Supervised Classification of CYP2D6 Genotype and Metabolizer Phenotype with Postmortem Tramadol-Exposed Finns

Frank R. Wendt, Nicole M.M. Novroski, Anna Liina Rahikainen, Antti Sajantila, Bruce Budowle

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Cytochrome p450 family 2, subfamily D, polypeptide 6 (CYP2D6) may be used to infer the metabolizer phenotype (MP) of an individual as poor, intermediate, extensive/normal, or ultrarapid. Metabolizer phenotypes may suggest idiosyncratic drug responses as contributing factors to cause and/or manner of death in postmortem investigations. Application of CYP2D6 has used long-range amplification of the locus and restriction enzyme digestion to detect single-nucleotide variants (SNVs) associated with MPs. This process can be cumbersome and requires knowledge of genotype phase. Phase may be achieved using long-read DNA sequencing and/or computational methods; however, both can be error prone, which may make it difficult or impractical for implementation into medicolegal practice. CYP2D6 was interrogated in postmortem autopsied Finns using supervised machine learning and feature selection to identify SNVs indicative of MP and/or rate of tramadol O-demethylation (T:M1). A subset of 18 CYP2D6 SNVs could predict MP/T:M1 with up to 96.3% accuracy given phased data. These data indicate that phase contributes to classification accuracy when using CYP2D6 data. Of these 18 SNVs, 3 are novel loci putatively associated with T:M1. These findings may enable design of small multiplexes for easy forensic application of MP prediction when cause and/or manner of death is unknown.

Original languageEnglish
Pages (from-to)8-18
Number of pages11
JournalAmerican Journal of Forensic Medicine and Pathology
Volume40
Issue number1
DOIs
Publication statusPublished - 1 Mar 2019

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Keywords

  • CYP2D6
  • pharmacogenomics
  • supervised machine learning
  • tramadol

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