Database and browser that provides a central resource to archive and display association between genetic variation and high-throughput molecular-level phenotypes. This effort originated with the NIH GTEx roadmap project: however the scope of this resource will be extended to include any available genotype/molecular phenotype datasets.

MCRA data for eQTL and computational analysis

Many disease-associated variants are thought to be regulatory but are not present in existing catalogues of expression quantitative trait loci (eQTL). We hypothesise that these variants may regulate expression in specific biological contexts, such as stimulated immune cells. Here, we used human iPSC-derived macrophages to map eQTLs across 24 cellular conditions. We found that 76% of eQTLs detected in at least one stimulated condition were also found in naive cells. The percentage of response eQTLs (reQTLs) varied widely across conditions (3.7% - 28.4%), with reQTLs specific to a single condition being rare (1.11%). Despite their relative rarity, reQTLs were overrepresented (p=0.05, Fisher's exact test) among disease-colocalizing eQTLs. We nominated an additional 21.7% of disease effector genes at GWAS loci via colocalization of reQTLs, with 38.6% of these not found in the Genotype–Tissue Expression (GTEx) catalogue. Our study highlights the diversity of genetic effects on expression and demonstrates how condition-specific regulatory variation can enhance our understanding of common disease risk alleles.

This dataset contains summary statistics for eQTL (Expression Quantitative Trait Loci) analyses for 120 human fetal brains from the second trimester of gestation (12 to 19post-conception weeks). Expression matrices, covariates, and summary statistics are provided for all tested eQTL and for top eQTL for all genes.The data are contained within a single .zip archive file. Individual data files are of openly accessible .txt text file format containing p- or q- values by SNP, and .bed Browser Extensible Data format files, containing annotation track data such as chromosomal coordinates. Data files of multiple GB in size are stored in individual .gz gzip compressed files.The related study investigates genetic influences on gene expression in the human fetal brain and their relationship with a variety of postnatal brain-related traits, including susceptibility to neuropsychiatric disorders. This dataset represents the first eQTL dataset derived exclusively from the human fetal brain, and is based on initial deep RNA sequencing and genotyping.The detailed breakdown of the files in this dataset is provided below and in README.md.Gene Level Analyses:

• expression_gene.bed.gz

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normalised, variance-stabilising transformed count data (29,875 genes)

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columns: chr, gene_start, gene_end, gene_id, samples...

• all_eqtls_gene.txt.gz· nominal p-values for all SNPs within 1 MB of each gene· columns: gene_id, variant_id, tss_distance, ma_samples, ma_count, maf, pval_nominal, slope, slope_se

• top_eqtls_gene.txt.gz· q-values for most significant eQTL for each gene (includes nominal p-value thresholds that can be used to filter significant SNPs)· columns: chr, snp_start, snp_end, gene_id, num_var, beta_shape1, beta_shape2, true_df, pval_true_df, variant_id, tss_distance, minor_allele_samples, minor_allele_count, maf, ref_factor, pval_nominal, slope, slope_se, pval_perm, pval_beta, qval, pval_nominal_threshold

Transcript Level Analyses: - expression_transcript.bed.gz · normalised, variance-stabilising transformed count data (144,448 transcripts)· columns: chr, transcript_start, transcript_end, transcript_id, samples... - all_eqtls_transcript.txt.gz· nominal p-values for all SNPs within 1 MB of each transcript· columns: transcript_id, variant_id, tss_distance, ma_samples, ma_count, maf, pval_nominal, slope, slope_se - top_eqtls_transcript.txt.gz· q-values for most significant eQTL for each transcript (includes nominal p-value thresholds that can be used to filter significant SNPs)· columns: columns: chr, snp_start, snp_end, transcript_id, num_var, beta_shape1, beta_shape2, true_df, pval_true_df, variant_id, tss_distance, minor_allele_samples, minor_allele_count, maf, ref_factor, pval_nominal, slope, slope_se, pval_perm, pval_beta, qval, pval_nominal_thresholdCovariates (Used For Both Gene Level and Transcript-Level Analyses) - covariates.txt· columns: Sample, Sex, PCW, RIN, ReadLength, PC1, PC2, PC3, PEER1, PEER2, PEER3, PEER4, PEER5, PEER6, PEER7, PEER8, PEER9, PEER10

Genome-wide association studies (GWAS) have been pivotal to increasing our understanding of intestinal disease. However, the mode by which genetic variation results in phenotypic change remains largely unknown, with many associated polymorphisms likely to modulate gene expression. Analyses of expression quantitative trait loci (eQTL) to date indicate that as many as 50% of these are tissue specific. Here we report a comprehensive eQTL scan of intestinal tissue. Subjects who had undergone ileal pouch anal anastomosis and closure of ileostomy at least one year prior to recruitment were prospectively enrolled at Mount Sinai Hospital in Toronto. Endoscopically and histologically normal tissue biopsies from the afferent limb of these individuals were obtained and preserved in RNAlater. Total RNA was extracted with the QIAGEN miRNeasy Kit and mRNA analysis was performed on Affymetrix Human Gene 1.0 ST arrays. DNA was obtained from whole-blood samples from the same individuals and genotyped using the Illumina beadchips. Cis- and trans-eQTL analyses were carried out on 173 subjects encompassing the expression levels of 19,047 unique autosomal genes listed in the NCBI database and over 580K dbSNPs (Call Rate ≥ 95%; MAF ≥ 5%; Hardy–Weinberg equilibrium (HWE) χ2 p-values ≥ 10-6). This work was done in a custom software pipeline and the Kruskal-Wallis test was used to compare expression values across different genotypes. False discovery rate correction for multiple testing was applied at an alpha level of 5%.This Series includes the Affymetrix data.

Spatial eQTL data for traits in "Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidity"

A large fraction of human genes are regulated by genetic variation near the transcribed sequence (cis-eQTL, expression quantitative trait locus), and many cis-eQTLs have implications for human disease. Less is known regarding the effects of genetic variation on expression of distant genes (trans-eQTLs) and their biological mechanisms. In this work, we use genome-wide data on SNPs and array-based expression measures from mononuclear cells obtained from a population-based cohort of 1,799 Bangladeshi individuals to characterize cis- and trans-eQTLs and determine if observed trans-eQTL associations are mediated by expression of transcripts in cis with the SNPs showing trans-association, using Sobel tests of mediation. We observed 434 independent trans-eQTL associations at a false-discovery rate of 0.05, and 189 of these trans-eQTLs were also cis-eQTLs (enrichment P<0.0001). Among these 189 trans-eQTL associations, 39 were significantly attenuated after adjusting for a cis-mediator based ...

Catalog provides uniformly processed gene expression and splicing QTLs from all available public studies on human. Expression and splicing QTLs recomputed from public datasets.

Genome-wide association studies (GWAS) have transformed our understanding of the genetics of complex traits such as autoimmune diseases, but how risk variants contribute to pathogenesis remains largely unknown. Identifying genetic variants that affect gene expression (expression quantitative trait loci, or eQTLs) is crucial to addressing this. eQTLs vary between tissues and following in vitro cellular activation, but have not been examined in the context of human inflammatory diseases. We performed eQTL mapping in five primary immune cell types from patients with active inflammatory bowel disease (n = 91), anti-neutrophil cytoplasmic antibody-associated vasculitis (n = 46) and healthy controls (n = 43), revealing eQTLs present only in the context of active inflammatory disease. Moreover, we show that following treatment a proportion of these eQTLs disappear. Through joint analysis of expression data from multiple cell types, we reveal that previous estimates of eQTL immune cell-type specificity are likely to have been exaggerated. Finally, by analysing gene expression data from multiple cell types, we find eQTLs not previously identified by database mining at 34 inflammatory bowel disease-associated loci. In summary, this parallel eQTL analysis in multiple leucocyte subsets from patients with active disease provides new insights into the genetic basis of immune-mediated diseases.

Previous studies had shown that integration of genome wide expression profiles, in metabolic tissues, with genetic and phenotypic variance, provided valuable insight into the underlying molecular mechanisms. We used RNA-Seq to characterize hypothalamic transcriptome in 99 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP), a reference resource population for cardiovascular and metabolic traits. We report numerous novel transcripts supported by proteomic analyses, as well as novel non coding RNAs. High resolution genetic mapping of transcript levels in HMDP, reveals both local and trans expression Quantitative Trait Loci (eQTLs) demonstrating 2 trans eQTL 'hotspots' associated with expression of hundreds of genes. We also report thousands of alternative splicing events regulated by genetic variants. Finally, comparison with about 150 metabolic and cardiovascular traits revealed many highly significant associations. Our data provides a rich resource for understanding the ...

aAnnotated in either the KEGG of Ingenuity database.Correlation in the Gene Atlas data set was used to infer annotation for ten of the genes with no previously known role in oxidative phosphorylation (bolded).

Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.

Abstract

Natural Killer (NK) cells are innate lymphocytes with central roles in immunosurveillance and are implicated in autoimmune pathogenesis. The degree to which regulatory variants affect NK gene expression is poorly understood. We performed expression quantitative trait locus (eQTL) mapping of negatively selected NK cells from a population of healthy Europeans (n=245). We find a significant subset of genes demonstrate eQTL specific to NK cells and these are highly informative of human disease, in particular autoimmunity. An NK cell transcriptome-wide association study (TWAS) across five common autoimmune diseases identified further novel associations at 27 genes. In addition to these cis observations, we find novel master-regulatory regions impacting expression of trans gene networks at regions including 19q13.4, the Killer cell Immunoglobulin-like Receptor (KIR) Region, GNLY, MC1R and UVSSA. Our findings provide new insights into the unique biology of NK cells, demonstrating markedly different eQTL from other immune cells, with implications for disease mechanisms.

Preprint

https://www.biorxiv.org/content/10.1101/2021.05.10.443088v1

Dataset

nk_raw_for_zenodo.txt: Matrix of raw gene expression at 47,209 probes in primary human NK cells from 245 healthy individuals of European ancestry. Gene expression is quantified using the Illumina HumanHT-12 v4 BeadChip gene expression array platform. Column names represent Array Address ID for each probe, and row names represent pseudonymised sample identifiers, which can be matched to sample genotypes. Sample genotypes are available at the European Genome-Phenome Archive with accession ID EGAS00000000109).

probes_passing_QC.txt: List of probes passing quality control; probe sequences mapping to a unique genomic locus, and probe sequences not containing common genomic variation (minor allele frequency >1%), n=29,002. Column names are Array Address ID (probeID), Ensembl ID (ensembl), Gene ID (gene), and Illumina probe ID (ilmn).

Supplementary Table S1. List of relevant studies of gene expression with available eQTL data. The table contains information of gene expression eQTL data used in the current study: source name (CEDAR, GTEx, blood eQTL by Westra et al.), tissue name and corresponding article DOI.

Part of the article: Williams FMK et al. "Sequence variation at 8q24.21 and risk of back pain"

eQTL summary statistics and GWAS colocalization posterior probabilities from eQTL calling in an admixed subcohort of GTEx v8 with local and global ancestry adjustments. For the original, non-peer-reviewed preprint, see https://www.biorxiv.org/content/10.1101/836825v1.

For the related source code, see https://doi.org/10.5281/zenodo.3924788 or https://github.com/nicolerg/gtex-admixture-la.

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.

Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved i..., , , ## Description of the data and file structure

Includes cell-cycle assignments in cell_cycle_feb02152022.tsvÂ . Each row contains the cell-cycle assignment for cells analyzed in the experiment. Columns include the data set, cell barcode, cell-cycle assignment, and Seurat-based cluster assignments prior to manual cell-cycle assignment.

Additionally, output data structures from Cell Ranger and Vartrix are included in processed.tar.gz. When expanded, processed/.*/filtered_feature_bc_matrix/ contains for each single-cell experiment (experiments are indicated as the folder names in . * and are described in provided R code referenced below at github):

1. barcodes.tsv are cell barcodes used in analysis, each line indicates a cell barcode that was used for downstream analysis
2. features.tsv.gz are gene features used in analysis, each line indicates a transcript (systematic gene name and common gene name are provided)
3. matrix.mtx.gz are the UMI counts per transcript in sparse ma...

In a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus we measured interferon (IFN) status, anti-IL-6 drug exposure and genome-wide gene expression at three time points (379 samples from 157 individuals). We mapped eQTLs by applying a random intercept linear mixed model using the first 25 principal components of gene expression and the first 5 principal components of genotyping as covariates. We used subject as a random effect. We defined cis eQTL as the SNP within 250kb upstream of the GENCODE transcription start site of the gene or 250kb downstream of the transcription end site.Expression_levels.txt: Gene expression data log2(cpm+1)eQTL_summary_statistics.txt: Cis eQTL summary statistics for all gene SNP pairs tested

Objectives: Genome-wide association studies (GWASs) have revealed many candidate SNPs, but the mechanisms by which these SNPs influence diseases are largely unknown. In order to decipher the underlying mechanisms, several methods have been developed to predict disease-associated genes based on the integration of GWAS and eQTL data (e.g., Sherlock and COLOC). A number of studies have also incorporated information from gene networks into GWAS analysis to reprioritize candidate genes. Methods: Motivated by these two different approaches, we have developed a statistical framework to integrate information from GWAS, eQTL, and protein-protein interaction (PPI) data to predict disease-associated genes. Our approach is based on a hidden Markov random field (HMRF) model, and we called the resulting computational algorithm GeP-HMRF (a GWAS-eQTL-PPI-based HMRF). Results: We compared the performance of GeP-HMRF with Sherlock, COLOC, and NetWAS methods on 9 GWAS datasets, using the disease-related genes in the MalaCards database as the standard, and found that GeP-HMRF significantly improves the prediction accuracy. We also applied GeP-HMRF to an age-related macular degeneration disease (AMD) dataset. Among the top 50 genes predicted by GeP-HMRF, 7 are reported by the MalaCards database to be AMD-related with an enrichment p value of 3.61 × 10–119. Among the top 20 genes predicted by GeP-HMRF, CFHR1, CGHR3, HTRA1, and CFH are AMD-related in the MalaCards database, and another 9 genes are supported by the literature. Conclusions: We built a unified statistical model to predict disease-related genes by integrating GWAS, eQTL, and PPI data. Our approach outperforms Sherlock, COLOC, and NetWAS in simulation studies and 9 GWAS datasets. Our approach can be generalized to incorporate other molecular trait data beyond eQTL and other interaction data beyond PPI.

Each row of this table corresponds to a tissue. There are three set of columns: L—model including first two genotype PCs and first 5 PEER factors in addition to the factor of interest, M—model including first two genotype PCs in addition to the factor of interest, and S—model including only the factor of interest. For each model we report four columns: number of significant findings at q-value levels 0.05, 0.10 and 0.25 as well as total number of genes tested. (CSV)