iPINBPA: an integrative network-based functional module discovery tool for genome-wide association studies

Area of Science:

• Genetics
• Bioinformatics
• Systems Biology

Background:

• Genome-wide association studies (GWAS) identify genetic variants associated with diseases.
• Integrating protein-protein interaction (PPI) networks can enhance the biological interpretation of GWAS findings.
• Existing methods may not fully leverage both statistical association signals and network topology.

Purpose of the Study:

• To introduce a novel method, integrative protein-interaction-network-based pathway analysis (iPINBPA), for analyzing GWAS data.
• To enhance the identification and prioritization of genetic associations by merging statistical and physical interaction evidence.
• To develop a tool for discovering biologically relevant sub-networks within large GWAS datasets.

Main Methods:

• iPINBPA utilizes a guilt-by-association approach to weight nodes in a PPI network based on GWAS association strength.
• Gene-wise p-values from GWAS are integrated with node weights using the Liptak-Stouffer method.
• A greedy search algorithm identifies enriched sub-networks (modules) with high node weights and low p-values.

Main Results:

• The performance of iPINBPA was evaluated using concentrated receiver operating characteristic (CROC) curves on multiple sclerosis (MS) GWAS and ImmunoChip data.
• iPINBPA identified sub-networks with smaller sizes and higher enrichment compared to other state-of-the-art methods.
• The method demonstrated superior performance in prioritizing genetic associations within biological networks.

Conclusions:

• iPINBPA provides a novel strategy for integrating topological network connectivity with GWAS association signals.
• This method offers an attractive tool for analyzing large GWAS datasets and uncovering biologically meaningful genetic associations.
• The approach enhances the discovery of disease-related pathways by combining diverse biological data types.