ArticleBioinformatics (Oxford, England)2020
PheGWAS: a new dimension to visualize GWAS across multiple phenotypes.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrating transcriptomic and polygenic risk scores to enhance predictive accuracy for ischemic stroke subtypes.Human genetics · 2025Article
- Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records.Journal of intellectual disability research : JIDR · 2024Article
- Phenome-wide association study and precision medicine of cardiovascular diseases in the post-COVID-19 era.Acta pharmacologica Sinica · 2023Review
- A genome-wide association study for allergen component sensitizations identifies allergen component-specific and allergen protein group-specific associations.The journal of allergy and clinical immunology. Global · 2023Article
- 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
- Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome.Comprehensive Physiology · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationPheGWAS was developed to enhance exploration of phenome-wide pleiotropy at the genome-wide level through the efficient generation of a dynamic visualization combining Manhattan plots from GWAS with PheWAS to create a 3D 'landscape'. Pleiotropy in sub-surface GWAS significance strata can be explored in a sectional view plotted within user defined levels. Further complexity reduction is achieved by confining to a single chromosomal section. Comprehensive genomic and phenomic coordinates can be displayed.
resultsPheGWAS is demonstrated using summary data from Global Lipids Genetics Consortium GWAS across multiple lipid traits. For single and multiple traits PheGWAS highlighted all 88 and 69 loci, respectively. Further, the genes and SNPs reported in Global Lipids Genetics Consortium were identified using additional functions implemented within PheGWAS. Not only is PheGWAS capable of identifying independent signals but also provides insights to local genetic correlation (verified using HESS) and in identifying the potential regions that share causal variants across phenotypes (verified using colocalization tests). AVAILABILITY AND IMPLEMENTATION: The PheGWAS software and code are freely available at (https://github.com/georgeg0/PheGWAS). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.