Cardiovascular disease-associated non-coding variants disrupt GATA4-DNA binding and regulatory functions.

Abstract

Genome-wide association studies have identified thousands of cardiovascular disease (CVD)-associated variants, with over 90% of them being mapped within the non-coding genome. Non-coding variants in regulatory regions of the genome, such as promoters, enhancers, silencers, and insulators, can alter the function of tissue-specific transcription factors (TFs) and their gene regulatory function. In this work, we used a computational approach to identify and test CVD-associated single-nucleotide polymorphisms (SNPs) that alter the DNA binding of the human cardiac transcription factor GATA4. Using a gapped k-mer support vector machine (GKM SVM) model, we scored CVD-associated SNPs localized in gene regulatory elements in expression quantitative trait loci (eQTL) detected in cardiac tissue to identify variants altering GATA4-DNA binding. We prioritized four variants that resulted in a total loss of GATA4 binding (rs1506537 and rs56992000) or the creation of new GATA4 binding sites (rs2941506 and rs2301249). The identified variants also resulted in significant changes in transcriptional activity proportional to the altered DNA-binding affinities. In summary, we present a comprehensive analysis comprising in silico, in vitro, and cellular evaluation of CVD-associated SNPs predicted to alter GATA4 function.