mhtplot.trunc {gap}R Documentation

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)
[Package gap version 1.5-3 Index]