ArticleInternational journal of legal medicine2018
Full-gene haplotypes refine CYP2D6 metabolizer phenotype inferences.
Article in International journal of legal medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Pharmacogenetics and Forensic Toxicology: A New Step towards a Multidisciplinary Approach.Toxics · 2021Review
- The Metabolization Profile of theGenes · 2020Review
- A genome-wide association study of tramadol metabolism from post-mortem samples.The pharmacogenomics journal · 2020Article
- Completed suicides of citalopram users-the role of CYP genotypes and adverse drug interactions.International journal of legal medicine · 2019Article
- Increasing the reach of forensic genetics with massively parallel sequencing.Forensic science, medicine, and pathology · 2017Review
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
CYP2D6 is a critical pharmacogenetic target, and polymorphisms in the gene region are commonly used to infer enzyme activity score and predict resulting metabolizer phenotype: poor, intermediate, extensive/normal, or ultrarapid which can be useful in determining cause and/or manner of death in some autopsies. Current genotyping approaches are incapable of identifying novel and/or rare variants, so CYP2D6 star allele definitions are limited to polymorphisms known a priori. While useful for most predictions, recent studies using massively parallel sequencing data have identified additional polymorphisms in CYP2D6 that are predicted to alter enzyme function but are not considered in current star allele nomenclature. The 1000 Genomes Project data were used to produce full-gene haplotypes, describe their distribution in super-populations, and predict enzyme activity scores. Full-gene haplotypes generated lower activity scores than current approaches due to inclusion of additional damaging polymorphisms in the star allele. These findings are critical for clinical implementation of metabolizer phenotype prediction because a fraction of the population may be incorrectly considered normal metabolizers but actually may be poor or intermediate metabolizers.
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