Evaluation of genetic variation contributing to differences in gene expression between populations

Integrating Epigenomics into Pharmacogenomic Studies

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, non-genetic 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.

Beyond the HapMap Genotypic Data: Prospects of Deep Resequencing Projects

The International HapMap Project provides a key resource of genotypic data on human samples including lymphoblastoid cell lines derived from individuals of four major world populations of African, European, Japanese and Chinese ancestry. Researchers have utilized this resource to identify genetic elements that correlate with various phenotypes such as risks of common diseases, individual drug response and gene expression variation. However, recent comparative studies have suggested that the currently available HapMap genotypic data may not capture a substantial proportion of rare or untyped SNPs in these populations, implying that the HapMap SNPs may not be sufficient for comprehensive association studies. In this paper, three large-scale deep resequencing projects covering the HapMap samples: ENCODE (Encyclopedia of DNA Elements), SeattleSNPs and NIEHS (National Institute of Environmental Health Sciences) Environmental Genome Project are discussed. Prospectively, once integrated with the HapMap resource, these efforts will greatly benefit the next wave of association studies and data mining using these cell lines.

Identification of genetic variants and gene expression relationships associated with pharmacogenes in humans

OBJECTIVES

The very important pharmacogenes (VIPs) were selected by Pharmacogenetic Research Network (National Institutes of Health-PGRN) owing to their significant effects on drug treatment both at the pharmacokinetic and pharmacodynamic levels. Our objective was to identify single nucleotide polymorphisms (SNPs) that potentially affected the expression of these genes or potential SNP-gene interactions involved to improve our understanding of genetic effects on drug therapy.

BASIC METHODS

Gene expression was evaluated in 176 International HapMap lymphoblastoid cell lines derived from CEU (CEPH, Utah residents with ancestry from northern and western Europe; n=87) and YRI (Yoruba in Ibadan, Nigeria; n=89) using Affymetrix GeneChip Human Exon 1.0 ST arrays (Affymetrix Laboratory, Affymetrix Inc., Santa Clara, California, USA) with interrogation of greater than 17,000 human genes. Genome-wide association was performed between over two million publicly available HapMap SNPs and gene expression.

MAIN RESULTS

The expression of two PGRN-VIPs (GSTT1 and GSTM1) are significantly associated with SNPs within 2.5 Mb of the genes; whereas the expression of three and ten PGRN-VIPs are significantly associated with distant-acting SNPs in CEU and YRI, respectively. In addition, three and four PGRN-VIPs harbor SNPs that are distantly associated with other gene expressions in CEU and YRI, respectively.

PRINCIPAL CONCLUSION

Using this information, one may identify genetic variants that are significantly associated with the expression of any set of genes of interest; or evaluate potential gene-gene interaction through SNP expression relationships.

SNPinProbe_1.0: a database for filtering out probes in the Affymetrix GeneChip human exon 1.0 ST array potentially affected by SNPs

The Affymetrix GeneChip(R) Human Exon 1.0 ST array (exon array) is designed to measure both gene-level and exon-level expression in human samples. This exon array contains approximately 1.4 million probesets consisting of approximately 5.4 million probes and profiles over 17,000 well-annotated gene transcripts in the human genome. As with all expression arrays, the exon array is vulnerable to SNPs within probes, because these SNPs can affect the hybridization of the probes and thus produce misleading expression values. In some cases, this could result in dramatic fluctuations of the exon-level expression. For this reason, we performed a genome-wide search for SNPs within regions that hybridize to probes by evaluating approximately 18 million SNPs in dbSNP (Build 129) and about 5.4 million probes in the exon array. We identified 597,068 probes within 350,382 probe sets that hybridized to regions containing SNPs. These affected probes and/or probesets can be filtered in the data processing procedure thus controlling for potential false expression phenotypes when using this exon array.

AVAILABILITY

http://cid-fb2a64e541add2be.skydrive.live.com/browse.aspx/Affy%7C_HuEx%7C_1.0ST?uc=2.

Effect of polymorphisms within probe-target sequences on olignonucleotide microarray experiments

Hybridization-based technologies, such as microarrays, rely on precise probe-target interactions to ensure specific and accurate measurement of RNA expression. Polymorphisms present in the probe–target sequences have been shown to alter probe- hybridization affinities, leading to reduced signal intensity measurements and resulting in false-positive results. Here, we characterize this effect on exon and gene expression estimates derived from the Affymetrix Exon Array. We conducted an association analysis between expression levels of probes, exons and transcripts and the genotypes of neighboring SNPs in 57 CEU HapMap individuals. We quantified the dependence of the effect of genotype on signal intensity with respect to the number of polymorphisms within target sequences, number of affected probes and position of the polymorphism within each probe. The effect of SNPs is quite severe and leads to considerable false-positive rates, particularly when the analysis is performed at the exon level and aimed at detecting alternative splicing events. Finally, we propose simple solutions, based on ‘masking’ probes, which are putatively affected by polymorphisms and show that such strategy results in a large decrease in false-positive rates, with a very modest reduction in coverage of the transcriptome.

Exploring the evolutionary history of the differentially expressed genes between human populations: action of recent positive selection

Though debates exist on the early human evolutionary models such as the “Out of Africa” theory, which hypothesizes that modern humans migrated from Africa to Europe about 50,000 to 100,000 years ago, Africans and Europeans were geographically separated with minimal gene flow for tens of thousands of years. The variations between the current European and African populations, therefore, should have evolved during this timeframe. To gain more insights into the evolutionary history of human phenotypes including gene expression, it is critical to tell how recent positive selection has played a role in the variations observed in the current populations. Using the list of differentially expressed genes we previously identified between the HapMap samples derived from individuals of African (from Ibadan, Nigeria) and European (from Utah, USA) ancestry, we searched for evidence of selection among these differential genes. We found that 27 differentially expressed genes (out of 356 tested) between these two European and African populations have been under recent positive selection. Our findings suggest that the variation between these two populations appears to be affected primarily by neutral genetic drift and/or stabilizing selection and to a lesser degree by positive selection. Further annotation enrichment analyses showed that these 27 genes under selection were overrepresented in certain Gene Ontology biological processes, molecular functions and cellular components such as transcription, lipid binding and lysosome. Our results can provide unique insights into the evolutionary history of the variation in the gene expression phenotype between these two human populations.

HapMap filter 1.0: a tool to preprocess the HapMap genotypic data for association studies

The International HapMap Project provides a resource of genotypic data on single nucleotide polymorphisms (SNPs), which can be used in various association studies to identify the genetic determinants for phenotypic variations. Prior to the association studies, the HapMap dataset should be preprocessed in order to reduce the computation time and control the multiple testing problem. The less informative SNPs including those with very low genotyping rate and SNPs with rare minor allele frequencies to some extent in one or more population are removed. Some research designs only use SNPs in a subset of HapMap cell lines. Although the HapMap website and other association software packages have provided some basic tools for optimizing these datasets, a fast and user-friendly program to generate the output for filtered genotypic data would be beneficial for association studies. Here, we present a flexible, straight-forward bioinformatics program that can be useful in preparing the HapMap genotypic data for association studies by specifying cell lines and two common filtering criteria: minor allele frequencies and genotyping rate. The software was developed for Microsoft Windows and written in C++.

AVAILABILITY

The Windows executable and source code in Microsoft Visual C++ are available at Google Code (http://hapmap-filter-v1.googlecode.com/) or upon request. Their distribution is subject to GNU General Public License v3.

Genetic architecture of transcript-level variation in humans

We report here the results of testing the pairwise association of 12,747 transcriptional gene-expression values with more than two million single-nucleotide polymorphisms (SNPs) in samples of European (CEPH from Utah; CEU) and African (Yoruba from Ibadan; YRI) ancestry. We found 4,677 and 5,125 significant associations between expression quantitative nucleotides (eQTNs) and transcript clusters in the CEU and the YRI samples, respectively. The physical distance between an eQTN and its associated transcript cluster was referred to as the intrapair distance. An association with 4 Mb or less intrapair distance was defined as local; otherwise, it was defined as distant. The enrichment analysis of functional categories shows that genes harboring the local eQTNs are enriched in the categories related to nucleosome and chromatin assembly; the genes harboring the distant eQTNs are enriched in the categories related to transmembrane signal transduction, suggesting that these biological pathways are likely to play a significant role in regulation of gene expression. We highlight in the EPHX1 gene a deleterious nonsynonymous SNP that is distantly associated with gene expression of ORMDL3, a susceptibility gene for asthma.

The HapMap Resource is Providing New Insights into Ourselves and its Application to Pharmacogenomics

The exploration of quantitative variation in complex traits such as gene expression and drug response in human populations has become one of the major priorities for medical genetics. The International HapMap Project provides a key resource of genotypic data on human lymphoblastoid cell lines derived from four major world populations of European, African, Chinese and Japanese ancestry for researchers to associate with various phenotypic data to find genes affecting health, disease and response to drugs. Recent progress in dissecting genetic contribution to natural variation in gene expression within and among human populations and variation in drug response are two examples in which researchers have utilized the HapMap resource. The HapMap Project provides new insights into the human genome and has applicability to pharmacogenomics studies leading to personalized medicine.

Cell-based Models for Discovery of Pharmacogenomic Markers of Anticancer Agent Toxicity

The field of pharmacogenomics is challenging because of the multigenic nature of drug response and toxicity. The candidate gene approach has been traditionally utilized to determine the contribution of genetic variation to a particular phenotype; however, the sequencing of the human genome and the genetic resource provided by the International HapMap Project has allowed researchers to perform genome-wide studies without a priori knowledge. Recent work has demonstrated the usefulness of cell-based models for pharmacogenomic discovery using the HapMap samples, which are a panel of well-genotyped, human lymphoblastoid cell lines (LCLs) derived from 90 Utah residents with ancestry from northern and western Europe (CEU), 90 Yoruba in Ibadan, Nigeria (YRI), 45 Japanese in Tokyo, Japan (JPT) and 45 Han Chinese in Beijing, China (CHB). Using these cell-based models, investigators are able to study not only individual variation in drug response, but also population differences in drug response. Finally, besides single nucleotide polymorphisms (SNPs) and gene expression, these cell-based models can also be used to investigate other genetic (e.g. copy number variants, CNVs), epigenetic or environmental factors responsible for drug response.