kinship.BLUP: Genomic prediction by kinship-BLUP (deprecated)

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***This function has been superseded by kin.blup; please refer to its help page.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

Value

$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

Arguments

y: Vector ($n.obs \times 1$) of observations. Missing values (NA) are omitted.
G.train: Matrix ($n.train \times m$) 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 ($n.pred \times m$) 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 ($n.obs \times p$) of fixed effects. If not passed, a vector of 1's is used to model the intercept.
Z.train: 0-1 matrix ($n.obs \times n.train$) 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 ($K = e^{-D^2/\theta^2}$) and "EXP" is an exponential kernel ($K = e^{-D/\theta}$), 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 $\theta$.
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).

References

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

Examples

Run this code

#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])

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