Epigenome-augmented eQTL-hotspots reveal genome-wide transcriptional programs in 36 human tissues

Abstract

Abstract Expression quantitative trait loci (eQTLs) are used to inform the mechanisms of transcriptional regulation in eukaryotic cells. However, the specificity of genome-wide eQTL identification is limited by stringent control for false discoveries. Here, we described a method based on the non-homogeneous Poisson process to identify 125 489 regions with highly frequent, multiple eQTL associations, or ‘eQTL-hotspots’, from the public database of 59 human tissues or cell types. We stratified the eQTL-hotspots into two classes with their distinct sequence and epigenomic characteristics. Based on these classifications, we developed a machine-learning model, E-SpotFinder, for augmented discovery of tissue- or cell-type-specific eQTL-hotspots. We applied this model to 36 tissues or cell types. Using augmented eQTL-hotspots, we recovered 655 402 eSNPs and reconstructed a comprehensive regulatory network of 2 725 380 cis-interactions among eQTL-hotspots. We further identified 52 012 modules representing transcriptional programs with unique functional backgrounds. In summary, our study provided a framework of epigenome-augmented eQTL analysis and thereby constructed comprehensive genome-wide networks of cis-regulations across diverse human tissues or cell types.