3613 results for "*geneti*"

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• Dataset that contains runs of submission 161

  Description of runs dataset of submission 161

  Dataset EGAD50000000253

• Dataset that contains runs of submission 163

  Description of runs dataset of submission 163

  Dataset EGAD50000000257

• Dataset that contains runs of submission 168

  Description of runs dataset of submission 168

  Dataset EGAD50000000264

• Dataset that contains runs of submission 174

  Description of runs dataset of submission 174

  Dataset EGAD50000000269

• Dataset that contains runs of submission 240

  This is the description of runs dataset of submission 240

  Dataset EGAD50000000349

• Dataset that contains runs of submission 299

  This is the description of runs dataset of submission 299

  Dataset EGAD50000000403

• Dataset that contains runs of submission 117

  Description of runs dataset of submission 117

  Dataset EGAD50000000166

• Dataset that contains runs of submission 152

  Description of runs dataset of submission 152

  Dataset EGAD50000000235

• Dataset that contains runs of submission 74

  This is the description of runs dataset of submission 74

  Dataset EGAD50000000456

• Dataset that contains runs of submission 16

  Description of runs dataset of submission 16

  Dataset EGAD50000000295

• Dataset that contains runs of submission 15

  Description of runs dataset of submission 15

  Dataset EGAD50000000082

• Dataset that contains runs of submission 27

  Description of runs dataset of submission 27

  Dataset EGAD50000000112

• Dataset that contains runs of submission 19

  Description of runs dataset of submission 19

  Dataset EGAD50000000086

• Dataset that contains runs of submission 36

  Description of runs dataset of submission 36

  Dataset EGAD50000000204

• Dataset that contains runs of submission 37

  Description of runs dataset of submission 37

  Dataset EGAD50000000206

• CINECA_synthetic_cohort_ita_ingestion_test

  Please note: This synthetic data set (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk\n\nThis dataset (CINECA_synthetic_cohort_EUROPE_UK1) consists of 2521 samples which have genetic data based on 1000 Genomes data (https://www.nature.com/articles/nature15393), and synthetic subject attributes and phenotypic data derived from UKBiobank (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779). These data were initially derived using the TOFU tool (https://github.com/spiros/tofu), which generates randomly generated values based on the UKBiobank data dictionary. Categorical values were randomly generated based on the data dictionary, continuous variables generated based on the distribution of values reported by the UK Biobank showcase, and date / time values were random. Additionally we split the phenotypes and attributes into 4 main classes - general, cancer, diabetes mellitus, and cardiac. We assigned the general attributes to all the samples, and the cardiac / diabetes mellitus / cancer attributes to a proportion of the total samples. Once the initial set of phenotypes and attributes were generated, the data data was checked for consistency and where possible dependent attributes were calculated from the independent variables generated by TOFU. For example, BMI was calculated from height and weight data, and age at death generated by date of death and date of birth. These data were then loaded to the development instance of Biosamples (https://www.ebi.ac.uk/biosamples/) which accessioned each of the samples. \nThe genetic data are derived from the 1000 Genomes Phase 3 release (https://www.internationalgenome.org/category/phase-3/). The genotype data consists of a single joint call vcf files with call genotypes for all 2504 samples, plus bed, bim, fam, and nosex files generated via plink for these samples and genotypes. The genotype data has had a variety of errors introduced to mimic real data and as a test for quality control pipelines. These include gender mismatches, ethnic background mislabelling and low call rates for a randomly chosen subset of sample data as well as deviations from Hardy Weinberg equilibrium and low call rates for a random selection of variants. Additionally 40 samples have raw genetic data available in the form of both bam and cram files, including unmapped data. The gender of the samples in the 1000 genomes data has been matched to the synthetic phenotypic data generated for these samples. The genetic data was then linked to the synthetic data in BioSamples, and submitted to EGA.

  Dataset EGAD50000000369

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__Oesophageal_adenocarcinoma

  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 (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.

  Study EGAS00001003702

• GCAT | Genomes for life: cohort study of the genomes of Catalonia

  The GCAT Study have recruited 20 000 participants aged 40–65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. A total of 5459 genomic profiles have been characterised by comprehensive genotyping. Genome-wide genotypes have been generated using Illumina Infinium SNP-bead array technology. We chose the Multi-Ethnic Global (MEGAEX, V.2) consortium array, a multipurpose, multiethnic genotyping array with two million selected markers (including previously described germline mutations, insertions-deletions (InDels) and SNPs).We have strictly followed the standard manufacturer recommended automated protocol for the Infinium HTS Assay scanned with a HiScan confocal scanner (Illumina, San Diego, California, USA). Genome Studio V.2011.1 has been used for raw data analysis. Genotyping was performed at the Genomics and Bioinformatics Unit of the PMPPC Institute for Health Science Research Germans Trias i Pujol, in Badalona, Spain. Future plans The first follow-up study started in December 2017 and will end by March 2018. Residences of all subjects will be geocoded during the following year. Several genomic analyses are ongoing, and metabolomic and genomic integrations will be performed to identify underlying genetic variants, as well as environmental factors that influence metabolites. http://dx.doi.org/10.1136/bmjopen-2017-018324

  Study EGAS00001003018

• Single-cell RNA sequencing reveals cell-type specific eQTLs in peripheral blood mononuclear cells

  This proof-of-concept study shows that single-cell RNA sequencing in ~25,000 peripheral blood mononuclear cells (PBMCs) of 45 donors allows for the identification of cell type-specific cis expression quantitative trait loci effects (cis-eQTLs). Moreover, the significant top-eQTL effects show consistent allelic direction (94%) with previously published whole blood deepSAGE data. These cell type-specific eQTLs may give us additional insight into the downstream consequences of genetic risk factors for immune-mediated diseases.

  Study EGAS00001002560

• Characterization_of_genomic_landscape_of_Peripheral_T_cell_Lymphomas__not_otherwise_specified__PTCL_NOS_

  Peripheral T-cell lymphomas not otherwise specified (PTCL-NOS) represent a heterogeneous group of nodal and extra-nodal mature T-cell lymphomas, with a low prevalence in Western countries. PTCL-NOSs account for about 25% of all PTCLs and are currently diagnosed based on exclusion criteria, as this lymphomas lack unifying morphological, phenotypic and genomic features. Cytogenetic and FISH analysis of PTCL-NOS samples have not revealed recurrent pathogenetic abnormalities, while gene expression profiling has shown only partial ability to segregate cases representing homogeneous clinic-pathological entities. This underscores the need to look at PTCL-NOS with innovative and high-throughput approaches to identify recurrent genetic lesions that could further our understanding of the biology of this heterogeneous group of diseases, provide better diagnostic tools and perhaps new targets for innovative treatments. Our aim is to study ~15 patients affected by PTCL-NOS. Out study will be funded by a private, non-profit Italian cancer research fund (Associazione Italiana per la Ricerca sul Cancro, www.airc.it) based on a grant owned by Anna Dodero and Cristiana Carniti, hematologists at INT. Samples will be analysed by whole genome sequencing using Illumina X10 machines, on a 150bp-PE protocol. Data will be analysed using the pipeline available in Team 78, under the supervision of Peter Campbell, the WTSI faculty who will oversee the project, and by Francesco Maura, visiting scientist at the WTSI.

  Study EGAS00001002057

• GLASS-NL DNA-Methylation

  Study EGAS00001007546

• Low-coverage whole genome sequencing for a highly selective cohort of severe COVID-19 patients

  Background Despite advances in identification of genetic markers associated to severe COVID-19, the full genetic characterisation of the disease remains elusive. Imputation of low-coverage whole genome sequencing (lcWGS) has emerged as a competitive method to study such disease-related genetic markers as they enable genotyping of most common genetic variants used for genome wide association studies. This study aims at exploring the potential use of imputation in lcWGS for a highly selected severe COVID-19 patient cohort. Findings We generated an imputed dataset of 79 variant call format (VCF) patient files using the GLIMPSE1 tool, each containing, on average, 9.5 million single nucleotide variants. The validation assessment of imputation accuracy yielded a squared Pearson correlation of approximately 0.97 across sequencing platforms, showing that GLIMPSE1 can be used to confidently impute variants with minor allele frequency up to approximately 2% in Spanish ancestry individuals. We conducted a comprehensive analysis on the patient cohort, examining hospitalisation and intensive care utilisation, sex and age-based differences, and clinical phenotypes using a standardised set of medical terms specifically developed to characterise severe COVID-19 symptoms for this cohort. Conclusion This dataset highlights the utility and accuracy of lcWGS imputation in the study of COVID-19 severity, setting a precedent for other applications in resource-constrained environments. The methods and findings presented here may be leveraged in future genomic projects, providing vital insights for health challenges like COVID-19.

  Study EGAS00001007573

• NOTCH1 fusion genes in pediatric T-cell lymphoblastic lymphoma hallmark a common high-risk subgroup with blood TARC levels as possible biomarker

  T-cell lymphoblastic lymphoma (T-LBL) is a common pediatric malignancy accounting for approximately 20% of the non-Hodgkin lymphomas during childhood. Survival rates of T-LBL are ~80%, but outcome after relapse is dismal, with salvage rates reaching only ~15. Considering the extremely poor prognosis after relapse and absence of clinically relevant high-risk genetics, there is an urgent need for the identification of molecular risk factors and new prognostic biomarkers in T-LBL, as well as identification of new therapeutic strategies. In this study we present a novel entity of high-risk pediatric T-LBL patients characterized by previously unknown NOTCH1 gene fusions and highly elevated blood TARC levels

  Study EGAS00001007703

• Timing chromosomal amplification events using patterns of somatic mutations in high hyperdiploid acute lymphoblastic leukemia

  This dataset included samples with high hyperdiploid acute lymphoblastic leukemia (ALL) collected from the Division of Clinical Genetics, Lund University, Sweden. All samples were subjected to whole genome sequencing using the Illumina HiSeqX platform. Paired-end sequencing (2x150bp) was done to ~60x coverage for diagnostic samples and ~30x coverage for remission samples. The paired-end reads were aligned to the human reference genome GRCh37 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.25/) by the Burrows-Wheeler Aligner tool (version 0.7.17). Duplicate reads marking and local realignment were performed by GATK (version 4.0.11.0).

  Study EGAS00001007052

• Profibrotic priming of airway cell types in idiopathic pulmonary fibrosis and drug inhibition in single-cell resolution

  The cause and etiology of idiopathic pulmonary fibrosis, a severe chronic lung disorder with limited treatment options, is still poorly understood. Early genetic studies hinted that airway epithelial cells play an important role in the development of IPF while more recent single-cell RNA sequencing atlases from explant IPF lungs have focused on macrophages. In this study, we used air-liquid interface cultures of primary cells taken from the subsegmental bronchi of IPF patients, reflecting early-stage fibrosis, to interrogate the single-cell transcriptional landscape of the airway mucosa. We identified IPF-enriched cell-cell interaction pathways. Moreover, we also observed that IPF cell types shared “core” signatures, where epithelial cells had a pro-inflammatory signature while fibroblasts had an early activation signature. Harnessing the in vitro culture system, we also treated the cells with nintedanib, pirfenidone, and saracatinib, an Src kinase inhibitor that could inhibit IPF progression, to investigate their effects on the previously described IPF signatures. Unfortunately, only certain signatures were positively affected by the three drugs. This study provides more insight into the disease mechanisms of IPF and is a key resource for the IPF community to further investigate the effects of pharmacological inhibition.

  Study EGAS00001005794