Dataset Information

Race does not explain genetic heterogeneity in pharmacogenomic pathways.

ABSTRACT:

Introduction

Polymorphic alleles in the human genome have been identified as affecting numerous drug responses. Currently, genotyping of all patients before starting a drug regimen is impractical. Since many polymorphisms occur at varying rates in different racial groups, we investigated whether a patient's race could predict presence of drug-relevant genetic variants well enough to be used as a substitute for individual genotyping.

Methods

We performed hierarchical clustering and principal components analysis on tagSNPs from three pathways (irinotecan, 5-fluorouracil and insulin) across 270 individuals from four racial groups available from the International HapMap Project.

Results

For the drug pathways, irinotecan and 5-fluorouracil, individuals from each race were widely dispersed, although several subclusters consisted entirely of individuals from a single racial group. Principal components analysis confirmed race was not a major contributor to the SNP data variance. Interestingly, individuals tended to cluster more by race across the endogenous insulin signaling pathway SNPs.

Conclusions

Most genetic variation was determined by individual variation, not racial grouping, indicating race is not adequate as a surrogate to individualized therapy.

SUBMITTER: Yen-Revollo JL

PROVIDER: S-EPMC2632586 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Race does not explain genetic heterogeneity in pharmacogenomic pathways.

Yen-Revollo Jane L JL Auman J Todd JT McLeod Howard L HL

Pharmacogenomics 20081101 11

<h4>Introduction</h4>Polymorphic alleles in the human genome have been identified as affecting numerous drug responses. Currently, genotyping of all patients before starting a drug regimen is impractical. Since many polymorphisms occur at varying rates in different racial groups, we investigated whether a patient's race could predict presence of drug-relevant genetic variants well enough to be used as a substitute for individual genotyping.<h4>Methods</h4>We performed hierarchical clustering and ...[more]

PMID: 19018720

Similar Datasets

Project description:Fifty years ago, Richard Lewontin found that the vast majority of human genetic variation falls within (~85%) rather than between (~15%) racial groups. This result has been replicated numerous times since and is widely taken to support the notion that genetic differences between racial groups are trivial and thus irrelevant for clinical decision-making. The aim of this study was to consider how the apportionment of pharmacogenomic variation within and between racial and ethnic groups relates to risk disparities for adverse drug reactions. We confirmed that the majority of pharmacogenomic variation falls within (97.3%) rather than between (2.78%) the three largest racial and ethnic groups in the United States: Black, Hispanic, and White. Nevertheless, pharmacogenomic variants showing far greater within than between-group variation can have high predictive value for adverse drug reactions, particularly for minority racial and ethnic groups. We predicted excess adverse drug reactions for minority Black and Hispanic groups, compared to the majority White group, and considered these results in light of the apportionment of genetic variation within and between groups. For 85% within and 15% between group variation, there are 700 excess adverse drug reactions per 1,000 patients predicted for a recessive effect model and 300 for a dominant model. We found high numbers of predicted Black and Hispanic excess adverse drug reactions for widely prescribed platinum chemotherapy compounds, such as cisplatin and oxaliplatin, as well as controlled narcotics, including fentanyl and tramadol. Our results indicate that race and ethnicity, while imprecise proxies for genetic diversity, correlate with patterns of pharmacogenomic variation in a way that is clearly relevant to medical treatment decisions. The effects of this variation is particularly pronounced for Black and Hispanic minority groups, owing to genetic differences from the majority White group. Treatment decisions that are made based on (assumed) White pharmacogenomic variant frequencies can be harmful for minority groups. Ignoring clinically relevant genetic differences among racial and ethnic groups, however well-intentioned, will exacerbate rather than ameliorate health disparities.

Project description:The relevance of race and ethnicity to genetics and medicine has long been a matter of debate. An emerging consensus holds that race and ethnicity are social constructs and thus poor proxies for genetic diversity. The goal of this study was to evaluate the relationship between race, ethnicity, and clinically relevant pharmacogenomic variation in cosmopolitan populations. We studied racially and ethnically diverse cohorts of 65,120 participants from the United States All of Us Research Program (All of Us) and 31,396 participants from the United Kingdom Biobank (UKB). Genome-wide patterns of pharmacogenomic variation-6311 drug response-associated variants for All of Us and 5966 variants for UKB-were analyzed with machine learning classifiers to predict participants' self-identified race and ethnicity. Pharmacogenomic variation predicts race/ethnicity with averages of 92.1% accuracy for All of Us and 94.3% accuracy for UKB. Group-specific prediction accuracies range from 99.0% for the White group in UKB to 92.9% for the Hispanic group in All of Us. Prediction accuracies are substantially lower for individuals who identified with more than one group in All of Us (16.7%) or as Mixed in UKB (70.7%). There are numerous individual pharmacogenomic variants with large allele frequency differences between race/ethnicity groups in both cohorts. Frequency differences for toxicity-associated variants predict hundreds of adverse drug reactions per 1000 treated participants for minority groups in All of Us. Our results indicate that race and ethnicity can be used to stratify pharmacogenomic risk in the US and UK populations and should not be discounted when making treatment decisions. We resolve the contradiction between the results reported here and the orthodoxy of race and ethnicity as non-genetic, social constructs by emphasizing the distinction between global and local patterns of human genetic diversity, and we stress the current and future limitations of race and ethnicity as proxies for pharmacogenomic variation.

Dataset Information

Race does not explain genetic heterogeneity in pharmacogenomic pathways.

Introduction

Methods

Results

Conclusions

Publications

Race does not explain genetic heterogeneity in pharmacogenomic pathways.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets