mpp_CIM {mppR} | R Documentation |
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.
|
Details
For more details about the different models, see documentation of the
function mpp_SIM
. The function returns a -log10(p-value) QTL
profile.
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.
|
Author(s)
Vincent Garin
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")
[Package
mppR version 1.5.0
Index]