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Simulate genetic values based on a multiplicative model for GxE interaction - Simulation with `AlphaSimR`

Source: R/multiplicative_gxe.R
multi_asr_output.Rd

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` population object generated with multi_asr_input.

Usage

multi_asr_output(
  pop,
  ntraits = 1,
  nenvs,
  nreps = 1,
  cov.mat,
  return.effects = FALSE
)

Arguments

pop

An `AlphaSimR` population object (Pop-class or HybridPop-class) 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.

Value

A data frame with columns 'env', 'rep', and genotype 'id', 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

# 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(4.9, 5.4, 235.2, 228.5) # Trait 1 x 2 environments, Trait 2 x 2 environments

# 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
)
#> Warning message: 
#>  'nterms' is less than rank of 'corA', 99.79% of variation captured with 3 terms
#> Warning message: 
#>  'nterms' and/or rank of 'corA' are less than number of environment-within-trait combinations, values in 'mean' will be approximated


# 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)

# \dontshow{
SP$nThreads <- 1L
# }

SP$addTraitA(
  nQtlPerChr = 20,
  mean = input_asr$mean,
  var = input_asr$var,
  corA = input_asr$corA
)

pop <- newPop(FOUNDERPOP)
#> Error in get("SP", envir = .GlobalEnv): object 'SP' not found


# 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
)
#> Error in eval(expr, envir, enclos): object 'pop' not found