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Gene-Environment Interactions in the Vascular Endothelium

Gene-Environment Interactions in the Vascular Endothelium
血管内皮的基因-环境相互作用
批准号:
10318186
负责人:
Anthony Scott Findley
金额:
$5.18万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-12-31 至 2022-12-30

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中文摘要
翻译
摘要:血管内皮中基因与环境的相互作用
英文摘要
Abstract: Gene‐Environment Interactions in the Vascular Endothelium Genome‐wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits. However, only a limited number of environmental factors are measured in GWAS. Thus, some of the genetic effect sizes measured may be underestimated when the underlying environment is composed of many exposures. Controlling for, or accurately measuring, all possible environmental factors in a GWAS setting is a formidable challenge. Instead, molecular phenotypes (gene expression, chromatin accessibility) measured in tightly controlled cellular environments provide a more tractable setting in which to study gene‐environment interactions (GxE) in the absence of other confounding variables. In this proposed research, I will develop methods to investigate causes and consequences of GxE, and I will apply them to analyze the vascular endothelium at the molecular, interindividual, and phenotypic levels. I will use data we have already collected from human umbilical vein endothelial cells (HUVECs) from 17 healthy donors, for 3 treatment conditions (dexamethasone, retinoic acid, and caffeine) and appropriate vehicle‐controls. We genotyped and performed RNA‐seq and ATAC‐seq to model genetic and environmental effects on gene regulation and chromatin accessibility in the vascular endothelium, a common site of pathology in cardiovascular disease (e.g., atherosclerosis). I will first identify transcription factors (TFs) which regulate response to each treatment and predict regulatory variants which affect gene expression in response to treatment. I will then develop a joint allele‐specific expression (ASE) and quantitative trait loci (QTL) mapping approach to experimentally identify GxE‐QTLs in our dataset and validate the computational predictions of the effects of regulatory variants. These variants will be used to fine map and functionally annotate GWAS SNPs associated with cardiovascular disease. Ultimately, findings discovered here will provide insights into the mechanisms for GxE in cardiovascular disease, and the developed methods will be broadly applicable to the study of GxE in other cell types and environmental conditions.
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