Single-cell RNA sequencing reveals cell-type specific eQTLs in peripheral blood mononuclear cells
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ABSTRACT:
PROVIDER: EGAS00001002560 | EGA |
REPOSITORIES: EGA
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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
Project description:The human immune system displays remarkable variation between individuals, leading to differences in susceptibility to autoimmune disease. We present single cell RNA sequence data from 1,267,768 peripheral blood mononuclear cells from 982 healthy human subjects. For 14 cell types, we identified 26,597 independent cis-expression quantitative trait loci (eQTLs) and 990 trans-eQTL, with the majority showing cell type specific effects on gene expression. We subsequently show how eQTLs have dynamic allelic effects in B cells transitioning from naïve to memory states and demonstrate how commonly segregating alleles lead to inter-individual variation in immune function. Finally, utilizing a Mendelian randomization approach, we identify the causal route by which 305 risk loci contribute to autoimmune disease at the cellular level. This work brings together genetic epidemiology with scRNA-seq to uncover drivers of inter-individual variation in the immune system.
2022-04-08 | GSE196829 | GEO
Project description:The human immune system displays remarkable variation between individuals, leading to differences in susceptibility to autoimmune disease. We present single cell RNA sequence data from 1,267,768 peripheral blood mononuclear cells from 982 healthy human subjects. For 14 cell types, we identified 26,597 independent cis-expression quantitative trait loci (eQTLs) and 990 trans-eQTL, with the majority showing cell type specific effects on gene expression. We subsequently show how eQTLs have dynamic allelic effects in B cells transitioning from naïve to memory states and demonstrate how commonly segregating alleles lead to inter-individual variation in immune function. Finally, utilizing a Mendelian randomization approach, we identify the causal route by which 305 risk loci contribute to autoimmune disease at the cellular level. This work brings together genetic epidemiology with scRNA-seq to uncover drivers of inter-individual variation in the immune system.
2022-04-08 | GSE196735 | GEO
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: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:Here, we performed scRNA-seq on 64,018 fibroblasts from 79 donors and mapped expression quantitative trait loci (eQTLs) at the level of individual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an individual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generated scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type specific eQTLs in iPSCs, and show that that the eQTLs in fibroblasts almost entirely disappear during reprogramming.
2021-02-22 | E-MTAB-10060 | biostudies-arrayexpress
Project description:Profiles of sequence variants that influence gene transcription are very important for understanding mechanisms that affect phenotypic variation and disease susceptibility. Using genotypes at 1.4 million SNPs and a comprehensive transcriptional profile of 15,454 coding genes and 6,113 lincRNA genes obtained from peripheral blood cells of 298 Japanese individuals, we mapped expression quantitative trait loci (eQTLs). We identified 3,804 cis-eQTLs (within 500 kb from target genes) and 165 trans-eQTLs (>500 kb away or on different chromosomes). Cis-eQTLs were often located in transcribed or adjacent regions of genes; among these regions, 5’ untranslated regions and 5’ flanking regions had the largest effects. Epigenetic evidence for regulatory potential accumulated in public databases explained the magnitude of the effects of our eQTLs. Cis-eQTLs were often located near the respective target genes, if not within genes. Large effect sizes were observed with eQTLs near target genes, and effect sizes were obviously attenuated as the eQTL distance from the gene increased. Using a very stringent significance threshold, we identified 165 large-effect trans-eQTLs. We used our eQTL map to assess 8,069 disease-associated SNPs identified in 1,436 genome-wide association studies (GWAS). We identified genes that might be truly causative, but GWAS might have failed to identify for 148 out of the GWAS-identified SNPs; for example, TUFM (P=3.3E-48) was identified for inflammatory bowel disease (early onset); ZFP90 (P=4.4E-34) for ulcerative colitis; and IDUA (P=2.2E-11) for Parkinson's disease. We identified four genes (P<2.0E-14) that might be related to three diseases and two hematological traits; each expression is regulated by trans-eQTLs on a different chromosome than the gene. The study subjects were 301 apparently healthy individuals residing in Nagahama City, Japan. Whole blood was collected from each participant when in a non-stimulated state.
2014-06-23 | E-GEOD-53351 | biostudies-arrayexpress
Project description:We leveraged single nuclei RNA-seq from 192 human brain samples to map cis-eQTLs in 8 brain cell types. We found extensive cell-type specific genetic effect on gene expression and integrated our results with GWAS to identify putative novel risk genes for brain disorders and the cell types in which the disease mechanisms are likely active.
| EGAS00001006345 | EGA
Project description:A proportion of the genetic variants underlying complex phenotypes do so through their effects on gene expression, so an important challenge in complex trait analysis is to discover the genetic basis for the variation in transcript abundance. So far, the potential of mapping both quantitative trait loci (QTLs) and expression quantitative trait loci (eQTLs) in rodents has been limited by the low mapping resolution inherent in crosses between inbred strains. We provide a megabase resolution map of thousands of eQTLs in hippocampus, lung, and liver samples from heterogeneous stock (HS) mice in which 843 QTLs have also been mapped at megabase resolution. We exploit dense mouse SNP data to show that artifacts due to allele-specific hybridization occur in _30% of the cis-acting eQTLs and, by comparison with exon expression data, we show that alternative splicing of the 3_ end of the genes accounts for <1% of cis-acting eQTLs. Approximately one third of cis-acting eQTLs and one half of trans-acting eQTLs are tissue specific. We have created an important systems biology resource for the genetic analysis of complex traits in a key model organism.
2009-10-22 | E-MTAB-88 | biostudies-arrayexpress
Project description:In this study we use RNAseq to explore allele specific expression (ASE) in adipose tissue of male and female F1 mice, produced from reciprocal crosses of C57BL/6J and DBA/2J strains. Comparison of the identified cis-eQTLs, to local-eQTLs, that were obtained from adipose tissue expression in two previous population based studies in our laboratory, yields poor overlap between the two mapping approaches, while both local-eQTL studies show highly concordant results. Specifically, local-eQTL studies show ~60% overlap between themselves, while only 15-20% of local-eQTLs are identified as cis by ASE, and less than 50% of ASE genes are recovered in local-eQTL studies. Utilizing recently published ENCODE data, we also find that ASE genes show significant bias for SNPs prevalence in DNase I hypersensitive sites that is ASE direction specific. We suggest a new approach to analysis of allele specific expression that is more sensitive and accurate than the commonly used fisher or chi-square statistics. Our analysis indicates that technical differences between the cis and local-eQTL approaches, such as differences in genomic background or sex specificity, account for relatively small fraction of the discrepancy. Therefore, we suggest that the differences between two eQTL mapping approaches may facilitate sorting of SNP-eQTL interactions into true cis and trans, and that a considerable portion of local-eQTL may actually represent trans interactions. 4 samples - male and female, BxD and DxB adipose of pooled RNA (3 animals per pool) were analyzed with high coverage RNAseq data.
2014-06-05 | E-GEOD-58239 | biostudies-arrayexpress