Predicting disease-related subnetworks for type 1 diabetes using a new network activity score. OMICS 2012 Oct;16(10):566-78
Date
08/25/2012Pubmed ID
22917479Pubmed Central ID
PMC3459426DOI
10.1089/omi.2012.0029Scopus ID
2-s2.0-84867092043 (requires institutional sign-in at Scopus site) 10 CitationsAbstract
In this study we investigated the advantage of including network information in prioritizing disease genes of type 1 diabetes (T1D). First, a naïve Bayesian network (NBN) model was developed to integrate information from multiple data sources and to define a T1D-involvement probability score (PS) for each individual gene. The algorithm was validated using known functional candidate genes as a benchmark. Genes with higher PS were found to be more likely to appear in T1D-related publications. Next a new network activity metric was proposed to evaluate the T1D relevance of protein-protein interaction (PPI) subnetworks. The metric considered the contribution both from individual genes and from network topological characteristics. The predictions were confirmed by several independent datasets, including a genome wide association study (GWAS), and two large-scale human gene expression studies. We found that novel candidate genes in the T1D subnetworks showed more significant associations with T1D than genes predicted using PS alone. Interestingly, most novel candidates were not encoded within the human leukocyte antigen (HLA) region, and their expression levels showed correlation with disease only in cohorts with low-risk HLA genotypes. The results suggested the importance of mapping disease gene networks in dissecting the genetics of complex diseases, and offered a general approach to network-based disease gene prioritization from multiple data sources.
Author List
Gao S, Jia S, Hessner MJ, Wang XAuthors
Martin J. Hessner PhD Professor in the Pediatrics department at Medical College of WisconsinShuang Jia Biostatistician III in the Pediatrics department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
AlgorithmsArea Under Curve
Bayes Theorem
Computer Simulation
Diabetes Mellitus, Type 1
Gene Regulatory Networks
Genetic Linkage
Genome-Wide Association Study
Humans
Models, Genetic
ROC Curve
Software
Transcriptome
