Gene Regulatory Networks and Individual-Specific Regulatory Disruptions

  • Des Weighill, PhD

    Postdoctoral Research Associate, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill


The majority of disease-associated genetic variants are thought to have regulatory effects, including disruption of transcription factor (TF) binding and alteration of downstream gene expression. Identifying the way in which each person’s genotype affects their individual gene regulatory network would provide important insight into disease etiology and enable improved genotype-specific disease risk assessments and treatments. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, eQTL data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses message passing to integrate this prior network with gene expression and TF protein-protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer GRNs for two blood-derived cell lines and identified genotype-associated, cell-line specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET GRNs for three cell types from each of 119 individuals and identified cell type-specific regulatory differences associated with diseases related to those cell types. Because genotypes are the only individual-level data required, EGRET can be readily applied to disease cohorts where only genetic information is available. 

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