Generates a pedigreed population with markers and a heritable trait, for examples, tests and teaching. Founders are unrelated; later generations are produced by mating sampled parents, and each offspring inherits one allele from each parent at every marker, so the simulated genotypes and the pedigree agree with each other.
Usage
simulateBreeding(
n_ind = 50,
n_marker = 200,
n_founder = 10,
genotyped = NULL,
n_qtl = 20,
h2 = 0.4,
missing = 0
)Arguments
- n_ind
Number of individuals in total.
- n_marker
Number of markers.
- n_founder
Number of unrelated founders.
- genotyped
Number of individuals with genotypes, taken from the most recent ones. Defaults to all of them.
- n_qtl
Number of markers with an effect on the trait.
- h2
Narrow-sense heritability of the simulated trait.
- missing
Proportion of genotype calls set to
NA.
Value
A BreedingExperiment object with a phenotype column and the
true breeding value in colData.
Details
Set genotyped below n_ind to leave some individuals ungenotyped, which is
the situation Hmatrix() is designed for.
The function draws random numbers but does not set the seed itself. Call
set.seed() beforehand if you need the same population twice.
Examples
set.seed(1)
be <- simulateBreeding(n_ind = 25, n_marker = 100)
be
#> class: BreedingExperiment
#> markers: 100 individuals genotyped: 25
#> assays(1): genotype
#> phenotypes(2): phenotype trueBV
#> sequences(5): chr1 chr2 chr3 chr4 chr5
#> pedigree: 25 individuals (25 genotyped, 0 not; 10 founders)
head(as.data.frame(colData(be)))
#> phenotype trueBV
#> ind001 0.04962359 0.8492997
#> ind002 2.09499329 -0.4066208
#> ind003 -0.69175387 -0.4045620
#> ind004 -1.27278074 -0.1810128
#> ind005 -1.83665981 -0.1741050
#> ind006 -1.12827745 -0.4129922