Genetic profiling of CYP2D6 variants known to alter enzyme activity - Branfield, Nicholas
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CYP2D6 is responsible for the metabolism of 20-25% of currently prescribed drugs and thus the study of its genetic variation are of particular importance to pharmacogenetics, as the various combinations of variant alleles result in a diverse set of metabolic profiles. However, little is currently known about the frequency of functionally important CYP2D6 SNPs in black South Africans. Therefore, this study aims to genotype four functionally important SNPs across the coding region of CYP2D6, in order to determine the allelic frequencies in black South Africans. In this study 90 unrelated black…mehr

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Produktbeschreibung
CYP2D6 is responsible for the metabolism of 20-25% of currently prescribed drugs and thus the study of its genetic variation are of particular importance to pharmacogenetics, as the various combinations of variant alleles result in a diverse set of metabolic profiles. However, little is currently known about the frequency of functionally important CYP2D6 SNPs in black South Africans. Therefore, this study aims to genotype four functionally important SNPs across the coding region of CYP2D6, in order to determine the allelic frequencies in black South Africans. In this study 90 unrelated black Bantu-speaking participants were genotyped, using the tetra ARMS-PCR method for each of the four SNPs: rs16947, rs1135840, rs1065852 and rs28371706. These frequencies were compared with those from other populations, using 1000 Genomes Project data. Knowledge of the novel frequencies, of these polymorphisms, and their resulting metabolic classes, would prove important for reducing both adversedrug reactions and improving drug dosage calculations, with respect to the black South African population.
Autorenporträt
Nicholas Branfield, Bachelor of Health Science with Honours in Human Genetics - The University of the Witwatersrand (2013). Software Engineer at Parcelninja, Johannesburg South Africa. Diverse set of interests, mainly focused at Computer Science and Medicine but any large data set or complicated problem will do.