3311 results for "*geneti*"

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• Chromatin 3D interactions mediate genetic effects on gene expression (genotypes)

  This dataset contains the imputed genotypes for the gencord samples. Genotyping was done using Illumina OMNI2.5M. Imputation was done using SHAPEIT2/IMPUTE2 with 1000 genomes project phase 3 reference panel.

  Dataset EGAD00001004790

• Genetics of stroke outcome exome sequencing

  Exome sequencing performed on DNA from 94 anterior ischemic stroke cases

  Dataset EGAD00001004808

• The genotype of LAM disease

  Tumor biopsies from LAM disease were retrospectively analyzed by multiple techniques to characterize the alterations in patients ,to elucidate the landscape of genetic/genomic alterations.

  Dataset EGAD00010001689

• Chromatin 3D interactions mediate genetic effects on gene expression (ChIP-seq)

  ChIP-seq data for Lymphoblastoid Cell Lines (LCL) and Fibroblasts (FIB) from the Gencord Cohort: - 160 LCLs assayed for H3K27ac, H3K4me1 and H3K4me3, - 78 FIB assayed for H3K4me3 and 79 FIB assayed for H3K27ac and H3K4me1 This dataset was generated as part of the following study: Delaneau et al (2019). Chromatin 3D interactions mediate genetic effects on gene expression.

  Dataset EGAD00001004871

• Chromatin 3D interactions mediate genetic effects on gene expression (RNA-seq)

  RNA-seq data for 168 Lymphoblastoid Cell Lines (LCL) and 78 Fibroblasts (FIB) from the Gencord Cohort. This dataset was generated as part of the following study: Delaneau et al (2019). Chromatin 3D interactions mediate genetic effects on gene expression.

  Dataset EGAD00001004872

• Genetic alterations in benign breast biopsies of subsequent breast cancer patient

  This dataset consists of amplicon sequencing of fibrocystic breast tissues, subsequent cancer tissue and germline control of 17 patients. The target genes include all exons of 27 protein-coding genes and 2 non-coding genes, as well as mutation hotspots in three cancer genes, frequently mutated in breast cancer. Ion AmpliSeq libraries were generated and sequenced on the Ion S5 XL system.

  Dataset EGAD00001004874

• 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

• Small-molecule inhibitors in melanoma - Kenski / Kong - WES (2019-04-11)

  Small-molecule inhibitors targeting the most commonly activated pathway in melanoma, MAPK pathway (either alone or in combination) are already given to melanoma patients for few years, and initially reduce tumour burden dramatically, eventually melanomas become resistant and tumours progress while on treatment. Resistance to this treatment occurs by acquisition of additional mutations or other alterations that affect the mitogen-activated protein kinase (MAPK) pathway by either direct or indirect signalling. Many resistance mechanisms somehow lead to reactivation of extracellular signal-regulated kinase (ERK), thereby restoring signalling of the oncogenic BRAF/MEK/ERK pathway. In addition, PI3K pathway activation contributes to resistance to BRAF inhibition. Less frequent but equally important to the phenomenon of targeted drug resistance is the observation that B15-20% of BRAF mutant melanoma patients fail to respond to BRAF inhibition already early on treatment, owing to intrinsic resistance. These patients have little therapeutic options, unless immunotherapy can be given. To better understand the resistance mechanisms in MAPK inhibitor-treated melanoma patients and melanoma biology, our lab generated a big panel of MAPK inhibitor resistant melanoma cell lines by continuous drug exposure. The understanding of the genetic landscape and gene expression as well as cross resistance to other treatment regimens, and other aspects of melanoma biology such as phenotype switch, will allow us to better exploit new therapeutic strategies for melanoma patients. . This dataset contains all the data available for this study on 2019-04-11.

  Dataset EGAD00001004952

• Whole exome sequencing of trio with primary immunodeficiency (IL2RB)

  Whole exome paired-end sequencing data was performed on a trio (patient + parents) who has primary immunodeficiency to identify the genetic cause of the immunodeficiency. Analysis revealed a novel homozygous mutation in IL2RB.

  Dataset EGAD00001004963

• PIVUS study - Longitudinal transcriptomics - Advanced aging

  We performed RNA sequencing in whole-blood from the same 65 individuals from the PIVUS study at ages 70 and 80 (130 samples) to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging. Each individual has four fastq files, two for each age. Consecutive sample IDs refer to the sample individual, e.g. PIVUS003 and PIVUS004 are the age 70 and age 80 samples of the first individual.

  Dataset EGAD00001004965

• 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

• 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

• 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

• 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

• 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

• RNA-seq as a tool for evaluating human embryo competence

  The majority of embryos that are created through IVF do not implant. It seems plausible that rates of implantation would improve if we had a better understanding of molecular factors affecting embryo competence. Currently, the process of selecting an embryo for uterine transfer utilizes an ad-hoc combination of morphological criteria, the kinetics of development, and genetic testing for aneuploidy. However, no single criterion can ensure selection of a viable embryo. In contrast, RNA-sequencing of embryos could yield highly dimensional data, which may provide additional insight and illuminate the discrepancies among current selection criteria. Indeed, recent advances enabling the production of RNA-sequencing (RNA-seq) libraries from single cells have facilitated the application of this technique to the study of some transcriptional events in early human development. However, these studies have not assessed the quality of their constituent embryos relative to commonly used embryological criteria. Here, we perform proof-of-principle advancement to clinical selection procedures by generating high quality RNA-seq libraries from a trophectoderm biopsy as well as the remaining whole embryo. We combine state-of-the-art embryological methods with low-input RNA-seq to develop the first transcriptome-wide approach for use in future predictive embryology studies. Specifically, we demonstrate the capacity of RNA-seq as a promising tool in preimplantation screening by showing that biopsies of an embryo can capture valuable information content available in the whole embryo from which they are derived. Furthermore, we show that this technique can be used to generate a RNA-based digital karyotype, and to identify candidate competence-associated genes. Together, these data establish the foundation for a future RNA-based diagnostic in IVF.

  Dataset EGAD00001005044

• UAMS Smoldering Myeloma Timeline Cohort

  To better understand the pattern of genetic changes over time, we performed whole exome sequencing of sequential bone marrow samples from 9 patients taken overtime including some paired SMM/newly diagnosed MM/Relapse MM samples. Samples from 9 patients (9 controls and 53 tumors) underwent whole exome sequencing with an additional capture for the IGH, IHK, IGL, and MYC loci. DNA was obtained from either CD138+ cells from the bone marrow of smoldering myeloma patients through time (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 NimbleGen's MedExome. After PCR amplification hybridized libraries underwent further amplification before being sequenced on a NextSeq500 (Illumina) using 75 bp paired end reads.

  Dataset EGAD00001005056