Causal gene regulatory network inference using enhancer activity as a causal anchor
Résumé
Transcription control plays a crucial role in establishing a unique gene expression signature
for each of the hundreds of mammalian cell types. Though gene expression data has been widely used
to infer the cellular regulatory networks, the methods mainly infer correlations rather than causality. We
propose that a causal inference framework successfully used for eQTL data can be extended to infer
causal regulatory networks using enhancers as causal anchors and enhancer RNA expression as a
readout of enhancer activity.
We developed statistical models and likelihood-ratio tests to infer causal gene regulatory
networks using enhancer RNA (eRNA) expression information as a causal anchor and applied the
framework to eRNA and transcript expression data from the FANTOM consortium. Predicted causal
targets of transcription factors (TFs) in mouse embryonic stem cells, macrophages and erythroblastic
leukemia overlapped significantly with experimentally validated targets from ChIP-seq and perturbation
data. 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. We
predicted TF targets from concerted variation of eRNA and TF and target promoter expression levels
within a single cell type as well as across multiple cell types. Importantly, TFs with high-confidence
predictions were largely different between these two analyses, demonstrating that variability within a cell
type is highly relevant for target prediction of cell type specific factors. Finally, we generated a compendium
of high-confidence TF targets across diverse human cell and tissue types.
Domaines
Sciences du Vivant [q-bio]
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