Dataset Information

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.

SUBMITTER: Baye TM

PROVIDER: S-EPMC2991422 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mapping genes that predict treatment outcome in admixed populations.

Baye T M TM Wilke R A RA

The pharmacogenomics journal 20101005 6

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 biogeograph ...[more]

PMID: 20921971

Similar Datasets

Project description:Admixed populations arise when two or more previously isolated populations interbreed. Mapping asthma susceptibility loci in an admixed population using admixture mapping (AM) involves screening the genome of individuals of mixed ancestry for chromosomal regions that have a higher frequency of alleles from a parental population with higher asthma risk as compared with parental population with lower asthma risk. AM takes advantage of the admixture created in populations of mixed ancestry to identify genomic regions where an association exists between genetic ancestry and asthma (in contrast to between the genotype of the marker and asthma). The theory behind AM is that chromosomal segments of affected individuals contain a significantly higher-than-average proportion of alleles from the high-risk parental population and thus are more likely to harbor disease-associated loci. Criteria to evaluate the applicability of AM as a gene mapping approach include: (1) the prevalence of the disease differences in ancestral populations from which the admixed population was formed; (2) a measurable difference in disease-causing alleles between the parental populations; (3) reduced linkage disequilibrium (LD) between unlinked loci across chromosomes and strong LD between neighboring loci; (4) a set of markers with noticeable allele-frequency differences between parental populations that contributes to the admixed population (single nucleotide polymorphisms (SNPs) are the markers of choice because they are abundant, stable, relatively cheap to genotype, and informative with regard to the LD structure of chromosomal segments); and (5) there is an understanding of the extent of segmental chromosomal admixtures and their interactions with environmental factors. Although genome-wide association studies have contributed greatly to our understanding of the genetic components of asthma, the large and increasing degree of admixture in populations across the world create many challenges for further efforts to map disease-causing genes. This review, summarizes the historical context of admixed populations and AM, and considers current opportunities to use AM to map asthma genes. In addition, we provide an overview of the potential limitations and future directions of AM in biomedical research, including joint admixture and association mapping for asthma and asthma-related disorders.

Dataset Information

Mapping genes that predict treatment outcome in admixed populations.

Publications

Mapping genes that predict treatment outcome in admixed populations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets