Abstract
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.
Highlights
Asthma is a common chronic inflammatory disease of the airways, characterized by varying age at onset and clinical presentation[1]
Identification of a module enriched with childhood-onset asthma-associated genes
Gene-level P-values were taken as the best SNP P-values among all SNPs mapped to the gene and were corrected for the gene length bias using circular genomic permutation of SNP P-values, which allows taking into account the linkage disequilibrium (LD) between SNPs
Summary
Asthma is a common chronic inflammatory disease of the airways, characterized by varying age at onset and clinical presentation[1]. Detecting sets of functionally related genes that jointly affect disease risk Among many of these approaches stands the network-assisted analysis that integrates GWAS results with protein-protein interaction (PPI) network to identify gene modules (subnetworks) enriched in association signals. Longer genes represented by more SNPs are more likely to have small P-values by chance[9, 10] Another challenge of network-assisted analysis is that most algorithms for searching gene modules are sensitive to the input data: the PPI network and GWAS results. We conducted a network-assisted analysis by integrating childhood-onset asthma (COA) GWAS results with experimentally verified human PPI network information to identify a set of interconnected genes that significantly contributes to COA risk. Inspection of the interconnected components of this module together with functional enrichment analysis revealed biologically meaningful processes that underlie the risk of childhood-onset asthma
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