mppGE_proc_fast: MPP GxE QTL analysis

In vincentgarin/mppGxE: MPP GxE QTL analysis

MPP GxE QTL analysis

Description

QTL detection in MPP characterized in multiple environments using 'fast' algorithm.

Usage

mppGE_proc_fast(
  pop.name = "MPP",
  trait.name = "trait1",
  mppData,
  trait,
  EnvNames = NULL,
  Q.eff = "par",
  VCOV = "UN",
  thre.cof = 4,
  win.cof = 50,
  window = 20,
  thre.QTL = 4,
  win.QTL = 20,
  text.size = 18,
  n.cores = 1,
  maxIter = 100,
  msMaxIter = 100,
  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.
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 = "par".
VCOV	VCOV Character expression defining the type of variance covariance structure used. 'CS' for compound symmetry assuming a unique genetic covariance between environments. 'CSE' for cross-specific within environment error term. 'CS_CSE' for both compound symmetry plus cross-specific within environment error term. 'UN' for unstructured environmental variance covariance structure allowing a specific genotypic covariance for each pair of environments. Default = 'UN'
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.
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.
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.
maxIter	maximum number of iterations for the lme optimization algorithm. Default = 100.
msMaxIter	maximum number of iterations for the optimization step inside the lme optimization. Default = 100.
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. Default = NULL.

Details

The procedure is the following:

1. Simple interval mapping (SIM) to select cofactor (mppGE_SIM_fast).

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

3. Estimation of QTLs additive allelic effect (QTL_effect_GE).

4. Estimation of the global QTLs effect R squared and individual QTL effect R squared (QTL_R2_GE).

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.
Q_eff	list containing the estimated QTL allelic effects.
R2	List containing R squared statistics of the QTL effects

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

1. The SIM and CIM results in a RData file (SIM.RData, CIM.RData).

2. The list of cofactors (cofactors.RData).

3. The list of QTL (QTLs.RData).

4. The list of QTL allelic effects (QTL_effects.RData).

5. The QTL R squared statistics (QTL_R2.RData)

6. The number of detected QTLs and adjusted R2 (Glb_res.RData)

7. The plot of the CIM profile (QTL_profile.pdf) with dotted vertical lines representing the cofactors positions and the plot of the genetic effects per cross or parents obtained with plot_genEffects_GE (gen_eff.pdf) with dashed lines representing the QTL positions.

Author(s)

Vincent Garin

See Also

mppGE_CIM_fast, mppGE_SIM_fast, QTL_effect_GE, QTL_R2_GE

Examples

## Not run: 

data(mppData_GE)

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

QTL <- mppGE_proc_fast(pop.name = 'EUNAM', trait.name = 'DMY', mppData = mppData_GE,
                  trait = c('DMY_CIAM', 'DMY_TUM'), 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.