plot.vGWAS: Variance GWA Manhattan Plot

In kullrich/vGWAS: Variance Heterogeneity Genome-wide Association Study - Reimplementation

Variance GWA Manhattan Plot

Description

The function plots the variance GWA result for the given scan object.

Usage

## S3 method for class 'vGWAS'
plot(x, sig.threshold = NULL, low.log.p = 0,
pch = 16, cex = 0.6, col.manhattan = c("slateblue4", "olivedrab"),
col.sig.threshold = "darkgoldenrod", ...)

Arguments

x	a result object from vGWAS scan. It can be any list or data.frame that contains chromosome, marker.map, and p.value, with class = 'vGWAS'. See vGWAS.
sig.threshold	a numeric value giving the significance threshold for -log(pvalues, 10). If NULL, Bonferroni correction will be used.
low.log.p	a numeric value giving the lower limit of the -log(pvalues, 10) to plot.
pch	point character. See par.
cex	size of points. See par.
col.manhattan	two colors as a vector for the Manhattan plot.
col.sig.threshold	one color for the significance threshold
...	not in use

Value

a plot for viewing vGWAS result.

Author(s)

Xia Shen

References

Shen, X., Pettersson, M., Ronnegard, L. and Carlborg, O. (2011): Inheritance beyond plain heritability: variance-controlling genes in Arabidopsis thaliana. PLoS Genetics, 8, e1002839.

See Also

package-vGWAS, vGWAS

Examples

# ----- load data ----- #
data(pheno)
data(geno)
data(chr)
data(map)
# ----- variance GWA scan ----- #
vgwa <- vGWAS(phenotype = pheno, geno.matrix = geno,
marker.map = map, chr.index = chr, pB = FALSE)
# ----- visualize the scan ----- #
plot(vgwa)
summary(vgwa)
# ----- calculate the variance explained by the strongest marker ----- #
vGWAS.variance(phenotype = pheno,
marker.genotype = geno[, vgwa[["p.value"]] == min(vgwa[["p.value"]])])
# ----- genomic control ----- #
vgwa2 <- vGWAS.gc(vgwa)
plot(vgwa2)
summary(vgwa2)

kullrich/vGWAS documentation built on June 10, 2025, 3:56 a.m.