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Simulate genetic values based on a multiplicative model for GxE interaction - `AlphaSimR` input parameters

Source: R/multiplicative_gxe.R
multi_asr_input.Rd

Creates a list of input parameters for `AlphaSimR` to simulate 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 utilises the ability of `AlphaSimR` to simulate correlated traits. The wrapper function multi_asr_input() is used to specify the input parameters required in `AlphaSimR`. After simulating the genetic values, the wrapper function multi_asr_output can be used to generate a data frame with output values.

Usage

multi_asr_input(
  ntraits = 1,
  nenvs = 2,
  mean = 0,
  var = 1,
  corA = NULL,
  nterms = NULL
)

Arguments

ntraits

Number of traits to be simulated.

nenvs

Number of environments to be simulated (minimum of two).

mean

A vector of mean genetic values for each trait or each environment-within-trait combination. If only one value is specified, all combinations will be assigned the same mean.

var

A vector of additive genetic variances for each trait or each environment-within-trait combination. If only one value is specified, all combinations will be assigned the same variance.

corA

A matrix of additive genetic correlations between environment-within-trait combinations. By default, a diagonal matrix is constructed.

nterms

A scalar defining the number of multiplicative terms to be simulated. By default, the number of terms is set to the number of environment-within-trait combinations. Note: when nterms is less than the number of environment-within-trait combinations, the values in mean will be approximated.

Value

A list with input parameters for `AlphaSimR`, which are used to simulate correlated genetic values based on a multiplicative model for GxE interaction. Covariates are also supplied for use in multi_asr_output.

Details

Currently supports additive traits only, but other (non-additive) traits are being implemented.

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(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
)
#> Warning message: 
#>  'nterms' is less than rank of 'corA', 99.79% of variation captured with 3 terms