NETMAGE: A human disease phenotype map generator for the network-based visualization of phenome-wide association study results

Abstract Background Disease complications, the onset of secondary phenotypes given a primary condition, can exacerbate the long-term severity of outcomes. However, the exact cause of many of these cross-phenotype associations is still unknown. One potential reason is shared genetic etiology—common genetic drivers may lead to the onset of multiple phenotypes. Disease-disease networks (DDNs), where nodes represent diseases and edges represent associations between diseases, can provide an intuitive way of understanding the relationships between phenotypes. Using summary statistics from a phenome-wide association study (PheWAS), we can generate a corresponding DDN where edges represent shared genetic variants between diseases. Such a network can help us analyze genetic associations across the diseasome, the landscape of all human diseases, and identify potential genetic influences for disease complications. Results To improve the ease of network-based analysis of shared genetic components across phenotypes, we developed the humaN disEase phenoType MAp GEnerator (NETMAGE), a web-based tool that produces interactive DDN visualizations from PheWAS summary statistics. Users can search the map by various attributes and select nodes to view related phenotypes, associated variants, and various network statistics. As a test case, we used NETMAGE to construct a network from UK BioBank (UKBB) PheWAS summary statistic data. Our map correctly displayed previously identified disease comorbidities from the UKBB and identified concentrations of hub diseases in the endocrine/metabolic and circulatory disease categories. By examining the associations between phenotypes in our map, we can identify potential genetic explanations for the relationships between diseases and better understand the underlying architecture of the human diseasome. Our tool thus provides researchers with a means to identify prospective genetic targets for drug design, using network medicine to contribute to the exploration of personalized medicine.

On Page 9, you said "Out of the 2189 edges for which phi correlations could be calculated, 1811 (82.73%) appeared in the DDN. This behavior suggests that our genetic associations identified by our PheWAS results serve as a reasonable approximation of disease co-occurrences". This is expected because both phi correlation and PheWAS analyses were performed on the same dataset. If a pair of disease highly co-occur in the dataset, you would expect a strong correlation on their genetic associations analyzed on the same dataset. However, it may not be generalizable that the genetic associations from PheWAS are a reasonable approximation to disease co-occurrences. The disease-SNP relationships from the PheWAS analysis result are bipartite. Even though NETMAGE focuses on the projected disease-disease network, the information about how specific SNPs link to their corresponding disease pairs is important. For example, in your UKBB-based network (https://hdpm.biomedinfolab.com/ddn/ukbb), when a specific disease is selected, a subgraph of the selected disease and other disease linked to the selected one are showing, but sonly a lump of SNPs without linking to their specific disease pair is provided. This is not helpful. Also annotating those SNPs their genetic context could be very useful for users to quickly grasp the nature of the genetic associations in the subgraph.

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