Similar Datasets
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:Single-cell eQTL mapping identifies cell type specific genetic control of autoimmune disease
| PRJNA807386 | ENA
Project description:Single-cell eQTL mapping identifies cell type specific genetic control of autoimmune disease [scRNAseq_data]
| PRJNA807049 | ENA
Project description:Single-cell eQTL mapping identifies cell type specific genetic control of autoimmune disease [Genotype_data]
| PRJNA807390 | ENA
Project description:BackgroundThe discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression.ResultsHere, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map 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 generate 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 the eQTLs in fibroblasts almost entirely disappear during reprogramming.ConclusionsThis work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.
| S-EPMC7934233 | biostudies-literature
Project description:Genome-wide gene expression quantitative trait loci (eQTL) mapping have been focused on single-nucleotide polymorphisms and have helped interpret findings from diseases mapping studies. The functional effect of structure variants, especially short insertions and deletions (indel) has not been well investigated. Here we impute 1,380,133 indels based on the latest 1,000 Genomes Project panel into three eQTL data sets from multiple tissues. Imputation of indels increased 9.9% power and identifies indel-specific eQTLs for 325 genes. We find introns and vicinities of UTRs are more enriched of indel eQTLs and 3.6 (single-tissue)-9.2%(multi-tissue) of previous identified eSNPs were taggers of eindels. Functional analyses identifies epigenetics marks, gene ontology categories and disease GWAS loci affected by SNPs and indels eQTLs showing tissue-consistent or tissue-specific effects. This study provides new insights into the underlying genetic architecture of gene expression across tissues and new resource to interpret function of diseases and traits associated structure variants.
| S-EPMC4929061 | biostudies-literature
Project description:Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.
| S-EPMC11019359 | biostudies-literature
Project description:Background & aimsAssociation studies have greatly refined the important role of the major histocompatibility complex (MHC) region in autoimmune hepatitis (AIH). However, the effects of human leucocyte antigen (HLA) polymorphisms on AIH are not well established. The aim of this study is to systematically characterise the association of MHC variants with AIH in our well-defined cohort of patients.MethodsWe performed an imputation-based analysis on the extensive association observed within the MHC region using the Han-MHC reference panel, and tested the comprehensive associations of HLA polymorphisms with AIH in 1622 Chinese AIH type 1 patients and 10,466 population controls.ResultsA total of 588 HLA variants were significantly associated with AIH, with HLA-B∗35:01 (p = 8.17 × 10-304; odds ratio [OR] = 7.32) contributing the strongest signal. Stepwise conditional analysis revealed additional independent signals at HLA-B∗08:01 (p = 1.35 × 10-33; OR = 4.26) and rs7765379 (p = 5.08 × 10-18; OR = 1.66). A strong link between the lead HLA variant and clinical phenotypes of AIH was observed: patients with HLA-B∗35:01 were less frequently positive for ANA and tended to have higher serum AST and ALT levels at diagnosis, but lower serum IgG levels.ConclusionsOur study reveals three novel and independent variants at HLA-B∗35:01, HLA-B∗08:01, and rs7765379 associated with AIH across the whole MHC region in the Han Chinese population. The findings illustrate the value of the MHC region in AIH and provide a new perspective for the immunogenetics of AIH.Impact and implicationsThis study revealed three novel and independent variants associated with autoimmune hepatitis across the whole major histocompatibility complex region in the Han Chinese population. These findings are significant in identifying autoantigens, providing insights into the activation of the autoimmune processes, and further advancing our understanding of the immunogenetic basis underlying autoimmune hepatitis.
| S-EPMC10711477 | biostudies-literature
Project description:Understanding how the genetic control of gene expression varies between cell types and contexts is key for our understanding of complex traits including disease. To this end, we leveraged single cell RNA sequencing (scRNA-seq) to characterize the genetic architecture of gene regulation in an organ with one of the most cellularly diverse organs, the human lung. We profile these effects across two conditions, tissue samples from healthy controls and patients with pulmonary fibrosis (PF), a chronic, progressive condition characterized by the scarring of lung tissue. In total, we have generated expression profiles of 475,047 cells from primary human lung tissue from 116 individuals, including 67 with PF and 49 unaffected donors. Employing a pseudo-bulk approach, we have mapped expression quantitative trait loci (eQTL) across 38 cell types, identifying shared and cell type-specific effects. Further, we identify disease-interaction eQTL demonstrating this class of associations are more likely to be cell-type specific and linked to key drivers of dysregulation in PF. Finally, we connect PF risk variants implicated by genome-wide association studies to their regulatory targets in disease-relevant cell types. This study represents the first use of scRNA-seq to identify cell type level eQTL in the human lung, and one of only a small number of studies to carry out these characterizations in solid tissues. These results provide valuable insights into lung biology and disease risk.
2023-03-20 | GSE227136 | GEO