MEAGA

MEAGA performs minimum distance-based enrichment analysis for genetic association studies by integrating protein-protein interaction networks to detect enriched biological pathways and functions in complex-trait and disease genetics.

Key Features:

• Graphical Algorithm Techniques: Employs graphical algorithms to perform functional and pathway enrichment tests while integrating network information from biological interactomes such as protein-protein interaction networks.
• Sub-graph Identification: Identifies sub-graphs among genes and measures their closeness within an interaction database to assess spatial relationships between associated genes.
• Statistical Summarization: Computes a statistic that summarizes both the number and the total distances of sub-graphs to quantify overlap between observed genetic signals and predefined function or pathway gene sets.
• Sampling Technique for P-values: Uses a sampling technique to approximate empirical and multiple testing-corrected P-values.

Scientific Applications:

• Simulation Studies: Demonstrated increased statistical power to identify disease-associated pathways in simulation studies.
• Case Study - Psoriasis: Applied to a meta-analysis of psoriasis using Immunochip datasets, revealing significant enrichment of immune-related functions and closer proximity among associated genes within the protein-protein interaction network.

Methodology:

Integrates protein-protein interaction networks; identifies sub-graphs and shortest distances among associated genes within the interactome; computes a combined statistic summarizing number and total distances of sub-graphs; and uses sampling to approximate empirical and multiple testing-corrected P-values, with application to Immunochip and Metabochip marker sets and comparison to count-based strategies.