4075 results for "*geneti*"

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• Congenital Heart Disease in UK Families

  This project aims to identify highly penetrant coding variants increasing the risk of Congenital Heart Disease (CHD) performing whole exome sequencing on DNA samples from 23 affected individuals, selected from 10 families with presumed Autosomal Recessive Inheritance. This is a collaboration with Prof. Eamonn Maher and Dr. Chirag Patel from the Department of Medical and Molecular Genetics, University of Birmingham plans to sequence 23 indexed Agilent whole exome pulldown libraries on 75Bp PE HiSeq (Illumina)

  Dataset EGAD00001000343

• Exome sequencing of patients with rare neurological disorders

  Exome sequencing of patients and their families with diverse rare neurological disorders. Some families have prior linkage data identifying a specific chromosomal interval or interest, other families do not have linkage data available. Many of these families come from special populations whose demography or preference for consanguineous marriages make them particularly tractable for genetic studies.

  Dataset EGAD00001000346

• Balanced Ependymoma

  We propose to definitively characterise the somatic genetics of a number of pediatric malignant tumours including ependymoma, high grade glioma and central nervous system primitive neurectodermal tumours through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing.

  Dataset EGAD00001000350

• Balanced Brain Tumour Whole Genome Sequencing

  We propose to definitively characterise the somatic genetics of a number of pediatric malignant tumours including ependymoma, high grade glioma and central nervous system primitive neurectodermal tumours through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing.

  Dataset EGAD00001000369

• Assessment of genetic and epigenetic variation in human IPS cells

  In order to progress human induced pluripotent stem cells (hiPSCs) towards the clinic, several outstanding questions must be addressed. It is possible to reprogram different somatic cell types into hiPSCs but it is unlcear whether some cell types carry through fewer mutations through reprogramming (either due to mutations present in the primary cells, or mutations accumulated during reprogramming). Through in depth analysis of hiPSCs generated from different somatic cells, it will be possible to assess the variation in genetic stability of different cell types.

  Dataset EGAD00001000384

• Genetic factors underlying premature coronary heart disease in patients with normal coronary arteries

  The study will analyse by exome sequencing 42 Greek patients with premature MI and no vessel disease to identify genetic factors underlying this condition.

  Dataset EGAD00001000402

• Subclonal diversification of primary breast cancer

  Cancers are ecosystems of genetically related clones, competing across space and time for limited resources. To understand the clonal structure of primary breast cancer, we applied genome and targeted sequencing to 295 samples from 49 patients’ tumors. The extent of subclonal diversification varied considerably among patients and encompassed many spatial patterns, including local growth, intraductal dissemination and clonal intermixture. Landmarks of disease progression, such as acquiring invasive or metastatic potential, arose within detectable subclones of antecedent lesions, suggesting that subclonal mutations could be relevant if actionable. No defined temporal order of mutation was evident, with the commonest genes, including PIK3CA, TP53, BRCA2, PTEN and MYC, mutated early in some, late in others, often exhibiting parallel evolution across subclones. Signatures of homologous recombination deficiency correlated with response to neoadjuvant chemotherapy. Thus, the interplay of mutation, growth and competition drives clonal structures of breast cancer that are complex, variable across patients and clinically relevant.

  Dataset EGAD00001000965

• Harnessing transposons for drug resistance gene discovery in cancer

  This study involves a forward genetic screen to identify common insertion sites in drug resistant clones. We will be utilising piggybac transposon systems in order to generate multiple drug resistant clones in a range of human cancer cell lines.

  Dataset EGAD00001000980

• Multi-omics analysis defines core genomic alterationsin pheochromocytomas and paragangliomas

  Pheochromocytomas and paragangliomas (PCC/PGL) are neural crest derived tumors with a very strong genetic component. We report the first integrated genomic portrayal of a large collection of PCC/PGL. SNP array analysis revealed distinct copy-number patterns associated with genetic background. Whole-exome sequencing showed a low mutation rate of 0.3 mutations per megabase, with few recurrent somatic mutations in genes not previously associated with PCC/PGL. DNA methylation arrays and miRNA sequencing identified DNA methylation changes and miRNA expression clusters strongly associated with mRNA expression profiling. Overexpression of the miRNA cluster 182/96/183 was specific of SDHB-mutated tumors and induced invasive traits, whereas silencing of the imprinted DLK1-MEG3 miRNA cluster appeared as a potential driver in a subgroup of sporadic tumors. Altogether, the complete genomic landscape of PCC/PGL is mainly driven by distinct germline and/or somatic mutations in susceptibility genes and reveals different molecular entities, characterized by a set of unique genomic alterations.

  Dataset EGAD00001000986

• Mechanisms of patient response to Dabrafenib in Melanoma

  Samples will be from the BRF113683 (BREAK-3) study which is a Phase III Randomized, Open-label Study Comparing GSK2118436 to Dacarbazine (DTIC) in Previously Untreated Subjects With BRAF Mutation Positive Advanced (Stage III) or Metastatic (Stage IV) Melanoma (n=250 enrolled)*NGS [Agilent capture (Sanger V2 panel): 360 genes and 20 gene fusions; Illumina HiSEQ Sequencing]*CNV: [via NGS or Affy SNP 6.0 or Illumina Omni (TBD)]Bioinformatics: Analysis will be performed using core Sanger informatics pipelines similar to those previously described (Papaemmanuil E et al. (2013) Blood. 22:3616 -3627). Briefly, copy number analysis will be performed using the ASCAT algorithm, and base substitutions, small insertions and deletions using the CAVEMAN and Pindel algorithms, respectively. Statistical approaches including generalized linear models will be used to predict clinical variables such as maximum clinical response and duration of response using genetic data. Sanger and EBI to conduct analysis; Raw data and correlation with clinical endpoints to be analyzed by both EBI/Sanger and GSK (unique pipeline analyses to increase call confidence)

  Dataset EGAD00001001375