Mapping genes that predict treatment outcome in admixed populations

Pharmacogenomics J. 2010 Dec;10(6):465-77. doi: 10.1038/tpj.2010.71. Epub 2010 Oct 5.


There is great interest in characterizing the genetic architecture underlying drug response. For many drugs, gene-based dosing models explain a considerable amount of the overall variation in treatment outcome. As such, prescription drug labels are increasingly being modified to contain pharmacogenetic information. Genetic data must, however, be interpreted within the context of relevant clinical covariates. Even the most predictive models improve with the addition of data related to biogeographical ancestry. The current review explores analytical strategies that leverage population structure to more fully characterize genetic determinants of outcome in large clinical practice-based cohorts. The success of this approach will depend upon several key factors: (1) the availability of outcome data from groups of admixed individuals (that is, populations recombined over multiple generations), (2) a measurable difference in treatment outcome (that is, efficacy and toxicity end points), and (3) a measurable difference in allele frequency between the ancestral populations.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Aryl Hydrocarbon Hydroxylases / genetics
  • Biotransformation / genetics
  • Chromosome Mapping*
  • Cytochrome P-450 CYP2C9
  • Databases, Genetic
  • Female
  • Gene Frequency
  • Genetics, Population*
  • Humans
  • Male
  • Pharmacogenetics*
  • Racial Groups
  • Sex Factors
  • Treatment Outcome


  • CYP2C9 protein, human
  • Cytochrome P-450 CYP2C9
  • Aryl Hydrocarbon Hydroxylases