Ancestry-Related Differences in Gene Expression: Findings May Enhance Understanding of Health Disparities Between Populations

Zhang, Wei; Dolan, M. Eileen

doi:10.2217/14622416.9.5.489

Cited by 13 publications

(11 citation statements)

References 34 publications

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“…Notably, both the enriched GO term “response to stimulus” and the enriched KEGG pathway “antigen processing and presentation” are related to immune response. We previously found that transcript clusters (gene-level) differentially expressed between the CEU and YRI samples were enriched in immune response genes (Zhang and Dolan 2008a; Zhang et al 2008a). It has been reported that African Americans may be more susceptible to infection by certain bacteria than Caucasians (Noble and Miller 1980) and some genetic polymorphisms that may lead to different antimicrobial response (Jordan et al 2005).…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have shown that common genetic variants account for the population differences in gene expression (Spielman et al 2007; Storey et al 2007; Stranger et al 2007; Zhang et al 2008a, b) and transcript isoform variation within the unrelated CEU samples (Hull et al 2007; Kwan et al 2007, 2008). We tried to investigate if the differences in allele frequency of common genetic variants contribute to the observed differences in transcript isoform variation between the CEU and YRI samples.…”

Section: Discussionmentioning

confidence: 99%

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Identification of common genetic variants that account for transcript isoform variation between human populations

et al. 2008

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In addition to the differences between populations in transcriptional and translational regulation of genes, alternative pre-mRNA splicing (AS) is also likely to play an important role in regulating gene expression and generating variation in mRNA and protein isoforms. Recently, the genetic contribution to transcript isoform variation has been reported in individuals of recent European descent. We report here results of an investigation of the differences in AS patterns between human populations. AS patterns in 176 HapMap lymphoblastoid cell lines derived from individuals of European and African ancestry were evaluated using the Affymetrix GeneChip® Human Exon 1.0 ST Array. A variety of biological processes such as response to stimulus and transcription were found to be enriched among the differentially spliced genes. The differentially spliced genes also include some involved in human diseases that have different prevalence or susceptibility between populations. The genetic contribution to the population differences in transcript isoform variation was then evaluated by a genome-wide association using the HapMap genotypic data on single nucleotide polymorphisms (SNPs). The results suggest that local and distant genetic variants account for a substantial fraction of the observed transcript isoform variation between human populations. Our findings provide new insights into the complexity of the human genome as well as the health disparities between the two populations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of common genetic variants that account for transcript isoform variation between human populations

et al. 2008

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“…The HapMap LCLs have been used to build cell-based models for pharmacogenomic discovery but currently lacks epigenomic information. This information could be integrated with publicly available genotypic, gene expression and drug sensitivity data on the same cell line (Zhang and Dolan 2008). Additional tissues including the liver or skin should be considered for pharmacogenomic discovery by integrating genomic, epigenomic, expression and drug metabolism data.…”

Section: Discussionmentioning

confidence: 99%

Integrating epigenomics into pharmacogenomic studies

Zhang¹,

Huang²,

Dolan³

2008

PGPM

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The goal of personalized medicine is to recommend drug treatment based on an individual’s genetic makeup. Pharmacogenomic studies utilize two main approaches: candidate gene and whole-genome. Both approaches analyze genetic variants such as single nucleotide polymorphisms (SNPs) to identify associations with drug response. In addition to DNA sequence variations, nongenetic but heritable epigenetic systems have also been implicated in regulating gene expression that could influence drug response. The International HapMap Project lymphoblastoid cell lines (LCLs) have been used to study genetic determinants responsible for expression variation and drug response. Recent studies have demonstrated that common genetic variants, including both SNPs and copy number variants (CNVs) account for a substantial fraction of natural variation in gene expression. Given the critical role played by DNA methylation in gene regulation and the fact that DNA methylation is currently the most studied epigenetic system, we suggest that profiling the variation in DNA methylation in the HapMap samples will provide new insights into the regulation of gene expression as well as the mechanisms of individual drug response at a new level of complexity. Epigenomics will substantially add to our knowledge of how genetics explains gene expression and pharmacogenomics.

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“…58–60 Because the HapMap project includes cell lines derived from individuals representing 11 distinct ethnic groups (with approximately 90 individuals per ethnic group), it can also be used as a tool toward discovery of the genetic contribution to pharmacoethnic differences in chemotherapeutic susceptibility. 61–63 Although EBV-transformation may introduce a potential confounder of cellular sensitivity to drug 64 , the Dolan lab has not found any association between EBV copy number and pharmacological phenotypes 65 . Each cell line’s unique sensitivity to drug-induced cell growth inhibition is measured, and the phenotypes are then subjected to GWAS using the publicly available HapMap genotypes to associate chemotherapy-related sensitivity with germline single nucleotide polymorphisms (SNPs).…”

Section: Cell-based Models To Identify Genetic Markersmentioning

confidence: 99%

Using Germline Genomics to Individualize Pediatric Cancer Treatments

Pinto

Cohn

Dolan

2012

Clinical Cancer Research

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The amazing successes in cure rates for children with cancer over the last century have come in large part from identifying clinical, genetic and molecular variables associated with response to therapy in large cooperative clinical trials and stratifying therapies according to the predicted risk of relapse. There is an expanding interest in identifying germline genomic variants, as opposed to genetic variants within the tumor, that are associated with susceptibility to toxicity and for risk of relapse. This review highlights the most important germline pharmacogenetic and pharmacogenomic studies in pediatric oncology. Incorporation of germline genomics into risk-adapted therapies will likely lead to safer and more effective treatments for children with cancer.

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Ancestry-Related Differences in Gene Expression: Findings May Enhance Understanding of Health Disparities Between Populations

Cited by 13 publications

References 34 publications

Identification of common genetic variants that account for transcript isoform variation between human populations

Identification of common genetic variants that account for transcript isoform variation between human populations

Integrating epigenomics into pharmacogenomic studies

Using Germline Genomics to Individualize Pediatric Cancer Treatments

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