Description Usage Arguments Details Value Note Author(s) References See Also Examples
This function fits genomic prediction models based on phenotypic and genotypic data in an object of class gpData
. The possible models are Best Linear Unbiased Prediction (BLUP) using a pedigree-based or a marker-based genetic relationship matrix and Bayesian Lasso (BL) or Bayesian Ridge regression (BRR).
BLUP models are fitted using the REML implementation of the regress
package (Clifford and McCullagh, 2012). The Bayesian regression models are fitted using the Gibbs-Sampler of the BGLR
package (de los Campos and Perez, 2010). The covariance structure in the BLUP model is defined by an object of class relationshipMatrix
. The training set for the model fit consists of all individuals with phenotypes and genotypes. All data is restricted to individuals from the training set used to fit the model.
1 2 |
gpData |
object of class |
model |
|
kin |
object of class |
predict |
|
trait |
|
repl |
|
markerEffects |
|
fixed |
A formula for fixed effects. The details of model specification are the same as for |
random |
A formula for random effects of the model. Specifies the matrices to include in the covariance structure. Each term is either a symmetric matrix, or a factor. Independent Gaussian random effects are included by passing the corresponding block factor. For mor details see |
... |
further arguments to be used by the genomic prediction models, i.e. prior values and MCMC options for the |
By default, an overall mean is added to the model. If no kin
is specified and model = "BLUP"
, a G-BLUP model will be fitted. For BLUP, further fixed and random effects can be added through the arguments fixed
and random
.
The marker effects hatm in the RR-BLUP model (available with markerEffects
) are calculated as
m = X'*Ginv*ghat
with X being the marker matrix, G=XX' and ghat the vector of predicted genetic values.
Only a subset of the individuals - the training set - is used to fit the model. This contains all individuals with phenotypes and genotypes. If kin
does not match the dimension of the training set (if, e.g. ancestors are included), the respective rows and columns from the trainings set are choosen.
Object of class gpMod
which is a list of
fit |
The model fit returned by the genomic prediction method |
model |
The model type, see 'Arguments' |
y |
The phenotypic records for the individuals in the training set |
g |
The predicted genetic values for the individuals in the training set |
m |
Predicted SNP effects (if available) |
kin |
Matrix |
The verbose output of the BLR
function is written to a file BLRout.txt
in the working directory to prevent the screen output from overload.
Valentin Wimmer, Hans-Juergen Auinger and Theresa Albrecht
Clifford D, McCullagh P (2012). regress: Gaussian Linear Models with Linear Covariance Structure. R package version 1.3-8, URL http://www.csiro.au.
Gustavo de los Campos and Paulino Perez Rodriguez, (2010). BLR: Bayesian Linear Regression. R package version 1.2. http://CRAN.R-project.org/package=BGLR
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ## Not run:
library(synbreedData)
data(maize)
maizeC <- codeGeno(maize)
# pedigree-based (expected) kinship matrix
K <- kin(maizeC,ret="kin",DH=maize$covar$DH)
# marker-based (realized) relationship matrix
# divide by an additional factor 2
# because for testcross prediction the kinship of DH lines is used
U <- kin(maizeC,ret="realized")/2
# BLUP models
# P-BLUP
mod1 <- gpMod(maizeC,model="BLUP",kin=K)
# G-BLUP
mod2 <- gpMod(maizeC,model="BLUP",kin=U)
# Bayesian Lasso
prior <- list(varE=list(df=3,S=35),lambda = list(shape=0.52,rate=1e-4,value=20,type='random'))
mod3 <- gpMod(maizeC,model="BL",prior=prior,nIter=6000,burnIn=1000,thin=5)
summary(mod1)
summary(mod2)
summary(mod3)
## End(Not run)
|
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