qtl (version 1.42-8)

effectplot: Plot phenotype means against genotypes at one or two markers

Description

Plot the phenotype means for each group defined by the genotypes at one or two markers (or the values at a discrete covariate).

Usage

effectplot(cross, pheno.col=1, mname1, mark1, geno1, mname2, mark2,
           geno2, main, ylim, xlab, ylab, col, add.legend=TRUE,
           legend.lab, draw=TRUE, var.flag=c("pooled","group"))

Arguments

cross

An object of class cross.

pheno.col

Column number in the phenotype matrix to be drawn in the plot. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations.

mname1

Name for the first marker or pseudomarker. Pseudomarkers (that is, non-marker positions on the imputation grid) may be referred to in a form like "5@30.3", for position 30.3 on chromosome 5.

mark1

Genotype data for the first marker. If unspecified, genotypes will be taken from the data in the input cross object, using the name specified in mname1.

geno1

Optional labels for the genotypes (or classes in a covariate).

mname2

Name for the second marker or pseudomarker (optional).

mark2

Like mark1 (optional).

geno2

Optional labels for the genotypes (or classes in a covariate).

main

Optional figure title.

ylim

Optional y-axis limits.

xlab

Optional x-axis label.

ylab

Optional y-axis label.

col

Optional vector of colors for the different line segments.

add.legend

A logical value to indicate whether to add a legend.

legend.lab

Optional title for the legend.

draw

A logical value to indicate generate the plot or not. If FALSE, no figure will be plotted and this function can be used to calculate the group means and standard errors.

var.flag

The method to calculate the group variance. "pooled" means to use the pooled variance and "group" means to calculate from individual group.

Value

A data.frame containing the phenotype means and standard errors for each group.

Details

In the plot, the y-axis is the phenotype. In the case of one marker, the x-axis is the genotype for that marker. In the case of two markers, the x-axis is for different genotypes of the second marker, and the genotypes of first marker are represented by lines in different colors. Error bars are plotted at \(\pm\) 1 SE.

The results of sim.geno are used; if they are not available, sim.geno is run with n.draws=16. The average phenotype for each genotype group takes account of missing genotype data by averaging across the imputations. The SEs take account of both the residual phenotype variation and the imputation error.

See Also

plotPXG, find.marker, effectscan, find.pseudomarker

Examples

# NOT RUN {
data(fake.f2)
# }
# NOT RUN {
# impute genotype data
# }
# NOT RUN {
fake.f2 <- sim.geno(fake.f2, step=5, n.draws=64)
# }
# NOT RUN {
########################################
# one marker plots
########################################
### plot of genotype-specific phenotype means for 1 marker
mname <- find.marker(fake.f2, 1, 37) # marker D1M437
effectplot(fake.f2, pheno.col=1, mname1=mname)

### output of the function contains the means and SEs
output <- effectplot(fake.f2, mname1=mname)
output

### plot a phenotype
# Plot of sex-specific phenotype means,
# note that "sex" must be a phenotype name here
effectplot(fake.f2, mname1="sex", geno1=c("F","M"))
# alternatively:
sex <- pull.pheno(fake.f2, "sex")
effectplot(fake.f2, mname1="Sex", mark1=sex, geno1=c("F","M"))

########################################
# two markers plots
########################################

### plot two markers
# plot of genotype-specific phenotype means for 2 markers
mname1 <- find.marker(fake.f2, 1, 37) # marker D1M437
mname2 <- find.marker(fake.f2, 13, 24) # marker D13M254
effectplot(fake.f2, mname1=mname1, mname2=mname2)

### plot two pseudomarkers
#####  refer to pseudomarkers by their positions
effectplot(fake.f2, mname1="1@35", mname2="13@25")

#####  alternatively, find their names via find.pseudomarker
pmnames <- find.pseudomarker(fake.f2, chr=c(1, 13), c(35, 25))
effectplot(fake.f2, mname1=pmnames[1], mname2=pmnames[2])

### Plot of sex- and genotype-specific phenotype means 
mname <- find.marker(fake.f2, 13, 24) # marker D13M254
# sex and a marker
effectplot(fake.f2, mname1=mname, mname2="Sex",
           mark2=sex, geno2=c("F","M"))

# Same as above, switch role of sex and the marker
# sex and marker
effectplot(fake.f2, mname1="Sex", mark1=sex,
           geno1=c("F","M"), mname2=mname)

# X chromosome marker
mname <- find.marker(fake.f2, "X", 14) # marker DXM66
effectplot(fake.f2, mname1=mname)

# Two markers, including one on the X
mnames <- find.marker(fake.f2, c(13, "X"), c(24, 14))
effectplot(fake.f2, mname1=mnames[1], mname2=mnames[2])
# }