Analyzing the potential for incorrect haplotype calls with different pharmacogenomic assays in different populations: A simulation based on 1000 Genomes data

Matthias Samwald, Kathrin Blagec, Sebastian Hofer, Robert R. Freimuth

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Aim: Many currently available pharmacogenomic assays and algorithms interrogate a set of 'tag' polymorphisms for inferring haplotypes. We wanted to test the accuracy of such haplotype inferences across different populations. Materials and methods: We simulated haplotype inferences made by existing pharmacogenomic assays for seven important pharmacogenes based on full genome data of 2504 persons in the 1000 Genomes dataset. Results: A sizable fraction of samples did not match any of the haplotypes in the star allele nomenclature systems. We found no clear population bias in the accuracy of results of simulated assays. Conclusion: Haplotype nomenclatures and inference algorithms need to be improved to adequately capture pharmacogenomic diversity in human populations.

Original languageEnglish (US)
Pages (from-to)1713-1721
Number of pages9
JournalPharmacogenomics
Volume16
Issue number15
DOIs
StatePublished - Oct 2015

Keywords

  • errors
  • genetic diversity
  • genetic testing
  • medical nomenclature
  • pharmacogenomics

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Pharmacology

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