mppR (version 1.5.0)

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
)

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.

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.

Author

Vincent Garin

Details

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

See Also

mpp_SIM, QTL_select

Examples

# 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")