Modeling Transcriptional Regulation Using Gene Regulatory Networks Based on Multi-Omics Data Sources

Background: Transcriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans- acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. Results: We describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and the local chromatin context. These prediction models perform significantly better compared to models containing cis -regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis -regulatory rare variants for SKAT(sequence kernel association test) analyses of gene expression. Conclusions: Our models generate refined effect estimates for individual transcription factors, allow characterization of their roles across the genome, and provide a framework for integrating multiple data types into a single model of transcriptional regulation.