3311 results for "*geneti*"

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• Hereditary Cancer Diagnostics with I2HCP gene panel

  This dataset contains targetted DNA sequencing data generated in the context of genetic diagnostics of hereditary cancer. Target enrichment was performed with the I2HCP, a custom hereditary cancer gene panel based in Agilent SureSelect and developed by the Germans Trias i Pujol Research Institute (IGTP, Badalona, Catalonia, Spain) and the Catalan Institute of Oncology (ICO, L'Hospitalet de Llobregat, Catalonia, Spain). After the enrichment, 108 samples were sequenced in Illumina HiSeq machines and 130 in MiSeq machines. Some of the samples were sequenced on both platforms. The dataset also includes MLPA validation data for all identified copy-number alterations, annotated with exon resolution and information about negative MLPA results. All this data was generated in the context of routine diagnostics and compiled together with MPLA validation data for a genetic diagnostics oriented benchmark of germline CNV calling tools.

  Study EGAS00001004316

• Knee_OA_Functional_Genomics

  The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of knee osteoarthritis that culminates in the need for total joint replacement (TJR). This study is a feasibility pilot that integrates functional genomics data from diseased and non-diseased tissues of OA patients who have undergone TJR. For each tissue, we characterised epigenetic marks (methylation), gene transcription (RNASeq) and expression (quantitative proteomics). We also generated genotype data on the HumanCoreExome array for each individual.

  Study EGAS00001001899

• Dataset that contains runs of submission 299

  This is the description of runs dataset of submission 299

  Dataset EGAD50000000403

• APCDR Uganda GWAS: Genome-wide sequence variation and susceptibility loci for cardiometabolic traits in a sub-Saharan African population (UGWAS component)

  Genomic studies in African populations provide unique opportunities to understand disease aetiology, human genetic diversity and population history in a regional and a global context. To leverage the relative benefits of different strategies, we undertook a combined approach of genotyping and whole-genome sequencing (WGS) in a population-based study of 6,400 individuals from a geographically defined rural community in South-West Uganda. We present data from 4,778 individuals with genotypes for ~2.2 million SNPs from the Uganda GWAS resource (UGWAS), and sequence data on up to 1,978 individuals spanning 41.5M SNPs and 4.5M indels (UG2G); 343 individuals overlap between the two datasets. We highlight the value of the largest sequence panel from Africa to date as a global resource for variant discovery, imputation and understanding the mutational spectrum and its clinical relevance in African populations. Alongside phenotype data, we provide a rich new genomic resource for researchers in Africa and globally.

  Study EGAS00001001558

• APCDR Uganda GWAS: Genome-wide sequence variation and susceptibility loci for cardiometabolic traits in a sub-Saharan African population (UG2G component)

  Genomic studies in African populations provide unique opportunities to understand disease aetiology, human genetic diversity and population history in a regional and a global context. To leverage the relative benefits of different strategies, we undertook a combined approach of genotyping and whole-genome sequencing (WGS) in a population-based study of 6,400 individuals from a geographically defined rural community in South-West Uganda. We present data from 4,778 individuals with genotypes for ~2.2 million SNPs from the Uganda GWAS resource (UGWAS), and sequence data on up to 1,978 individuals spanning 41.5M SNPs and 4.5M indels (UG2G); 343 individuals overlap between the two datasets. We highlight the value of the largest sequence panel from Africa to date as a global resource for variant discovery, imputation and understanding the mutational spectrum and its clinical relevance in African populations. Alongside phenotype data, we provide a rich new genomic resource for researchers in Africa and globally.

  Study EGAS00001000545

• Integrated genomic, transcriptional and epigenomic analyses in germinal center-cell lymphomas link the mutation landscape with differential DNA methylation in Burkitt lymphoma

  Biologically and clinically diverse B-cell neoplasms, including Burkitt, follicular and diffuse large B-cell lymphoma, show features of germinal center (GC) B-cells. Here we present a comprehensive analysis of the epigenetic landscape of GC-B-cell lymphomas using whole genome bisulfite sequencing paired with genome and transcriptome sequencing from 29 primary tumor samples and four normal GC-B-cell samples. All lymphomas studied showed extensive genome-wide methylation losses as well as intragenic regions where DNA methylation levels were strongly correlated with expression of associated genes. Pathway analysis revealed that these intragenic regions were enriched in signaling networks differentially activated in Burkitt and follicular lymphomas and affected regulatory mechanisms deregulated by recurrent mutations in a large fraction of Burkitt lymphomas (including the ID3/TCF3 complex, the SWI/SNF remodeling complex and the inhibitory GI signaling pathway). Taken together, these results provide a global picture of Burkitt lymphoma pathomechanism and demonstrate a tight connection between epigenetic regulation and other levels of regulatory control in these tumors.

  Study EGAS00001001067

• A Universal Gut Metagenomic-Derived Signature Predicts Cirrhosis

  Dysregulation of the gut microbiome has been implicated in the progression of nonalcoholic fatty liver disease (NAFLD) to advanced fibrosis and cirrhosis. To determine the diagnostic capacity of this association, stool microbiomes were compared across 163 well-characterized participants encompassing non-NAFLD controls, NAFLD-cirrhosis patients and their first-degree relatives. Interrogation of shotgun metagenomic and untargeted metabolomic profiles using the Random Forest machine learning algorithm and differential abundance analysis identified discrete metagenomic and metabolomic signatures that were similarly effective in detecting cirrhosis (diagnostic accuracy 0.91, AUC). Combining the metagenomic signature with age and serum albumin levels accurately distinguished cirrhosis in etiologically and genetically distinct cohorts from geographically separated regions. Additional inclusion of serum aspartate aminotransferase levels, which are increased in cirrhosis patients, enabled discrimination of cirrhosis from earlier stages of fibrosis. These findings demonstrate that a core set of gut microbiome species may offer universal utility as a non-invasive diagnostic test for cirrhosis.

  Study EGAS00001004600

• The_Little_Princess_Knowledge_Bank_

  We propose to build a genetic knowledge bank of Wilms tumour that integrates clinical outcome data with a genetic, epigenetic, and transcriptomic survey of 1,000 Wilms tumours.

  Study EGAS00001004237

• Chromatin landscape of medulloblastoma reveals context dependent driver

  In this study, we defined subgroup specific chromatin landscape (H3K27ac, H3K27me3, RNA-Seq, 27ac hichip, WGS) of medulloblastoma to identify pathogenic epigenetic alterations that regulate expression of a context dependent driver gene.

  Study EGAS00001006741

• Reconstruction of the personal information from human genome reads in gut metagenome sequencing data

  Human DNA present in fecal samples can result in a small number of human reads in gut shotgun metagenomic sequencing data. However, it is currently unclear how much personal information can be reconstructed from such reads and this has not been quantitatively evaluated. Such a quantitative evaluation is necessary to clarify the ethical concerns related to data sharing and to enable the efficient use of human genetic information in stool samples, such as for research and forensics. Here, we used genomic approaches to reconstruct personal information from fecal metagenomes of 343 Japanese individuals with associated human genotype data. Our approach can be used to quantify the personal information contained within gut metagenome data.

  Study EGAS00001007027