mpp_CIM: MPP Composite Interval Mapping

Description

Compute QTL models along the genome using cofactors representing other genetic positions for control.

Usage

mpp_CIM(mppData, trait = 1, Q.eff = "cr", cofactors = NULL,
  window = 20, plot.gen.eff = FALSE, n.cores = 1)

Arguments

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

Character expression indicating the assumption concerning the QTL effects: 1) "cr" for cross-specific; 2) "par" for parental; 3) "anc" for ancestral; 4) "biall" for a bi-allelic. For more details see mpp_SIM. Default = "cr".

cofactors

Object of class QTLlist representing a list of selected position obtained with the function QTL_select or vector of character marker positions names. Default = NULL.

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 20.

plot.gen.eff

Logical value. If plot.gen.eff = TRUE, the function will save the decomposed genetic effects per cross/parent. These results can be plotted with the function plot.QTLprof to visualize a genome-wide decomposition of the genetic effects. This functionality is only available for the cross-specific, parental and ancestral models. Default value = FALSE.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

Value

Return:

CIM

Data.frame of class QTLprof. with five columns : 1) QTL marker names; 2) chromosomes; 3) interger position indicators on the chromosome; 4) positions in centi-Morgan; and 5) -log10(p-val). And if plot.gen.eff = TRUE, p-values of the cross or parental QTL effects.

Details

For more details about the different models, see documentation of the function mpp_SIM. The function returns a -log10(p-value) QTL profile.

Examples

Run this code

# NOT RUN {
# Cross-specific effect model
#############################

data(mppData)

SIM <- mpp_SIM(mppData = mppData, Q.eff = "cr")

cofactors <- QTL_select(Qprof = SIM, threshold = 3, window = 20)

CIM <- mpp_CIM(mppData = mppData, Q.eff = "cr", cofactors = cofactors,
               window = 20, plot.gen.eff = TRUE)

plot(x = CIM)
plot(x = CIM, gen.eff = TRUE, mppData = mppData, Q.eff = "cr")

# Bi-allelic model
##################

cofactors <- mppData$map[c(15, 63), 1]

CIM <- mpp_CIM(mppData = mppData, Q.eff = "biall", cofactors = cofactors,
               window = 20)

plot(x = CIM, type = "h")
                               
# }