Determination of which parental QTL effect show a significant interaction with the environment. Then, the function try to characterize the nature of the QTLxE effect by estimating the sensitivity of the parental allelic effects showing significant QTLxE interaction to environmental covariates provided by the user.
QTL_effect_QxEC(
mppData,
trait,
env_id = NULL,
VCOV = "UN",
ref_par = NULL,
QTL = NULL,
QmainQi = TRUE,
thre_QTL = 2,
all_main = TRUE,
EC,
Qmain_QxE = NULL,
QTLxEC_plot = 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
Numerical
value specifying the -log10(p-val) threshold
for a parental QTL allele to be considered as significant. By default,
thre_QTL = 2, which correspond to a p-value of 0.01.
Logical
value specifying if all the parental alleles
should be set as main effect in the QTLxEC model or if only the significant
parental allele should be introduced in the model as main effect and QTLxEC
effect if the QTLxE term is significant. Default = TRUE.
Numeric
matrix with environments as row and environmental
covariates (EC) as column. The cell i, j of EC specify the value of the
jth EC in environment i.
results from QTL_effect_main_QxE
Logical
value specifying if the data to
plot sensitivity curve with the function plot_QTLxEC should be returned.
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 first estimate the parental QTL allele main and QTLxE effect
using the function QTL_effect_main_QxE
. Then it determines
which parental allele shows a significant QTLxE effect by looking if the
-log10(p-val) of the parental QTLxE effect is superior or equal to
thre_QTL
and if the -log10(p-val) of QTLxE term is superior to one of
the main effect. Finally, given this information, the function replaces the
QTLxE term of the parental QTL allelic effect showing a significant QTLxE
effect with a main effect and QTLxEC term representing interaction between
the parental QTL allele and the environmental covariate (EC). The QTLxEC term can
be interpreted as a sensitivity of the QTL to the variation of the EC in the
different environments.
Two options are possible concerning the inclusion of the parental QTL allele
as main effect in the QTLxEC model. Either all parental allele are introduced
(all_main = TRUE
, default), or only the parental allele showing a
singificant main effect are introduced (all_main = FALSE
).
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>.
QTL_effect_main_QxE
if (FALSE) {
data(mppData_GE)
Qpos <- c("PZE.105068880", "PZE.106098900")
EC <- matrix(c(180, 310, 240, 280), 4, 1)
rownames(EC) <- c('CIAM', 'TUM', 'INRA', 'KWS')
colnames(EC) <- 'cum_rain'
Qeff <- QTL_effect_QxEC(mppData = mppData_GE,
trait = c('DMY_CIAM', 'DMY_TUM', 'DMY_INRA_P', 'DMY_KWS'),
env_id = c('CIAM', 'TUM', 'INRA', 'KWS'),
QTL = Qpos, EC = EC)
Qeff
}
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