Assocplots: a Python package for static and interactive visualization of multiple-group GWAS results

Ekaterina A. Khramtsova, Barbara Elaine Stranger

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Background: Over the last decade, genome-wide association studies (GWAS) have generated vast amounts of analysis results, requiring development of novel tools for data visualization. Quantile–quantile (QQ) plots and Manhattan plots are classical tools which have been utilized to visually summarize GWAS results and identify genetic variants significantly associated with traits of interest. However, static visualizations are limiting in the information that can be shown. Here, we present Assocplots, a Python package for viewing and exploring GWAS results not only using classic static Manhattan and QQ plots, but also through a dynamic extension which allows to interactively visualize the relationships between GWAS results from multiple cohorts or studies.

Availability and Implementation: The Assocplots package is open source and distributed under the MIT license via GitHub (https://github.com/khramts/assocplots) along with examples, documentation and installation instructions.

Contact: ekhramts@medicine.bsd.uchicago.edu or bstranger@medicine.bsd.uchicago.edu

Original languageEnglish (US)
Pages (from-to)432-434
Number of pages3
JournalBioinformatics (Oxford, England)
Volume33
Issue number3
DOIs
StatePublished - Feb 1 2017

ASJC Scopus subject areas

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

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