Abstract
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 in admixed population. Admixture influences populations in a manner different from geographic race or cultural ethnicity. When two genetically distinct populations interbreed, a set of unique relationships are created among sets of variants across the genome. This phenomenon results in the formation of long segments of DNA with distinguishable ancestral origins. Risk alleles carried within these segments can influence treatment outcome in a variety of contexts. As the scientific community moves toward characterization of outcome in diverse cohorts with DNA biobanks linked to comprehensive electronic medical records, a great need exists for the development of flexible, robust analytical strategies that leverage admixture to optimize the study of pharmacogenomics. We presented analytical strategies to develop ancestry informative marker (AIM) that leverage population structure to more fully characterize genetic determinants of outcome in large clinical practice-based admixed cohorts. Keywords: Admixture, ancestry, Ancestry Informative Markers, AIMs, admixture mapping, ancestry, biobank, database, pharmacogenetics Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1663. doi:1538-7445.AM2012-1663
Published Version
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