# Simulate genetic values based on an unstructured 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 an unstructured model for genotype-by-environment (GxE) interaction. The wrapper function `unstr_asr_output` requires an [`AlphaSimR`](https://CRAN.R-project.org/package=AlphaSimR) population object generated with unstr_asr_input . ```r unstr_asr_output(pop, ntraits = 1, nenvs, nreps = 1) ``` ## 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 unstr_asr_input . - `ntraits`: Number of simulated traits specified in unstr_asr_input . - `nenvs`: Number of simulated environments specified in unstr_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. ## Returns A data frame with columns 'env', genotype 'id', and 'rep', followed by the simulated genetic values for each trait. ## Examples ```r # Simulate genetic values with 'AlphaSimR' for two additive + dominance traits # in two environments based on an unstructured model. # 1. Define the genetic architecture of the simulated traits. # Mean genetic values and mean dominance degrees. mean <- c(4.9, 5.4, 235.2, 228.5) # Trait 1 x 2 environments, Trait 2 x 2 environments meanDD <- c(0.4, 0.4, 0.1, 0.1) # Trait 1 and 2, same value for both environments # Additive genetic variances and dominance degree variances. var <- c(0.086, 0.12, 15.1, 8.5) # Trait 1 x 2 environments, Trait 2 x 2 environments varDD <- 0.2 # Same value for all environment-within-trait combinations # 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) # Dominance degree correlations between the four environment-within-trait combinations. corDD <- diag(4) # Assuming independence input_asr <- unstr_asr_input( ntraits = 2, nenvs = 2, mean = mean, var = var, TcorA = TcorA, EcorA = EcorA, meanDD = meanDD, varDD = varDD, corDD = corDD ) # 2. Use input_asr to simulate genetic values with 'AlphaSimR' based on an # unstructured model. library("AlphaSimR") FOUNDERPOP <- quickHaplo( nInd = 10, nChr = 1, segSites = 20 ) SP <- SimParam$new(FOUNDERPOP) SP$addTraitAD( nQtlPerChr = 20, mean = input_asr$mean, var = input_asr$var, corA = input_asr$corA, meanDD = input_asr$meanDD, varDD = input_asr$varDD, corDD = input_asr$corDD, useVarA = TRUE ) # By default, the variances in 'var' represent additive genetic variances. # When useVarA = FALSE, the values represent total genetic variances. 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. gv_df <- unstr_asr_output( pop = pop, ntraits = 2, nenvs = 2, nreps = 2 ) ```

unstr_asr_output function