Featured Research

Free, Open-Source Tool Enhances Visualization of the Gap between Genotype and Phenotype

December 05, 2017

Whether it is genetic disease detection or mechanisms of agronomically important traits in plants, LinkageMapView helps scientists “bridge the gap between genotype and phenotype” with map visualizations that are simultaneously functional, visually pleasing, and publication-ready.

LinkageMapView was developed by bioinformaticians with the UNC Charlotte Bioinformatics Services Division (BiSD) at the North Carolina Research Campus (NCRC) in Kannapolis. The tool is described in a new paper published in the September edition of the journal Bioinformatics.

The Challenge

The major goal in genetic mapping is to identify quantitative trait loci (QTL), sections of DNA that are responsible for natural phenotypic information, and link them to the correct genotype. Linkage and QTL maps are vital to scientists studying plants, particularly researchers breeding crops to meet the ever-increasing demand for food created by a growing world population.

The problem is that over time, sequencing technologies that make it possible to develop complex QTL maps have become more advanced than available visualization techniques. Without a tool to effectively paint a picture of the relationships between genotypes and phenotypes as determined by a scientist’s research, findings cannot be disseminated, discussed, or advanced.

“Linkage maps were becoming so dense that you couldn’t fit all of the information on the map,” explained Research Assistant Professor and paper co-author Robert Reid, PhD. “This is where the problem lies, and LinkageMapView was designed to solve it.”


Robert Reid, PhD

The tool is designed to be used in “R,” a graphical programming environment commonly used by scientists that is free and open source. By improving upon the features of existing visualization tools, LinkageMapView has several characteristics that make it beneficial for scientists working with genetic maps:

  • Runs on all major OS platforms (Mac, Windows, Linux)
  • Integrates into existing map-building pipelines
  • Enables users to compare maps to promote collaboration between scientists
  • Highly customizable, including size, title, fonts, colors, and label display
  • Outputs in Adobe Portable Document Format (PDF)
  • Contains options to help the user highlight areas of significance
  • Creates high-resolution, publication-quality figures of chromosomes and linkage maps


Left: An original oat consensus map (Chaffin et al., 2016). Red lines denote marker locations to show where marker density is the highest. Right: Oat consensus map using LinkageMapView. Marker density is now represented by a color spectrum, allowing for easier interpretation of marker dense regions. The color palette is fully customizable. Individual chromosomes are given titles.

This is only the beginning of development for LinkageMapView. “The R parameter interface can be daunting for a novice R user,” said first author Lisa Ouellette, a bioinformatics graduate student who completed a majority of the technical design, programming, and testing for LinkageMapView. “Going forward, we want to improve LinkageMapView by making it available online with an easy, intuitive way to indicate your preferences.”

The featured image is an example of a genetic linkage map produced in LinkageMapView, courtesy of Cavagnaro et al., 2011.

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