SigMod

SigMod identifies strongly interconnected disease-associated gene modules by integrating GWAS association signals with gene network information to improve detection of disease-related genes.

Key Features:

• Integration of GWAS and Gene Networks: Combines GWAS association signals with gene network topology to identify modules of interconnected genes associated with disease.
• Binary Quadratic Optimization: Formulates module selection as a binary quadratic optimization problem.
• Exact Solution via Graph Min-Cut: Solves the binary quadratic optimization exactly using graph min-cut algorithms.
• Consideration of Edge Weights: Incorporates edge weights that quantify the confidence of gene–gene connections in the network.
• Rapid Selection Path Computation: Computes the selection path rapidly to enable efficient identification of candidate modules.
• Robustness Against Noise: Demonstrates robustness to noise in both GWAS results and network resources.

Scientific Applications:

• Simulation and benchmarking: Demonstrated improved performance relative to state-of-the-art network-assisted methods on simulated and real datasets.
• Childhood-onset asthma GWAS analysis: Identified a gene module enriched for high association signals containing functionally related genes relevant to asthma.

Methodology:

Formulates module identification as a binary quadratic optimization problem solved exactly using graph min-cut algorithms, incorporates edge weights representing connection confidence, and computes a selection path.