4051 results for "*geneti*"

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• IBD Whole Genome Sequencing (2019-04-01)

  We will sequence at 15X coverage the genomes of 1536 IBD patients. These samples are currently onsite at Sanger and made available for sequencing via our collaboration with the UK IBD Genetics consortium. 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 2019-04-01.

  Dataset EGAD00001004880

• Genetic vulnerability of knockout cancer lines (2019-04-01)

  Genome-wide CRISPR/Cas9 library screen was performed in isogenic cell lines. Three biological replicates were used. Cells were harvested at initiation of screen and at specific time points following cell culture. Using this approach we aim to identify genes specifically important in cell survival in engineered cell lines. Please perform 72, instead of 36, PCR reactions as the screen performed at 200x coverage. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004881

• The South Asia Rheumatic Heart Disease Genetics Network Data

  This dataset comprises over 850 individuals recruited in Uttar Pradesh, India, including cases of rheumatic heart disease based on echocardiographic diagnosis and controls recruited on the basis of normal echocardiograms. For this analysis all available samples were genotyped using the Illumina HumanCore-24 BeadChip platform.

  Dataset EGAD00001004882

• Investigation of mutational signatures associated with DNMT3A deficiency (2019-04-03)

  Mutational signatures have been shown to be attributable to specific genetic contexts, such as mutations in DNA repair genes. DNMT3A is a DNA methyltransferase that helps maintain the DNA methylation pattern in a site-specific manner and may participate in DNA repair or the stress response. We have identified an adult individual who is a germline mosaic for a DNMT3A mutation. We have obtained clonal lymphoblastoid cells (LCLs) from the subject representing both WT and mutant lines grown in the same individual for >50 years. These clones represent a unique opportunity to examine the mutational impact of the DNMT3A mutation in a well-controlled setting. Our goal is to perform WGS on whole blood, representing the pool, as well as several WT and several mutant clones, in order to investigate the contribution of DNMT3A to mutation rates and signatures. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004889

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: pancreas (2019-04-03)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (Illumina HiSeqX, 40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004893

• 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

• Somatic genetic basis of Wilms’ tumour

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

  Dataset EGAD00001004774

• Whole-genome sequencing of Tibetans from China

  Twenty-seven Tibetan samples from China were whole-genome sequenced to investigate high-altitude adaptation, population genetics and demographic history.

  Dataset EGAD00001004797

• Genetics of stroke outcome exome sequencing

  Exome sequencing performed on DNA from 94 anterior ischemic stroke cases

  Dataset EGAD00001004808

• RNA-seq data

  This dataset contains 200 RNA-seq bam files (142 SLE, 58 healthy individuals). RNA libraries were prepared with the Illumina TruSeq sample preparation kit and were sequenced on Illumina HiSeq2000. 49 bp paired-end reads were mapped to the GRCh37 reference human genome using the GEM mapper.This dataset was generated as part of the following study: Panousis et al (2019). Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity

  Dataset EGAD00001005037

• Genotype data

  This dataset contains the imputed genotypes for 197 individuals. All individuals were genotyped with the Illumina HumanCoreExome-24 array. The individuals were phased with SHAPEIT and imputed to the 1000 Genomes Project Phase III using IMPUTE2. This dataset was generated as part of the following study: Panousis et al (2019). Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity

  Dataset EGAD00001005038

• Expression data

  This dataset contains the RPKM and raw read counts of expression for all the individuals. This dataset was generated as part of the following study: Panousis et al (2019). Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity.

  Dataset EGAD00001005039

• Clinical phenotypes/covariates

  This dataset contains the clinical phenotypes/covariates information for all the individuals. This dataset was generated as part of the following study: Panousis et al (2019). Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity.

  Dataset EGAD00001005040

• eQTL summary statistics

  This dataset contains the eQTL summary statistics (nominal pass, significant eQTLs, best associated variant per gene). eQTL mapping was performed with fastQTL. This dataset was generated as part of the following study: Panousis et al (2019). Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity.

  Dataset EGAD00001005041

• MultiOMICS study of a pair of infant monozygotic twins with concordant B-cell ALL (WGS)

  B-cell acute lymphoblastic leukemia (B-cell ALL) is the most common cancer in childhood. Studying identical twins with B-cell ALL provides a unique and tractable model for deciphering the developmental timing of pre- and post-natal mutations contributing to clonal evolution. To date, this has mainly focused on major cytogenetic subgroups of childhood B-cell ALL, including MLL fusions, ETV6-RUNX1, hyperdiploidy, and BCR-ABL1. However, formal demonstration of the prenatal origin and “backtracking” the natural history of the leukemia remains understudied in “B-other”/Normal Karyotype (NK) B-cell ALL. To characterize the genetic landscape of this particular leukemia subtype, we performed whole genome DNA- and B-cell receptor (BCR)- on a pair of 8-month-old monozygotic twins diagnosed with concordant “B-other”/NK B-cell ALL.

  Dataset EGAD00001005017

• MultiOMICS study of a pair of infant monozygotic twins with concordant B-cell ALL (WGBS)

  B-cell acute lymphoblastic leukemia (B-cell ALL) is the most common cancer in childhood. Studying identical twins with B-cell ALL provides a unique and tractable model for deciphering the developmental timing of pre- and post-natal mutations contributing to clonal evolution. To date, this has mainly focused on major cytogenetic subgroups of childhood B-cell ALL, including MLL fusions, ETV6-RUNX1, hyperdiploidy, and BCR-ABL1. However, formal demonstration of the prenatal origin and “backtracking” the natural history of the leukemia remains understudied in “B-other”/Normal Karyotype (NK) B-cell ALL. To characterize the epigenetic landscape of this particular leukemia subtype, we performed DNA bisulfite-sequencing on a pair of 8-month-old monozygotic twins diagnosed with concordant “B-other”/NK B-cell ALL.

  Dataset EGAD00001005018

• ChIPseq Sequencing data for epigenetic subgroups of meningioma

  Bam files for 16 meningioma tumor samples; ChIPseq performed on Illumina HiSeq 2000

  Dataset EGAD00001005021

• sQTL summary statistics

  This dataset contains the sQTL summary statistics (nominal pass, significant sQTLs). sQTL mapping was performed with QTLtools. This dataset was generated as part of the following study: Panousis et al (2019). Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity.

  Dataset EGAD00001005042

• 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

• Investigating the genetics of immunity against Salmonella in humans (2019-09-05)

  This study entails whole genome sequencing of an interleukin (IL)-12 b-1 receptor-deficient individual who presented with a chronic systemic Salmonella Enteritidis infection that did not resolve with standard IFNg and antibiotic treatment. Whole genome sequencing of the patient's parents are also included. 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 2019-09-05.

  Dataset EGAD00001005311

• August 2019 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  August 2019 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001005335

• The identification of genetic vulnerabilities in head and neck cancers for the development of novel therapies (2020-01-15)

  CRISPR/Cas9 lethality screens in a set of Asian head and neck cancer cell lines to identify novel targets. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005784

• Somatic mutation and clonal evolution in premalignant lung disease - WGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulations of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from the lung tissue of individuals with a variety of lung diseases (COPD, UIP, IPF, Emphysema, pulmonary hypertension). This will allow us to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. Smoking is a large risk factor for developing many of these lung diseases so we are particularly keen to determining whether there is evidence of a smoking signature in these patients. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005786