4002 results for "*geneti*"

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• DNA methylation and Metabolic data from type 2 diabetes adolescents

  This data contains DNA methylation data obtained from the PBMCs obtained from type 2 diabetes adolescents and controls. There are 21 diabetic samples and 10 controls. This dataset also contains metabolic data obtained from the serum of 155 samples. There are 113 diabetic and 42 control samples.

  Dataset EGAD00001005271

• Transcriptional profiling of tauopathies in human IPS-derived neurons (2019-08-21)

  This project is a pilot study, in collaboration with Maria Grazia Spillantini and Mariangela Iovino (Cambridge Centre for Brain Repair), to investigate the utility of IPS-derived neurons for the study of neurodegenerative disorders. Our aim is to characterise the transcriptional consequences of tauopathies using neurons derived from differentiated IPSCs as a model system. We will use IPS cells derived from six individuals, four with known mutations in the tau protein, 2 without. RNA will be extracted at Day 0 and Day 65 of differentiation by which time the neuronal tauopathy is apparent. RNA will be extracted and the transcriptome of each line characterised using RNAseq. We will then search for genes that are differentially expressed between the transcriptomes of individuals with tau mutations versus those in controls. My lab will analyse the RNAseq data, comparing both affected and controls and both time-points, to establish candidate genes. Darren Logan’s lab, along with our collaborators, will experimentally verify and further investigate these genes in additional lines and animal models. From this analysis we will generate a list of candidate genes that are differentially expressed between cases and controls. This study will not only help us understand the molecular basis of tauopathies, but also identify gene candidates for biomarkers of neurodegenerative disease. It will serve as a proof of principle for future planned studies into generating and transcriptomically analysing an allelic series of Tau mutations in IPSCs with a controlled genetic background. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-08-21.

  Dataset EGAD00001005277

• Genetic landscape of SMM

  Identifying high risk smoldering myeloma patients and progression mechanism is a prerequisite to implement effective inception strategies and curve myeloma related morbidity and mortality. We hypothesize that genomics may help identify determinants of progression that may help predict outcome and offer effective chemo preventive targets. Eighty-two patients underwent a custom targeted panel sequencing with an additional capture for the translocation loci. These results were compared to 223 newly diagnosed patients (EGAS00001003223 and EGAD00001004117) and 17 MGUS and 10 early myeloma patients. DNA was obtained from either CD138+ cells from the bone marrow of multiple myeloma patients (tumor) or from stem cell harvests or peripheral blood cells from the same patient (control). 100 ng of DNA was fragmented, end-repaired, and adapters ligated using the HyperPlus kit (KAPA Biosystems). After PCR amplification the libraries were hybridized with probes against either a targeted panel consisting of 140 genes and chromosomal regions (Nimblegen) using SeqCap reagents (Nimblegen). Hybridized libraries underwent further amplification before being sequenced on a NextSeq500 (Illumina) using 75 bp paired end reads. Overall, these data highlight the importance of dysregulation of the MAPK pathway in the progression to MM.

  Dataset EGAD00001005285

• Somatic genetic basis of Wilms' tumour

  We investigated the somatic genetic basis of Wilms' tumour and found complex phylogenetic relations between tumours

  Dataset EGAD00001005134

• Somatic genetic basis of Wilms' tumour

  We investigated the somatic genetic basis of Wilms' tumour and found complex phylogenetic relations between tumours

  Dataset EGAD00001005135

• Somatic genetic basis of Wilms' tumour

  We investigated the somatic genetic basis of Wilms' tumour and found complex phylogenetic relations between tumours

  Dataset EGAD00001005136

• CyTOF of 27 DLBCLs

  Diffuse large B-cell lymphoma (DLBCL) is the most common histologic subtype of non-Hodgkin lymphoma and is notorious for its clinical heterogeneity. Patient outcomes can be predicted by cell-of-origin (COO) classification, demonstrating that the underlying transcriptional signature of malignant B-cells informs biological behavior in the context of standard combination chemotherapy regimens. In the current study, we used mass cytometry (CyTOF) to examine tumor phenotypes at the protein level with single cell resolution in a collection of 27 diagnostic DLBCL biopsy specimens from treatment naïve patients. We found that malignant B-cells from each patient occupied unique regions in 37-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Interestingly, variable MHC class II expression was found to be the greatest contributor to phenotypic diversity. Within individual tumors, a subset of cases showed multiple phenotypic subpopulations, and in one case we were able to demonstrate direct correspondence between protein-level phenotypic subsets and DNA mutation-defined subclones. In summary, CyTOF analysis can resolve both inter- and intra-tumoral heterogeneity among primary samples, and reveals that each case of DLBCL is unique and may be comprised of multiple, genetically distinct subclones.

  Dataset EGAD00001005419

• Single Cell RNAseq at various stages of HiPSCs differentiating toward definitive endoderm and endoderm derived lineages

  When comparing the differentiation capacities of pluripotent stem cell lines that have different genetic backgrounds, batch to batch experimental variablility poses a significant challenge, especially when trying to identify smaller effects. One way to address this issue is to differentiate several different lines in the same culture dish, thereby elimating experimental variation. In addition, it allows researchers to analyze many more lines with less experiments. Parallel single cell RNA-Seq exploits that individual cells are tagged and hence each cell can be reliably assigned to the donor of origin based on the genetic variants it contains. In addition, analyzing the genetic signature of single cells within a differentiating population can reveal differentation stages that are not easily detected in bulk RNAseq data. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001005741

• Whole Exome Sequencing of Spanish Patients diagnosed with rare ophtalmogenetic disorders.

  Whole Exome sequencing of a set of Spanish patients suffering rare genetic diseases. The set consists of 4 patients, one was diagnosed with Retinitis Pigmentosa (RP-1629), another one was diagnosed with Macular Dystrophy (MD-0235) and two were diagnosed with Leber's Congenital Amaurosis (LCA-0081 and LCA-0103).

  Dataset EGAD00001005746

• RNA-seq study of longitudinal blood cell samples drawn from children at risk of type 1 diabetes

  The appearance of type 1 diabetes (T1D)-associated autoantibodies is the first and only measurable parameter to predict progression toward T1D in genetically susceptible individuals. However, autoantibodies indicate an active autoimmune reaction, wherein the immune tolerance is already broken. Therefore, there is a clear and urgent need for new biomarkers that predict the onset of the autoimmune reaction preceding auto-antibody positivity or reflect progressive b-cell destruction. Here we report the mRNA sequencing-based analysis of 306 samples including fractionated samples of CD4+ and CD8+ T cells as well as CD4, CD8 cell fractions and unfractionated PBMC samples longitudinally collected from seven children who developed beta-cell autoimmunity (case subjects) at a young age and matched control subjects.

  Dataset EGAD00001005767