mpp_forward {mppR}

MPP QTL analysis using forward regression

Description

Multi-parent population QTL analysis model using a forward regression.

Usage

mpp_forward(
  pop.name = "MPP",
  trait.name = "trait1",
  mppData,
  trait = 1,
  Q.eff = "cr",
  ref.par = NULL,
  sum_zero = FALSE,
  threshold = 4,
  window = 30,
  backward = TRUE,
  alpha.bk = 0.05,
  plot.Qprof = FALSE,
  plot.gen.eff = FALSE,
  CI = FALSE,
  drop = 1.5,
  text.size = 18,
  n.cores = 1,
  verbose = TRUE,
  output.loc
)

Arguments

pop.name

Character name of the studied population. Default = "MPP".

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

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.

sum_zero

Optional Logical value specifying if the QTL effect of a parental or an ancestral model should be caculated using the sum to zero constraint. Default = FALSE.

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.Qprof

Logical value. If plot = TRUE, the function will plot the QTL profile last run of the forward regression. Default = FALSE.

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 ploted with the function plot.QTLprof to visualize a genome-wide decomposition of the genetic effects. This plot will be realized on the last run of the forward regression. This functionality is ony available for the cross-specific, parental and ancestral models. Default value = 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.

text.size

Numeric value specifying the size of graph axis text elements. Default = 18.

n.cores

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

verbose

Logical value indicating if the steps of the procedure should be printed. Default = TRUE.

output.loc

Path where a folder will be created to save the results. By default the function uses the current working directory.

Details

The function run a full MPP QTL detection using models with different possible assumptions concerning the number of alleles at the QTL position. For more details about the different models, see documentation of the function mpp_SIM. The procedure is the following:

1. Forward regression to determine the a multi-QTL model. The function selects successively QTL positions with -log10(pval) above the threshold. Those positions are added as cofactors for following detection run. The procedure stop when no more position has a -log10(pval) above the threshold (QTL_forward).

2. If backward = TRUE, backward elimination on the final list of detected QTLs.

3. Estimation of the QTL genetic effects and R squared statistics (QTL_gen_effects and QTL_R2).

4. If CI = TRUE, confidence interval calculation based on a CIM- (composite interval mapping removing all cofactors on the scanned chromosome) of the last run of the forward regression.

5. If plot.Qprof = TRUE, plot of the last run of the forward regression using plot.QTLprof.

6. If plot.gen.eff = TRUE, plot of the genetic effect distribution along the genome of the last run of the forward regression using plot.QTLprof.

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 output section.

QTL.effects

List of QTLs genetic effects. For details see QTL_gen_effects output section.

QTL.CI

If CI = TRUE, confidence interval information of the QTLs.

Some output files are also saved at the specified location (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 (plus confidence interval if CI = TRUE) and estimated QTL genetic effects per cross or parents (for details see QTL_gen_effects).

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

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

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

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

6. If plot.Qprof = TRUE, the plot of the last regression run (QTL_profile.pdf). If plot.gen.eff = TRUE, plot of the genetic effects per cross or parents (gen_eff.pdf) with dashed lines representing the QTL positions. For more details see plot.QTLprof

Author(s)

Vincent Garin

See Also

mpp_SIM, plot.QTLprof, QTL_gen_effects, QTL_forward, QTL_R2

Examples

## Not run: 

data(mppData)

# Specify a location where your results will be saved
my.loc <- "C:/.../..."

# Cross-specific model

USNAM_cr <- mpp_forward(pop.name = "USNAM", trait.name = "ULA",
                        mppData = mppData, plot.gen.eff = TRUE,
                        plot.Qprof = TRUE, CI = TRUE, output.loc = my.loc)



## End(Not run)