compsym_asr_input {FieldSimR}

Simulate genetic values based on a compound symmetry model for GxE interaction - 'AlphaSimR' input parameters

Description

Creates a list of input parameters for 'AlphaSimR' to simulate genetic values in multiple environments for one or more traits based on a compound symmetry model for genotype-by-environment (GxE) interaction.
This function utilises the ability of 'AlphaSimR' to simulate correlated traits. The wrapper function compsym_asr_input() is used to specify the input parameters required in 'AlphaSimR'. After simulating the genetic values, the wrapper function compsym_asr_output can be used to generate a data frame with output values.

Usage

compsym_asr_input(
  ntraits = 1,
  nenvs = 2,
  mean = 0,
  var = 1,
  prop.main = 0.5,
  corA = NULL,
  meanDD = NULL,
  varDD = NULL,
  prop.mainDD = NULL,
  corDD = NULL,
  relAA = NULL,
  prop.mainAA = NULL,
  corAA = 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 environment-within-trait combination. If only one value is specified, all combinations will be assigned the same mean.

var

A vector of genetic variances for each trait.
Note: When useVarA = TRUE is specified in 'AlphaSimR' (default), the values in var represent the additive genetic variances, otherwise they represent the total (additive + non-additive) genetic variances.

prop.main

A vector defining the proportion of main effect variance for each trait. If only one value is specified, all traits will be assigned the same proportion.
Note: 0 < prop.main < 1.

corA

A matrix of additive genetic correlations between traits. By default, a diagonal matrix is constructed.

meanDD

A vector of mean dominance degrees for each environment-within-trait combination (similar to mean). If only one value is specified, all combinations will be assigned the same mean. By default, meanDD = NULL and dominance is not simulated.

varDD

A vector of dominance degree variances for each trait.

prop.mainDD

A vector defining the proportion of dominance degree main effect variance for each trait (similar to prop.main). If only one value is specified, all traits will be assigned the same proportion.
Note: 0 < prop.mainDD < 1.

corDD

A matrix of dominance degree correlations between traits (similar to corA). If not specified and dominance is simulated, a diagonal matrix is constructed.

relAA

A vector defining the relative magnitude of additive-by-additive (epistatic) variance to additive genetic variance for each trait, that is in a diploid organism with allele frequency of 0.5. If only one value is specified, all traits will be assigned the same relative magnitude.

prop.mainAA

A vector defining the proportion of epistatic main effect variance for each trait (similar to prop.main). If only one value is specified, all traits will be assigned the same proportion.
Note: 0 < prop.mainAA < 1.

corAA

A matrix of epistatic correlations between traits (similar to corA). If not specified and epistasis is simulated, a diagonal matrix is constructed.

Details

The compound symmetry model assumes the same genetic variance for each environment and the same genetic covariance between each pair of environments. New functionality is being implemented which relaxes the former assumption (also see unstr_asr_output).

Note: 'AlphaSimR' can simulate different biological effects (see: SimParam).

• For additive traits use addTraitA().

• For additive + dominance traits use addTraitAD().

• For additive + epistatic traits use addTraitAE().

• For additive + dominance + epistatic traits use addTraitADE().

Check the useVarA argument of these functions when simulating non-additive traits.

Value

A list with input parameters for 'AlphaSimR', which are used to simulate correlated genetic values based on a compound symmetry model for GxE interaction.

Examples

# Simulate genetic values with 'AlphaSimR' for two additive + dominance traits
# in two environments based on a compound symmetry 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(0.08, 13) # Different values for Traits 1 and 2
varDD <- 0.2 # Same value for Traits 1 and 2

# Proportion of additive and dominance degree main effect variances.
prop.main <- c(0.4, 0.6) # Different values for Traits 1 and 2
prop.mainDD <- 0.4 # Same value for Traits 1 and 2

# Additive and dominance degree correlations between the two simulated traits.
corA <- matrix(c(
  1.0, 0.5,
  0.5, 1.0
), ncol = 2)
corDD <- diag(2) # Assuming independence

input_asr <- compsym_asr_input(
  ntraits = 2,
  nenvs = 2,
  mean = mean,
  var = var,
  prop.main = prop.main,
  corA = corA,
  meanDD = meanDD,
  varDD = varDD,
  prop.mainDD = prop.mainDD,
  corDD = corDD
)