qqman: an R package for creating Q-Q and manhattan plots from GWAS results

May 15, 2014

(This article was first published on Getting Genetics Done, and kindly contributed to R-bloggers)

Three years ago I wrote a blog post on how to create manhattan plots in R. After hundreds of comments pointing out bugs and other issues, I’ve finally cleaned up this code and turned it into an R package.

The qqman R package is on CRAN: http://cran.r-project.org/web/packages/qqman/

The source code is on GitHub: https://github.com/stephenturner/qqman

If you’d like to cite the qqman package (appreciated but not required), please cite this pre-print: Turner, S.D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. biorXiv DOI: 10.1101/005165 (2014).

Something wrong? Please file bug reports, feature requests, or anything else related to the code as an issue on GitHub rather than commenting here. Also, please post the code you’re using and some example data causing a failure in a publicly accessible place, such as a GitHub gist (no registration required). It’s difficult to troubleshoot if I can’t see the data where the code is failing. Want to contribute? Awesome! Send me a pull request.

Note: This release is substantially simplified for the sake of maintainability and creating an R package. The old code that allows confidence intervals on the Q-Q plot and allows more flexible annotation and highlighting is still available at the version 0.0.0 release on GitHub.

Here’s a shout-out to all the blog commenters on the previous post for pointing out bugs and other issues, and a special thanks to Dan Capurso and Tim Knutsen for useful contributions and bugfixes.

qqman package tutorial

First things first. Install the package (do this only once), then load the package (every time you start a new R session)

# only once:

# each time:

You can access this help any time from within R by accessing the vignette:


The manhattan package includes functions for creating manhattan plots and q-q plots from GWAS results. The gwasResults data.frame included with the package has simulated results for 16,470 SNPs on 22 chromosomes. Take a look at the data:

'data.frame':   16470 obs. of  4 variables:
$ SNP: chr "rs1" "rs2" "rs3" "rs4" ...
$ CHR: int 1 1 1 1 1 1 1 1 1 1 ...
$ BP : int 1 2 3 4 5 6 7 8 9 10 ...
$ P : num 0.915 0.937 0.286 0.83 0.642 ...
  SNP CHR BP      P
1 rs1 1 1 0.9148
2 rs2 1 2 0.9371
3 rs3 1 3 0.2861
4 rs4 1 4 0.8304
5 rs5 1 5 0.6417
6 rs6 1 6 0.5191
          SNP CHR  BP      P
16465 rs16465 22 530 0.5644
16466 rs16466 22 531 0.1383
16467 rs16467 22 532 0.3937
16468 rs16468 22 533 0.1779
16469 rs16469 22 534 0.2393
16470 rs16470 22 535 0.2630

How many SNPs on each chromosome?

   Var1 Freq
1 1 1500
2 2 1191
3 3 1040
4 4 945
5 5 877
6 6 825
7 7 784
8 8 750
9 9 721
10 10 696
11 11 674
12 12 655
13 13 638
14 14 622
15 15 608
16 16 595
17 17 583
18 18 572
19 19 562
20 20 553
21 21 544
22 22 535

Creating manhattan plots

Now, let's make a basic manhattan plot.


We can also pass in other graphical parameters. Let's add a title (main=), reduce the point size to 50%(cex=), and reduce the font size of the axis labels to 80% (cex.axis=):

manhattan(gwasResults, main = "Manhattan Plot", cex = 0.5, cex.axis = 0.8)

Let's change the colors and increase the maximum y-axis:

manhattan(gwasResults, col = c("blue4", "orange3"), ymax = 12)

Let's remove the suggestive and genome-wide significance lines:

manhattan(gwasResults, suggestiveline = F, genomewideline = F)

Let's look at a single chromosome:

manhattan(subset(gwasResults, CHR == 1))

Let's highlight some SNPs of interest on chromosome 3. The 100 SNPs we're highlighting here are in a character vector called snpsOfInterest. You'll get a warning if you try to highlight SNPs that don't exist.

 chr [1:100] "rs3001" "rs3002" "rs3003" "rs3004" "rs3005" ...
manhattan(gwasResults, highlight = snpsOfInterest)

We can combine highlighting and limiting to a single chromosome:

manhattan(subset(gwasResults, CHR == 3), highlight = snpsOfInterest, main = "Chr 3")

A few notes on creating manhattan plots:

  • Run str(gwasResults). Notice that the gwasResults data.frame has SNP, chromosome, position, and p-value columns named SNP, CHR, BP, and P. If you're creating a manhattan plot and your column names are different, you'll have to pass the column names to the chr=, bp=, p=, and snp= arguments. See help(manhattan) for details.
  • The chromosome column must be numeric. If you have “X,” “Y,” or “MT” chromosomes, you'll need to rename these 23, 24, 25, etc.
  • If you'd like to change the color of the highlight or the suggestive/genomewide lines, you'll need to modify the source code. Search for col="blue", col="red", or col="green3" to modify the suggestive line, genomewide line, and highlight colors, respectively.

Creating Q-Q plots

Creating Q-Q plots is straightforward - simply supply a vector of p-values to the qq() function. You can optionally provide a title.

qq(gwasResults$P, main = "Q-Q plot of GWAS p-values")

To leave a comment for the author, please follow the link and comment on their blog: Getting Genetics Done.

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