Cardiovascular Disease-Associated Non-Coding Variants Disrupt GATA4-DNA Binding and Regulatory Functions

Genome-wide association studies have mapped over 90% of cardiovascular disease (CVD)-associated variants 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) proteins 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 comprised of in silico, in vitro, and cellular evaluation of CVD-associated SNPs predicted to alter GATA4 function.

Graphical Abstract

Highlights

• An integrative computational approach combining functional genomics data and machine learning was implemented to prioritize potential causal genetic variants associated with cardiovascular disease (CVD).

• We prioritized and validated CVD-associated SNPs that created or destroyed genomic binding sites of the cardiac transcription factor GATA4.

• Changes in GATA4-DNA binding resulted in significant changes in GATA4-dependent transcriptional activity in human cells.

• Our results contribute to the mechanistic understanding of cardiovascular disease-associated non-coding variants impacting GATA4 function.