A computational approach for identification of core modules from a co-expression network and GWAS data

Area of Science:

• Genetics
• Systems Biology
• Computational Biology

Background:

• Complex traits are influenced by many genes.
• Identifying key regulatory genes is crucial for understanding disease.
• The omnigenic model provides a framework for genetic architecture.

Purpose of the Study:

• To describe a protocol for identifying "core" genes associated with disease phenotypes.
• To leverage the omnigenic model for discovering novel therapeutic targets.
• To outline a computational approach integrating multiple data sources.

Main Methods:

• Application of the omnigenic model to genetic architecture.
• Utilizing genome-wide association studies (GWAS) data.
• Integrating gene co-expression networks and public databases (GTEx, IMPC).

Main Results:

• Identification of gene modules enriched for "core-like" characteristics.
• A systematic approach to pinpoint potential disease-regulating genes.
• Provides a reproducible protocol for future research.

Conclusions:

• The described protocol enables the identification of novel core genes.
• These core genes represent promising candidates for therapeutic intervention in complex diseases.
• This integrative approach advances the study of genetic architecture.