Breed: Wrap-up Function

Here, we introduce a function breed() as a wrapper for matings and selections. n_gens defines how many generations are simulated, and n_select_A or n_select_B determines how many male or female progenies are selected as sires or dams in the next generation.

Breed: Wrap-up Function

breed(cohort_A         ::Cohort,
      cohort_B         ::Cohort;
      n_gens           ::Int64=1,
      n_select         ::Int64=cohort_A.n + cohort_B.n,
      n_select_A   ::Int64=cohort_A.n,
      n_select_B ::Int64=cohort_B.n,
      select_all_gens  ::Bool=false,
      args...)

breed(cohort::Cohort, n_gens::Int64, args...) =
    breed(cohort, cohort, n_gens; args...)

Arguments

Positional arguments

cohort_A : A cohort object that is treated as common mating parents. It's asssumed to be sires/males parents.
cohort_B : A cohort object that is a mating pool from which individuals are sampled to mate with cohort_A. It's assumed to be dams/female parents.

Keywords arguments

n_gens : An integer specifying how many mate-select generations are carried out.
n_select : Used when ratio_malefemale is set to 0. n_select individuals are selected as parents for the next generation.
n_select_A : Used when ratio_malefemale is not 0. n_select_A will be selected as male parents for the next generation.
n_select_B : Used when ratio_malefemale is not 0. n_select_B will be selected as female parents for the next generation.
select_all_gens : Default "false" and parents are not included in the next generation pool for selections. Set select_all_gens to "true" if the selections consider individuals from all generations.

Outputs

By default, two cohort objects will be returned. The first cohort is assumed to be male progenies and the other cohort are female progenies. The size of two cohorts will folow the ratio raiot_malefemale. When ratio_malefemale is set to 0, only one cohort will be returned.

Examples

We can have 10 sires and mate each sire with 5 dams for 5 generations. In each generation, we randomly select 10 male progenies as sires and all female progenies as dams for the next generation. We can derive such breeding scheme as below:

julia> args  = Dict(# mating arguments
                    :nA               => 10,
                    :nB_per_A         => 5,
                    :n_per_mate       => 2,
                    :ratio_malefemale => 1.0,
                    # selection arguments
                    :is_random        => true,
                    # breeding arguments
                    :n_gens           => 5,
                    :nA_select        => 10,
                    :select_all_gens  => true)

Simulate 10 sires and 50 dams as founders.

julia> sires = Founders(10)
julia> dams  = Founders(50)

Breed cohorts based on the defined arguments.

julia> sires, dams = breed(sires, dams; args...)
[ Info: Gen 0 -> Mean of BVs: [1.665 2.745], Variance of BVs: [1.008 0.479]
[ Info: Gen 1 -> Mean of BVs: [1.719 2.715], Variance of BVs: [0.96 0.546]
[ Info: Gen 2 -> Mean of BVs: [1.754 2.777], Variance of BVs: [0.936 0.724]
[ Info: Gen 3 -> Mean of BVs: [1.766 2.796], Variance of BVs: [0.927 0.775]
[ Info: Gen 4 -> Mean of BVs: [1.773 2.771], Variance of BVs: [0.951 0.781]
[ Info: Gen 5 -> Mean of BVs: [1.813 2.751], Variance of BVs: [0.952 0.804]
([ Info: Cohort (60 individuals)
[ Info: 
[ Info: Mean of breeding values: 
[ Info: [1.855 2.593]
[ Info: 
[ Info: Variance of breeding values: 
[ Info: [0.884 0.697]
, [ Info: Cohort (300 individuals)
[ Info: 
[ Info: Mean of breeding values: 
[ Info: [1.805 2.782]
[ Info: 
[ Info: Variance of breeding values: 
[ Info: [0.969 0.822]

The result is equivalent to the following mate-select iterations:

julia> for _ in 1:5
            males, females = mate(sires, dams, args...)
            sires         += select(males, n_sires, args...)
            dams          += females
       end