mppGE_SIM {mppR} | R Documentation |
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
)
Arguments
mppData |
An object of class |
trait |
|
VCOV |
VCOV |
ref_par |
Optional |
n.cores |
|
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} + x_{i_{q}p} * \beta_{pj} + \underline{GE}_{icj} + \underline{e}_{icj}
For further details see the vignette.
Value
Return:
SIM |
|
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
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)