gap (version 1.5-1)

mhtplot.trunc: Truncated Manhattan plot

Description

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

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,
  ...
)

Value

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

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.

Author

James Peters, Jing Hua Zhao

Details

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.

See Also

mhtplot.

Examples

if (FALSE) {
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()
}