mhtplot.trunc: Truncated Manhattan plot

In gap: Genetic Analysis Package

Truncated Manhattan plot

Description

Truncated Manhattan plot

Usage

mhtplot.trunc(
  x,
  chr = "CHR",
  bp = "BP",
  p = NULL,
  log10p = NULL,
  z = NULL,
  snp = "SNP",
  col = c("gray10", "gray60"),
  chrlabs = NULL,
  suggestiveline = -log10(1e-05),
  genomewideline = -log10(5e-08),
  highlight = NULL,
  annotatelog10P = NULL,
  annotateTop = FALSE,
  cex.mtext = 1.5,
  cex.text = 0.7,
  mtext.line = 2,
  y.ax.space = 5,
  y.brk1 = NULL,
  y.brk2 = NULL,
  trunc.yaxis = TRUE,
  cex.axis = 1.2,
  delta = 0.05,
  ...
)

Arguments

x	A data.frame.
chr	Chromosome.
bp	Position.
p	p values, e.g., "1.23e-600".
log10p	log10(p).
z	z statistic, i.e., BETA/SE.
snp	SNP. Pending on the setup it could either of variant or gene ID(s).
col	Colours.
chrlabs	Chromosome labels, 1,2,...22,23,24,25.
suggestiveline	Suggestive line.
genomewideline	Genomewide line.
highlight	A list of SNPs to be highlighted.
annotatelog10P	Threshold of -log10(P) to annotate.
annotateTop	Annotate top.
cex.mtext	axis label extension factor.
cex.text	SNP label extension factor.
mtext.line	position of the y lab.
y.ax.space	interval of ticks of the y axis.
y.brk1	lower -log10(P) break point.
y.brk2	upper -log10(P) break point.
trunc.yaxis	do not truncate y-axisx when FALSE.
cex.axis	extension factor for x-, y-axis.
delta	a value to enable column(s) of red points.
...	other options.

Details

To generate truncated Manhattan plot, e.g., of genomewide significance (P values) or a random variable that is uniformly distributed.

The rationale of this function is to extend mhtplot() to handle extremely small p values as often seen from a protein GWAS. Optionally, the function also draws an ordinary Manhattan plot.

Value

The plot is shown on or saved to the appropriate device.

Author(s)

James Peters, Jing Hua Zhao

See Also

mhtplot.

Examples

## Not run: 
options(width=120)
require(gap.datasets)
mhtdata <- within(mhtdata, {z=qnorm(p/2, lower.tail=FALSE)})
mhtplot.trunc(mhtdata, chr = "chr", bp = "pos", z = "z", snp = "rsn",
              y.brk1=6, y.brk2=10, y.ax.space=1, mtext.line=2.5)
# https://portals.broadinstitute.org/collaboration/
# giant/images/c/c8/Meta-analysis_Locke_et_al%2BUKBiobank_2018_UPDATED.txt.gz
gz <- gzfile("work/Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt.gz")
BMI <- within(read.delim(gz,as.is=TRUE), {Z <- BETA/SE})
print(subset(BMI[c("CHR","POS","SNP","P")],CHR!=16 & P<=1e-150))
library(Rmpfr)
print(within(subset(BMI, P==0, select=c(CHR,POS,SNP,Z)),
             {P <- format(2*pnorm(mpfr(abs(Z),100),lower.tail=FALSE));
              Pvalue <- pvalue(Z); log10P <- -log10p(Z)}))
png("BMI.png", res=300, units="in", width=9, height=6)
par(oma=c(0,0,0,0), mar=c(5,6.5,1,1))
mhtplot.trunc(BMI, chr="CHR", bp="POS", z="Z", snp="SNP",
              suggestiveline=FALSE, genomewideline=-log10(1e-8),
              cex.mtext=1.2, cex.text=1.2,
              annotatelog10P=156, annotateTop = FALSE,
              highlight=c("rs13021737","rs17817449","rs6567160"),
              mtext.line=3, y.brk1=200, y.brk2=280, trunc.yaxis=TRUE,
              cex.axis=1.2, cex=0.5,
              y.ax.space=20,
              col = c("blue4", "skyblue")
)
dev.off()

## End(Not run)

gap documentation built on Sept. 11, 2024, 5:36 p.m.