EGAS00001000689-sc-2019-07-29T08:54:41Z - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001005200 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 full summary statistics data include five molecular traits (eQTL, hQTL(H3K27ac), hQTL(H3K4me1), mQTL, and psiQTL) for three primary blood cells (Monocytes, Neutrophils, and T-cells). Each full summary statistics file contains the associations for all tested variants for each phenotype ID.
| EGAD00001005199 | EGA
Project description:Deciphering the impact of genetic variation on gene regulation is fundamental to understanding common, complex human diseases. In this study, we obtained genome-wide RNA-Seq and ChIP-Seq (for H3K4me3 and H3K27ac histone modifications) data in the human liver. We mapped quantitative trait loci (QTLs) of gene expression levels and histone modification states. We integrated our findings with summary statistics of genome-wide association studies (GWAS) and identified candidate genes, gene regulatory regions, and variants in GWAS loci.
2019-06-13 | GSE128072 | GEO
Project description: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.
| EGAD00001005041 | EGA
Project description:Background: Functional enrichment analysis of genome-wide association study (GWAS)-summary statistics has suggested that immune cell-types, and especially CD4+ T-cells, play an important role in asthma pathogenesis. Despite this, CD4+ T-cells are under-represented in asthma transcriptome studies. Objective: To identify differences in gene expression between asthmatics and healthy controls in CD4+ T-cells. Methods: CD4+ T-cells were isolated within 2 hours from collection from peripheral blood from people with well-established asthma (n=33) and healthy controls (n=12). 3'-RNA-Seq was used to generate gene expression data. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify sets of co-expressed genes (modules). The asthma-associated modules were tested for enrichment of GWAS-identified asthma genes and gene ontology (GO) biological processes. For the genes in the asthma-associated modules, integration of eQTL and GWAS summary statistics (colocalisation), and protein-protein interaction (PPI) data was used to identify master regulators. Results: After quality control, 43 samples were available for the analysis. WGCNA identified three modules associated with asthma, which are strongly enriched for GWAS-identified asthma genes, antigen processing/presentation and immune response to viral infections. Colocalisation analysis of eQTL and GWAS summary statistics, together with PPI data, identified PTPRC as a master regulator of asthma gene-expression profiles in CD4+ T-cells. Conclusion: Unstimulated CD4+ T-cells from peripheral blood from asthmatics have different expression profiles, compared to healthy controls, for sets of genes involved in immune response to viral infections and antigen processing/presentation . Integration of genetic and protein-protein interaction data identified PTPRC as a master regulator of genes in asthma.
2023-12-27 | GSE217904 | GEO
Project description:Summary statistics for a multi-cohort epigenome-wide association study. This includes summary statistics (effect-size, standard error, p-value) for 470,000 methylation markers.
| EGAD00010001029 | EGA
Project description:Transcript abundance in adipose and liver tissues was used to map expression quantitative trait loci (eQTL) underlying fat accretion, weight gain and liver weight. Gonadal fat and liver tissues were used from 48 individuals F2 selected from a cohort (1,200 mice) originated from a cross between M16i and MB2 mice. A complex polygenic architecture within the MB2 locus was observed which was comprised of several locally regulated eQTL (cis-eQTL) with significant correlations to obesity traits. Total RNA obtained from isolated gonadal fat and liver tissues were used to mesured transcript abundance in order to detect expression QTLs.
2010-09-15 | E-GEOD-20724 | biostudies-arrayexpress
Project description:Analyses of QTLs for expression levels (eQTLs) of the genes reveal genetic relationship between expression variation and the regulator, thus unlocking the information for identifying the regulatory network. Oligo-nucleotide expression microarrays hybridized with RNA can simultaneously provide data for molecular markers and transcript abundance. In this study, we used Affymetrix GeneChip Rice Genome Array to analyze eQTLs in rice shoots at 72 h after germination from 110 recombinant inbred lines (RILs) derived from a cross between Zhenshan 97 and Minghui 63. Totally 1,632 single feature polymorphisms (SFPs) plus 23 PCR markers were identified and placed into 601 recombinant bins, spanning 1,459 cM in length, which were used as markers to genotype the RILs. We obtained 16,372 expression traits (e-traits) each with at least one eQTL, resulting in 26,051 eQTLs in total, including both cis- and trans-eQTLs. We also identified 171 eQTL hotspots among rice genome, each of which controls transcript variations of many e-traits. Gene Ontology analysis revealed enrichment of certain functional categories of genes in some of the eQTL hotspots. In particular, eQTLs for e-traits involving DNA metabolic process was significantly enriched in several eQTL hotspots on chromosomes 3, 5 and 10. Several transcription factors colocalizing with cis-eQTLs showed significant correlations with hundreds of e-traits, indicating possible co-regulation. We also detected correlations between the QTLs for shoot dry weight and eQTLs, revealing possible candidate genes for the trait. These results provided the clues for identification and characterization of regulatory network in the whole genome at the transcriptional level.
2011-06-24 | GSE22564 | GEO
Project description:Gene expression microarray data in resting myeloid cells and CD4+ T cells, as well as in response to lipopolysaccharide (LPS) or phytohemagglutinin (PHA) stimulation for 24 hours, respectively. Myeloid cells and CD4+ T cells were purified from resting and stimulated peripheral blood mononuclear cells from cord blood samples of 152 individuals at birth. Raw array data and quantile normalised gene expression data are available. Full summary statistics of eQTL analysis and interaction tests for response eQTLs are available. Information such as sample attributes and experimental variables are available in the \\"Microarray_sample_info.txt\\".
2020-04-20 | E-MTAB-8977 | biostudies-arrayexpress