Effective regulatory relationships between genes differ from direct transcription factor-target interactions, and exhibit a distinct statistical signature

Gene expression patterns are determined by the interactions between thousands of transcriptional regulators and their target genes. However, mounting evidence of an apparent lack of causal and statistical association between transcription factors and their targets, raises the question of where the effective regulation of any particular gene lies. Using both a simple computational gene regulatory network model together with human expression and perturbation data for 796 known transcription factors and their confirmed targets, we demonstrate a widespread and generalized lack of statistical and causal association between the activity of regulators and direct targets. We introduce the concept of effective regulators: genes that, upon experimental down-regulation, have the strongest impact on a given target. Effective regulators form a small set of genes that display a distinct statistical signature, and networks constructed from effective regulators exhibit greater clustering and connectivity than canonical TF-based networks. Our results show that, while at the transcriptome-level, actual regulatory relationships between genes do not coincide with direct transcription factor–target interactions and are, instead, diffusely distributed throughout the transcriptional network, they can however be readily identified based on their distinct statistical properties.