netZooR

netZooR infers genotype-specific gene regulatory networks to characterize how genetic variants affect transcription factor binding and downstream gene regulation.

Key Features:

• Genotype-Specific Network Inference: netZooR incorporates EGRET (Estimating the Genetic Regulatory Effect on Transcription Factors) to construct genotype-specific gene regulatory networks (GRNs) for each individual, accounting for genetic variants that affect transcription factor (TF) binding.
• Integration of Multi-Omics Data: EGRET creates a genotype-informed TF–gene prior using TF motif predictions, expression quantitative trait loci (eQTL), individual genotypes, and predicted effects of genetic variants on TF binding, then refines this prior via message-passing integration of gene expression data and TF protein–protein interaction information.
• Validation and Application: Applied to blood-derived cell lines and three cell types from 119 individuals, netZooR-derived GRNs revealed genotype-associated regulatory differences validated by allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding, and identified cell type–specific regulatory variations linked to diseases.
• Insight into Complex Phenotypes: By reflecting individual genetic variation in GRNs, netZooR enables analysis of genetic regulatory associations that contribute to complex phenotypes.

Scientific Applications:

• Personalized Medicine: Understanding genotype-specific regulatory networks allows tailoring treatments to individual genetic profiles.
• Disease Research: Identifying cell type-specific regulatory differences associated with diseases aids in uncovering potential therapeutic targets and biomarkers.
• Genetic Epidemiology: Enhancing disease risk assessment by considering the regulatory effects of genetic variants.

Methodology:

EGRET creates a genotype-informed TF–gene prior from TF motif predictions, eQTLs, individual genotypes, and predicted variant effects on TF binding, then refines the prior using message passing that integrates gene expression data and TF protein–protein interaction networks.