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.

mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights

“…Table 2 provides detailed comparisons of mGWAS-Explorer with several bioinformatics resources that can be used for mGWAS, including Metabolomics GWAS Server [33,34], PheWeb [35], NETMAGE [37], and GePhEx [72]. The metabolomics GWAS server supports searching the results of two genome-wide association studies on the blood and urine metabolome in 7824 and 3861 individuals with European ancestry [33,34].…”

“…PheWeb is an excellent tool for developers to build a website to explore and visualize large-scale genetic associations [35]. NETMAGE focuses on visualizing disease-disease networks from sum-mary statistics [37], and GePhEx allows visualization and interpretation of relationships across multiple traits with genetic associations evidence [72].…”

“…A variety of databases currently exist to store the genotype-phenotype association datasets, including GWAS Catalog [27], PhenoScanner [28], OpenGWAS [29], Open Targets Genetics [30], PheLiGe [31], DisGeNET [32], as well as specific tools for mGWAS, such as the metabolomics GWAS server [33,34]. Valuable tools currently exist to allow users to perform cross-phenotype analysis [35][36][37][38][39][40]. However, these tools do not offer extra support beyond displaying and visualization, and none of them are dedicated to mGWAS.…”

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

“…Furthermore, PheWASs are disease- and variant-agnostic, meaning that the identification of these potential instances of pleiotropy remains unbiased ( Pendergrass et al 2013 , Hall et al 2014 ). The summary statistics from a PheWAS can be used to create corresponding shared-SNP DDNs (ssDDNs), where edges represent sets of associated SNPs that pass a desired threshold of significance and are shared between the two phenotypes ( Verma et al 2019 , Sriram et al 2021 , 2022 ). By analyzing a ssDDN, a researcher or clinician can evaluate how diseases are linked to one another, with immediate insight into potential shared genetic architecture through the identification of putative pleiotropic SNPs at specific genomic locations.…”

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

“…Furthermore, PheWASs are disease-and variant-agnostic, meaning that the identification of these potential instances of pleiotropy remains unbiased 6,7 . The summary statistics from a PheWAS can be used to create corresponding shared-SNP DDNs (ssDDNs), where edges represent sets of associated SNPs that pass a desired threshold of significance and are shared between the two phenotypes [8][9][10] . By analyzing a ssDDN, a researcher or clinician can evaluate how diseases are linked to one another, with immediate insight into potential shared genetic architecture through the identification of putative pleiotropic SNPs at specific genomic locations.…”