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Pharmacogenomics
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The need to shift pharmacogenetic research from candidate gene to genome-wide association studies

    Derek W Linskey

    Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA

    ,
    David C Linskey

    Independent Investigator, Seattle, WA 98109, USA

    ,
    Howard L McLeod

    Precision Medicine, Geriatric Oncology Consortium, Tampa, FL 33609, USA

    &
    Jasmine A Luzum

    *Author for correspondence: Tel.: +1 734 615 4851;

    E-mail Address: jluzum@med.umich.edu

    Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA

    Published Online:https://doi.org/10.2217/pgs-2021-0108

    The primary research approach in pharmacogenetics has been candidate gene association studies (CGAS), but pharmacogenomic genome-wide association studies (GWAS) are becoming more common. We are now at a critical juncture when the results of those two research approaches, CGAS and GWAS, can be compared in pharmacogenetics. We analyzed publicly available databases of pharmacogenetic CGAS and GWAS (i.e., the Pharmacogenomics Knowledgebase [PharmGKB®] and the NHGRI-EBI GWAS catalog) and the vast majority of variants (98%) and genes (94%) discovered in pharmacogenomic GWAS were novel (i.e., not previously studied CGAS). Therefore, pharmacogenetic researchers are not selecting the right candidate genes in the vast majority of CGAS, highlighting a need to shift pharmacogenetic research efforts from CGAS to GWAS.

    Papers of special note have been highlighted as: • of interest; •• of considerable interest

    References

    • 1. US FDA. Preventable adverse drug reactions: a focus on drug interactions (2018). https://www.fda.gov/drugs/drug-interactions-labeling/preventable-adverse-drug-reactions-focus-drug-interactions#ADRs:%20Prevalence%20and%20Incidence
    • 2. Ali MK, Bullard KM, Saaddine JB, Cowie CC, Imperatore G, Gregg EW. Achievement of goals in US diabetes care, 1999–2010. N. Engl. J. Med. 368(17), 1613–1624 (2013).
    • 3. Chow CK, Teo KK, Rangarajan S et al. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. JAMA 310(9), 959–968 (2013).
    • 4. Akyea RK, Kai J, Qureshi N, Iyen B, Weng SF. Sub-optimal cholesterol response to initiation of statins and future risk of cardiovascular disease. Heart 105(13), 975–981 (2019).
    • 5. Roden DM, Wilke RA, Kroemer HK, Stein CM. Pharmacogenomics: the genetics of variable drug responses. Circulation 123(15), 1661–1670 (2011).
    • 6. Pirmohamed M. Pharmacogenetics: past, present and future. Drug Discov. Today 16(19–20), 852–861 (2011).
    • 7. Goldstein DB, Tate SK, Sisodiya SM. Pharmacogenetics goes genomic. Nat. Rev. Genet. 4(12), 937–947 (2003).
    • 8. Relling MV, Schwab M, Whirl-Carrillo M et al. Clinical pharmacogenetics implementation consortium guideline for thiopurine dosing based on TPMT and NUDT15 genotypes: 2018 update. Clin. Pharmacol. Ther. 105(5), 1095–1105 (2019).
    • 9. Yang SK, Hong M, Baek J et al. A common missense variant in NUDT15 confers susceptibility to thiopurine-induced leukopenia. Nat. Genet. 46(9), 1017–1020 (2014). •• Despite decades of candidate gene association studies focusing on TPMT in thiopurine-induced leukopenia, this genome-wide association study (GWAS) discovered that variants in NUDT15 are associated with thiopurine-induced leukopenia.
    • 10. National Comprehensive Cancer Netword. Pediatric acute lymphoblastic leukemia. (2020) https://www.nccn.org/professionals/physician_gls/pdf/ped_all.pdf
    • 11. Daly AK. Genome-wide association studies in pharmacogenomics. Nat. Rev. Genet. 11(4), 241–246 (2010).
    • 12. Giacomini KM, Yee SW, Mushiroda T, Weinshilboum RM, Ratain MJ, Kubo M. Genome-wide association studies of drug response and toxicity: an opportunity for genome medicine. Nat. Rev. Drug Discov. 16(1), 1 (2017). •• Describes the challenges of pharmacogenomic GWAS and compares them to disease and trait GWAS.
    • 13. McInnes G, Yee SW, Pershad Y, Altman RB. Genomewide association studies in pharmacogenomics. Clin. Pharmacol. Ther. 110(3), 637–648 (2021). •• This paper is an in-depth and current evaluation of pharmacogenomic GWAS.
    • 14. Buniello A, MacArthur JAL, Cerezo M et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47(D1), D1005–d1012 (2019).
    • 15. Whirl-Carrillo M, McDonagh EM, Hebert JM et al. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92(4), 414–417 (2012).
    • 16. ClinCalc.com. ClinCalc DrugStats Database (2021). https://clincalc.com/DrugStats/Top200Drugs.aspx
    • 17. Casas JP, Cooper J, Miller GJ, Hingorani AD, Humphries SE. Investigating the genetic determinants of cardiovascular disease using candidate genes and meta-analysis of association studies. Ann. Hum. Genet. 70(Pt 2), 145–169 (2006).
    • 18. Morgan TM, Krumholz HM, Lifton RP, Spertus JA. Nonvalidation of reported genetic risk factors for acute coronary syndrome in a large-scale replication study. JAMA 297(14), 1551–1561 (2007). • Shows that previously studied candidate genes for coronary artery disease could not be validated.
    • 19. Samani NJ, Erdmann J, Hall AS et al. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357(5), 443–453 (2007). •• After years of candidate gene association studies focused on other genes and variants, this genome-wide association study discovered a novel loci, chromosome 9p21.3, as associated with the development of coronary artery disease.
    • 20. Schunkert H, Götz A, Braund P et al. Repeated replication and a prospective meta-analysis of the association between chromosome 9p21.3 and coronary artery disease. Circulation 117(13), 1675–1684 (2008). • Shows that the chromosome 9p21.3 loci that was discovered by GWAS is robustly replicated.
    • 21. Karaesmen E, Rizvi AA, Preus LM et al. Replication and validation of genetic polymorphisms associated with survival after allogeneic blood or marrow transplant. Blood 130(13), 1585–1596 (2017).
    • 22. Pandi MT, Williams MS, van der Spek P, Koromina M, Patrinos GP. Exome-wide analysis of the DiscovEHR cohort reveals novel candidate pharmacogenomic variants for clinical pharmacogenomics. Genes (Basel) 11(5), 561–572 (2020).
    • 23. Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19(8), 491–504 (2018).
    • 24. Her L, Zhu HJ. Carboxylesterase 1 and precision pharmacotherapy: pharmacogenetics and nongenetic regulators. Drug Metab. Dispos. 48(3), 230–244 (2020).
    • 25. Peters EJ, McLeod HL. Ability of whole-genome SNP arrays to capture ‘must have’ pharmacogenomic variants. Pharmacogenomics 9(11), 1573–1577 (2008).
    • 26. Gamazon ER, Skol AD, Perera MA. The limits of genome-wide methods for pharmacogenomic testing. Pharmacogenet. Genomics 22(4), 261–272 (2012).
    • 27. Rowan CG, Flory J, Gerhard T et al. Agreement and validity of electronic health record prescribing data relative to pharmacy claims data: a validation study from a US electronic health record database. Pharmacoepidemiol. Drug Saf. 26(8), 963–972 (2017).
    • 28. Burns EM, Rigby E, Mamidanna R et al. Systematic review of discharge coding accuracy. J. Public Health (Oxf.) 34(1), 138–148 (2012).
    • 29. Daly AK, Donaldson PT, Bhatnagar P et al. HLA-B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin. Nat. Genet. 41(7), 816–819 (2009).
    • 30. Lanfear DE, Luzum JA, She R et al. Polygenic score for β-blocker survival benefit in European ancestry patients with reduced ejection fraction heart failure. Circ. Heart Fail. 13(12), e007012 (2020). • Successful example of how pharmacogenetic researchers can use polygenic scores to overcome the low power of GWAS which typically assess the individual effects of genetic variants.