LinkageMapView—rendering high-resolution linkage and QTL maps

1Linkage studies over the years have been producing genetic maps (linkage maps) that act as invaluable tools in areas of genetic disease detection, anchoring genome sequence assemblies and elucidating genetic mechanisms of agronomical important traits in plants. The advent of high-throughput sequencing has allowed for better marker identification and greater map densities. This is a desirable outcome for solving many genetics based problems but visualizing these maps creates a new challenge. As the marker density increases, it becomes difficult to add labels, QTL regions and centimorgan values and still maintain readability.

2There is existing software that generates linkage and QTL maps and these fall into three categories. Rudimentary maps are available in software where the primary objective is QTL analysis. For example, R/qtl is widely used for QTL analysis but the plot functionality is not a strength. High-quality maps can be produced with feature-rich software such as MapChart. MapChart only runs on Windows. Lastly, there is web-based software like R/qtlcharts, but it is more useful for interactively exploring linkage and QTL maps. Here, we introduce LinkageMapView, an open source package in R that can produce publication quality genetic maps, can be run on any platform supported by R, and can be incorporated into a user’s existing R-based pipeline.

3LinkageMapView uses R base graphics for plotting, labels and optional colored segments. The main function, lmv.linkage.plot, is the only function a user needs to directly invoke. The generated maps are output in Adobe Portable Document Format (PDF). Because linkage maps can be dense with loci, LinkageMapView has many options that allow the user to reduce extraneous detail, minimize map dimensions, and highlight areas of importance.

4Input to LinkageMapView is a delimited text file with the first three columns containing linkage group name, cM position, and locus. Alternatively, input can be an R/qtl cross object.

5To generate the map, a user calls the lmv.linkage.plot function. Parameters allow users to customize most aspects of the map. Users can alter map size, title, fonts, colors and label display to emphasize markers or QTLs of importance. Some common parameters are discussed below.

6mapthese: An ordered vector of linkage group names to print.

7showonly: A vector of loci labels to print. All other loci labels will not be printed although their presence is depicted with a line segment across the chromosome to indicate loci density.

8dupnbr: If TRUE, the first locus name at a position will be shown followed by a count of the duplicate markers at that position.

9ruler: If TRUE, instead of printing position numbers on each chromosome, a ruler is printed on the left side.

10markerformatlist: A list of loci with the desired R font, color, and size for the labels. This list overrides any other specifications for the loci listed.

11qtldf: An optional R data frame containing QTL information:

12autoconnadj: If TRUE (the default), loci with the same name and in adjacent linkage groups will be connected with a line.

13conndf: An optional data frame containing the homologs to connect with a line. If autoconnadj is TRUE, this list will be merged with the automatic list.

14revthese: An optional vector of linkage group names to reverse. The end position becomes position 0 and position 0 becomes the largest position.

15posonleft: An optional boolean vector the length of the number linkage groups to map, indicating if the positions should be plotted on the left (TRUE) or on the right (FALSE).

16The maps from two use cases are shown in Figure 1.

17The first case (Fig. 1A) demonstrates using a cross object from R/qtl as input to LinkageMapView (Listing 1) using all default formats.

18Listing 1. Sample commands to generate linkage group map from R/qtl cross object.

19library(qtl)

20data(hyper)

21lmv.linkage.plot(hyper,"qtlhyper.pdf", mapthese = c(1, 4, 6, 7, 15))

22The second case (Fig. 1B) demonstrates using LinkageMapView to produce a comparative map with homologs connected, a QTL mapped, a cM ruler and several color and font options.

23LinkageMapView is a freely available and open source tool to produce publication quality linkage and QTL maps. It is implemented in R to provide easy integration with R QTL analysis programs and so it can be run on any platform. The plethora of options provides the user with flexibility in dealing with high-density linkage maps. These characteristics make LinkageMapView more useful than currently available linkage map plotting tools.