Weighted gene coexpression network analysis strategies applied to mouse weight

Area of Science:

• Genetics
• Systems Biology
• Bioinformatics

Background:

• Complex traits are polygenic, making identification of underlying genetic regulatory loci challenging.
• Gene coexpression network analysis is effective for identifying groups of differentially regulated genes.
• Systems-oriented genetic approaches integrating gene expression and genotype data are crucial.

Purpose of the Study:

• To apply weighted gene coexpression network analysis (WGCNA) to liver gene expression and genotype data from a mouse inter-cross.
• To identify physiologically relevant gene modules and their genetic drivers.
• To explore differences in network structure between lean and obese mice.

Main Methods:

• Weighted Gene Coexpression Network Analysis (WGCNA) was used on liver gene expression and genotype data.
• Single-network analysis identified conserved gene modules across mouse crosses.
• Differential network analysis compared gene expression networks between lean and obese mice.

Main Results:

• A physiologically interesting gene module was identified and found in two distinct mouse crosses.
• Module quantitative trait loci (mQTLs) perturbing this module were discovered, along with key genetic drivers.
• Differential network analysis revealed altered connectivity and module structure in obese mice, suggesting an epidermal growth factor (EGF) pathway.

Conclusions:

• WGCNA is a powerful tool for identifying genetic drivers and pathways underlying complex traits.
• Integrating network properties with genetic data aids in understanding the gene-trait relationship.
• The study highlights the utility of WGCNA in dissecting complex genetic architectures.