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R/qtlcharts: Interactive Graphics for Quantitative Trait Locus Mapping
Sunday, 2015/02/08 | 06:12:42

Karl W. Broman1

+ Author Affiliations

1.Address for correspondence: Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, 2126 Genetics-Biotechnology Center, 425 Henry Mall, Madison, WI 53706. E-mail: kbroman@biostat.wisc.edu

Genetics February 1, 2015 vol. 199 no. 2 359-361




Every data visualization can be improved with some level of interactivity. Interactive graphics hold particular promise for the exploration of high-dimensional data. R/qtlcharts is an R package to create interactive graphics for experiments to map quantitative trait loci (QTL) (genetic loci that influence quantitative traits). R/qtlcharts serves as a companion to the R/qtl package, providing interactive versions of R/qtl’s static graphs, as well as additional interactive graphs for the exploration of high-dimensional genotype and phenotype data.


INTERACTIVE graphics have enormous value for the exploration of high-dimensional genetic data. Visualizations of high-dimensional data must be a compressed summary, but with interactive visualizations, features may be linked to underlying details or to different views of the data. Moreover, interactive graphs offer the ability to zoom into dense figures, and sets of linked graphic panels offer greater opportunity to make connections across diverse data types.


Interactive data visualization has a long history. For example, an early innovation, brushed scatterplots, is due to Becker and Cleveland (1987). While numerous tools for interactive data visualization have been developed, for example Mondrian (Theus and Urbanek 2008) (http://www.theusrus.de/Mondrian) and SpotFire (http://spotfire.tibco.com/), until recently interactive visualization has been a specialized craft, and the tools have not been widely adopted as part of routine data analysis. But there has been a recent expansion in the general use and development of complex and rich interactive graphics tools, motivated in part by the JavaScript library D3 (Bostock et al. 2011) (http://d3js.org), the development of HTML5 and scalable vector graphics (SVG), and the power of modern web browsers. Moreover, as these graphical tools are deployed as web pages, they are immediately accessible to users.


Figure 2 Examples of the basic panels that form the core of R/qtlcharts. (A) Heat map; (B) LOD curves; (C) scatterplot; (D) a set of confidence intervals.
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