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We previously identified associations with ovarian cancer outcome at five genetic loci. To identify putatively causal genetic variants and target genes, we prioritized two ovarian outcome loci (1q22 and 19p12) for further study. Bioinformatic and functional genetic analyses indicated that MEF2D and ZNF100 are targets of candidate outcome variants at 1q22 and 19p12, respectively. At 19p12, the chromatin interaction of a putative regulatory element with the ZNF100 promoter region correlated with candidate outcome variants. At 1q22, putative regulatory elements enhanced MEF2D promoter activity and haplotypes containing candidate outcome variants modulated these effects. In a public dataset, MEF2D and ZNF100 expression were both associated with ovarian cancer progression-free or overall survival time. In an extended set of 6,162 epithelial ovarian cancer patients, we found that functional candidates at the 1q22 and 19p12 loci, as well as other regional variants, were nominally associated with patient outcome; however, no associations reached our threshold for statistical significance (p<1×10-5). Larger patient numbers will be needed to convincingly identify any true associations at these loci.
Pubmed ID: 29029385 RIS Download
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Software environment and programming language for statistical computing and graphics. R is integrated suite of software facilities for data manipulation, calculation and graphical display. Can be extended via packages. Some packages are supplied with the R distribution and more are available through CRAN family.It compiles and runs on wide variety of UNIX platforms, Windows and MacOS.
View all literature mentionsProject 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 mentionsOpen source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.
View all literature mentionsA computer program for phasing observed genotypes and imputing missing genotypes.
View all literature mentionsCell line OVCAR-8 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line ES-2 is a Cancer cell line with a species of origin Homo sapiens (Human)
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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