GeneticsGenomicsPeer-reviewed journal article
Interpreting coronary artery disease risk through gene-environment interactions in gene regulation
Anthony S FindleyiD, Allison L. RichardsiD, Cristiano PetriniiD, Adnan AlaziziiD, Elizabeth R. DomaniD, Alexander G Shanku, Gordon O. Davis, N. Hauff, Yoram Sorokin, Xiaoquan WeniD, Roger Piqué-RegiiD, Francesca LucaiD
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Abstract
GWAS and eQTL studies identified thousands of genetic variants associated with complex traits and gene expression. Despite the important role of environmental exposures in complex traits, only a limited number of environmental factors are measured in these studies. Measuring molecular phenotypes in tightly controlled cellular environments provides a more tractable setting to study gene-environment interactions in the absence of other confounding variables.\n\nWe performed RNA-seq and ATAC-seq in endothelial cells exposed to retinoic acid, dexamethasone, caffeine, and selenium to model genetic and environmental effects on gene regulation in the vascular endothelium, a common site of pathology in cardiovascular disease. We found that genes near regions of differentially accessible chromatin were more likely to be differentially expressed (OR = [3.41, 6.52], p < 10-16). Furthermore, we confirmed that environment-specific changes in transcription factor binding are a key mechanism for cellular response to environmental stimuli. SNPs in these transcription response factor footprints for dexamethasone, caffeine, and retinoic acid were enriched in GTEx eQTLs from artery tissues indicating that these environmental conditions are latently present in GTEx samples. Additionally, SNPs in footprints for response factors in caffeine are enriched in colocalized eQTLs for coronary artery disease (CAD), suggesting a role for caffeine in CAD risk. Interestingly, each treatment may amplify or buffer genetic risk for CAD, depending on the particular SNP considered.
- organism: human
- assay: bulk RNA-seq
- assay: ATAC-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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