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PheWAS-ME: A web-app for interactive exploration of multimorbidity patterns in PheWAS

Authors :
Nick Strayer
Yu Shyr
Yaomin Xu
Jana K. Shirey-Rice
Jill M. Pulley
Joshua C. Denny
Source :
Bioinformatics
Publication Year :
2019
Publisher :
Cold Spring Harbor Laboratory, 2019.

Abstract

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.

Details

Language :
English
Database :
OpenAIRE
Journal :
Bioinformatics
Accession number :
edsair.doi.dedup.....56040f5fe0a79f45b44be121c62ec2b4
Full Text :
https://doi.org/10.1101/19009480