OBJECTIVES: Numerous functional polymorphisms in the CYP2C19 gene have been identified; some alleles (e.g. CYP2C19*2 and CYP2C19*3) are associated with poor metabolism of CYP2C19 substrate drugs. Studies have found that the proportion of poor metabolizers, explained by CYP2C19*2 and CYP2C19*3, varies from less than 50% to more than 90% of poor metabolizers. Therefore, phenotype-genotype correlation studies should cover more than CYP2C19*2 and CYP2C19*3. A broader coverage, however, requires an easy-to-use and high-throughput genotyping platform. This broader coverage should also include the recently identified functional allele, CYP2C19*10, which involves a nucleotide change adjacent to the altered nucleotide change in CYP2C19*2. The currently used restriction fragment length polymorphism-based method for genotyping CYP2C19*2 cannot distinguish between CYP2C19*2 and CYP2C19*10. We aim to develop a simple platform that can genotype all CYP2C19 functional alleles. METHODS: We have developed a thin-film biosensor chip platform to genotype 16 exonic CYP2C19 variants, including two sets of two adjacent single nucleotide polymorphisms and 12 single single nucleotide polymorphisms, using a ligation strategy. RESULTS: We demonstrate that this is a rapid, accurate, and inexpensive method for genotyping CYP2C19 variants using individual's genomic DNA samples. We further demonstrate that this genotyping platform can be used to construct a haplotype structure of the CYP2C19 variants in a population, and to assign a haplotype combination to each individual on the basis of his/her genotype results. CONCLUSION: This assay can be applied in pharmacogenomic studies in both basic research and clinical laboratories. It is also an ideal technology for pharmacogenomic tests in both developed and developing countries.
OBJECTIVES: Numerous functional polymorphisms in the CYP2C19 gene have been identified; some alleles (e.g. CYP2C19*2 and CYP2C19*3) are associated with poor metabolism of CYP2C19 substrate drugs. Studies have found that the proportion of poor metabolizers, explained by CYP2C19*2 and CYP2C19*3, varies from less than 50% to more than 90% of poor metabolizers. Therefore, phenotype-genotype correlation studies should cover more than CYP2C19*2 and CYP2C19*3. A broader coverage, however, requires an easy-to-use and high-throughput genotyping platform. This broader coverage should also include the recently identified functional allele, CYP2C19*10, which involves a nucleotide change adjacent to the altered nucleotide change in CYP2C19*2. The currently used restriction fragment length polymorphism-based method for genotyping CYP2C19*2 cannot distinguish between CYP2C19*2 and CYP2C19*10. We aim to develop a simple platform that can genotype all CYP2C19 functional alleles. METHODS: We have developed a thin-film biosensor chip platform to genotype 16 exonic CYP2C19 variants, including two sets of two adjacent single nucleotide polymorphisms and 12 single single nucleotide polymorphisms, using a ligation strategy. RESULTS: We demonstrate that this is a rapid, accurate, and inexpensive method for genotyping CYP2C19 variants using individual's genomic DNA samples. We further demonstrate that this genotyping platform can be used to construct a haplotype structure of the CYP2C19 variants in a population, and to assign a haplotype combination to each individual on the basis of his/her genotype results. CONCLUSION: This assay can be applied in pharmacogenomic studies in both basic research and clinical laboratories. It is also an ideal technology for pharmacogenomic tests in both developed and developing countries.
Authors: H Irene Su; Mary D Sammel; Luke Velders; Michelle Horn; Corrie Stankiewicz; Jennifer Matro; Clarisa R Gracia; Jamie Green; Angela DeMichele Journal: Fertil Steril Date: 2009-04-18 Impact factor: 7.329
Authors: Matthew T Oetjens; Joshua C Denny; Marylyn D Ritchie; Niloufar B Gillani; Danielle M Richardson; Nicole A Restrepo; Jill M Pulley; Holli H Dilks; Melissa A Basford; Erica Bowton; Dan R Masys; Russell A Wilke; Dan M Roden; Dana C Crawford Journal: Pharmacogenomics Date: 2013-05 Impact factor: 2.533