mppR (version 1.5.0)

mppGE_SIM: MPP GxE Simple Interval Mapping

Description

Computes single QTL models along the genome using an approximate mixed model computation. An initial variance covariance (VCOV) structure is calculated using function from the nlme package. Then, this information is used to estimate the QTL global and within parental effect significance using a Wald test.

Usage

mppGE_SIM(
  mppData,
  trait,
  VCOV = "UN",
  ref_par = NULL,
  n.cores = 1,
  maxIter = 100,
  msMaxIter = 100
)

Value

Return:

SIM

Data.frame of class QTLprof. with five columns : 1) QTL marker or in between position names; 2) chromosomes; 3) integer position indicators on the chromosome; 4) positions in centi-Morgan; and 5) -log10(p-val) of the global QTL effect across environments 6) p-values of the within environment QTL effects (one column per environment); and p-values of the within environment parental QTL allelic effects (one column per parent environment combination).

Arguments

mppData

An object of class mppData.

trait

Character vector specifying which traits (environments) should be used.

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'

ref_par

Optional Character expression defining the parental allele that will be used as reference for the parental model. Default = NULL

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.

Author

Vincent Garin

Details

The estimated model is the following:

\(\underline{y}_{icj} = E_{j} + C_{cj} + x_{i_{q}p} * \beta_{pj} + \underline{GE}_{icj} + \underline{e}_{icj}\)

For further details see the vignette.

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_CIM, mppGE_proc

Examples

data(mppData_GE)

SIM <- mppGE_SIM(mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'))

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

plot(x = SIM, QTL = Qpos)

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