mpp_proc {mppR}R Documentation

MPP QTL analysis

Description

Multi-parent population QTL analysis.

Usage

mpp_proc(
  pop.name = "MPP",
  trait.name = "trait1",
  mppData,
  trait = 1,
  Q.eff = "cr",
  plot.gen.eff = FALSE,
  thre.cof = 3,
  win.cof = 50,
  N.cim = 1,
  window = 20,
  thre.QTL = 3,
  win.QTL = 20,
  backward = TRUE,
  alpha.bk = 0.05,
  ref.par = NULL,
  sum_zero = 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".

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 ony available for the cross-specific, parental and ancestral models. Default value = FALSE.

thre.cof

Numeric value representing the -log10(p-value) threshold above which a position can be peaked as a cofactor. Default = 3.

win.cof

Numeric value in centi-Morgan representing the minimum distance between two selected cofactors. Default = 50.

N.cim

Numeric value specifying the number of time the CIM analysis is repeated. Default = 1.

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 20.

thre.QTL

Numeric value representing the -log10(p-value) threshold above which a position can be selected as QTL. Default = 3.

win.QTL

Numeric value in centi-Morgan representing the minimum distance between two selected QTLs. Default = 20.

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. Terms with p-values above this value will iteratively be removed. Default = 0.05.

ref.par

Optional Character expression defining the parental allele that will be used as reference to compute QTL effects for 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 calculated using the sum to zero constraint. Default = FALSE.

CI

Logical value. If CI = TRUE, the function will compute a -log10(pval) drop confidence interval for each QTL after calculating a CIM- profile (without cofactors on the scanned chromosome). 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 mpp_proc should be printed. Default = TRUE.

output.loc

Path where a folder will be created to save the results.

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. Simple interval mapping (SIM) to select cofactor (mpp_SIM).

  2. Composite interval mapping (CIM) with selected cofactors (mpp_CIM).

  3. Optional backward elimination on the list of QTL candidates (backward = TRUE) (mpp_back_elim).

  4. Computation of the QTL genetic effects (QTL_gen_effects) and proportion of the phenotypic variation explained by the QTLs (R squared) (QTL_R2).

  5. Optional QTL confidence interval computation from a CIM- profile (excluding cofactors on the scanned chromosome) (argument CI=TRUE).

Value

Return:

List containing the following items:

n.QTL

Number of detected QTLs.

cofactors

Data.frame with cofactors positions.

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 SIM and CIM results in a text file (SIM.txt, CIM.txt).

  3. The list of cofactors (cofactors.txt).

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

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

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

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

  8. The plot of the CIM profile (QTL_profile.pdf) with dotted vertical lines representing the cofactors positions. 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_back_elim, mpp_CIM, mpp_perm, mpp_SIM, plot.QTLprof, QTL_gen_effects, QTL_R2

Examples

 

data(mppData)

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

# Cross-specific model

USNAM_cr <- mpp_proc(pop.name = "USNAM", trait.name = "ULA",
                     mppData = mppData, plot.gen.eff = TRUE, CI = TRUE,
                     verbose = FALSE, output.loc = my.loc)
[Package mppR version 1.5.0 Index]