcomes Res. 25(8), 1428–1438 (2022). https://doi.org/10.1016/j.jval.2022.01.006

    Article  Google Scholar 

  5. Carr, D., Alfirevic, A., Pirmohamed, M.: Pharmacogenomics: current state-of-the-art. Genes 5(2), 430–443 (2014)

    Article  Google Scholar 

  6. Chang, K.L., Weitzel, K., Schmidt, S.: Pharmacogenetics: using genetic information to guide drug therapy. Am. Fam. Physician 92(7), 588 (2015)

    Google Scholar 

  7. Lee, S.J.: Clinical application of CYP2C19 pharmacogenetics toward more personalized medicine. Front. Genet. 3, 318 (2013)

    Article  Google Scholar 

  8. Nebert, D.W.: Polymorphisms in drug-metabolizing enzymes: what is their clinical relevance and why do they exist? Am. J. Hum. Genet. 60(2), 265 (1997)

    Google Scholar 

  9. Haga, S.B., Tindall, G., O’Daniel, J.M.: Public perspectives about pharmacogenetic testing and managing ancillary findings. Genet. Test. Mol. Biomarkers 16(3), 193–197 (2012)

    Article  Google Scholar 

  10. Ginsburg, G.S., Phillips, K.A.: Precision medicine: from science to value. Health affairs (Project Hope) 37(5), 694–701 (2018). https://doi.org/10.1377/hlthaff.2017.1624

    Article  Google Scholar 

  11. OwusuObeng, A., Fei, K., Levy, K., Elsey, A., Pollin, T., Ramirez, A., Horowitz, C.: Physician-reported benefits and barriers to clinical implementation of genomic medicine: a multi-site IGNITE-network survey. J. Pers. Med. 8(3), 24 (2018)

    Google Scholar 

  12. Hresko, A., Haga, S.: Insurance coverage policies for personalized medicine. J. Pers. Med. 2(4), 201–216 (2012)

    Article  Google Scholar 

  13. Quazi, S.: Artificial intelligence and machine learning in precision and genomic medicine. Med. Oncol. (Northwood, London, England) 39(8), 120 (2022). https://doi.org/10.1007/s12032-022-01711-1

    Article  Google Scholar 

  14. Phillips, K.A., Douglas, M.P., Trosman, J.R., Marshall, D.A.: What goes around comes around”: lessons learned from economic evaluations of personalized medicine applied to digital medicine. Value Health: J. Int. Soc. Pharmacoeconomics Outcomes Res. 20(1), 47–53 (2017). https://doi.org/10.1016/j.jval.2016.08.736

    Article  Google Scholar 

  15. Tam, V., Patel, N., Turcotte, M., Bossé, Y., Paré, G., Meyre, D.: Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 1 (2019)

    Google Scholar 

  16. Verma, M.: Genome-wide association studies and epigenome-wide association studies go together in cancer control. Future Oncol. 12(13), 1645–1664 (2016)

    Article  Google Scholar 

  17. Carlsten, C., Brauer, M., Brinkman, F., Brook, J., Daley, D., McNagny, K., Pui, M., Royce, D., Takaro, T., Denburg, J.: Genes, the environment and personalized medicine: we need to harness both environmental and genetic data to maximize personal and population health. EMBOreports 15(7), 736–739 (2014). https://doi.org/10.15252/embr.201438480

  18. Daly, A.K.: Pharmacogenomics of adverse drug reactions. Genome Med. 5(1), 5 (2013)

    Article  MathSciNet  Google Scholar 

  19. Visscher, P.M., Brown, M.A., McCarthy, M.I., Yang, J.: Five years of GWAS discovery. Am. J. Hum. Genet. 90(1), 7–24 (2012)

    Article  Google Scholar 

  20. Motsinger-Reif, A.A., et al.: Genome-wide association studies in pharmacogenomics: successes and lessons. Pharmacogenet. Genomics 23(8), 383 (2013)

    Article  Google Scholar 

  21. Welter, D., et al.: The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42(D1), D1001–D1006 (2013)

    Article  Google Scholar 

  22. Levy-Sakin, M., Pastor, S., Mostovoy, Y., Li, L., Leung, A. K., McCaffrey, J., Chung, C.Y., et al.: Genome maps across 26 human populations reveal population-specific patterns of structural variation. Nat. Commun. 10(1), 1025 (2019)

    Google Scholar 

  23. Cannon, M.E., Mohlke, K.L.: Deciphering the emerging complexities of molecular mechanisms at GWAS loci. Am. J. Hum. Genet. 103(5), 637–653 (2018)

    Article  Google Scholar 

  24. Alekseyev, Y.O., et al.: A next-generation sequencing primer—how does it work and what can it do? Acad. Pathol. 5, 2374289518766521 (2018)

    Article  Google Scholar 

  25. Voelkerding, K.V., Dames, S.A., Durtschi, J.D.: Next-generation sequencing: from basic research to diagnostics. Clin. Chem. 55(4), 641–658 (2009)

    Article  Google Scholar 

  26. Yin, Y., Butler, C., Zhang, Q.: Challenges in the application of NGS in the clinical laboratory. Hum. Immunol. 82(11), 812–819 (2021). https://doi.org/10.1016/j.humimm.2021.03.011

    Article  Google Scholar 

  27. Mardis, E.R., Wilson, R.K.: Cancer genome sequencing: a review. Hum. Mol. Genet. 18(R2), R163–R168 (2009)

    Article  Google Scholar 

  28. Mardis, E.R.: Next-generation sequencing platforms. Annu. Rev. Anal. Chem. 6, 287–303 (2013)

    Article  Google Scholar 

  29. Butler, J.M.: Advanced topics in forensic DNA typing: methodology. Academic Press (2011)

    Google Scholar 

  30. Bruijns, B., Tiggelaar, R., Gardeniers, H.: Massively parallel sequencing techniques for forensics: a review. Electrophoresis 39(21), 2642–2654 (2018)

    Article  Google Scholar 

  31. Kozich, J.J., Westcott, S.L., Baxter, N.T., Highlander, S.K., Schloss, P.D.: Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79(17), 5112–5120 (2013)

    Article  Google Scholar 

  32. Weirather, J. L., de Cesare, M., Wang, Y., Piazza, P., Sebastiano, V., Wang, X.J., Buck, D., Au, K.F.: Comprehensive comparison of Pacific biosciences and oxford nanopore technologies and their applications to transcriptome analysis. F1000Research 6, 100 (2017). https://doi.org/10.12688/f1000research.10571.2

  33. Bell, G.C., Crews, K.R., Wilkinson, M.R., Haidar, C.E., Hicks, J.K.,Baker, D.K., Freimuth, R.R. et al.: Development and use of active clinical decision support for preemptive pharmacogenomics. J. Am. Med. Inform. Assoc. 21(e1), e93–e99 (2013)

    Google Scholar 

  34. Nayarisseri, A., et al.: Artificial intelligence, big data and machine learning approaches in precision medicine and drug discovery. Currentdrugtargets 22(6), 631–655 (2021). https://doi.org/10.2174/1389450122999210104205732

    Article  Google Scholar 

  35. Méndez Hernández, R., Ramasco Rueda, F.: Biomarkers as prognostic predictors and therapeutic guide in critically ill patients: clinical evidence. J. Pers. Med. 13(2), 333 (2023). https://doi.org/10.3390/jpm13020333

    Article  Google Scholar 

  36. Riley, R.D., Hayden, J.A., Steyerberg, E.W., Moons, K.G., Abrams, K.,Kyzas, P.A., Hemingway, H.: Prognosis research strategy (PROGRESS) 2: prognostic factor research. PLoS Med. 10(2), e1001380 (2013)

    Google Scholar 

  37. Hlatky, M.A., Greenland, P., Arnett, D.K., Ballantyne, C.M., Criqui, M.H., Elkind, M.S., Go, A.S., Harrell, F.E., Jr, Hong, Y., Howard, B.V., Howard, V.J., Hsue, P.Y., Kramer, C.M., McConnell, J.P., Normand, S.L., O’Donnell, C.J., Smith, S.C., Jr., Wilson, P.W.: American Heart Association Expert Panel on Subclinical Atherosclerotic Diseases and Emerging Risk Factors and the Stroke Council. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation 119(17), 2408–2416 (2009). https://doi.org/10.1161/CIRCULATIONAHA.109.192278

  38. Liu, X., Xiao, C., Yue, K., Chen, M., Zhou, H., Yan, X.: Identification of multiomics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma. Sci. Rep. 12(1), 12084 (2022). https://doi.org/10.1038/s41598-022-16341-w

    Article  Google Scholar 

  39. Dahiru, T.: P—value, a true test of statistical significance? A cautionary note. Ann. Ib. Postgrad. Med. 6(1), 21–26 (2008). https://doi.org/10.4314/aipm.v6i1.64038

    Article  Google Scholar 

  40. Moons, K.G.: Criteria for scientific evaluation of novel markers: a perspective. Clin. Chem. 56(4), 537–541 (2010)

    Article  Google Scholar 

  41. Bragg, L.M., Stone, G., Butler, M.K., Hugenholtz, P., Tyson, G.W.: Shining a light on dark sequencing: characterising errors in Ion Torrent PGM data. PLoS Comput. Biol. 9(4), e1003031 (2013)

    Article  Google Scholar 

  42. Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.M., Zietz, M., Hoffman, M.M., Xie, W., Rosen, G.L., Lengerich, B.J., Israeli, J., Lanchantin, J., Woloszynek, S., Carpenter, A.E., Shrikumar, A., Xu, J., Cofer, E.M., Greene, C.S. et al.: Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15(141), 20170387 (2018). https://doi.org/10.1098/rsif.2017.0387

  43. Pirmohamed, M., Park, B.K.: Genetic susceptibility to adverse drug reactions. Trends Pharmacol. Sci. 22(6), 298–305 (2001). https://doi.org/10.1016/s0165-6147(00)01717-x

    Article  Google Scholar 

  44. Johnson, J.A., Caudle, K.E., Gong, L., Whirl‐Carrillo, M., Stein, C.M., Scott, S.A., Anderson, J.L.: Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for pharmacogenetics‐guided warfarin dosing: 2017 update. Clin. Pharmacol. Ther. 102(3), 397–404 (2017)

    Google Scholar 

  45. Lee, C.H., Jung, K.H., Cho, D.J., Jeong, S.K.: Effect of warfarin versus aspirin on blood viscosity in cardioembolic stroke with atrial fibrillation: a prospective clinical trial. BMC Neurol. 19(1), 82 (2019)

    Article  Google Scholar 

  46. Allyn-Feuer, A., Ade, A., Luzum, J.A., Higgins, G.A., Athey, B.D.: The pharmacoepigenomics informatics pipeline defines a pathway of novel and known warfarin pharmacogenomics variants. Pharmacogenomics 19(5), 413–434 (2018). https://doi.org/10.2217/pgs-2017-0186

  47. Westervelt, P., Cho, K., Bright, D.R., Kisor, D.F.: Drug-gene interactions: inherent variability in drug maintenance dose requirements. P & T: Peer-Rev. J. Formul Manag. 39(9), 630–637 (2014)

    Google Scholar 

  48. Höppner, W., Primorac, D.: Pharmacogenetics in clinical practice: experience with 16 commonly used drugs. Zagreb: St. Catherine Specialty Hospital (2016)

    Google Scholar 

  49. Turner, R.M., Bula, M., Pirmohamed, M.: Personalized medicinein cardiovascular disease. Diagn. Mol. Pathol. 457–471 (2017). https://doi.org/10.1016/b978-0-12-800886-7.00036-4

  50. Lee, K.E., et al.: Effects of CYP4F2 gene polymorphisms on warfarin clearance and sensitivity in Korean patients with mechanical cardiac valves. Ther. Drug Monit. 34(3), 275–282 (2012). https://doi.org/10.1097/FTD.0b013e318256a77c

    Article  Google Scholar 

  51. Shendre, A., Dillon, C., Limdi, N.A.: Pharmacogenetics of warfarin dosing in patients of African and European ancestry. Pharmacogenomics 19(17), 1357–1371 (2018). https://doi.org/10.2217/pgs-2018-0146

    Article  Google Scholar 

  52. Allabi, A.C., et al.: Functional impact of CYP2C95, CYP2C96, CYP2C98, and CYP2C911 in vivo among black Africans. Clin. Pharmacol. Ther. 76(2), 113–118 (2004). https://doi.org/10.1016/j.clpt.2004.04.001

    Article  Google Scholar 

  53. Lindley, K.J., et al.: Warfarin dosing in patients with CYP2C9*5 variant alleles. Clin. Pharmacol. Ther. 111(4), 950–955 (2022). https://doi.org/10.1002/cpt.2549

    Article  Google Scholar 

  54. Takeuchi,F., McGinnis, R., Bourgeois, S., Barnes, C., Eriksson, N., Soranzo, N., Holm, L.: A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet. 5(3), e1000433 (2009)

    Google Scholar 

  55. Wigle, T.J., Tsvetkova, E.V., Welch, S.A., Kim, R.B.: DPYD and fluorouracil-based chemotherapy: mini review and case report. Pharmaceutics 11(5), 199 (2019). https://doi.org/10.3390/pharmaceutics11050199

    Article  Google Scholar 

  56. Longley, D.B., Harkin, D.P., Johnston, P.G.: 5-fluorouracil: mechanisms of action and clinical strategies. Nat. Rev. Cancer 3(5), 330–338 (2003). https://doi.org/10.1038/nrc1074

    Article  Google Scholar 

  57. Lam, S.W., Guchelaar, H.J., Boven, E.: The role of pharmaco- genetics in capecitabine efficacy and toxicity. Cancer Treat. Rev. 50, 9–22 (2016). https://doi.org/10.1016/j.ctrv.2016.08.001

    Article  Google Scholar 

  58. Dean, L., Kane, M.: Capecitabine Therapy and DPYD Genotype. In: Pratt, V.M. et al. (eds.), Medical Genetics Summaries. National Center for Biotechnology Information (US) (2016)

    Google Scholar 

  59. deMan, F.M., Veerman, G.M., Oomen-de Hoop, E., Deenen, M. J., Meulendijks, D., Mandigers, C.M., Mathijssen, R.H.: Comparison of toxicity and effectiveness between fixed-dose and body surface area-based dose capecitabine. Ther. Adv. Med. Oncol. 11, 1758835919838964 (2019)

    Google Scholar 

  60. Peng, T., Ouyang, Y., Tong, K.: Rechallenge capecitabine after fluoropyrimidine-induced cardiotoxicity in rectal cancer: a case report. Medicine 98(2) (2019)

    Google Scholar 

  61. Balyan, R., Hahn, D., Huang, H., Chidambaran, V.: Pharmacokinetic and pharmacodynamic considerations in developing a response to the opioid epidemic. Expert Opin. Drug Metab. Toxicol. 16(2), 125–141 (2020). https://doi.org/10.1080/17425255.2020.1721458

    Article  Google Scholar 

  62. Dagostino, C., et al.: CYP2D6 genotype can help to predict effectiveness and safety during opioid treatment for chronic low back pain: results from a retrospective study in an Italian cohort. Pharmacogenomics Pers. 11, 179–191 (2018). https://doi.org/10.2147/PGPM.S181334

    Article  Google Scholar 

  63. Carranza-Leon, D., Dickson, A.L., Gaedigk, A., Stein, C.M., Chung, C.P.: CYP2D6 genotype and reduced codeine analgesic effect in real- world clinical practice. Pharmacogenomics J. 21(4), 484–490 (2021). https://doi.org/10.1038/s41397-021-00226-8

  64. Wong, A.K., Somogyi, A.A., Rubio, J., Philip, J.: The role of pharmacogenomics in opioid prescribing. Curr. Treat. Options Oncol. 23(10), 1353–1369 (2022). https://doi.org/10.1007/s11864-022-01010-x

    Article  Google Scholar 

  65. Kaye, A.D., Garcia, A.J., Hall, O.M., Jeha, G.M., Cramer, K.D., Granier, L., Urman, R.D.: Update on the pharmacogenomics of pain management. Pharmacogenomics Pers. Med., 12, 125 (2019)

    Google Scholar 

  66. Dai, D.P., et al.: In vitro functional assessment of 22 newly identified CYP2D6 allelic variants in the Chinese population. Basic Clin. Toxicol. 117(1), 39–43 (2015). https://doi.org/10.1111/bcpt.12363

    Article  Google Scholar 

  67. Ramsey, L.B., Ong, H.H., Schildcrout, J.S., Shi, Y., Tang, L.A., Hicks, J.K.,El Rouby, N., Cavallari, L.H., Tuteja, S., Aquilante, C. L., Beitelshees, L., Lemkin, D.L., Blake, K.V., Williams, H., Cimino, J.J., Davis, B.H., Limdi, N.A., Empey, P.E., Horvat, C.M., Kao, D.P.: IGNITE Pharmacogenetics Working Group. Prescribing Prevalence of Medications With Potential Genotype-Guided Dosing in Pediatric Patients. JAMAnetworkopen 3(12), e2029411 (2020). https://doi.org/10.1001/jamanetworkopen.2020.29411

  68. Gammal, R.S., et al.: Pharmacogenetics for safe codeine use in sickle cell disease. Pediatrics 138(1), e20153479 (2016). https://doi.org/10.1542/peds.2015-3479

    Article  Google Scholar 

  69. Bugada, D., Lorini, L.F., Fumagalli, R., Allegri, M.: Genetics and opioids: towards more appropriate prescription in cancer pain. Cancers 12(7), 1951 (2020). https://doi.org/10.3390/cancers12071951

    Article  Google Scholar 

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  1. International Burch University, Francuske Revolucije Bb, Ilidža, 71210, Bosnia and Herzegovina

    Dženita Omerkić & Adna Ašić

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  1. Dženita Omerkić
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  2. Adna Ašić
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  1. Verlab Research Institute for Biomedical Engineering, Medical Devices, and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina

    Almir Badnjević

  2. Verlab Research Institute for Biomedical Engineering, Medical Devices, and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina

    Lejla Gurbeta Pokvić

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Omerkić, D., Ašić, A. (2024). An Overview of Personalized Medicine Development Through Recent Advances in Genome-Wide Association Studies. In: Badnjević, A., Gurbeta Pokvić, L. (eds) MEDICON’23 and CMBEBIH’23. MEDICON CMBEBIH 2023 2023. IFMBE Proceedings, vol 94. Springer, Cham. https://doi.org/10.1007/978-3-031-49068-2_29

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