mppGE_CIM {mppR}

MPP GxE Composite Interval Mapping

Description

Computes multi-QTL models with cofactors 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_CIM(
  mppData,
  trait,
  VCOV = "UN",
  VCOV_data = "unique",
  cofactors = NULL,
  cof_red = FALSE,
  cof_pval_sign = 0.1,
  window = 20,
  ref_par = NULL,
  n.cores = 1,
  maxIter = 100,
  msMaxIter = 100
)

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'

VCOV_data

Character specifying if the reference VCOV should be formed taking all cofactors into consideration ("unique") or if different VCOVs should be formed by removing the cofactor information that is too close of a tested QTL position ("minus_cof"). Default = "unique"

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.

cof_red

Logical value specifying if the cofactor matrix should be reduced by only keeping the significant allele by environment interaction. Default = FALSE

cof_pval_sign

Numeric value specifying the p-value significance of an allele by environment term to be kept in the model. Default = 0.1

window

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

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.

Details

The estimated model is the following:

\underline{y}_{icj} = E_{j} + C_{cj} + \sum_{qc=1}^{n_{cof}} x_{i_{qc}p} + \beta_{pj} + x_{i_{q}p} * \beta_{pj} + \underline{GE}_{icj} + \underline{e}_{icj}

For further details see the vignette.

It is possible to calculate one initial VCOV using a null model with all the cofactors (VCOV_data = "unique") or one VCOV per combination of cofactors (VCOV_data = "minus_cof"). In the later case, the cofactor that fall witin a distance of window on the left and right of a QTL position is removed for the calculation of the initial VCOV. Therefore, N_cof + 1 VCOV are calculated.

Value

Return:

CIM

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).

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>.

mppGE_SIM, mppGE_proc

Examples

data(mppData_GE)

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

CIM <- mppGE_CIM(mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'),
                     cofactors = cofactors, window = 20)
                     
Qpos <- QTL_select(CIM)
                      
plot(CIM)

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