Towards a Comprehensive Pharmacogenetic Profile for Predicting Opiate Metabolizer Phenotype

Purpose: The gene encoding cytochrome p450 family 2 subfamily D polypeptide 6 (CYP2D6) is a key pharmacogenetic marker for an enzyme which confers poor, intermediate, extensive, and ultrarapid phase I metabolism of many endogenous toxins and foreign compounds, including marketed opiate-based drugs. The pharmacogenetics of opiate metabolism is particularly important due to the relatively high incidence of addiction and overdose of opiates. Recently, trans-acting opiate metabolism and analgesic response enzymes (UGT2B7, ABCB1 [also called p-glycoprotein and/or multi-drug resistant protein], OPRM1, and COMT) have been incorporated into pharmacogenetic studies to generate more comprehensive metabolic profiles of patients. While meaningful, these studies are limited in that demography is not documented during sample selection, and use of targeted genotyping approaches inherently cannot detect novel variants. With use of massively parallel sequencing, it is possible to identify additional polymorphisms that fine tune, or refine, previous pharmacogenetic findings. Methods: The 1000 Genomes Project data were analyzed in two phases: (1) To describe population genetic variation and summary statistics for these five genes in self-reported healthy individuals in five super- and 26 sub-populations; and (2) To utilize individual polymorphism data to form full-gene haplotypes of the five genes of interest in the same sample set to use full-gene information to refine metabolizer phenotype estimates. Both phases of this work were performed using R Studio®, Excel-based workbooks, Genetic Data Analysis, and TreeView. Results: A summary is provided of population statistics, variant effect predictions, and clustering of super- and sub-populations based on pharmacogenetically relevant polymorphisms in five genes whose protein products are associated with opiate metabolism. Comparisons of current standards versus full-gene metabolizer phenotype predictions indicate that a full-gene approach provides better resolution of metabolizer phenotype. These data also indicate that a substantial portion of extensive metabolizers may be incorrectly classified as such due to novel damaging polymorphisms elsewhere in the gene. Conclusions: The results of these studies serve as substantial baseline population genetic data of individual pharmacogenetically relevant polymorphisms and highlight the advantage of using full-gene sequence information to infer metabolizer phenotypes.