Abstract
Significance Abdominal aortic aneurysm (AAA) is a common and severe disease with major genetic risk factors. In this study we generated enhancer-promoter contact data to identify regulatory elements in AAA-relevant cell types and identified changes in their predicted chromatin accessibility between AAA patients and controls. We integrated this information with disease-associated variants in regulatory elements and gene bodies to further understand the etiology and pathogenetic mechanisms of AAA. Our study combined whole-genome sequencing data with gene regulatory relations in disease-relevant cell types to reveal the important roles of the interleukin 6 pathway and ERG and KLF regulation in AAA pathogenesis.
Highlights
Abdominal aortic aneurysm (AAA) is a common degenerative cardiovascular disease whose pathobiology is not clearly understood
Based on the ATAC-seq (Assay for Transposase Accessible Chromatin with high-throughput sequencing) data from human aortic endothelial cells (HAEC) and aortic smooth muscle cells (AoSMC), we identified the cell-type-specific regulatory elements (REs) in HAEC and AoSMC and classified them into two categories: 1) open REs which overlapped with at least one ATAC-seq peak (OpenLoop) and 2) nonopen REs which did not overlap with any ATAC-seq peaks (NonOpenLoop)
We investigated these cell-type-specific REs at the network level based on the genome-wide association studies (GWAS) summary statistics, identifying AoSMC as the most relevant cell type for AAA
Summary
Abdominal aortic aneurysm (AAA) is a common degenerative cardiovascular disease whose pathobiology is not clearly understood. We performed analysis of whole-genome sequencing data in AAA patients versus controls with the aim of detecting diseaseassociated variants that may affect gene regulation in human aortic smooth muscle cells (AoSMC) and human aortic endothelial cells (HAEC), two cell types of high relevance to AAA disease. To support this analysis, we generated H3K27ac HiChIP data for these cell types and inferred cell-type-specific gene regulatory networks. We subsequently applied the constructed networks to AAA GWAS and gene expression data to Significance
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