Mapping genes that predict treatment outcome in admixed populations

Abstract

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.

Figure 1

Schematic presentations of an admixed individual. Admixture occurs when two or more populations merge to form a new population. The two ancestral population, 1 and 2, are represented by blue and red chromosomes, respectively. Individuals in the subsequent generation may or may not receive an intact chromosome 1 or 2 from their ancestor. As generations continue, mosaics develop for chromosomes 1 and 2 as a result of recombination at meiosis. The admixed individual’s genomes are a mosaic of the two initial ancestral chromosomes.

Figure 2

Possible outcomes in ancestry-dependent pharmacogenomic studies. ADR, adverse drug reactions.

Figure 3

United States (US) death rates due to myocardial infarction (*2000–2006; reprinted with permission from the US Census Bureau). Age-adjusted average death rates, ranging from 195/100 000 (lightest shade) to 747/100 000 (darkest shade), are shown for the Continental United States.

Figure 4

Proposed model to integrate DNA biobanks (for example, phenotypic data and genotypic data) with ancestry informative markers (AIMs) to adjust population structure and/or map genes in practice based pharamcogenetic/genomic studies.