mppR: MQE_proc – R documentation

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MQE_proc

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, threshold = 4, window = 30, backward = TRUE,
  alpha.bk = 0.05, plot.MQE = FALSE, 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`.
`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.
`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:

Forward regression to determine a MQE model with different possible assumptions for the QTL effect at different loci. The function use.
Optional backward elimination (backward = TRUE) on the final list of detected QTLs.
Estimation of the QTL genetic effects and R squared statistics.
Optional plot (plot.MQE = TRUE) of the last CIM run of the forward regression using the function.

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.

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

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.
The list of QTLs (QTL.txt).
The QTL R squared statistics (QTL_R2.txt) (for details see QTL_R2).
General results of the QTL detection process: Number of QTL and global adjusted and non-adjusted R squared statistics. (QTL_genResults.txt).
if plot.MQE = TRUE, a plot of the last QTL detection run profile (plot_MQE.pdf).

Author(s)

Vincent Garin

Examples

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)

mppR

Multi-Parent Population QTL Analysis

v1.2.1

GPL-3

Authors

Vincent Garin [aut, cre], Valentin Wimmer [aut], Dietrich Borchardt [ctb, dtc], Fred van Eeuwijk [ctb, ths], Marcos Malosetti [ctb, ths]

Initial release

2020-02-10