RTN

RTN infers transcriptional regulatory networks and identifies master regulators by analyzing gene expression to map transcription factor activity and connect regulators to phenotypes.

Key Features:

• Conditional Mutual Information: Uses conditional mutual information to assess modulators of transcription factor activity.
• Master Regulator Analysis: Identifies and analyzes master regulators within gene expression networks.
• eQTL and VSE Linking: Links master regulators with genetic markers via expression quantitative trait loci (eQTL) and VSE analysis.
• Haplotype Block Mapping: Utilizes haplotype block structure mapped to the human genome to facilitate exploration of risk-associated SNPs identified in GWAS.
• GWAS Integration: Integrates GWAS-identified risk-associated SNPs with regulatory networks to explore genetic contributions to phenotypes.
• Large-Scale Expression Networks: Analyzes networks derived from large transcriptional profile datasets (for example, a network from 2,000 transcriptional profiles).

Scientific Applications:

• FGFR2 and Breast Cancer Risk: Elucidated molecular mechanisms underlying breast cancer risk associated with the FGFR2 locus using eQTL analysis.
• Master Regulator Identification: Identified SPDEF, ERα, FOXA1, GATA3, and PTTG1 as master regulators of FGFR2 signaling from a network derived from 2,000 transcriptional profiles.
• ERα Occupancy Response: Demonstrated that ERα occupancy is responsive to FGFR2 signaling, implicating ERα, FOXA1, and GATA3 in regulation of breast cancer susceptibility genes.
• Therapeutic Insight: Findings align with observed effects of anti-oestrogen treatment in breast cancer prevention, supporting a role for FGFR2 signaling in mediating risk.

Methodology:

Uses conditional mutual information to assess transcription factor modulators; performs eQTL and VSE analyses linking regulators to genetic markers using haplotype block structure mapped to the human genome; and integrates GWAS-identified SNPs with transcriptional network analysis.