Prioritizing cardiovascular disease-associated variants altering NKX2-5 and TBX5 binding through an integrative computational approach

Abstract

Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are heavily influenced by genetic factors. Genome-wide association studies (GWAS) have mapped > 90% of CVD-associated variants within the non-coding genome, which can alter the function of regulatory proteins, like transcription factors (TFs). However, due to the overwhelming number of GWAS single nucleotide polymorphisms (SNPs) (>500,000), prioritizing variants for in vitro analysis remains challenging. In this work, we implemented a computational approach that considers support vector machine (SVM)-based TF binding site classification and cardiac expression quantitative trait loci (eQTL) analysis to identify and prioritize potential CVD-causing SNPs. We identified 1,535 CVD-associated SNPs that occur within TF footprints and putative enhancers in the human heart and 14,218 variants in linkage disequilibrium (LD) with genotype-dependent gene expression in cardiac tissue. Using ChIP-seq data from two essential cardiac TFs (NKX2-5 and TBX5) in human induced pluripotent stem cell-derived cardiomyocytes (hIPSC-CM), we trained a large-scale gapped k-mer SVM (LS-GKM-SVM) model to identify CVD-associated SNPs that altered NKX2-5 and TBX5 binding sites. The model was tested by scoring human heart TF genomic footprints within putative enhancers and measuring in vitro binding through electrophoretic mobility shift assay (EMSA). Five variants predicted to alter NKX2-5 (rs59310144, rs6715570, and rs61872084) and TBX5 (rs7612445 and rs7790964) binding were prioritized for in vitro validation based on the magnitude of the predicted change in binding and because they are eQTLs in cardiac tissue. All five variants altered NKX2-5 and TBX5 DNA binding. In summary, we present a bioinformatic approach that considers tissue-specific eQTL analysis and SVM-based TF binding site classification to prioritize CVD-associated variants for in vitro experimental analysis.