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Prediction of CYP2D6 phenotype from genotype across world populations

PURPOSE: Owing to its highly polymorphic nature and major contribution to the metabolism and bioactivation of numerous clinically used drugs, CYP2D6 is one of the most extensively studied drug-metabolizing enzymes and pharmacogenes. CYP2D6 alleles confer no, decreased, normal, or increased activity...

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Autores principales: Gaedigk, Andrea, Sangkuhl, Katrin, Whirl-Carrillo, Michelle, Klein, Teri, Leeder, J. Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5292679/
https://www.ncbi.nlm.nih.gov/pubmed/27388693
http://dx.doi.org/10.1038/gim.2016.80
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author Gaedigk, Andrea
Sangkuhl, Katrin
Whirl-Carrillo, Michelle
Klein, Teri
Leeder, J. Steven
author_facet Gaedigk, Andrea
Sangkuhl, Katrin
Whirl-Carrillo, Michelle
Klein, Teri
Leeder, J. Steven
author_sort Gaedigk, Andrea
collection PubMed
description PURPOSE: Owing to its highly polymorphic nature and major contribution to the metabolism and bioactivation of numerous clinically used drugs, CYP2D6 is one of the most extensively studied drug-metabolizing enzymes and pharmacogenes. CYP2D6 alleles confer no, decreased, normal, or increased activity and cause a wide range of activity among individuals and between populations. However, there is no standard approach to translate diplotypes into predicted phenotype. METHODS: We exploited CYP2D6 allele-frequency data that have been compiled for Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines (>60,000 subjects, 173 reports) in order to estimate genotype-predicted phenotype status across major world populations based on activity score (AS) assignments. RESULTS: Allele frequencies vary considerably across the major ethnic groups predicting poor metabolizer status (AS = 0) between 0.4 and 5.4% across world populations. The prevalence of genotypic intermediate (AS = 0.5) and normal (AS = 1, 1.5, or 2) metabolizers ranges between 0.4 and 11% and between 67 and 90%, respectively. Finally, 1 to 21% of subjects (AS >2) are predicted to have ultrarapid metabolizer status. CONCLUSIONS: This comprehensive study summarizes allele frequencies, diplotypes, and predicted phenotype across major populations, providing a rich data resource for clinicians and researchers. Challenges of phenotype prediction from genotype data are highlighted and discussed. Genet Med 19 1, 69–76.
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spelling pubmed-52926792017-02-10 Prediction of CYP2D6 phenotype from genotype across world populations Gaedigk, Andrea Sangkuhl, Katrin Whirl-Carrillo, Michelle Klein, Teri Leeder, J. Steven Genet Med Original Research Article PURPOSE: Owing to its highly polymorphic nature and major contribution to the metabolism and bioactivation of numerous clinically used drugs, CYP2D6 is one of the most extensively studied drug-metabolizing enzymes and pharmacogenes. CYP2D6 alleles confer no, decreased, normal, or increased activity and cause a wide range of activity among individuals and between populations. However, there is no standard approach to translate diplotypes into predicted phenotype. METHODS: We exploited CYP2D6 allele-frequency data that have been compiled for Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines (>60,000 subjects, 173 reports) in order to estimate genotype-predicted phenotype status across major world populations based on activity score (AS) assignments. RESULTS: Allele frequencies vary considerably across the major ethnic groups predicting poor metabolizer status (AS = 0) between 0.4 and 5.4% across world populations. The prevalence of genotypic intermediate (AS = 0.5) and normal (AS = 1, 1.5, or 2) metabolizers ranges between 0.4 and 11% and between 67 and 90%, respectively. Finally, 1 to 21% of subjects (AS >2) are predicted to have ultrarapid metabolizer status. CONCLUSIONS: This comprehensive study summarizes allele frequencies, diplotypes, and predicted phenotype across major populations, providing a rich data resource for clinicians and researchers. Challenges of phenotype prediction from genotype data are highlighted and discussed. Genet Med 19 1, 69–76. Nature Publishing Group 2017-01 2016-07-07 /pmc/articles/PMC5292679/ /pubmed/27388693 http://dx.doi.org/10.1038/gim.2016.80 Text en Copyright © 2017 Official journal of the American College of Medical Genetics and Genomics http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Original Research Article
Gaedigk, Andrea
Sangkuhl, Katrin
Whirl-Carrillo, Michelle
Klein, Teri
Leeder, J. Steven
Prediction of CYP2D6 phenotype from genotype across world populations
title Prediction of CYP2D6 phenotype from genotype across world populations
title_full Prediction of CYP2D6 phenotype from genotype across world populations
title_fullStr Prediction of CYP2D6 phenotype from genotype across world populations
title_full_unstemmed Prediction of CYP2D6 phenotype from genotype across world populations
title_short Prediction of CYP2D6 phenotype from genotype across world populations
title_sort prediction of cyp2d6 phenotype from genotype across world populations
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5292679/
https://www.ncbi.nlm.nih.gov/pubmed/27388693
http://dx.doi.org/10.1038/gim.2016.80
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