mppGE_CIM_fast: MPP GxE Composite Interval Mapping using fast MM GLS...

In vincentgarin/mppGxE: MPP GxE QTL analysis

MPP GxE Composite Interval Mapping using fast MM GLS algorithm

Description

Computes a multi QTL models along the genome using different models. Add extra elements to provide more information about the procedure. The initial mixed are calculated with functions from the nlme package.

Usage

mppGE_CIM_fast(
  mppData,
  trait,
  Q.eff = "cr",
  VCOV = "UN",
  cofactors = NULL,
  window = 20,
  n.cores = 1,
  maxIter = 100,
  msMaxIter = 100
)

Arguments

mppData	An object of class mppData.
trait	Character vector specifying which traits (environments) should be used.
Q.eff	Character expression indicating the assumption concerning the QTL effects: 1) "cr" for cross-specific; 2) "par" for parental; 3) "anc" for ancestral; 4) "biall" for a bi-allelic. Default = "cr".
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'
cofactors	Object of class QTLlist representing a list of selected marker positions obtained with the function QTL_select() or a vector of character marker positions names. Default = NULL.
window	Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 20.
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.

Value

Return:

CIM	Data.frame of class QTLprof. with five columns : 1) QTL marker or in between position names; 2) chromosomes; 3) interger position indicators on the chromosome; 4) positions in centi-Morgan; 5) -log10(p-val); and 6 onwards) if p-values of the cross or parental QTL allelic effects.

Author(s)

Vincent Garin

References

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2021). nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1-152, <URL: https://CRAN.R-project.org/package=nlme>.

See Also

mppGE_SIM_fast

Examples

library(mppR)

data(mppData_GE)

cofactors <- mppData_GE$map$mk.names[c(35, 61)]

CIM <- mppGE_CIM_fast(mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'),
                 Q.eff = 'par')

Qpos <- QTL_select(Qprof = CIM, threshold = 3, window = 50)

plot.QTLprof(x = CIM, QTL = Qpos)

plot_genEffects_GE(mppData = mppData_GE, nEnv = 2, EnvNames = c('CIAM', 'TUM'),
                   Qprof = CIM, Q.eff = 'par', QTL = Qpos, text.size = 14)

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