3613 results for "*geneti*"

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• GDAP - Genome Diversity in Africa Project (2021-02-12)

  The Genomic Diversity in Africa Project (GDAP) started with the plan to develop a genomic resource from African populations, characterise genomic diversity and population history, and facilitate clinical studies in Africa. Currently, 25 individuals from 24 ethnolinguistic groups have been whole-genome sequenced at high depth totalling 585 individuals. An additional 41 individuals have been sequenced with 10X Genomics libraries. At this stage, the initial curation of this dataset has been finished and we are performing the analysis in coordination with our collaborators. The current state of the GDAP represents a very diverse panel of African populations that maximizes geographical and ethnic variation and represents a great starting point to achieve the aforementioned goals. However, southern sub-Saharan countries, Bantu speakers and hunter gatherer groups are currently underrepresented, despite being crucial to understand the evolutionary history of the continent. After extensive effort to collate studies documentation, we finally have the opportunity to sequence 600 new individuals from these groups, including countries as Gabon, Rwanda and Zambia, and address these deficiencies. We aim to proceed with the same strategy: to sequence at high depth 25 individuals with standard PCR free libraries, with 2 additional individuals with 10X Genomics Chromium libraries per ethnolinguistic group. The former allows a good representation of variants down to low frequency in any given population, and the latter allows accurate phasing and the analysis of structural variation. By including these new populations, we want to investigate three crucial questions in African history in addition to the initial objectives: the Bantu expansion, the evolutionary history of hunter gatherers and the transatlantic slave trade. Additionally, the expanded dataset will help us better discover the genetic variation present in Africa and characterize the African pangenome. 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 2021-02-12.

  Dataset EGAD00001006965

• Influence of the Microbiome on Epigenetic Mechanisms in IBD

  Live CD4 T cells were sorted from inflamed and non-inflamed tissue samples of IBD patients or from healthy and IBD blood samples. ATAC-Seq libraries were generated from live CD4 T cells sorted from i) inflamed and non-inflamed tissue samples, ii) healthy and IBD blood samples, or from iii) CD4 T cell subsets polarised from healthy blood samples. After isolating crude nuclei, live CD4+ T cells were treated with Tagment DNA buffer and Tagment DNA Enzyme (Nextera DNA Library Prep Kit, Illumina), and then the DNA was purified by MinElute PCR Purification Kit (Qiagen). Transposed DNA fragments were amplified using specific adapters followed by purification with MinElute PCR Purification Kit (Qiagen). Fragments from 240-360pb were selected in the PippinHT system (Sage Science). The quality of the library and its DNA concentration were assessed by Bioanalyzer instruments (Agilent Technologies) and ultimately submitted for sequencing using Illumina HiSeq 2500 sequencer, V4 chemistry. On the other hand, single cell RNA-Seq libraries were generated exclusively from inflamed and non-inflamed tissue samples of Crohn’s disease patients. Briefly, live CD4 T cells were captured and encapsulated before cDNA amplification using the 10X Genomics Chromium Platform. Samples were prepared as outlined by 10x genomics Single Cell 3’ Reagent Kits v2 user guide. Samples were sequenced on a HiSeq 2500 with the following run parameters: Read 1 – 26 cycles, read 2 – 98 cycles, index 1 – 8 cycles.

  Dataset EGAD00001011066

• Capture-based NGS

  Capture-based NGS obtained using the "all-CLL" panel (also known as SOPHiA DDM (TM) Community CLL Clonality Solution). Library preparation was performed following SOPHiA GENETICS recommendations using 200 ng genomic DNA. Libraries were sequenced on a MiSeq instrument (2x300 bp, Illumina) aiming at a mean coverage of 1,000x.

  Dataset EGAD00001011151

• Whole blood RNA sequencing of individuals from Nepal

  This study utilized blood samples collected in Lalitpur, Nepal as part of the Strategic Typhoid Alliance across Africa and Asia (STRATAA) study. The dataset comprises whole blood RNAseq of 376 febrile individuals from Nepal. Blood was collected in PAXgene tubes and sent to Monash University (Melbourne, Australia) where RNA was extracted using the PAXgene Blood RNA kit before being sent to the Wellcome Sanger Institute (Hinxton, UK) for sequencing. Library prep used NEBNext Ultra II RNA custom kits on an Agilent Bravo WS automation platform with poly(A) pulldown. After PCR, plates were purified using Agencourt AMPure XP SPRI beads and libraries were quantified using Biotium Accuclear Ultra high sensitivity dsDNA Quantitative kits. Pooled libraries were normalised to 2.8 nM. Samples were globin depleted using KAPA RNA HyperPrep with RiboErase. Libraries were then subjected to 2x100bp paired-end sequencing on Illumina NovaSeq. Each library was sequenced to an average of 80 million reads. The STRATAA study was approved by the Nepal Health Research Council (NHRC, ref 283 306/2015) and OxTREC (Oxford Tropical Research Ethics Committee, ref 39-15). All participants provided informed consent for human genetic tests.

  Dataset EGAD00001011131

• Longitudinal RNA-seq datasets from patients after abdominal surgery

  This dataset consists of RNA-seq data of 126 whole-blood samples derived from patients after abdominal surgery. Total-RNA collected preoperatively and at 3-time points postoperatively (2-6, 24 and 48hrs) were analysed using RNA-sequencing. RNA was collected in PAXGene (Qiagen) tubes and extracted using the PAXGene RNA extraction kit, with a DNase step to remove contaminating DNA. Globin and ribosomal RNA were removed via a Ribo-zero kit (Illumina) and library preparation was carried out using the TruSeq Stranded Total RNA Library Prep Kit (Illumina). Next generation sequencing was performed on the NovaSeq sequencing platform (Illumina) at the Wellcome Centre for Human Genetics (WCHG) in Oxford.

  Dataset EGAD00001011102

• Germline genome sequencing samples from the Hereditary Cancer Syndromes (ICCon) Cancer Flagship

  A prospective study of individuals with suspicion of a hereditary cancer syndrome for whom previous clinical targeted genetic testing was either not informative or was not available. To identify pathogenic disease-causing variants explaining participant presentation, germline whole-genome sequencing and a comprehensive cancer virtual gene panel analysis were undertaken.

  Dataset EGAD00001011260

• Clinical and genetic factors associated with tumor response to neoadjuvant (chemo)radiotherapy, survival and recurrence risk in rectal cancer

  Dataset consists of 216 samples, with each sample having a BAM, BAI and VCF file.

  Dataset EGAD00001011297

• 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

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

  We generated a dataset consisting of 79 VCF files, and respective FASTQ and CRAM files, methodically generated using the GLIMPSE1 imputation algorithm leveraging the 1000 Genomes Project Phase 3 dataset as the reference panel of haplotypes. In total this dataset is composed of approximately 325 GB of FASTQ data, 156 GB of CRAM data, and 6 GB of VCF data. Our samples were specifically derived from sequenced DNA from a highly selective cohort of patients, mostly comprised of Iberian Populations in Spain (IBS) individuals but also containing some individuals with other genetic backgrounds, who presented severe COVID-19 symptoms during the initial wave of the SARS-CoV-2 pandemic in Madrid, Spain. On average, each VCF file in this rich dataset contains 9.49 million high-confidence single nucleotide variants [95%CI: 9.37 million - 9.61 million].

  Dataset EGAD00001011363

• Oxidative phosphorylation is a key ontogenetic feature of monocyte immunometabolism promoting myeloid differentiation after birth

  The dataset consists of 12 samples of monocytes, which were analyzed using RNA-sequencing (RNA-seq). These samples were categorized into four distinct groups, each comprising three samples. The dataset was designed to investigate the transcriptomic and metabolic profiles of monocytes across different age groups and stimulation conditions. 3 neonatal controls, 3 neonatal LPS stimulated, 3 adult controls and 3 adult LPS stimulated samples.

  Dataset EGAD00001011340

• An MTOR mutation hitchhikes through renal embryogenesis, driving multifocal, multiphenotypic tumours - RNA

  Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the seeding of cells predisposed to disease within mature organs, creating a field effect. We characterise an embryonic cancer mutation that drives multifocal, multiphenotypic renal tumours in a 14-year-old girl. Their shared MTOR mutation, absent from normal tissues, increases protein flexibility which enables a FAT domain hinge to dramatically increase mTORC1 activity. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

  Dataset EGAD00001011646

• An MTOR mutation hitchhikes through renal embryogenesis, driving multifocal, multiphenotypic tumours - scRNA

  Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the seeding of cells predisposed to disease within mature organs, creating a field effect. We characterise an embryonic cancer mutation that drives multifocal, multiphenotypic renal tumours in a 14-year-old girl. Their shared MTOR mutation, absent from normal tissues, increases protein flexibility which enables a FAT domain hinge to dramatically increase mTORC1 activity. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

  Dataset EGAD00001011647

• Genetic landscape of hepatocellular carcinoma

  Genetic landscape of hepatocellular carcinoma

  Dataset EGAD00001000131

• RNA-seq of Liver Cancer

  The present series corresponds to 161 RNA-seq samples from tumors with matched WES or WGS. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Dataset EGAD00001003993

• Exome sequencing of advanced hepatocellular carcinoma

  The aim of our project is to decipher the genomic of advanced hepatocellular carcinoma using whole exome sequencing. To this purpose, we aim to compare genetic landscape of advanced hepatocellular carcinoma with early tumor in order to understand the mechanisms of tumor progression. This work will also help to identify new therapeutic targets potentially useful to treat patients at advanced stage. This dataset contain whole exome sequencing aligned reads for 41 tumor with matched normal samples

  Dataset EGAD00001004555

• An MTOR mutation hitchhikes through renal embryogenesis, driving multifocal, multiphenotypic tumours - WGS

  Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the seeding of cells predisposed to disease within mature organs, creating a field effect. We characterise an embryonic cancer mutation that drives multifocal, multiphenotypic renal tumours in a 14-year-old girl. Their shared MTOR mutation, absent from normal tissues, increases protein flexibility which enables a FAT domain hinge to dramatically increase mTORC1 activity. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

  Dataset EGAD00001011645

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Kidney (1)

  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. 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.

  Dataset EGAD00001006427

• MutWP1 CRUK Grand Challenge Mutographs of Cancer Colorectal

  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. 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.

  Dataset EGAD00001007510

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyoma RNA

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014788

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyosarcoma RNA

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014790

• Peripheral blood RNA-sequencing in 4,732 participants of the INTERVAL cohort

  We performed bulk RNA-sequencing on peripheral blood collected from 4,732 blood donors recruited as part of the INTERVAL study. Using these data, we mapped gene expression and splicing quantitative trait loci (QTLs). Then, we integrated these data with protein, metabolite and lipid QTLs in the same individuals. The study aimed to identify the shared genetic etiology across transcriptional phenotypes, molecular traits and health outcomes in humans.

  Dataset EGAD00001008015

• Using genetics to identify cell types and mechanisms underlying susceptibility to primary sclerosing cholangitis

  Primary sclerosing cholangitis (PSC) is a T-cell mediated, chronic inflammatory condition of the biliary tree that is strongly associated with inflammatory bowel disease. Genome-wide association studies have identified 22 non-HLA genetic risk variants associated PSC. Identifying the genes impacted by these variants has proven difficult as the majority lie in non-coding regions of the genome. Knowledge of the genes and biological pathways these non-coding variants are perturbing is vital to understanding the disease biology. One means of assessing the impact of non-coding variants within disease associated loci upon genes is via colocalisation with eQTL. Many eQTL are cell-type specific, requiring the analysis of disease relevant cell types to detect colocalisation. We have collected PSC-relevant T-cell-subtypes from the peripheral blood of PSC patients via fluorescence activated cell sorting in preparation for RNA sequencing and mapping of eQTL. Samples were collected at the Norfolk and Norwich University Hopital, for which local ethical approval has been granted. Lysed cell samples will be transferred to WTSI and DNA/RNA will be extracted from lysed cell samples by T143 before genotyping (DNA) and custom library preparation and sequencing (RNA). 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 EGAD00001011815

• PSC

  Genetic characterisation of primary sclerosing cholangitis

  Dataset EGAD00010002633

• Genetic landscape of Extranodal NK/T-cell lymphoma

  Whole-exome sequence (WES) data of tumor-normal pairs from 40 ENKTCL patients and RNA sequence (RNA-seq) data of tumors from 20 ENKTCL patients.

  Dataset EGAD00001009852

• Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (normal data)

  There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers. This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.

  Dataset EGAD00001010295