A genome-wide association (GWA) study often involves analyzing the effects of 100,000s of single nucleotide polymorphisms (SNPs) on a disease outcome or trait. Visualizing such high density data can often prove tricky, especially if the investigator is interested in specific regions.
I have recently discovered a free tool called WGAviewer from the Duke University (http://people.genome.duke.edu/~dg48/WGAViewer/) that can greatly help with the visualization part (it does not perform any analysis). The software is based on Java so should be platform independent (I only used and tested it for Windows so far). Some of the key features includes:
- QQ plots
- Manhattan plots with *interactive zoom* in and out
- Zoom to a region by gene name or region easily and visualize results
- Ablity to select and annotate the top N snps
- Automatic update of annotation on Ensembl and HapMap data
- Calculate LD linkage for a particular region etc
- Take publication quality snapshot pictures
One of the hassles I found was formatting the data for input. The documentations suggest several ways of making the data input using MAP files etc in the manual. However, the easiest way I found was to simply create a space-separated ASCII file (using R or even Excel) with the following columns: rsid, chromosome (1-22, X, Y, XY, M), Map (coordinate on the chromosome) and -logP (log base 10 of p-values).
SNP chromosome Map -logP MitoA10045G M 10045 2.04858284222835 MitoT9900C M 9900 0.233064674990652 MitoT9951C M 9951 0.0641728753170715 rs1000000 12 125456933 1.16139248878691 rs10000010 4 21227772 0.149317624784192 rs10000023 4 95952929 1.15832462919552 rs10000030 4 103593179 0.106028436059944 rs10000041 4 165841405 0.221366644208304 rs1000007 2 237416793 0.213983677592946 ...
You will need to create and load one file per analysis which is bit annoying if you have many analyses to visualize. I hope they add new features to visualize and (even better) compare different results in the near future. Imagine being able to superimpose manhattan plots from two different studies or techniques together!