Update Friday, May 14, 2010: See this newer post on LocusZoom. If you caught Cristen Willer's seminar here a few weeks ago you saw several beautiful figures in the style of a manhattan plot, but zoomed in around a region of interest, with several other useful information overlays.

If you caught Soumya Raychaudhuri's seminar last week you heard a lot about the tool he developed at the broad called GRAIL - Gene Relationships Across Implicated Loci. You've got GWAS results and now you want to prioritize SNPs to follow up in replication or functional studies.

"The hunt for the genetic roots of common diseases has hit a blank wall." ...quoting the first sentence in Nicholas Wade's New York Times article reviewing this PLoS Biology research paper by David Goldstein and his colleagues at Duke University. Also be sure to see Richard Robinson's synopsis of this paper, both published this week in PLoS Bio.

**** UPDATE, May 15 2014 ***** The functions described here have now been wrapped into an R package. View the updated blog post or see the online package vignette for how to install and use. If you'd still like to use the old code described here, you can access this at version 0.0.0 on GitHub.

I have used this chunk of code on numerous occasions to plot GWAS data, so I thought I'd share!The variables needed are a log p-value (or some other statistic) and the absolute genomic position of each SNP (distance from the beginning of chromosome 1). If you need the offsets to compute this absolute position, they are listed in MB in the xline(---) portion of the plot.

Way back will wrote on this topic.  See his previous post for Stata code for doing this.  Unfortunately the R package that was used to create QQ-plots here has been removed from CRAN, so I wrote my own using ggplot2 and some code I received from Daniel Shriner at NHGRI. Of course you can use R's built-in qqplot() function, but I could never figure out a way to add the diagonal using base graphics.

Often when presenting statistics from a candidate gene study, or a region of interest from a genome-wide association study, it is useful to see various SNP-wise values in the context of linkage disequilibrium patterns.

Strict quality control procedures are extremely important for any genome-wide association study.  One of the first steps you should take when running QC on your GWAS is to look for related samples in your dataset.  This does two things for you.

***Update Monday, November 09, 2009: The sma package is no longer available on CRAN.  See this updated post for making QQ-plots in R using ggplot2.*** Nearly everyone who has read a paper on a genome-wide association study should now be familiar with the QQ-plot.

GWAS reviews are a dime a dozen these days, but I found this one in particular that's a good up-to-date review suitable for people relatively new to the field. While this one has a focus on psychiatric genetics, it has a short summary on topics like positional methods, candidate gene studies, common variation, rare variation, CNVs, GWAS design, and issues concerning power and sample size.