Dataset Information

Disease-associated loci share features with loci that regulate gene expression in response to environmental perturbations

ABSTRACT: Many of the genetic loci associated with disease are expected to have context-dependent regulatory effects that are underrepresented in the transcriptomes of healthy, steady-state adult tissues. To understand genetic gene regulation across diverse environmental conditions and cellular contexts, we perturbed a broad array of human cell types with three environmental exposures in vitro. With single-cell RNA-sequencing data from 1.4 million cells, we identified hundreds of dynamic eQTLs that regulate molecular responses to perturbation across multiple cellular contexts. These response eQTLs exhibit distinct signatures of regulatory complexity, evolutionary constraint, and functional enrichment that distinguish them from standard, steady- state eQTLs. Similar to GWAS loci, response eQTLs are enriched in distal enhancers and are linked to genes that experienced strong selective constraint, involve in complex regulatory landscapes, and diverse biological functions. We also observed that response eQTLs are more likely than standard eQTLs to colocalize with loci that affect complex traits. Our results highlight the complexity of genetic regulatory effects and suggest that our ability to interpret disease-associated loci may benefit from the pursuit of studies of gene-by-environment interactions in diverse biological contexts.

ORGANISM(S): Homo sapiens

PROVIDER: GSE292909 | GEO | 2025/06/24

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Most loci identified in genome wide association studies (GWAS) of complex traits reside in non-coding DNA and may contribute to phenotype via changes in gene regulation. The discovery of expression quantitative trait loci (?eQTLs?) can thus be used to more precisely identify modest but real disease associations and provide insights into their underlying molecular mechanisms. This is particularly true for analyses of expression in non-transformed cells from tissues relevant to the complex traits of interest. We have conducted two independent studies to identify genetic, including both SNPs and copy-number variants, and environmental determinants of human liver gene expression variation. We analyzed two sets of primary livers (primary dataset: n=220; replication dataset: n=60) using Agilent and Illumina expression arrays and Illumina SNP genotyping (550K). At least 30% of genetic and non-genetic factors that meet genome-wide significance (p <1 x10-9) in one study fail to replicate in the second study, suggesting that artifacts, like unknown SNPs that affect RNA-probe hybridization or hidden confounding variables, often result in statistically significant but biologically irrelevant correlations. These data confirm the value of independent replications to enrich for truly predictive eQTLs, and given our study design we are able to identify hundreds of reproducible correlations. We show that such information can be used to provide insights into disease-relevant phenotypes, with specific examples including eQTLs related to lipid levels (e.g. LDL cholesterol), immune system function (e.g. HLA), and drug response (e.g. warfarin). Furthermore, in the interest of both fine-mapping and mechanistic annotation, we hypothesized that promoters and 3?UTRs are enriched for causal eQTL variants. Therefore, we re-sequenced the promoter and 3?UTR regions of 25 genes with eQTLs, cloned each discovered haplotype, and quantified their impact on transcription using a luciferase-based assay. These data reveal multiple examples of robust, haplotype-specific in vitro functional differences that correlate directly with in vivo expression levels. This suggests that many eQTLs can be rapidly fine-mapped to one or a few single-nucleotide variants and mechanistically characterized using such assays. Integration of functional assays with eQTL discovery, and eQTLs with complex trait associations, is a powerful means to exploit GWAS data and improve their biological interpretability. RNA expression levels were quantified on Illumina gene expression microarrays for 60 normal human livers. Expression quantitative trait loci were identified by genome wide association mapping.

Dataset Information

Disease-associated loci share features with loci that regulate gene expression in response to environmental perturbations

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets