# kinship.BLUP {rrBLUP}

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***This function has been superseded by `kin.blup`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)

### 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 4.3. License: GPL-3