Demo: Step by Step
Step 0. Load XSim
julia> using XSimStep 1. Setup Genome and Phenome
The demo example simulates 10 chromosomes with 100 loci each. And 2 independent traits are controlled by 3 and 8 QTLs, respectively.
julia> build_genome(n_chr=10, n_loci=100)
[ Info: --------- Genome Summary ---------
[ Info: Number of Chromosome : 10
[ Info:
[ Info: Chromosome Length (cM): 1000.0
[ Info: [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]
[ Info:
[ Info: Number of Loci : 1000
[ Info: [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
[ Info:
[ Info: Genotyping Error : 0.0
[ Info: Mutation Rate : 0.0
[ Info:
julia> build_phenome([3, 8])
[ Info: --------- Phenome Summary ---------
[ Info: Number of Traits : 2
[ Info: Heritability (h2) : [0.5, 0.5]
┌ Info:
│ Genetic_Variance =
│ 2×2 Array{Float64,2}:
│ 1.0 0.0
└ 0.0 1.0
┌ Info:
│ Residual_Variance =
│ 2×2 Array{Float64,2}:
│ 1.0 0.0
└ 0.0 1.0
[ Info: Number of QTLs : [3 8]Step 2. Initialize Founders
Simulate 3 founder sires
julia> n_sires = 3
julia> sires = Founders(n_sires)
[ Info: Cohort (3 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [2.232 -0.609]
[ Info:
[ Info: Variance of breeding values:
[ Info: [2.374 0.873]Simulate 20 founder dams
julia> n_dams = 20
julia> dams = Founders(n_dams)
[ Info: Cohort (20 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [2.117 -0.369]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.519 0.732]Step 3. Mating
Here, the first cohort in the function mate is cohort_A, and the second one is cohort_B. We let each sire mate with 5 dams, and each mating event will produce 1 male and 1 female progenies when :ratio_malefemale is set to 1.0.
julia> args_mate = Dict(:nA => 3,
:nB_per_A => 5,
:n_per_mate => 2,
:ratio_malefemale => 1.0)
julia> males, females = mate(sires, dams; args_mate...)
[ Info: --------- Mating Summary ---------
[ Info: Generated 30 individuals from 3 cohort_A individuals
[ Info: Every cohort_A individual mates with 5 individuals from cohort_B
[ Info: And each mating reproduces 2 progenies
[ Info:
[ Info: --------- Offsprings Summary ---------
([ Info: Cohort (15 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [2.294 -0.592]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.846 0.33]
, [ Info: Cohort (15 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [1.614 -0.586]
[ Info:
[ Info: Variance of breeding values:
[ Info: [1.313 0.527]
)Users can check how the mating was performed by calling get_pedigree() function. The first column is individual ID, and the second and the third correspond to the individual's sire and dam, respectively. We can use + to concatenate two cohort.
julia> progenies = males + females
julia> get_pedigree(progenies)
30×3 Array{Int64,2}:
24 2 22
25 2 22
26 2 7
27 2 7
28 2 11
29 2 11
30 2 6
31 2 6
32 2 17
33 2 17
34 1 23
35 1 23
36 1 19
37 1 19
38 1 4
39 1 4
40 1 16
41 1 16
42 1 13
43 1 13
44 3 20
45 3 20
46 3 18
47 3 18
48 3 12
49 3 12
50 3 9
51 3 9
52 3 5
53 3 5Step 4. Selection
Next, although the heritability is set to 0.5, we can reassign it in :h2 with a new value (or a vector for multiple traits). The argument :weights allows us to weight two traits differently in the selction. This example we select 3 sires from the 15 male progenies and 10 dams from the 15 female progenies.
julia> args_select = Dict(:h2 => [.8, .5],
:weights=> [.6, .4])
julia> sires = select(males, 3; args_select...)
[ Info: --------- Selection Summary ---------
[ Info: Select 3 individuals out of 15 individuals
[ Info: Selection differential (P): [0.794 0.972]
[ Info: Selection response (G): [0.68 1.078]
┌ Info:
│ Residual_Variance =
│ 2×2 Array{Float64,2}:
│ 0.25 0.0
└ 0.0 1.0
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (3 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [2.591 0.597]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.972 0.095]
julia> dams = select(females, 10; args_select...)
[ Info: --------- Selection Summary ---------
[ Info: Select 10 individuals out of 15 individuals
[ Info: Selection differential (P): [0.328 0.17]
[ Info: Selection response (G): [0.37 0.237]
┌ Info:
│ Residual_Variance =
│ 2×2 Array{Float64,2}:
│ 0.25 0.0
└ 0.0 1.0
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (10 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [2.883 -0.093]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.56 1.007]Step 5. Expand to Multiple Generations
We can expand the described mate() and select() to :n_gens generations. By assigning :n_select_A and :n_select_B to specify how many progenies will be passed to the next generation.
Expand to Multiple Generations
julia> args_breed = Dict(:n_gens => 5,
:n_select_A => 3,
:n_select_B => 20)
julia> sires, dams = breed(sires, dams; args_breed..., args_mate..., args_select...)
[ Info: Gen 0 -> Mean of BVs: [2.816 0.066], Variance of BVs: [0.598 0.863]
[ Info: Gen 1 -> Mean of BVs: [3.063 0.429], Variance of BVs: [0.998 1.014]
[ Info: Gen 2 -> Mean of BVs: [3.596 0.483], Variance of BVs: [0.481 0.524]
[ Info: Gen 3 -> Mean of BVs: [4.001 0.84], Variance of BVs: [0.138 0.574]
[ Info: Gen 4 -> Mean of BVs: [4.18 0.965], Variance of BVs: [0.065 0.807]
[ Info: Gen 5 -> Mean of BVs: [4.18 1.182], Variance of BVs: [0.065 0.515]
([ Info: Cohort (3 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [4.192 1.523]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.03 0.83]
, [ Info: Cohort (15 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [4.177 1.114]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.074 0.477]julia> summary(sires + dams)
Dict{String,Any} with 3 entries:
"mu_g" => [4.18 1.182]
"var_g" => [0.065 0.515]
"n" => 18Modularism
Codes below are equivalent to the breed() function
julia> for i in 1:5
males, females = mate(sires, dams; args_mate...)
sires = select(males, 3; args_select...)
dams = select(females, 20; args_select...)
end
julia> summary(sires + dams)
Dict{String,Any} with 3 entries:
"mu_g" => [4.259 2.05]
"var_g" => [0.024 0.454]
"n" => 18Complete Codes
# Load XSim
using XSim
import Random
Random.seed!(95616)
# Build genome and phenome
build_genome(n_chr=10, n_loci=100)
build_phenome([3, 8])
# Initialize founders
n_sires = 3
n_dams = 20
sires = Founders(n_sires)
dams = Founders(n_dams)
# Define parameters
args = Dict(# mating
:nA => 3,
:nB_per_A => 5,
:n_per_mate => 2,
:ratio_malefemale => 1.0,
# selection
:h2 => [.8, .5],
:weights => [.6, .4],
# breeding
:n_gens => 5,
:n_select_A => 3,
:n_select_B => 20)
# Breeding program
sires, dams = breed(sires, dams; args...)