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About the Authors:
Qing-Rong Chen
* E-mail: [email protected]
Affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
Ying Hu
Affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
Chunhua Yan
Affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
Kenneth Buetow
Affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
Daoud Meerzaman
Affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
Introduction
Gene expression levels can be considered as quantitative traits and genetic polymophisms associated with transcript levels are referred as expression quantitative trait loci (eQTL). Substantial eQTL mapping studies have detected significant levels of polymorphism controlling individual genes, indicating that germline variations can affect gene expression networks and gene expression levels are heritable [1]–[3]. Most of these global eQTL analyses have been conducted in cell lines and normal tissues. Genome-wide association studies (GWAS) in cancer have identified a significant number of cancer susceptibility regions associated with specific cancers (http://www.genome.gov/gwastudies/). Trait-associated single nucleotide polymorphisms (SNPs) from GWAS are enriched for eQTLs for many phenotypes [4]. While several studies have combined GWAS findings and eQTL analysis to evaluate the effect of the trait-associated risk polymorphisms on transcript abundance in tumors [5]–[7], some eQTL studies have also investigated global germline impact on gene expression in tumors [5]–[9]. A systematic analysis of germline influence on gene expression tumors could identify novel alleles that influence tumorigenesis but are undetectable by analysis of normal tissue [8].
Glioblastoma multiforme (GBM) remains to be the most common and lethal primary brain tumor despite improvements in clinical care over the last 20 years. It is important to understand the inherited genetic contribution to tumor gene expression to gain insight into the underlying biology for this rapidly fatal disease. Previous studies have looked at the somatic variations and gene expression patterns observed in tumors to identify possible causal genes and pathways in GBM [10]–[11]. In the work described below we examine the role of...