GBLUP.fit {IPLGP}

Muti-trait GBLUP Model

Description

Built the muti-trait GBLUP model using the phenotypic and genotypic data of a training population by 'mmer' from R package 'sommer'. Then, output the fitted values of the training population.

Usage

GBLUP.fit(t1, t2, t3, t4, t5, geno = NULL, K = NULL, outcross = FALSE)

Arguments

t1

vector. The phenotype of trait1. The missing value must be coded as NA. The length of all triat must be the same.

t2

vector. The phenotype of trait2. The missing value must be coded as NA. The length of all triat must be the same.

t3

vector. The phenotype of trait3. The missing value must be coded as NA. The length of all triat must be the same.

t4

vector. The phenotype of trait4. The missing value must be coded as NA. The length of all triat must be the same.

t5

vector. The phenotype of trait5. The missing value must be coded as NA. The length of all triat must be the same.

geno

matrix. An n*p matrix with n individuals and p markers of the training population. The markers must be coded as 1, 0, or -1 for alleles AA, Aa, or aa. The missing value must have been already imputed.

K

matrix. An n*n matrix denotes the genomic relationship matrix of the training population if geno is set to be NULL.

outcross

logical. A logical variable, if outcross is set to be TRUE, the crop is regarded as an outcross crop. The kinship matrix of dominance effects are also considered in the model. The geno data must be given when outcross being TRUE.

Value

fitted.value

The fitted values.

fitted.A

The additive effect part of fitted values.

fitted.D

The dominance effect part of fitted values.

mu

The average value of fitted values.

Note

Due to restrictions on the use of the funtion 'mmer', if an unknown error occurs during use, please try to input the phenotype data as the format shown in the example.

References

Habier D, Fernando RL, Dekkers JCM. 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389-2397.

VanRaden PM. 2008. Efficient methods to compute genomic predictions. J Dairy Sci. 91:4414-4423.

See Also

mmer

Examples

# generate simulated data
set.seed(2000)
t1 <- rnorm(50,30,10)
t2 <- rnorm(50,10,5)
t3 <- rnorm(50,20,20)
t4 <- NULL
t5 <- NULL

# run with the marker score matrix
geno.test <- matrix(sample(c(1, -1), 5000, replace = TRUE), 50, 100)
result1 <- GBLUP.fit(t1, t2, t3, t4, t5, geno = geno.test)
result1$fitted.value

# run with the genomic relationship matrix
K.test <- geno.test%*%t(geno.test)/ncol(geno.test)
result2 <- GBLUP.fit(t1, t2, t3, t4, t5, K = K.test)
result2$fitted.value