Decomposition of the QTL effect into main component across environments and QTLxE component.
QTL_effect_main_QxE(
mppData,
trait,
env_id = NULL,
VCOV = "UN",
ref_par = NULL,
QTL = NULL,
QmainQi = TRUE,
maxIter = 100,
msMaxIter = 100
)Return:
List with one data.frame per QTL that contains the following
elements:
QTL parent allele main effect expressed as deviation with respect to the reference parent
QTL parent allele effect in environment j expressed as deviation with respect to the reference parent
Significance of the parent main effect expressed as the -log10(p-val)
Significance of the parent QTLxE effect expressed as the -log10(p-val)
An object of class mppData.
Character vector specifying which traits (environments) should be used.
Character vector specifying the environment names.
By default, E1, ... En
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'
Optional Character expression defining the parental
allele that will be used as reference for the parental model. Default = NULL
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.
logical value specifying how the QTL parental allele
main effects are estimated. For further explanation see the details section.
Default = TRUE
maximum number of iterations for the lme optimization algorithm. Default = 100.
maximum number of iterations for the optimization step inside the lme optimization. Default = 100.
Vincent Garin
The function estimate the QTL parent allele main effect across environments as well the QTLxE effect. The significance of the QTL parental main effect as well as the QTLxE effect are also estimated and returned as -log10(p-value).
The function use two models, one where the QTL parent allele effect are considered to be different in each environments (QTLxE model) and a model where the QTL parental effect are assumed to be constant across environment (QTL main model). Concerning the model to estimate the QTL main effect, there are two option, the first (default) option (QmainQi = TRUE), estimate a model where only the ith QTL is defined with a main effect and the other position are assumed to have parental effect that vary in each environment (same as the QTLxE model). In that case, the function estimate as many QTL main model as there are QTL positions to get the main effect estimate of each QTL position. The alternative option (QmainQi = FALSE), calculate a single model where all QTL are defined with a main effect term. The estimated main effect obtained with the two options are generally very similar. The second option is less time consumming.
The QTL main allelic effect is the deviation of the parental allelic effect with respect to the reference parent (e.g. the central or recurrent parent in a NAM population)
The estimation is performed using an exact mixed model with function from R
package nlme. The significance of the allele effect is assessed using a
Wald test.
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>.
if (FALSE) {
data(mppData_GE)
Qpos <- c("PZE.105068880", "PZE.106098900")
Qeff <- QTL_effect_main_QxE(mppData = mppData_GE,
trait = c('DMY_CIAM', 'DMY_TUM', 'DMY_INRA_P', 'DMY_KWS'),
env_id = c('CIAM', 'TUM', 'INRA', 'KWS'),
QTL = Qpos)
Qeff
}
Run the code above in your browser using DataLab