# Simulate genetic values based on a multiplicative model for GxE interaction - Simulation with `AlphaSimR` Creates a data frame of simulated genetic values in multiple environments for one or more traits based on a (reduced rank) multiplicative model for genotype-by-environment (GxE) interaction. This function requires an [`AlphaSimR`](https://CRAN.R-project.org/package=AlphaSimR) population object generated with multi_asr_input . ```r multi_asr_output( pop, ntraits = 1, nenvs, nreps = 1, cov.mat, return.effects = FALSE ) ``` ## Arguments - `pop`: An [`AlphaSimR`](https://CRAN.R-project.org/package=AlphaSimR) population object ([Pop-class](https://gaynorr.github.io/AlphaSimR/reference/Pop-class.html) or [HybridPop-class](https://gaynorr.github.io/AlphaSimR/reference/HybridPop-class.html)) generated with multi_asr_input . - `ntraits`: Number of traits specified in multi_asr_input . - `nenvs`: Number of environments specified in multi_asr_input . - `nreps`: A vector defining the number of replicates in each environment. If only one value is specified, all environments will be assigned the same number. - `cov.mat`: A matrix of covariates that will be used to construct the genetic values, typically generated with multi_asr_input . - `return.effects`: When `TRUE` (default is `FALSE`), a list is returned with additional entries containing the genotype slopes for each multiplicative term. ## Returns A data frame with columns 'env', genotype 'id', and 'rep', followed by the simulated genetic values for each trait. When `return.effects = TRUE`, a list is returned with additional entries containing the genotype slopes for each multiplicative term. ## Examples ```r # Simulate genetic values with 'AlphaSimR' for two additive traits in two # environments based on a multiplicative model with three terms. # 1. Define the genetic architecture of the simulated traits. # Mean genetic values. mean <- c(5, 240) # Trait 1, Trait 2 # Additive genetic variances. var <- c(0.086, 0.12, 15.1, 8.5) # Trait 1 x 2 environments, Trait 2 x 2 environments # Additive genetic correlations between the two simulated traits. TcorA <- matrix(c( 1.0, 0.6, 0.6, 1.0 ), ncol = 2) # Additive genetic correlations between the two simulated environments. EcorA <- matrix(c( 1.0, 0.2, 0.2, 1.0 ), ncol = 2) # Construct separable additive genetic correlation matrix corA <- kronecker(TcorA, EcorA) input_asr <- multi_asr_input( ntraits = 2, nenvs = 2, mean = mean, var = var, corA = corA, nterms = 3 ) # 2. Use input_asr to simulate genetic values in 'AlphaSimR' based on a # multiplicative model with three terms. library("AlphaSimR") FOUNDERPOP <- quickHaplo( nInd = 10, nChr = 1, segSites = 20 ) SP <- SimParam$new(FOUNDERPOP) SP$addTraitA( nQtlPerChr = 20, mean = input_asr$mean, var = input_asr$var, corA = input_asr$corA ) pop <- newPop(FOUNDERPOP) # 3. Create a data frame with simulated genetic values for the two traits in the two # environments, with two replicates of each genotype. # The covariates are obtained from input_asr. gv_ls <- multi_asr_output( pop = pop, ntraits = 2, nenvs = 2, nreps = 2, cov.mat = input_asr$cov.mat, return.effects = TRUE ) ```

multi_asr_output function