PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.
- Resource Type
- Academic Journal
- Authors
- Strayer N; Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA.; Shirey-Rice JK; Department of Medical Administration, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.; Shyr Y; Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA.; Denny JC; Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37203, USA.; Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.; Pulley JM; Department of Medical Administration, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.; Department of Medicine, Office of Research, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.; Xu Y; Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA.; Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37203, USA.
- Source
- Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
- Subject
- Language
- English
Summary: Electronic health records (EHRs) linked with a DNA biobank provide unprecedented opportunities for biomedical research in precision medicine. The Phenome-wide association study (PheWAS) is a widely used technique for the evaluation of relationships between genetic variants and a large collection of clinical phenotypes recorded in EHRs. PheWAS analyses are typically presented as static tables and charts of summary statistics obtained from statistical tests of association between a genetic variant and individual phenotypes. Comorbidities are common and typically lead to complex, multivariate gene-disease association signals that are challenging to interpret. Discovering and interrogating multimorbidity patterns and their influence in PheWAS is difficult and time-consuming. We present PheWAS-ME: an interactive dashboard to visualize individual-level genotype and phenotype data side-by-side with PheWAS analysis results, allowing researchers to explore multimorbidity patterns and their associations with a genetic variant of interest. We expect this application to enrich PheWAS analyses by illuminating clinical multimorbidity patterns present in the data.
Availability and Implementation: A demo PheWAS-ME application is publicly available at https://prod.tbilab.org/phewas_me/. Sample datasets are provided for exploration with the option to upload custom PheWAS results and corresponding individual-level data. Online versions of the appendices are available at https://prod.tbilab.org/phewas_me_info/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity_explorer).
Supplementary Information: Supplementary data are available at Bioinformatics online.
(© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)