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.