gap (version 1.2.3-1)

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. The rationale of this function is to extend mhtplot() to handle extremely small p values as often seen from a protein GWAS; for R will break down when p <= 1e-324.

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, cex.y = 1, y.ax.space = 5, y.brk1, y.brk2,
                    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

cex.y

y axis numbers

y.ax.space

interval of ticks of the y axis

y.brk1

lower -log10(P) break point

y.brk2

upper -log10(P) break point

delta

a value to enable column(s) of red points

...

other options

Value

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

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=10, y.brk2=12, mtext.line=2.5)

# https://portals.broadinstitute.org/collaboration/
# giant/images/0/0f/Meta-analysis_Locke_et_al+UKBiobank_2018.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, cex.axis=1.2, cex.y=1.2, cex=0.5,
              y.ax.space=20,
              col = c("blue4", "skyblue")
)
dev.off()
# }