The widely collected and analyzed genetic data help in understanding the underlying mechanisms of heterogeneous diseases. Cellular components interact in a network fashion where genes are nodes and edges are the interactions. The failure in individual genes lead to dys-regulation of sub-groups of genes which causes a disease phenotype, and this dys-functional region is called a disease module. Disease module identification in complex diseases such as asthma and cancer is a huge challenge. Despite the development of numerous sophisticated methods there is a still no gold standard. In this study we apply different parameter settings to test the performance of a widely used method for disease module detection in multi-omics data called Weighted Gene Co-expression Network Analysis (WGCNA). A systematic approach is used to identify disease modules in asthma and arthritis diseases. The accuracy of obtained modules is validated by a pathway scoring algorithm (PASCAL) and GWAS SNP enrichment. Our results differ between the tested data sets and therefore we cannot conclude with recommendations for an optimal setting that could perform best for multiple data sets using this method.

It is currently understood that diseases are typically not caused by rogue errors in genetics but have both molecular and environmental causes from myriad overlapping interactions within an interactome. Genetic errors, such as that seen by a single-nucleotide polymorphism can lead to a dysfunctional cell, which in turn can lead to systemic disruptions that result in disease phenotypes. Perturbations within the interactome, as can be caused by many such errors, can be organized into a pathophenotype, or “disease module”. Disease modules are sets of correlated variables that can represent many of a disease’s activities with subgraphs of nodes and edges. Many methods for inferring disease modules are available today, but the results each one yields is not only variable between methods but also across datasets and trial attempts. In this study, several such inference methods for deriving disease modules are evaluated by combining them to create “consensus” modules. The method of focus is Double-Specific Betweenness (S2B), which uses betweenness centrality across separate diseases to derive new modules. This study, however, uses S2B to combine the results of independent inference methods rather than separate diseases to derive new modules. Pre-processed asthma and arthritis data are compared using various combinations of inference methods. The performance of each result is validated using Pathway Scoring Algorithm. The results of this study suggest that combining methods of inference using MODifieR or S2B may be beneficial for deriving meaningful disease modules.

Inflammatory bowel disease encompasses a variety of heterogeneous chronic inflammatory diseases that affect the gastrointestinal tract, where Crohn’s disease and ulcerative colitis are the principal examples. The etiology of these, and many other complex human diseases, remain largely unknown and therefore pose relevant targets for novel research strategies. One such strategy is the in silico application of network theory derived methods to data sourced from publicly available repositories of e.g. gene expression data. Specifically, methods generating graphs of interconnected elements enriched by differentially expressed genes—disease modules—were inferred with data available through the Gene Expression Omnibus. Based on a previous method, the current project aimed to evaluate disease modules, combined from stand-alone inferential methods, in disease consensus modules: representing pathophenotypical motifs for the diseases of interest. The modules found to be significantly enriched by genome-wide association study inferred single-nucleotide polymorphisms, as validated using the Pathway Scoring Algorithm, were subsequently subjects for further analysis using Kyoto Encyclopedia of Genes and Genomes-pathway enrichment, and literature searches. The results of this study adheres to previous findings relating to the employed method, but lack any novelty pertaining the diseases of interest. However, the results substantiate the preceding methods’ conclusion by including parameters that increase statistical validity. In addition, the study contributed to peripheral results concerning both the methodology of consensus module methods, and the elucidation of inflammatory bowel disease etiology and disease subtype differentiation, that pose interesting subjects for future investigation.