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Glioma, the most common central nervous system cancer in adults, has poor prognosis. Here we identify a new SNP associated with glioma risk, rs1920116 (near TERC), that reached genome-wide significance (Pcombined = 8.3 × 10(-9)) in a meta-analysis of genome-wide association studies (GWAS) of high-grade glioma and replication data (1,644 cases and 7,736 controls). This region has previously been associated with mean leukocyte telomere length (LTL). We therefore examined the relationship between LTL and both this new risk locus and other previously established risk loci for glioma using data from a recent GWAS of LTL (n = 37,684 individuals). Alleles associated with glioma risk near TERC and TERT were strongly associated with longer LTL (P = 5.5 × 10(-20) and 4.4 × 10(-19), respectively). In contrast, risk-associated alleles near RTEL1 were inconsistently associated with LTL, suggesting the presence of distinct causal alleles. No other risk loci for glioma were associated with LTL. The identification of risk alleles for glioma near TERC and TERT that also associate with telomere length implicates telomerase in gliomagenesis.
Pubmed ID: 24908248 RIS Download
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Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.
View all literature mentionsA database and Java tool designed to integrate multiple datasets, and provides analysis and visualization of associations between sequence variation and gene expression in eQTL studies. Genevar allows researchers to investigate eQTL (expression quantitative trait loci) associations within a gene locus of interest in real time. The database and application can be installed on a standard computer in database mode and, in addition, on a server to share discoveries among affiliations or the broader community over the internet via web services protocols. (entry from Genetic Analysis Software)
View all literature mentionsA computer program for phasing observed genotypes and imputing missing genotypes.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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