Dataset Information

Systematic identification of genotype-dependent enhancer variants in eosinophilic esophagitis [RNA-Seq]

ABSTRACT: Eosinophilic esophagitis (EoE) is a rare atopic disorder associated with esophageal dysfunction, including difficulty swallowing, food impaction, and inflammation. EoE develops in a small subset of people with food allergies under the influence of environmental and genetic risk factors. Genome wide association studies (GWAS) have identified 31 independent risk loci for the disease, and linkage disequilibrium (LD) expansion of these loci nominates a set of 531 variants that are potentially causal. These risk variants are non-coding, suggesting a likely role in altered gene regulatory mechanisms. To systematically interrogate the gene regulatory activity of these variants, we designed a massively parallel reporter assay (MPRA) containing the alleles of each variant within their 170 bp genomic sequence context cloned into a GFP reporter library. Transfection of the MPRA library into TE-7 esophageal epithelial cells, HaCaT skin keratinocytes, and Jurkat T cells revealed cell-type-specific gene regulation. We identify 32 allelic enhancer variants (allelic enVars) that regulate reporter gene expression in a genotype-dependent manner in at least one cellular context. By annotating these variants with expression quantitative trait loci (eQTL) and chromatin looping data in related tissues and cell types, we identify putative target genes affected by genetic variation in EoE patients, including TSLP and multiple genes at the HLA locus. Transcription factor enrichment analyses reveal possible roles for cell-type specific regulators, including GATA-3, a key regulator of type 2 inflammation. Collectively, our approach reduces the large set of EoE-associated variants to a set of 32 with allelic regulatory activity, providing new functional insights into the effects of genetic variation in this disease.

ORGANISM(S): Homo sapiens

PROVIDER: GSE232336 | GEO | 2023/11/26

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Genome-wide association studies (GWAS) have successfully identified 145 genomic regions that contribute to schizophrenia risk, but linkage disequilibrium (LD) makes it challenging to discern causal variants. Computational finemapping prioritized thousands of credible variants, ~98% of which lie within poorly characterized non-coding regions. To functionally validate their regulatory effects, we performed a massively parallel reporter assay (MPRA) on 5,173 finemapped schizophrenia GWAS variants in primary human neural progenitors (HNPs). We identified 440 variants with allelic regulatory effects (MPRA-positive variants), with 72% of GWAS loci containing at least one MPRA-positive variant. Transcription factor binding had modest predictive power for predicting the allelic activity of MPRA-positive variants, while GWAS association, finemap posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. Furthermore, 64% of MPRA-positive variants did not exhibit eQTL signature, suggesting that MPRA could identify yet unexplored variants with regulatory potentials. MPRA-positive variants differed from eQTLs, as they were more frequently located in distal neuronal enhancers. Therefore, we leveraged neuronal 3D chromatin architecture to identify 273 genes that physically interact with MPRA-positive variants. These genes annotated by chromatin interactome displayed higher mutational constraints and regulatory complexity than genes annotated by eQTLs, recapitulating a recent finding that eQTL- and GWAS-detected variants map to genes with different properties. Finally, we propose a model in which allelic activity of multiple variants within a GWAS locus can be aggregated to predict gene expression by taking chromatin contact frequency and accessibility into account. In conclusion, we demonstrate that MPRA can effectively identify functional regulatory variants and delineate previously unknown regulatory principles of schizophrenia.

Dataset Information

Systematic identification of genotype-dependent enhancer variants in eosinophilic esophagitis [RNA-Seq]

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure