PubMed · 8256376
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K Brøsen. 1993-11-15. [CYP2D6 genotype determination].. https://pubmed.ncbi.nlm.nih.gov/8256376/
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The CYP2D6 gene is a highly polymorphic pharmacogene involved in the metabolism of 25% of commonly used drugs. We aim to assess the feasibility of extracting relevant pharmacogenomic information from Whole Genome Sequencing (WGS) data and to highlight any difference in CYP2D6 allele frequencies between the northeastern Italian and European populations. To achieve this aim, WGS was performed on two cohorts: 664 individuals from six different isolated communities (FIC) and 123 outbred Italian individuals (FOP). In silico CYP2D6 genotyping was performed and allele frequencies from the FIC cohort were compared to those of FOP and European individuals from 1000 Genomes. Interestingly, 18 alleles identified in FIC were absent in the control cohorts. In particular, 13 individuals carried the extremely rare CYP2D6*28x2 allele, whose activity is unknown. Moreover, we identified a carrier of the CYP2D6*34x2 allele, which has never been described before. The population structure and genetic differentiation of the cohorts were investigated, revealing that the genetic isolates differ only slightly from the outbred and the European populations, but still offer new insight into CYP2D6 heterogeneity. The findings described here will be relevant to tailoring the treatments in the northeastern Italian population.
OBJECTIVES: Selecting a maximally informative subset of polymorphisms to predict a clinical outcome, such as drug response, requires appropriate search methods due to the increased dimensionality associated with looking at multiple genotypes. In this study, we investigated the ability of several pattern recognition methods to identify the most informative markers in the CYP2D6 gene for the prediction of CYP2D6 metabolizer status. METHODS: Four data-mining tools were explored: decision trees, random forests, artificial neural networks, and the multifactor dimensionality reduction (MDR) method. Marker selection was performed separately in eight population samples of different ethnic origin to evaluate to what extent the most informative markers differ across ethnic groups. RESULTS: Our results show that the number of polymorphisms required to predict CYP2D6 metabolic phenotype with a high accuracy can be dramatically reduced owing to the strong haplotype block structure observed at CYP2D6. MDR and neural networks provided nearly identical results and performed the best. CONCLUSION: Data-mining methods, such as MDR and neural networks, appear as promising tools to improve the efficiency of genotyping tests in pharmacogenetics with the ultimate goal of pre-screening patients for individual therapy selection with minimum genotyping effort.
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