Common regulatory variation impacts gene expression in a cell type dependent manner
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE17080 | GEO | 2009/07/31
SECONDARY ACCESSION(S): PRJNA119799
REPOSITORIES: GEO
Similar Datasets
Project description:Studies correlating genetic variation to gene expression facilitate the interpretation of common human phenotypes and disease. As functional variants may be operating in a tissue-dependent manner, we performed gene expression profiling and association with genetic variants (SNPs) on three cell types of 85 individuals. After excluding 10 outlier individuals detected through principal component analysis, we detected cell type-specific genetic effects, with 69 - 80% of regulatory variants operating in a cell type-specific manner and identified multiple eQTLs per gene, unique or shared among cell types and positively correlated with the number of transcripts per gene. Cell type specific eQTLs were found at larger distances from genes and lower effect size similar to known enhancers. These data suggest that the complete regulatory variant repertoire can only be uncovered in the context of cell type specificity. Total RNA was extracted from fibroblasts, LCLs, and T-cells of the 85 unrelated individuals of the GenCord. Two one-quarter scale Message Amp II reactions (Ambion, Foster City, CA) were performed for each RNA extraction with 200 ng of total RNA. 1.5 μg of cRNA was hybridized to a commercial whole-genome expression array (WG-6 v3 Expression BeadChip, Illumina) to quantify transcript abundance. In total there were two technical replicates (labeling and hybridization) for each RNA sample.
2010-08-20 | E-GEOD-17080 | biostudies-arrayexpress
Project description:Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation.
2014-12-19 | PXD001406 | Pride
Project description:The goal of the study was to identify genes whose aberrant expression can contribute to diabetic retinopathy. We determined differential response in gene expression to high glucose in lymphoblastoid cell lines derived from matched type 1 diabetic individuals with and without retinopathy. Those genes exhibiting the largest difference in glucose response between diabetic subjects with and without retinopathy were assessed for association to diabetic retinopathy utilizing genotype data from a meta-genome-wide association study. All genetic variants associated with gene expression (expression QTLs; eQTLs) of the glucose response genes were tested for association with diabetic retinopathy. We detected an enrichment of the glucose response gene eQTLs among small association p-values for diabetic retinopathy. Among these, we identified FLCN as a susceptibility gene for diabetic retinopathy. Expression of FLCN in response to glucose is greater in individuals with diabetic retinopathy compared to diabetic individuals without retinopathy. Three large, independent cohorts of diabetic individuals revealed an enhanced association of FLCN eQTL to diabetic retinopathy. Mendelian randomization confirmed a direct positive effect of increased FLCN expression on retinopathy in diabetic individuals. Together, our studies integrating genetic association and gene expression implicate FLCN as a disease gene in diabetic retinopathy.
2020-03-10 | GSE146615 | GEO
Project description:Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation. We measured level of translation transcriptome-wide in lymphoblastoid cell lines derived from 72 HapMap Yoruba individuals using ribosome profiling assay, for which we have transcript level, protein level (62 out of 72) and genotype information collected.
2014-12-19 | E-GEOD-61742 | biostudies-arrayexpress
Project description:Systematic characterization of how genetic variation modulates gene regulation in a cell type specific context is essential for understanding complex traits. To address this question, we profiled gene expression and chromatin state of cells from healthy retinae of 20 human donors with a single-cell multiomics approach, and performed genomic sequencing. sc-eQTLs, sc-caQTL, sc-ASCA and sc-ASE were mapped in major retinal cell types. By integrating these results, we identified and characterized regulatory elements and genetic variants effective on gene regulation in individual cell types. Most of the identified sc-eQTLs and sc-caQTLs exhibit cell type-specific effects. Interestingly, the cis-elements harboring genetic variants with cell type-specific effects tend to be accessible in multiple cell types. Lastly, we identified the enriched cell types, fine-mapped candidate causal variants and genes, and uncovered cell type-specific regulatory mechanism underlying GWAS loci.
2023-11-07 | GSE247157 | GEO
Project description:While genetic variants are known to be associated with overall gene abundance in stimulated immune cells, less is known about their effects on alternative isoform usage. Analyzing RNA-seq profiles of monocyte-derived dendritic cells from 243 individuals, we uncovered thousands of unannotated isoforms synthesized in response to influenza infection and type 1 interferon stimulation. We identified more than a thousand quantitative trait loci (QTLs) associated with alternate isoform usage (isoQTLs), many of which are independent of expression QTLs for the same gene. Compared to eQTLs, isoQTLs are enriched for splice sites and untranslated regions, but depleted of sequences upstream of annotated transcription start sites. Both eQTLs and isoQTLs explain a significant proportion of the disease heritability attributed to common genetic variants.
2018-10-04 | GSE92904 | GEO
Project description:Quantification of gene expression levels at the single cell level has revealed that gene expression can vary substantially even across a population of homogeneous cells. However, it is currently unclear what genomic features control variation in gene expression levels, and whether common genetic variants may impact gene expression variation. Here, we take a genome-wide approach to identify expression variance quantitative trait loci (vQTLs). To this end, we generated single cell RNA-seq (scRNA-seq) data from induced pluripotent stem cells (iPSCs) derived from 53 Yoruba individuals. We collected data for a median of 95 cells per individual and a total of 5,447 single cells, and identified 241 mean expression QTLs (eQTLs) at 10% FDR, of which 82% replicate in bulk RNA-seq data from the same individuals. We further identified 14 vQTLs at 10% FDR, but demonstrate that these can also be explained as effects on mean expression. Our study suggests that dispersion QTLs (dQTLs), which could alter the variance of expression independently of the mean, have systematically smaller effect sizes than eQTLs. We estimate that at least 300 cells per individual and 400 individuals would be required to have modest power to detect the strongest dQTLs in iPSCs. These results will guide the design of future studies on understanding the genetic control of gene expression variance.
2018-09-21 | GSE118723 | GEO
Project description:Innate immune responses vary by pathogen and host genetics. We generated transcriptomes of monocytes from 215 individuals, with and without stimulation (3h or 6h) by fungal, Gram-negative or Gram-positive bacterial pathogens. Monocytes showed a conserved response to bacterial pathogens and a distinct antifungal response. Mapping expression quantitative trait loci (eQTLs) identified 745 response eQTLs (reQTLs) and corresponding genes showing pathogen-specific effects. Compared to all differentially expressed genes, reQTL-regulated genes were more frequently upregulated, indicating cell-activating gene regulation. ReQTL-regulated gene products are essential in NOD-like, C-type lectin, Toll-like and complement receptor-signaling pathways. ReQTLs functionally characterize risk variants identified through genome-wide association studies for autoimmunity, inflammatory or infectious diseases and cancer. Thus, in addition to the transcriptional response of monocytes, their genetic regulation differs for bacterial and fungal pathogens. ReQTLs help explain interindividual variation in immune response to infection and provide candidate genes for variants associated with a range of diseases.
2022-03-03 | GSE177040 | GEO
Project description:We hypothesized that mapping genetic variants associated with promoter and enhancer functions can provide novel insights into the mechanism through which eQTLs (expression quantitative trait loci) exert their effects on gene expression. To this end, we quantified genome-wide promoter usage and enhancer activity and tested the resulting molecular phenotypes for association with nearby genetic variants to discover cis-QTLs. To perform such analyzes, we have produced deep transcriptome profiling of 154 unrelated central European individuals, applying deepCAGE (Cap Analysis of Gene Expression) to nuclear enriched total RNA extracted from EBV transformed lymphoblastoid cell lines (LCLs). The LCLs were either purchased from Coriell Cell Repository (CEU, n=86) or from the GenCord collection (n=68, Gutierrez-Arcelus M et al. Elife 2013).
2017-09-15 | E-MTAB-5835 | biostudies-arrayexpress
Project description:<p>While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE (Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics) project was established. Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes, which represent 61% of all protein-coding genes expressed in these cell types. Strikingly, a large fraction (41%) of these genes showed a strong cis- association with genotype only in a single cell type. We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell- specific manner. These datasets will help reveal the effects of disease risk-associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis (<a href="http://dice-database.org">http://dice-database.org</a>). </p>
| phs001703 | dbGaP