# kinship.BLUP: Genomic prediction by kinship-BLUP (deprecated) In rrBLUP: Ridge Regression and Other Kernels for Genomic Selection

## Description

***This function has been superseded by kin.blup; please refer to its help page.

## Usage

 1 2 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 \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/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).

## 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

## References

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

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 #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]) 

### Example output




rrBLUP documentation built on Jan. 29, 2018, 1:04 a.m.