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Enhancers Decoding the Mechanisms Underlying CAD Risk

Periodic Reporting for period 1 - EnDeCAD (Enhancers Decoding the Mechanisms Underlying CAD Risk)

Reporting period: 2019-01-01 to 2020-06-30

In recent years, genome-wide association studies (GWAS) have discovered hundreds of single nucleotide polymorphisms (SNPs) which are significantly associated with coronary artery disease (CAD). However, the SNPs identified by GWAS explain typically only small portion of the trait heritability and vast majority of variants do not have known biological roles. This is explained by variants lying within noncoding regions such as in cell type specific enhancers and additionally ‘the lead SNP’ identified in GWAS may not be the ‘the causal SNP’ but only linked with a trait associated SNP. Therefore, a major priority for understanding disease mechanisms is to understand at the molecular level the function of each CAD loci. In this study we aim to bring the functional characterization of SNPs associated with CAD risk to date by focusing our search for causal SNPs to enhancers of disease relevant cell types, namely endothelial cells, macrophages and smooth muscle cells of the vessel wall, hepatocytes and adipocytes. By combination of massively parallel enhancer activity measurements, collection of novel eQTL data throughout cell types under disease relevant stimuli, identification of the target genes in physical interaction with the candidate enhancers and establishment of correlative relationships between enhancer activity and gene expression we hope to identify causal enhancer variants and link them with target genes to obtain a more complete picture of the gene regulatory events driving disease progression and the genetic basis of CAD. Linking these findings with our deep phenotypic data for cardiovascular risk factors, gene expression and metabolomics has the potential to improve risk prediction, biomarker identification and treatment selection in clinical practice. Ultimately, this research strives for fundamental discoveries and breakthrough that advance our knowledge of CAD and provides pioneering steps towards taking the growing array of GWAS for translatable results.
To better understand and annotate the cell type specific expression of ncRNAs during cellular differentiation and in response to proatherogenic stimulus, we have characterized eRNAs and miRNAs in hepatocytes and endothelial cells (Linna Kuosmanen et al, 2020; Viiri et al, 2020). As a proof of principle of functional characterization of noncoding variants, we have investigated an enhancer region that harbors a lncRNA called OLMALINC. We demonstrate that OLMALINC regulates the expression of stearoyl‐coenzyme A desaturase in the liver that could explain its association with statin use and serum triglyceride levels (Benhammou et al, 2019). From this single-locus analysis, we have recently expanded to genome-wide analysis of variants in endothelial cells and identified hundreds of expression and epigenetic QTLs that supports evidence of the genetic predisposition to CAD being manifested through the endothelium (Stolze et al, 2020). Similarly, we have completed extensive genome-wide characterization of regulatory variants active in liver hepatocytes and identified hundreds of potential target genes based on chromatin interaction data and cis-eQTL analysis (Selvarajan et al, submitted). Importantly, we demonstrate that enhancer variants nearby known risk genes (APOB, APOE, APOC1 and LIPA) could represent another way by which genetic variation regulates serum lipoprotein levels.
We have implemented several strategies to detect functional regulatory variants in disease-relevant cell types. To this end, we have validated several risk loci in enhancer regions specific to endothelial cells (VEGFC, FGD6, and KIF26B) and hepatocytes (GALK1, APOB, LIPA, CLTCL1, UBE2Z and MAT1A). This analysis will be further extended to smooth muscle cells, macrophages and adipocytes during the next reporting period. Ultimately, our study will provide the pioneering steps towards deeper understanding of the genetic basis of CAD.
Schematic overview