Project description:Whole-genome array comparative genomic hybridization (aCGH) of human ependymoma tumors. DOP-PCR products were spotted in triplicate onto NexterionTM Slide E epoxysilane-coated slides (PEQLAB, Erlangen, Germany) using a spotting robot (VersArray ChipWriterTM Pro system,BioRad, Munich, Germany) at 20C and 40% humidity. After spotting, slides were cross-linked,baked for 1 hr at 80C, and cross-linked again. Fresh frozen tumor material was collected during tumor resection. Copy number aberrations represent the status at diagnosis.
Project description:Whole-genome array comparative genomic hybridization (aCGH) of human ependymoma tumors. DOP-PCR products were spotted in triplicate onto NexterionTM Slide E epoxysilane-coated slides (PEQLAB, Erlangen, Germany) using a spotting robot (VersArray ChipWriterTM Pro system,BioRad, Munich, Germany) at 20C and 40% humidity. After spotting, slides were cross-linked,baked for 1 hr at 80C, and cross-linked again.
Project description:The Genetic Association Information Network (GAIN) Data Access Committee was established in June 2007 to provide prompt and fair access to data from six genome-wide association studies through the database of Genotypes and Phenotypes (dbGaP). Of 945 project requests received through 2011, 749 (79%) have been approved; median receipt-to-approval time decreased from 14 days in 2007 to 8 days in 2011. Over half (54%) of the proposed research uses were for GAIN-specific phenotypes; other uses were for method development (26%) and adding controls to other studies (17%). Eight data-management incidents, defined as compromises of any of the data-use conditions, occurred among nine approved users; most were procedural violations, and none violated participant confidentiality. Over 5 years of experience with GAIN data access has demonstrated substantial use of GAIN data by investigators from academic, nonprofit, and for-profit institutions with relatively few and contained policy violations. The availability of GAIN data has allowed for advances in both the understanding of the genetic underpinnings of mental-health disorders, diabetes, and psoriasis and the development and refinement of statistical methods for identifying genetic and environmental factors related to complex common diseases.