Creating manhattan plots

Now, let’s make a basic manhattan plot.

manhattan(gwasResults)

We can also pass in other graphical parameters. Let’s add a title (main=), increase the y-axis limit (ylim=), reduce the point size to 60% (cex=), and reduce the font size of the axis labels to 90% (cex.axis=). While we’re at it, let’s change the colors (col=), remove the suggestive and genome-wide significance lines, and supply our own labels for the chromosomes:

manhattan(gwasResults, main="Manhattan Plot", ylim=c(0,10), cex=0.6, cex.axis=0.9, col=c("blue4", "orange3"), suggestiveline=F, genomewideline=F, chrlabs=c(1:20, "P", "Q"))

Now, 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.

str(snpsOfInterest)

 chr [1:100] "rs3001" "rs3002" "rs3003" "rs3004" "rs3005" "rs3006" "rs3007" ...

manhattan(gwasResults, highlight=snpsOfInterest)

We can combine highlighting and limiting to a single chromosome, and use the xlim graphical parameter to zoom in on a region of interest (between position 200-500):

manhattan(subset(gwasResults, CHR==3), highlight=snpsOfInterest, xlim=c(200, 500), main="Chr 3")

We can also annotate SNPs based on their p-value. By default, this only annotates the top SNP per chromosome that exceeds the annotatePval threshold.

manhattan(gwasResults, annotatePval=0.01)

We can also annotate all SNPs that meet a threshold:

manhattan(gwasResults, annotatePval=0.005, annotateTop=FALSE)

Finally, the manhattan function can be used to plot any value, not just p-values. Here, we’ll simply call the function passing to the p= argument the name of the column we want to plot instead of the default “P” column. In this example, let’s create a test statistic (“zscore”), plot that instead of p-values, change the y-axis label, and remove the default log transformation. We’ll also remove the genomewide and suggestive lines because these are only meaningful if you’re plotting -log10(p-values).

# Add test statistics
gwasResults <- transform(gwasResults, zscore=qnorm(P/2, lower.tail=FALSE))
head(gwasResults)

  SNP CHR BP         P    zscore
1 rs1   1  1 0.9148060 0.1069785
2 rs2   1  2 0.9370754 0.0789462
3 rs3   1  3 0.2861395 1.0666287
4 rs4   1  4 0.8304476 0.2141275
5 rs5   1  5 0.6417455 0.4652597
6 rs6   1  6 0.5190959 0.6447396

# Make the new plot
manhattan(gwasResults, p="zscore", logp=FALSE, ylab="Z-score", genomewideline=FALSE, suggestiveline=FALSE, main="Manhattan plot of Z-scores")

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. You can modify the source code (e.g., fix(manhattan)) to change the line designating the axis tick labels (labs <- unique(d$CHR)) to set this to whatever you’d like it to be.
• 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.

qq(gwasResults$P)

We can optionally supply many other graphical parameters.

qq(gwasResults$P, main="Q-Q plot of GWAS p-values",
   xlim=c(0,7), ylim=c(0,12), pch=18, col="blue4", cex=1.5, las=1)