Genetic ancestry plays a central role in population pharmacogenomics

Commun Biol. 2021 Feb 5;4(1):171. doi: 10.1038/s42003-021-01681-6.

Abstract

Recent studies have pointed out the essential role of genetic ancestry in population pharmacogenetics. In this study, we analyzed the whole-genome sequencing data from The 1000 Genomes Project (Phase 3) and the pharmacogenetic information from Drug Bank, PharmGKB, PharmaADME, and Biotransformation. Here we show that ancestry-informative markers are enriched in pharmacogenetic loci, suggesting that trans-ancestry differentiation must be carefully considered in population pharmacogenetics studies. Ancestry-informative pharmacogenetic loci are located in both protein-coding and non-protein-coding regions, illustrating that a whole-genome analysis is necessary for an unbiased examination over pharmacogenetic loci. Finally, those ancestry-informative pharmacogenetic loci that target multiple drugs are often a functional variant, which reflects their importance in biological functions and pathways. In summary, we develop an efficient algorithm for an ultrahigh-dimensional principal component analysis. We create genetic catalogs of ancestry-informative markers and genes. We explore pharmacogenetic patterns and establish a high-accuracy prediction panel of genetic ancestry. Moreover, we construct a genetic ancestry pharmacogenomic database Genetic Ancestry PhD ( http://hcyang.stat.sinica.edu.tw/databases/genetic_ancestry_phd/ ).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Pharmacological / analysis
  • Biomarkers, Pharmacological / metabolism
  • Biotransformation / genetics*
  • Databases, Genetic
  • Gene Frequency
  • Gene-Environment Interaction
  • Genetic Variation / physiology
  • Genome, Human / physiology
  • Homozygote
  • Humans
  • Inactivation, Metabolic / genetics
  • Linkage Disequilibrium
  • Pharmacogenetics*
  • Polymorphism, Single Nucleotide
  • Precision Medicine / methods
  • Precision Medicine / trends
  • Principal Component Analysis
  • Proteome / drug effects
  • Proteome / metabolism
  • Racial Groups / genetics*
  • Transcriptome / drug effects
  • Transcriptome / physiology

Substances

  • Biomarkers, Pharmacological
  • Proteome