Predicting Disease-Related Subnetworks for Type 1 Diabetes Using a New Network Activity Score

Abstract

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 largescale 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.

A Systems Biology Approach to Investigating Sex Differences in Cardiac Hypertrophy

“…This provided a network with 178 genes and 313 interactions. We used an in‐house software, jActiveModulesTopo to identify the subnetworks that were most relevant to the sex–hypertrophy interaction (ie, connected sets of genes with high levels of sex–hypertrophy difference), using a simulated annealing method and setting the search depth at 2, and the result is given in Figure , with 17 genes and 22 interactions. This subnetwork is centered on PPARα, a clear hub with 8 interactions with other members, whereas the interactions for other genes ranged from 1 to 4.…”

“…Only interactions with a confidence score >700 were kept. Protein–protein interaction subnetworks with gene expression variations significantly associated with a factor (sex, hypertrophy, and the interaction between sex and hypertrophy) were identified using jActiveModulesTopo, a software package for trait‐relevant subnetwork identification that takes network topology into consideration …”

Collective effects of common single nucleotide polymorphisms and genetic risk prediction in type 1 diabetes

“…The HLA region on chromosome 6 may provide most of them . Previous studies used specific major effect genes in HLA‐region or in combination with non‐HLA regions to establish T1D risk prediction models . But these models do not give robust TPR values (AUC < 0.85) to be meaningful in practical use.…”

Identification of highly synchronized subnetworks from gene expression data

“…(5) was developed recently by us to predict candidate disease genes for type 1 diabetes, where $z_i$ is the z-score of disease relevance of gene i . There again we demonstrated the advantage of incorporating network structural information [16]. …”