map.plot: Creating a genetic map plot

Description

This function was designed to create a genetic map plot using a data frame indicating the Linkage Group (LG), Position and marker names (Locus).

Usage

map.plot(data, trait = NULL, trait.scale = "same", 
        col.chr = NULL, col.trait = NULL, type = "hist", cex = 0.4,
        lwd = 1, cex.axis = 0.4, cex.trait=0.8, jump = 5)

Arguments

data

the data frame with 3 columns with names; Locus, LG and Position

trait

if something wants to be plotted next the linkage groups the user must indicate the name of the column containing the values to be ploted, i.e. p-values, LOD scores, X2 segregation distortion values, etc.

trait.scale

is trait is not NULL, this is a character value indicating if the y axis limits for the trait plotted next to the chromosomes should be the same or different for each linkage group. The default value is "same", which means that the same y axis limit is conserved across linkage groups. For giving an individual y axis limit for each linkage group write "diff".

col.chr

a vector with color names for the chromosomes. If NULL they will be drawn in gray-black scale.

col.trait

a vector with color names for the dots, lines or histogram for the trait plotted next to the LG's

type

a character value indicating if the trait should be plotted as scatterplot `dot`, histogram `hist`, line `line` next to the chromosomes.

cex

the cex value determining the size of the cM position labels in the LGs

lwd

the width of the lines in the plot

cex.axis

the cex value for sizing the labels of LGs and traits plotted (top labels)

cex.trait

the cex value for sizing the dots or lines of the trait plotted

jump

a scalar value indicating how often should be drawn a number next to the LG indicating the position. The default is 5 which means every 5 cM a label will be drawn, i.e. 0,5,10,15,... cM.

Value

If all parameters are correctly indicated the program will return:

$plot.data: a plot with the LGs and the information used to create a plot

References

Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744

Examples

Run this code

#random population of 200 lines with 1000 markers
M <- matrix(rep(0,200*1000),1000,200)
for (i in 1:200) {
  M[,i] <- ifelse(runif(1000)<0.5,-1,1)
}
colnames(M) <- 1:200
set.seed(1234)
geno <- data.frame(Locus=paste("m",1:500, sep="."),LG=sort(rep(c(1:5),100)),
                   Position=rep(seq(1,100,1),5),
                   X2=rnorm(500,10,4), check.names=FALSE)
geno$Locus <- as.character(geno$Locus)
## look at the data, 5LGs, 100 markers in each
## X2 value for segregation distortion simulated
head(geno)
tail(geno)
table(geno$LG) # 5 LGs, 100 marks
map.plot(geno, trait="X2", type="line")
map.plot(geno, trait="X2", type="hist")
map.plot(geno, trait="X2", type="dot")

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