Causal gene regulatory network inference using enhancer activity as a causal anchor

Il s'agit d'un pre-print sur bioRxiv. Prevoyez-vous de faciliter l'import des métadonnées via le DOINOTICE A REPRENDRE PAS DE CLE UT AU 3 SEPTEMBRE 2018Transcription control plays a crucial role in establishing a unique gene expression signaturefor each of the hundreds of mammalian cell types. Though gene expression data has been widely usedto infer the cellular regulatory networks, the methods mainly infer correlations rather than causality. Wepropose that a causal inference framework successfully used for eQTL data can be extended to infercausal regulatory networks using enhancers as causal anchors and enhancer RNA expression as areadout of enhancer activity.We developed statistical models and likelihood-ratio tests to infer causal gene regulatorynetworks using enhancer RNA (eRNA) expression information as a causal anchor and applied theframework to eRNA and transcript expression data from the FANTOM consortium. Predicted causaltargets of transcription factors (TFs) in mouse embryonic stem cells, macrophages and erythroblasticleukemia overlapped significantly with experimentally validated targets from ChIP-seq and perturbationdata. We further improved the model by taking into account that some TFs might act in a quantitative,dosage-dependent manner, whereas others might act predominantly in a binary on/off fashion. Wepredicted TF targets from concerted variation of eRNA and TF and target promoter expression levelswithin a single cell type as well as across multiple cell types. Importantly, TFs with high-confidencepredictions were largely different between these two analyses, demonstrating that variability within a celltype is highly relevant for target prediction of cell type specific factors. Finally, we generated a compendiumof high-confidence TF targets across diverse human cell and tissue types