EnvBV_blup {GEmetrics}

Compute the best linear unbiased prediction and the conditional variance matrix of environment-specific breeding values

Description

This function calculates the best linear unbiased prediction and the conditional variance matrix of environment-specific breeding values

Usage

EnvBV_blup(Pheno, K, Omega_G, Omega_E)

Arguments

Pheno

a data frame with three columns: "Y" for phenotypic values, "Genotype" for genotype names and "Environment" for environment names. All genotypes names must be included in the set or row and column names of the "K" matrix. The number of environments must be at least two

K

a square kinship or genomic relationship matrix for N genotypes whose row and column names include those of the "Genotype" column of the "Pheno" matrix

Omega_G

a square matrix with genetic covariances between J environments whose row and column names correspond to those of the "Environment" column of the "Pheno" matrix

Omega_E

a square matrix with error covariances between J environments whose row and column names correspond to those of the "Environment" column of the "Pheno" matrix

Value

a list of two elements: a "G_hat" matrix of best linear unbiased prediction of environment-specific breeding values for the N genotypes (as rows) in J environments (as columns), and a square conditional variance matrix "P" of environment-specific breeding values of dimension NJxNJ where each row block of size N correspond to an environment and the rows of each block correspond to genotypes

Examples

## Set seed for reproductibility
set.seed(123)

## Load "wheat" dataset from BGLR
data("wheat",package = "BGLR")

## Generate a design data frame for all genotypes in 5 environments
Design <- expand.grid(Genotype=rownames(wheat.A),Environment=paste0("Env",1:5))

## Set sparseness by discarding 80% of the combinations
Design <- Design[-sample(nrow(Design),round(nrow(Design)*4/5)),]

## Simulate phenotypic data with default parameter values
DataSim <- Simulate_MET_data(Design=Design,K=wheat.A)


## Calculate the blup and the conditional variance matrix using simulated variance components
## this step can take several seconds
## note that variance can also be estimated (e.g. using BGLR)
BlupEnvBV <- EnvBV_blup(Pheno=DataSim$Pheno,K=wheat.A,Omega_G=DataSim$Omega_G,
                        Omega_E=DataSim$Omega_E)

## Display results
head(BlupEnvBV$G_hat)
BlupEnvBV$P[1:5,1:5]