The textile plot (Kumasaka and Shibata 2007) is a parallel coordinate plot in which the ordering, locations and scales of the axes are simultaneously chosen so that the connecting lines, each of which represents a case, are aligned as horizontally as possible. Plots of this type can accommodate numerical data as well as ordered or unordered categorical data, or a mixture of these different data types.

We introduce the textile plot to provide a visualization of linkage disequilibrium (LD) structure so as to maximize deep insight into genetic variation present in the multiple SNP genotype data. LD is a major concern in many genetic studies since the density of SNP markers has markedly increased. The dramatic increase in the number of SNPs may cause such problems in statistical analyses as multiple comparison in hypothesis testing and collinearity in logistic regression model due to the presence of complex LD structure. It is necessary to make inference about underlying genetic variation through LD structure before applying statistical models to the data.

The plot can accentuate LD by specific geometrical shapes. The LD between adjacent SNPs is shown by line crossings between adjacent loci so that low-grade line crossing indicates high dependency of alleles between SNPs and vertical dispersion of genotypes approximates the structure of pairwise correlation coefficients for all SNPs. Application of this technique to simulated and real data illustrated the potential usefulness of the textile plot as an aid to the interpretation of LD among the multiple SNP genotype data. This is the first attempt to introduce the plot to the field of genetics.

Click the download button below. The installer will guide you to install the software in your own computer. The appearance of Textile Plot software is very similar to ordinary web browser, but the target is not a HTML file but data. Lately, the csv or tsv format file of SNP genotype file with one line header is available to use (e.g. see the example files in detail). Just open the file from file menu at the top left, or simply drag and drop it to the window.

The toolbar at the left side of the window will greatly supports you to look at and interpret complex SNP genotype data. The first tool is for selecting or deleting SNP loci from the display. The second tool enables us to highlight samples, and to select or delete the samples in the data set. Usual union or intersection operation is available by pressing SHIFT key or ALT key. The third tool is for zooming up a part of the display by means of vertical axis. If SHIFT key is pressed during the dragging mouse, it enables us to zoom up by means of horizontal axis. The forth tool makes us to rearrange the phisical order of the SNPs according to your own preferences. Just grab, drag and release the SNP. Then you will see the other aspect of the data. The optimal SNP ordereing is also available from "View" -> "Order of SNPs" in the menu bar.

Here we provide some simulated and real data examples. Example1.tsv is a simulated SNP genotype data under Hardy-Weinberg equilibrium, where the degree of linkage disequilibrium (LD) decays from left to right. Example2.csv is a simulated SNP genotype data under Hardy-Weinberg equilibrium, here the first and second SNP are in absolute LD and the second and third SNPs are in complete LD while the third and fourth are nearly in linkage equilibrium. The third example is a part of famous HapMap data (Phase II, with samples comprised of 45 CHB, 45 JPN, 60 YRI and 60 CEU) surrounding HLA-A region. Here we note that, the software accepts any kind of genotype representations (e.g. "AA", "Aa", "AG", "C A", "1 2", etc) except for a numerical representation like "00", "12", "22", etc. But the missing genotyope MUST be denoted by "NA". The author also try to accomodate several other formats such as HapMap format, PLINK format and so on.

Publication

Kumasaka N, Nakamura Y & Kamatani N (2010) The Textile Plot: a new linkage disequilibrium display of multiple-single nucleotide polymorphism genotype data. PLoS ONE 5(4): e10207. doi:10.1371/journal.pone.0010207.