The more complex data are, the higher the number of possibilities to extract partial information from those data. These possibilities arise by adopting diﬀerent analytic approaches. The heterogeneity among these approaches and in particular the heterogeneity in results they produce are challenging for follow-up studies, including replication, validation and translational studies. Furthermore, they complicate the interpretation of ﬁndings with wide-spread relevance. Here, we take the example of statistical epistasis networks derived from genome-wide association studies with single nucleotide polymorphisms as nodes. Even though we are only dealing with a single data type, the epistasis detection problem suﬀers from many pitfalls, such as the wide variety of analytic tools to detect them, each highlighting diﬀerent aspects of epistasis and exhibiting diﬀerent properties in maintaining false positive control. To reconcile diﬀerent network views to the same problem, we considered 3 network aggregation methods and discussed their performance in the context of epistasis network aggregation. We furthermore applied a latent class method as best performer to real-life data on inﬂammatory bowel disease (IBD) and highlighted its beneﬁts to increase our understanding about IBD underlying genetic architectures.