Comparing bulk and single-cell methodologies and models to profile gene expression, chromatin accessibility and regulatory links in endothelial cells treated with TNFα.

Genome-wide association studies (GWAS) have identified thousands of non-coding variants associated with complex traits and diseases. However, identifying the causal genes regulated by those variants remains challenging. Regulatory links can be inferred from direct physical interaction (e.g. chromosome conformation capture) or probabilistic models. These statistical models take advantage of gene expression and chromatin accessibility profiles generated in cells and tissues by bulk or single-cell (sc) methodologies. We tested whether using bulk or sc RNAseq/ATACseq data and corresponding predictive enhancer-to-gene models impact the prioritization of causal GWAS genes. Using non-treated and TNFα-treated human endothelial cells in vitro, we show that bulk and sc RNAseq/ATACseq profiles highlight the same biology. Despite these similarities, we show using GWAS results for coronary artery disease (CAD) and diastolic blood pressure (DBP) that applying bulk- or sc-based enhancer-to-gene models can yield differences in terms of captured heritability, fine-mapped variants and linked genes. For instance, at one CAD locus, the bulk-based ABC model predicts a regulatory link with