Identifying High-Dimensional Genomic Factors Modulating Biological Networks Across MultiOmics Data.

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Biological traits interact in complex networks, but genomic influence on these interactions is unclear.
• Understanding these interactions is crucial for deciphering biological systems.

Purpose of the Study:

• To introduce GFBioNet, an efficient computational method for identifying genomic factors that modulate trait associations in biological networks.
• To enable scalable analysis of high-dimensional multi-omics data while controlling the false discovery rate (FDR).

Main Methods:

• A two-stage strategy: 1. Baseline network estimation using Gaussian graphical models. 2. Testing genomic factor modulation of network edges (trait-trait relationships).
• Explicit control of the false discovery rate (FDR) for robust findings.

Main Results:

• Simulations confirm reliable FDR control and high statistical power.
• GFBioNet identified host genetic variants affecting oral microbiome networks.
• Gut microbes were found to modulate metabolite networks in colorectal cancer.
• Somatic mutations and copy-number alterations were shown to reshape gene expression networks in lung adenocarcinoma.

Conclusions:

• GFBioNet is a versatile tool for uncovering the genomic architecture of biological networks.
• The method expands network analysis to evaluate modifiers of trait-trait relationships in multi-omics studies.