title: Systematic genetic analysis identifies Cis-eQTL target genes associated with glioblastoma patient survival.
abstract: Prior expression quantitative trait locus (eQTL) studies have demonstrated heritable variation determining differences in gene expression. The majority of eQTL studies were based on cell lines and normal tissues. We performed cis-eQTL analysis using glioblastoma multiforme (GBM) data sets obtained from The Cancer Genome Atlas (TCGA) to systematically investigate germline variation's contribution to tumor gene expression levels. We identified 985 significant cis-eQTL associations (FDR<0.05) mapped to 978 SNP loci and 159 unique genes. Approximately 57% of these eQTLs have been previously linked to the gene expression in cell lines and normal tissues; 43% of these share cis associations known to be associated with functional annotations. About 25% of these cis-eQTL associations are also common to those identified in Breast Cancer from a recent study. Further investigation of the relationship between gene expression and patient clinical information identified 13 eQTL genes whose expression level significantly correlates with GBM patient survival (p<0.05). Most of these genes are also differentially expressed in tumor samples and organ-specific controls (p<0.05). Our results demonstrated a significant relationship of germline variation with gene expression levels in GBM. The identification of eQTLs-based expression associated survival might be important to the understanding of genetic contribution to GBM cancer prognosis.
authors.affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Qing-Rong
authors.initials: QR
authors.lastname: Chen
authors.affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Ying
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Yan
authors.affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Buetow
authors.affiliation: Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Daoud
authors.initials: D
authors.lastname: Meerzaman
mesh_terms: D001921:Brain / Q000378:metabolism / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis / Q000453:epidemiology / Q000235:genetics*; D015972:Gene Expression Regulation, Neoplastic; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing; D055106:Genome-Wide Association Study; D005838:Genotype; D005909:Glioblastoma / Q000175:diagnosis / Q000453:epidemiology / Q000235:genetics*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D011379:Prognosis; D040641:Quantitative Trait Loci; D016019:Survival Analysis