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kinship.BLUP {rrBLUP}

Genomic prediction by kinship-BLUP (deprecated)
Package: 
rrBLUP
Version: 
4.2

Description

***This function has been superseded by kin.blup; please refer to its help page.

Usage

kinship.BLUP(y, G.train, G.pred=NULL, X=NULL, Z.train=NULL, 
     K.method="RR", n.profile=10, mixed.method="REML", n.core=1)

Arguments

y
Vector (n.obs     imes 1) of observations. Missing values (NA) are omitted.
G.train
Matrix (n.train     imes m) of unphased genotypes for the training population: n.train lines with m bi-allelic markers. Genotypes should be coded as {-1,0,1}; fractional (imputed) and missing (NA) alleles are allowed.
G.pred
Matrix (n.pred     imes m) of unphased genotypes for the prediction population: n.pred lines with m bi-allelic markers. Genotypes should be coded as {-1,0,1}; fractional (imputed) and missing (NA) alleles are allowed.
X
Design matrix (n.obs     imes p) of fixed effects. If not passed, a vector of 1's is used to model the intercept.
Z.train
0-1 matrix (n.obs     imes n.train) relating observations to lines in the training set. If not passed the identity matrix is used.
K.method
"RR" (default) is ridge regression, for which K is the realized additive relationship matrix computed with A.mat. The option "GAUSS" is a Gaussian kernel (K = e^{-D^2/θ^2}) and "EXP" is an exponential kernel (K = e^{-D/θ}), where Euclidean distances D are computed with dist.
n.profile
For K.method = "GAUSS" or "EXP", the number of points to use in the log-likelihood profile for the scale parameter θ.
mixed.method
Either "REML" (default) or "ML".
n.core
Setting n.core > 1 will enable parallel execution of the Gaussian kernel computation (use only at UNIX command line).

Values

$g.train
BLUP solution for the training set
$g.pred
BLUP solution for the prediction set (when G.pred != NULL)
$beta
ML estimate of fixed effects

For GAUSS or EXP, function also returns

$profile
log-likelihood profile for the scale parameter

References

Endelman, J.B. 2011. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250-255.

Examples

#random population of 200 lines with 1000 markers
G <- matrix(rep(0,200*1000),200,1000)
for (i in 1:200) {
  G[i,] <- ifelse(runif(1000)<0.5,-1,1)
}
 
#random phenotypes
g <- as.vector(crossprod(t(G),rnorm(1000)))
h2 <- 0.5 
y <- g + rnorm(200,mean=0,sd=sqrt((1-h2)/h2*var(g)))
 
#split in half for training and prediction
train <- 1:100
pred <- 101:200
ans <- kinship.BLUP(y=y[train],G.train=G[train,],G.pred=G[pred,],K.method="GAUSS")
 
#correlation accuracy
r.gy <- cor(ans$g.pred,y[pred])

Documentation reproduced from package rrBLUP, version 4.2. License: GPL-3