MQE_proc: Multi-QTL effect MPP analysis

In mppR: Multi-Parent Population QTL Analysis

Multi-QTL effect MPP analysis

Description

Build multi-QTL effects (MQE) models in which different QTL effects (cross-specific, parental, ancestral or bi-allelic) can be assumed at different loci.

Usage

MQE_proc(
  pop.name = "MPP_MQE",
  trait.name = "trait1",
  mppData = NULL,
  trait = 1,
  Q.eff,
  ref.par = NULL,
  threshold = 4,
  window = 30,
  backward = TRUE,
  alpha.bk = 0.05,
  plot.MQE = FALSE,
  CI = FALSE,
  drop = 1.5,
  n.cores = 1,
  verbose = TRUE,
  output.loc
)

Arguments

pop.name	Character name of the studied population. Default = "MPP_MQE".
trait.name	Character name of the studied trait. Default = "trait1".
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 vector of possible QTL effects the user want to test. Elements of Q.eff can be "cr", "par", "anc" or "biall". For details look at mpp_SIM.
ref.par	Optional Character expression defining the parental allele that will be used as reference to calculate the allelic effects of the parental model. For the ancestral model, the ancestral class containing the reference parent will be set as reference. This option can only be used if the MPP design is composed of a unique connected part. Default = NULL.
threshold	Numeric value representing the -log10(p-value) threshold above which a position can be considered as significant. Default = 4.
window	Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 30.
backward	Logical value. If backward = TRUE, the function performs a backward elimination on the list of selected QTLs. Default = TRUE.
alpha.bk	Numeric value indicating the significance level for the backward elimination. Default = 0.05.
plot.MQE	Logical value. If plot.MQE = TRUE, the function will plot the last run of the MQE model determination. Default = FALSE.
CI	Logical value. If CI = TRUE, the function will compute a -log10(pval) drop confidence interval for each QTL using the QTL profile of the last iteration. Default = FALSE.
drop	Numeric -log10(p-value) drop value at the limits of the interval. Default = 1.5.
n.cores	Numeric. Specify here the number of cores you like to use. Default = 1.
verbose	Logical value indicating if the steps of MQE_proc should be printed. Default = TRUE.
output.loc	Path where a folder will be created to save the results.

Details

The possible QTL effect that the user wants to allow must be specified in Q.eff. The procedure is the following:

1. Forward regression to determine a MQE model with different possible assumptions for the QTL effect at different loci. The function use.

2. Optional backward elimination (backward = TRUE) on the final list of detected QTLs.

3. Estimation of the QTL genetic effects and R squared statistics.

4. If plot.MQE = TRUE, plot of the last CIM run of the forward regression.

5. If CI = TRUE, confidence interval calculation based on a CIM- (CIM without cofactor on the selected chromosome) of the last run of the forward regression.

Value

Return:

List containing the following items:

n.QTL	Number of detected QTLs.
QTL	Data.frame with QTL positions.
R2	list containing R squared statistics of the QTL effects. for details see QTL_R2.
QTL.effects	List of genetic effects per QTL.
QTL.CI	If CI = TRUE, confidence interval information of the QTLs.

Some output files are also saved at the location specified (output.loc):

1. A QTL report (QTL_REPORT.txt) with: 1) the number of detected QTLs; 2) the global R squared statistics; 3) for each QTL, position information and estimated QTL genetic effect per cross or parents.

2. The list of QTLs (QTL.txt).

3. The QTL R squared statistics (QTL_R2.txt) (for details see QTL_R2).

4. General results of the QTL detection process: Number of QTL and global adjusted and non-adjusted R squared statistics. (QTL_genResults.txt).

5. if plot.MQE = TRUE, a plot of the last QTL detection run profile (plot_MQE.pdf).

6. If CI = TRUE, the QTL confidence intervals (QTL_CI.txt).

Author(s)

Vincent Garin

See Also

mpp_SIM, MQE_gen_effects

Examples

## Not run: 

data(mppData)

# Specify a location where your results will be saved
my.loc <- tempdir()

MQE <- MQE_proc(pop.name = "USNAM", trait.name = "ULA", mppData = mppData,
                Q.eff = c("par", "biall"), verbose = FALSE,
                output.loc = my.loc)
                 

## End(Not run)

mppR documentation built on Jan. 6, 2023, 1:23 a.m.