mppGE_CV: MPP GxE cross-validation

In vincentgarin/mppGxE: MPP GxE QTL analysis

MPP GxE cross-validation

Description

MPP GxE cross-validation.

Usage

mppGE_CV(
  pop.name = "MPP",
  trait.name = "trait1",
  mppData,
  trait,
  cv.ref = NULL,
  Rep = 5,
  k = 3,
  EnvNames = NULL,
  Q.eff = "cr",
  VCOV = "CS_CSRT",
  thre.cof = 4,
  win.cof = 50,
  N.cim = 1,
  window = 20,
  thre.QTL = 4,
  win.QTL = 20,
  alpha = 0.01,
  parallel = FALSE,
  cluster = NULL,
  workspace = 8e+06,
  verbose = TRUE,
  output.loc = NULL
)

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	Character vector specifying which traits (environments) should be used.
cv.ref	Numerical or character indicator to specify which trait of the mppData object should be used to check the prediction in the CV process. By default use 'trait'.
Rep	Numeric value representing the number of repetitions of the k-fold procedure. Default = 5.
k	Numeric value representing the number of folds for the within cross partition of the population. Default = 3.
EnvNames	Character expression indicating the environment or trait labels. By default: Env_1, 2, 3, etc.
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. Default = "cr".
VCOV	VCOV Character expression defining the type of variance covariance structure used. "ID" for identity, "CSRT" for within environment cross-specific residual terms, "CS_CSRT" for compound symmetry with within environment cross-specific residual terms. Default = "CS_CSRT".
thre.cof	Numeric value representing the -log10(p-value) threshold above which a position can be selected as cofactor. Default = 4.
win.cof	Numeric value in centi-Morgan representing the minimum distance between two selected cofactors. Default = 50 cM.
N.cim	Numeric value specifying the number of times 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 = 4.
win.QTL	Numeric value in centi-Morgan representing the minimum distance between two selected QTLs. Default = 20.
alpha	Numeric value indicating the level of significance for the backward elimination. Default = 0.01.
parallel	Logical value specifying if the function should be executed in parallel on multiple cores. To run the function in parallel user must provide clusters in the cluster argument. Parallelization is only available for 'ID' model. Default = FALSE.
cluster	Cluster object obtained with the function makeCluster() from the parallel package. Default = NULL.
workspace	Size of workspace for the REML routines measured in double precision words (groups of 8 bytes). The default is workspace = 8e6.
verbose	Logical value indicating if the steps of mpp_proc should be printed. It will not affect the printing of the other functions called by mpp_proc(), especially the printing of asreml(). Default = TRUE.
output.loc	Path where a folder will be created to save the results. Default = NULL.

Value

Return:

List containing the following results items:

p_vs	Matrix with : 1) the number of detected QTL; 2) the proportion of predicted genetic variance in the VS (p.vs) at the population level (average of within cross predictions) per environment.
QTL	Data.frame containing: 1) the list of QTL position detected at least one time during the entire CV process; 2) the number of times the position has been detected

The results elements returned as R object are also saved as text files at the specified output location (output.loc).

Author(s)

Vincent Garin

Examples

## Not run: 

library(asreml)

data(mppData_GE)

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

QTL <- mppGE_CV(pop.name = 'EUNAM', trait.name = 'DMY', mppData = mppData_GE,
                trait = c('DMY_CIAM', 'DMY_TUM'), Rep = 1, k = 2,
                EnvNames = c('CIAM', 'TUM'),  Q.eff = 'par',
                thre.cof = 3, thre.QTL = 3, verbose = FALSE,
                output.loc = my.loc)


## End(Not run)

vincentgarin/mppGxE documentation built on June 25, 2022, 2:45 p.m.