Visualizing genomic information across chromosomes with PhenoGram

Abstract

Background: With the abundance of information and analysis results being collected for genetic loci, user-friendly and flexible data visualization approaches can inform and improve the analysis and dissemination of these data. A chromosomal ideogram is an idealized graphic representation of chromosomes. Ideograms can be combined with overlaid points, lines, and/or shapes, to provide summary information from studies of various kinds, such as genome-wide association studies or phenome-wide association studies, coupled with genomic location information. To facilitate visualizing varied data in multiple ways using ideograms, we have developed a flexible software tool called PhenoGram which exists as a web-based tool and also a command-line program.

Results: With PhenoGram researchers can create chomosomal ideograms annotated with lines in color at specific base-pair locations, or colored base-pair to base-pair regions, with or without other annotation. PhenoGram allows for annotation of chromosomal locations and/or regions with shapes in different colors, gene identifiers, or other text. PhenoGram also allows for creation of plots showing expanded chromosomal locations, providing a way to show results for specific chromosomal regions in greater detail. We have now used PhenoGram to produce a variety of different plots, and provide these as examples herein. These plots include visualization of the genomic coverage of SNPs from a genotyping array, highlighting the chromosomal coverage of imputed SNPs, copy-number variation region coverage, as well as plots similar to the NHGRI GWA Catalog of genome-wide association results.

Conclusions: PhenoGram is a versatile, user-friendly software tool fostering the exploration and sharing of genomic information. Through visualization of data, researchers can both explore and share complex results, facilitating a greater understanding of these data.

Figure 1

Screen capture of the PhenoGram web-interface. The researcher will upload an input file, provide a title of the resultant figure, and then choose other options. Scrolling over each item, such as “Input File” will provide information on what the line means, as well as a link to example files that can be used with Phenogram when relevant. For example, scrolling over “Input file” indicates a tab-delimited input file is necessary, and provides a link to an example file that can be used with PhenoGram.

Figure 2

Using PhenoGram to plot the NHGRI GWA catalog association results for eight phenotypes. An Ideogram of all 22 chromosomes is plotted, along with the X and Y chromosomes. Lines are plotted on the chromosomes corresponding to the base-pair location of each SNP, and the line connects to colored circles representing the phenotype(s) associated with that SNP.

Figure 3

The different annotation spacing methods available with PhenoGram. PhenoGram has several options for modifying the spatial presentation of the circles or other annotation on PhenoGram plots: The default of standard spacing, the equal spacing method placing circles or other annotation at equal intervals along the chromosome, and proximity spacing that minimizes circle or annotation overlap while keeping points near their chromosomal locations. The option to plot a single chromosome was used for this figure.

Figure 4

The five phenotype color generation methods available in PhenoGram. For a small number of phenotypes, the color list method assigns easily-discernible colors. With a greater number of phenotypes, the standard generator attempts to maximize the color separation between the phenotypes. A random generator may also be used, as well as a method for web-safe colors. The grouped method makes it possible to plot phenotypes with the same designated identifier in a gradient of similar colors.

Figure 5

Adding in a shape to indicate a grouping, such as ancestry. Designation of ancestry in the PhenoGram input file will result in all phenotypes of each ancestry identifier being plotted with a unique shape in addition to showing a phenotype color. The input file can take up to three ancestry (or other group) identifiers.

Figure 6

Plotting lines at base-pair locations using PhenoGram. Each line represents a base-pair location genotyped on the immunochip genotyping array, an array with variants chosen for previous association with the autoimmune response and the immune system. By setting the lines to be transparent, areas of higher and lower genotyping density are made more visible. The very densely genotyped major histocompatibility region (MHC) on Chromosome 6 has been overplotted with color, as well as annotated, using PhenoGram.

Figure 7

The CNV detection results of two microarray methods. Regions of a custom DNA microarray targeted for genomic hotspots, in red, are plotted on top of blue Illumina DNA microarray CNV results.

Figure 8

Using PhenoGram to plot an expanded view of a specific region of a chromosome. PhenoGram provides the capability of showing a closer view of a specific region of a chromosome. This is particularly useful when there is a heightened density of information to be plotted for a specific chromosomal region. Here, NHGRI GWAS data is shown on a portion of chromosome six where there is a greater density of Crohn’s disease and rheumatoid arthritis phenotypes.

Figure 9

Cytogenetic band option within PhenoGram. It is possible to shade the chromosomes with predefined cytogenetic bands on any PhenoGram plot. Here, this option is used with simulated SNP-phenotype association data.