mppGE_SIM | R Documentation |
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.
mppGE_SIM(
mppData,
trait,
VCOV = "UN",
ref_par = NULL,
n.cores = 1,
maxIter = 100,
msMaxIter = 100
)
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. |
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.
Return:
SIM |
|
Vincent Garin
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_CIM
,
mppGE_proc
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.