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The Global Spectrum of Protein-coding Pharmacogenomic Diversity

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Date 2016 Oct 26
PMID 27779249
Citations 44
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Abstract

Differences in response to medications have a strong genetic component. By leveraging publically available data, the spectrum of such genomic variation can be investigated extensively. Pharmacogenomic variation was extracted from the 1000 Genomes Project Phase 3 data (2504 individuals, 26 global populations). A total of 12 084 genetic variants were found in 120 pharmacogenes, with the majority (90.0%) classified as rare variants (global minor allele frequency <0.5%), with 52.9% being singletons. Common variation clustered individuals into continental super-populations and 23 pharmacogenes contained highly differentiated variants (F>0.5) for one or more super-population comparison. A median of three clinical variants (PharmGKB level 1A/B) was found per individual, and 55.4% of individuals carried loss-of-function variants, varying by super-population (East Asian 60.9%>African 60.1%>South Asian 60.3%>European 49.3%>Admixed 39.2%). Genome sequencing can therefore identify clinical pharmacogenomic variation, and future studies need to consider rare variation to understand the spectrum of genetic diversity contributing to drug response.

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References
1.
Numanagic I, Malikic S, Pratt V, Skaar T, Flockhart D, Sahinalp S . Cypiripi: exact genotyping of CYP2D6 using high-throughput sequencing data. Bioinformatics. 2015; 31(12):i27-34. PMC: 4542776. DOI: 10.1093/bioinformatics/btv232. View

2.
Chen L, Shu Y, Liang X, Chen E, Yee S, Zur A . OCT1 is a high-capacity thiamine transporter that regulates hepatic steatosis and is a target of metformin. Proc Natl Acad Sci U S A. 2014; 111(27):9983-8. PMC: 4103324. DOI: 10.1073/pnas.1314939111. View

3.
Pirmohamed M, Ostrov D, Park B . New genetic findings lead the way to a better understanding of fundamental mechanisms of drug hypersensitivity. J Allergy Clin Immunol. 2015; 136(2):236-44. PMC: 4534769. DOI: 10.1016/j.jaci.2015.06.022. View

4.
Lim E, Wurtz P, Havulinna A, Palta P, Tukiainen T, Rehnstrom K . Distribution and medical impact of loss-of-function variants in the Finnish founder population. PLoS Genet. 2014; 10(7):e1004494. PMC: 4117444. DOI: 10.1371/journal.pgen.1004494. View

5.
Ramos E, Doumatey A, Elkahloun A, Shriner D, Huang H, Chen G . Pharmacogenomics, ancestry and clinical decision making for global populations. Pharmacogenomics J. 2013; 14(3):217-22. DOI: 10.1038/tpj.2013.24. View