The number of genetic factors associated with asthma remains limited. To identify new genes with an undetected individual effect but collectively influencing asthma risk, we conducted a network-assisted analysis that integrates outcomes of genome-wide association studies (GWAS) and protein-protein interaction networks. We used two GWAS datasets, each consisting of the results of a meta-analysis of nine childhood-onset asthma GWASs (5,924 and 6,043 subjects, respectively). We developed a novel method to compute gene-level P-values (fastCGP), and proposed a parallel dense-module search and cross-selection strategy to identify an asthma-associated gene module. We identified a module of 91 genes with a significant joint effect on childhood-onset asthma (P < 10(-5)). This module contained a core subnetwork including genes at known asthma loci and five peripheral subnetworks including relevant candidates. Notably, the core genes were connected to APP (encoding amyloid beta precursor protein), a major player in Alzheimer's disease that is known to have immune and inflammatory components. Functional analysis of the module genes revealed four gene clusters involved in innate and adaptive immunity, chemotaxis, cell-adhesion and transcription regulation, which are biologically meaningful processes that may underlie asthma risk. Our findings provide important clues for future research into asthma aetiology.