simer (version 1.0.0)

phenotype: Phenotype simulation

Description

Generate single-trait or multiple-trait phenotype by mixed model.

Usage

phenotype(SP = NULL, ncpus = 0, verbose = TRUE)

Value

the function returns a list containing

$pheno$pop

the population information containing environmental factors and other effects.

$pheno$pop.ind

the number of individuals in the base population.

$pheno$pop.rep

the repeated times of repeated records.

$pheno$pop.rep.bal

whether repeated records are balanced.

$pheno$pop.env

a list of environmental factors setting.

$pheno$phe.type

a list of phenotype types.

$pheno$phe.model

a list of genetic model of phenotype such as "T1 = A + E".

$pheno$phe.h2A

a list of additive heritability.

$pheno$phe.h2D

a list of dominant heritability.

$pheno$phe.h2GxG

a list of GxG interaction heritability.

$pheno$phe.h2GxE

a list of GxE interaction heritability.

$pheno$phe.h2PE

a list of permanent environmental heritability.

$pheno$phe.var

a list of phenotype variance.

$pheno$phe.corA

the additive genetic correlation matrix.

$pheno$phe.corD

the dominant genetic correlation matrix.

$pheno$phe.corGxG

the GxG genetic correlation matrix.

$pheno$phe.corPE

the permanent environmental correlation matrix.

$pheno$phe.corE

the residual correlation matrix.

Arguments

SP

a list of all simulation parameters.

ncpus

the number of threads used, if NULL, (logical core number - 1) is automatically used.

verbose

whether to print detail.

Author

Dong Yin

Details

Build date: Nov 14, 2018 Last update: Jan 28, 2025

References

Kao C and Zeng Z (2002) <https://www.genetics.org/content/160/3/1243.long>

Examples

# \donttest{
# Prepare environmental factor list
pop.env <- list(
  F1 = list( # fixed effect 1
    level = c("1", "2"),
    effect = list(tr1 = c(50, 30), tr2 = c(50, 30))
  ), 
  F2 = list( # fixed effect 2
    level = c("d1", "d2", "d3"),
    effect = list(tr1 = c(10, 20, 30), tr2 = c(10, 20, 30))
  ),
  C1 = list( # covariate 1
    level = c(70, 80, 90),
    slope = list(tr1 = 1.5, tr2 = 1.5)
  ),
  R1 = list( # random effect 1
    level = c("l1", "l2", "l3"),
    ratio = list(tr1 = 0.1, tr2 = 0.1)
  )
)

# Generate genotype simulation parameters
SP <- param.annot(qtn.num = list(tr1 = c(2, 8), tr2 = 10),
                  qtn.model = "A + D + A:D")
# Generate annotation simulation parameters
SP <- param.geno(SP = SP, pop.marker = 1e4, pop.ind = 1e2)
# Generate phenotype simulation parameters
SP <- param.pheno(
  SP = SP, 
  pop.ind = 100,
  pop.rep = 2, # 2 repeated record
  pop.rep.bal = TRUE, # balanced repeated record
  pop.env = pop.env,
  phe.type = list(
    tr1 = "continuous",
    tr2 = list(case = 0.01, control = 0.99)
  ),
  phe.model = list(
    tr1 = "T1 = A + D + A:D + F1 + F2 + C1 + R1 + A:F1 + E",
    tr2 = "T2 = A + D + A:D + F1 + F2 + C1 + R1 + A:F1 + E"
  ),
  phe.var = list(tr1 = 100, tr2 = 100)
)

# Run annotation simulation
SP <- annotation(SP)
# Run genotype simulation
SP <- genotype(SP)
# Run phenotype simulation
SP <- phenotype(SP)
# }