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

Nick Strayer, Jana K. Shirey-Rice, Yu Shyr, Joshua C. Denny, Jill M. Pulley, Yaomin Xu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1778-1780
Number of pages3
JournalBioinformatics
Volume37
Issue number12
DOIs
Publication statusPublished - 2021 Jun 15

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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