ArticleAmerican journal of human genetics2019
Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals.
Article in American journal of human genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pleiotropy in the Genetic Predisposition to Rheumatoid Arthritis: A Phenome-Wide Association Study and Inverse Variance-Weighted Meta-Analysis.Arthritis & rheumatology (Hoboken, N.J.) · 2020Pooled it
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Polygenic and pharmacogenomic contributions to medication dosing: a real-world longitudinal biobank study.Journal of translational medicine · 2025Article
- Phenome-wide comorbidity network analysis reveals clinical risk patterns in enthesopathy and enthesitis.medRxiv : the preprint server for health sciences · 2025Article
- The interplay of sex and genotype in disease associations: a comprehensive network analysis in the UK Biobank.Human genomics · 2025Article
- Phenome-wide association study in 25,639 pregnant Chinese women reveals loci associated with maternal comorbidities and child health.Cell genomics · 2024Article
- Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants.Nature communications · 2024Article
- CoPheScan: phenome-wide association studies accounting for linkage disequilibrium.Nature communications · 2024Article
- A novel method for multiple phenotype association studies based on genotype and phenotype network.PLoS genetics · 2024Article
- 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
- The association of immune-related genes and the potential role of IL10 with biliary atresia.Pediatric research · 2023Article
- Common genetic factors among autoimmune diseases.Science (New York, N.Y.) · 2023Review
- Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data.Bioinformatics (Oxford, England) · 2023Article
- Exploring novel disease-disease associations based on multi-view fusion network.Computational and structural biotechnology journal · 2023Article
- A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank.Communications biology · 2022Article
- Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study.Journal of medical Internet research · 2022Article
- NETMAGE: A human disease phenotype map generator for the network-based visualization of phenome-wide association study results.GigaScience · 2022Article
- Identifying Potential Therapeutic Applications and Diagnostic Harms of Increased Bilirubin Concentrations: A Clinical and Genetic Approach.Clinical pharmacology and therapeutics · 2022Observational
- Phenome-Wide Association Studies.JAMA · 2022Article
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12 authors.
Funding
Abstract
Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger's biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a "disease-disease" network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.
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