Simulate_MET_data {GEmetrics}

Simulate multi-environment trials data

Description

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

Usage

Simulate_MET_data(Design, K, h2 = 0.5, rho = 0.5, sd_mu = 1)

Arguments

Design

a data frame with two columns: "Genotype" for genotype names and "Environment" for environment names. All genotypes names must be included in the set of row/column names of the "K" matrix. The number of environments (J) must be at least two

K

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

h2

heritability (numeric value between 0 and 1 excluded) of observations in each environment: either a scalar to set a common heritability for all environments, or a vector of heritabilities associated with each environment of size J. The default value is a heritability of 0.5 for all environments

rho

genetic correlation (numeric value between -1 and 1 excluded) between environment pairs: either a scalar to set a common genetic correlation between all environment pairs, or a square correlation matrix of dimension JxJ. The default value is a genetic correlation of 0.5 between all environment pairs

sd_mu

standard deviation (positive numeric value) of the Gaussian distribution in which environment means are drawn. The default value is 1

Value

a list of two elements: a "Pheno" data frame consisting of the "Design" data frame to which a "Y" column containing simulated phenotypic values has been added, and a "EnvBV" matrix of dimension NxJ containing the simulated environment-specific breeding values

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 75% of the combinations
Design <- Design[-sample(nrow(Design),round(nrow(Design)*3/4)),]

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

## Simulated phenotypes
head(DataSim$Pheno)

## Simulated environment-specific breeding values
head(DataSim$EnvBV)

## Genetic covariance matrix between environments
DataSim$Omega_G

## Error covariance matrix between environments
DataSim$Omega_E