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 Citations (Scopus)

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
StatePublished - 1 Mar 2019

Fingerprint

Tramadol
Genotype
Phenotype
Nucleotides
Peptides
DNA Sequence Analysis
Digestion
Cytochrome P450 Family 2
Enzymes
Pharmaceutical Preparations

Keywords

  • CYP2D6
  • pharmacogenomics
  • supervised machine learning
  • tramadol

Cite this

Wendt, Frank R. ; Novroski, Nicole M.M. ; Rahikainen, Anna Liina ; Sajantila, Antti ; Budowle, Bruce. / Supervised Classification of CYP2D6 Genotype and Metabolizer Phenotype with Postmortem Tramadol-Exposed Finns. In: American Journal of Forensic Medicine and Pathology. 2019 ; Vol. 40, No. 1. pp. 8-18.
@article{4c49e4346b3645b899f5d6036e6f3162,
title = "Supervised Classification of CYP2D6 Genotype and Metabolizer Phenotype with Postmortem Tramadol-Exposed Finns",
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.",
keywords = "CYP2D6, pharmacogenomics, supervised machine learning, tramadol",
author = "Wendt, {Frank R.} and Novroski, {Nicole M.M.} and Rahikainen, {Anna Liina} and Antti Sajantila and Bruce Budowle",
year = "2019",
month = "3",
day = "1",
doi = "10.1097/PAF.0000000000000447",
language = "English",
volume = "40",
pages = "8--18",
journal = "American Journal of Forensic Medicine and Pathology",
issn = "0195-7910",
publisher = "Lippincott Williams and Wilkins Ltd.",
number = "1",

}

Supervised Classification of CYP2D6 Genotype and Metabolizer Phenotype with Postmortem Tramadol-Exposed Finns. / Wendt, Frank R.; Novroski, Nicole M.M.; Rahikainen, Anna Liina; Sajantila, Antti; Budowle, Bruce.

In: American Journal of Forensic Medicine and Pathology, Vol. 40, No. 1, 01.03.2019, p. 8-18.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Supervised Classification of CYP2D6 Genotype and Metabolizer Phenotype with Postmortem Tramadol-Exposed Finns

AU - Wendt, Frank R.

AU - Novroski, Nicole M.M.

AU - Rahikainen, Anna Liina

AU - Sajantila, Antti

AU - Budowle, Bruce

PY - 2019/3/1

Y1 - 2019/3/1

N2 - 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.

AB - 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.

KW - CYP2D6

KW - pharmacogenomics

KW - supervised machine learning

KW - tramadol

UR - http://www.scopus.com/inward/record.url?scp=85061138372&partnerID=8YFLogxK

U2 - 10.1097/PAF.0000000000000447

DO - 10.1097/PAF.0000000000000447

M3 - Article

C2 - 30507617

AN - SCOPUS:85061138372

VL - 40

SP - 8

EP - 18

JO - American Journal of Forensic Medicine and Pathology

JF - American Journal of Forensic Medicine and Pathology

SN - 0195-7910

IS - 1

ER -