title: iPINBPA: an integrative network-based functional module discovery tool for genome-wide association studies.
abstract: We introduce the integrative protein-interaction-network-based pathway analysis (iPINBPA) for genome-wide association studies (GWAS), a method to identify and prioritize genetic associations by merging statistical evidence of association with physical evidence of interaction at the protein level. First, the strongest associations are used to weight all nodes in the PPI network using a guilt- by-association approach. Second, the gene-wise converted p-values from a GWAS are integrated with node weights using the Liptak-Stouffer method. Finally, a greedy search is performed to find enriched modules, i.e., sub-networks with nodes that have low p-values and high weights. The performance of iPINBPA and other state-of-the-art methods is assessed by computing the concentrated receiver operating characteristic (CROC) curves using two independent multiple sclerosis (MS) GWAS studies and one recent ImmunoChip study. Our results showed that iPINBPA identified sub-networks with smaller sizes and higher enrichments than other methods. iPINBPA offers a novel strategy to integrate topological connectivity and association signals from GWAS, making this an attractive tool to use in other large GWAS datasets.
authors.affiliation: School of Computing, Queen's University, 25 Union Street, Goodwin Hall, Kingston, Ontario, K7L 3N6, Canada. lili@cs.queensu.ca.
authors.forename: Lili
authors.initials: L
authors.lastname: Wang
authors.forename: Parvin
authors.initials: P
authors.lastname: Mousavi
authors.forename: Sergio E
authors.initials: SE
authors.lastname: Baranzini
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D009103:Multiple Sclerosis / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D060066:Protein Interaction Maps; D012372:ROC Curve