E-ENAD-34
Ontology highlight
ABSTRACT:
PROVIDER: E-ENAD-34 | ExpressionAtlas | 2020-04-22
REPOSITORIES: ExpressionAtlas
Similar Datasets
Project description:Type 1 and type 2 diabetes (T1D and T2D) share pathophysiological characteristics, yet mechanistic links have remained elusive. T1D results from autoimmune destruction of pancreatic beta cells, while beta cell failure in T2D is delayed and progressive. Here we find a new genetic component of diabetes susceptibility in T1D non-obese diabetic (NOD) mice, identifying immune-independent beta cell fragility. Genetic variation in Xrcc4 and Glis3 alter the response of NOD beta cells to unfolded protein stress, enhancing the apoptotic and senescent fates. The same transcriptional relationships were observed in human islets, demonstrating the role for beta cell fragility in genetic predisposition to diabetes.
2016-02-24 | PXD000859 | Pride
Project description:DNA methylation is an epigenetic modification, influenced by both genetic and environmental variation, that plays a key role in transcriptional regulation and many organismal phenotypes. Although patterns of DNA methylation have been shown to differ between human populations, it remains to be determined how epigenetic diversity relates to the patterns of genetic and gene expression variation at a global scale. Here we measured DNA methylation at 485,000 CpG sites in five diverse human populations, and analyzed these data together with genome-wide genotype and gene expression data. We found that population-specific DNA methylation mirrors genetic variation, and has greater local genetic control than mRNA levels. We estimated the rate of epigenetic divergence between populations, which indicates far greater evolutionary stability of DNA methylation in humans than has been observed in plants. This study provides a deeper understanding of worldwide patterns of human epigenetic diversity, as well as initial estimates of the rate of epigenetic divergence in recent human evolution.
2017-08-01 | GSE101431 | GEO
Project description:BLUEPRINT: A human variation panel of genetic influences on epigenomes and transcriptomes in three immune cells (WGS)
| EGAD00001002663 | EGA
Project description:Immune Variation Project (ImmVar)
| PRJNA242428 | ENA
Project description:<p>Profiling of gene expression with microarrays holds great potential for human health, for illuminating disease pathways or providing biomarkers to monitor disease or its resolution. Using high-throughput approaches for genotyping, immunophenotyping and gene expression analysis, the project will examine the basis for the control of gene expression in human immune cells, and how it is influenced by natural genetic variation or aging. In practice, the project will combine several cross-informative approaches: 1) Building on the established sample/data pipelines and robust protocols of the Immunological Genome (ImmGen) project, and on established cohorts of ethnically diverse healthy volunteers at hand, microarray techniques will be used to generate whole-genome expression profiles from purified naive CD4+ lymphocytes and monocytes from 600 healthy volunteers. A dense genetic map will be established for all donors. The results will elucidate how variation in the human genome affects the expression of immune genes, of key importance in understanding gene variants that bring susceptibility to immune or inflammatory disease. Computational analysis of these rich data will allow the reconstruction of regulatory connections between genes, helping to establish general modules and those specific of a given immune cell type. These data will be complemented by an orthogonal data group, generated from a restricted subset of 10 individuals, in which we will profile a larger set of 28 carefully delineated cell populations that exist in human blood. This work will also benefit from powerful interspecies comparison with similar experiments being performed in mice by ImmGen. 2) In addition, RNA from the same set of 28 defined cell populations will be probed with microarrays that explore other aspects of the transcriptome: i) microRNAs and other non-coding RNAs; ii) exon or splice junction arrays that will establish a map of differential splicing in human blood leukocyte. 3) The composition and reactivity of blood cells from the same donors will be established at the time of sample collection using high-throughput flow cytometry, correlating immune phenotypes with gene expression and genetic variation. 4) Genetic variability conditions the baseline levels of gene expression, but also the responsiveness to activating challenges. With samples from the same donors, NanoString technology for transcript counting will be used to analyze the transcriptional response of defined gene sets, representing response signatures of T or dendritic cells, for a fine-grained dissection of responses to different triggers (different bacterial ligands for dendritic cells, different cytokine environment for T cells). In keeping with the resource aspect of this project, all data and interpretations will be made publicly available rapidly upon curation, allowing public querying and browsing of the data, by using and evolving the existing web architectures of the ImmGen project, of the Broad Institute, and of international repositories. RELEVANCE: Exploration with DNA chips of the genome's expression microarrays holds great potential for human health, to better understand disease and to serve as diagnostic tools. Using a combination of high-throughput genomic techniques and computational biology, we will perform a broad exploration of gene expression in human blood cells across groups of African-American, Asian and European ancestry, asking how these profiles are affected by genetic variation or by age. These results will provide an invaluable reference benchmark for the interpretation of genetic and immunological studies.</p> <p>Additionally, over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, in a second study we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (ATAC-QTLs). ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression.</p>
| phs000815 | dbGaP
Project description:The Genotype-Tissue Expression (GTEx) project aims to provide to the scientific community a resource with which to study human gene expression and regulation and its relationship to genetic variation. This project will collect and analyze multiple human tissues from donors who are also densely genotyped, to assess genetic variation within their genomes. By analyzing global RNA expression within individual tissues and treating the expression levels of genes as quantitative traits, variations in gene expression that are highly correlated with genetic variation can be identified as expression quantitative trait loci, or eQTLs. The present dataset contains RNA-seq from human reference/non-diseased tissues (thus, we have excluded K-562 cell line that is a chronic myelogenous leukemia cell line) from the Genotype-Tissue Expression (GTEx) Project (http://www.gtexportal.org/home/).
2016-12-07 | E-MTAB-5214 | biostudies-arrayexpress
Project description:Mapping regulatory variation in sensory neurons using IPS lines from the HIPSCI project
2020-02-06 | E-ENAD-33 | ExpressionAtlas
Project description:Genetic variation governs protein expression through both transcriptional and post-transcriptional processes. To investigate this relationship, we combined a multiplexed, mass spectrometry-based method for protein quantification with an emerging mouse model harboring extensive genetic variation from 8 founder strains. We collected genome-wide mRNA and protein profiling measurements to link genetic variation to protein expression differences in livers from 192 diversity outcross mice. We observed nearly 3,700 protein-level quantitative trait loci (pQTL) with an equal proportion of proteins regulated directly by their cognate mRNA as uncoupled from their transcript. Our analysis reveals an extensive array of at least five models for genetic variant control of protein abundance including direct protein-to-protein associations that act to achieve stoichiometric balance of functionally related enzymes and subunits of multimeric complexes.
2017-11-08 | MSV000081696 | MassIVE