plot.vGWAS: Variance GWA Manhattan Plot

In vGWAS: Variance Heterogeneity Genome-Wide Association Study

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 used.

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. Submitted.

See Also

vGWAS-package, vGWAS

Examples

## Not run: 

# ----- 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)
              
# ----- visualize the scan ----- #

plot(vgwa)

summary(vgwa)

# ----- calculate the variance explained by strongest the marker ----- #

vGWAS.heritability(phenotype = pheno, 
                   marker.genotype = geno[,vgwa$p.value == min(vgwa$p.value)])
                   
# ----- genomic control ----- #

vgwa2 <- vGWAS.gc(vgwa)
                                
plot(vgwa2) 

summary(vgwa2)

## End(Not run)

vGWAS documentation built on May 2, 2019, 6:48 p.m.