ArticleGigaScience2022
NETMAGE: A human disease phenotype map generator for the network-based visualization of phenome-wide association study results.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-wide association meta-analysis identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target.Nature genetics · 2023Pooled it
- Phenome-wide comorbidity network analysis reveals clinical risk patterns in enthesopathy and enthesitis.medRxiv : the preprint server for health sciences · 2025Article
- UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data.Nature communications · 2025Article
- The interplay of sex and genotype in disease associations: a comprehensive network analysis in the UK Biobank.Human genomics · 2025Article
- Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network.Bioinformatics (Oxford, England) · 2024Article
- Phenome-wide association network demonstrates close connection with individual disease trajectories from the HUNT study.PloS one · 2024Article
- mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights.Metabolites · 2022Article
- NETMAGE: A human disease phenotype map generator for the network-based visualization of phenome-wide association study results.GigaScience · 2022Article
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9 authors.
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Abstract
backgroundDisease 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.
resultsTo 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.
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