Selection
select(cohort ::Cohort,
n ::Int64,
criteria ::Union{String, Array} = "phenotypes";
h2 ::Union{Array{Float64}, Float64}=GLOBAL("h2"),
ve ::Union{Array{Float64}, Float64}=GLOBAL("Ve"),
weights ::Array{Float64, 1} =[1.0],
return_log ::Bool =false,
silent ::Bool =GLOBAL("silent")
select(cohort::Cohort, ratio::Float64; args...)Arguments
Positional arguments
cohort: Acohortfrom which individuals are selected.n:nindividuals are selected.ratio:ratioportion of individuals are selected.criteria:Criteriathat will be used for the selecition. Default "phenotypes", the options are ["phenotypes", "GBLUP", "random", array]. If set to "GBLUP", a genetic evaluation is carried out byJWASand the estimated breeding values will be thecriteria. It's also avaialbe to provdie thecriteria(e.g., phenotypes matrix) directly for the selection.
Keyword arguments
h2: The heritabilityh2of the simulated phenotypes.ve: The residual covarianceveof the simulated phenotypes.weight: Linear coefficients of traits for the selection. The selection is more sensitive to traits with greaterweight. Negativereturn_log: Defaultfalse. Settrueto return selection differential and selection response besides the selected cohort.silent: Defaultfalse. Settrueto mute the log messages.
Outputs
A selected cohort object will be returned. If return_log is set to true, a dictionary object containing the selected cohort, selection differential, and selection response will be returned.
Examples
Single Trait Selection
Set demo genome and phenome with single traits controlled by 50 QTLs.
julia> build_demo()
julia> build_phenome(50)
julia> summary()
[ Info: --------- Genome Summary ---------
[ Info: Number of Chromosome : 10
[ Info:
[ Info: Chromosome Length (cM): 1500.0
[ Info: [150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0]
[ 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:
[ Info: --------- Phenome Summary ---------
[ Info: Number of Traits : 1
[ Info: Heritability (h2) : [0.5]
┌ Info:
│ Genetic_Variance =
│ 1×1 Array{Float64,2}:
└ 1.0
┌ Info:
│ Residual_Variance =
│ 1×1 Array{Float64,2}:
└ 1.0
[ Info: Number of QTLs : [50]Initialize a cohort with 100 individuals
julia> cohort = Cohort(100)Select 30 Individuals
# Select top 30 individuals
julia> cohort_s = select(cohort, 30)
# Equivalent
julia> cohort_s = select(cohort, 0.3)
[ Info: --------- Selection Summary ---------
│ Residual_Variance =
│ 1×1 Array{Float64,2}:
└ 1.0
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (30 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [1.448]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.367]Assign Heritability h2 or Residual Covariance ve
# Assign heritability
julia> progenies = select(cohort, 30, h2=0.1)
# Equivalent in the case where genetic variance `vg` is 1.0
julia> progenies = select(cohort, 30, ve=9.0)
[ Info: --------- Selection Summary ---------
[ Info: Select 30 individuals out of 100 individuals
[ Info: Selection differential (P): [1.182]
[ Info: Selection response (G): [0.338]
┌ Info:
│ Residual_Variance =
│ 1×1 Array{Float64,2}:
└ 9.0
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (30 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [0.956]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.643]Negative Selection
Set weights=-1 to rank individuals in ascending order
julia> progenies = select(cohort, 30, weights=[-1])
[ Info: --------- Selection Summary ---------
[ Info: Select 30 individuals out of 100 individuals
[ Info: Selection differential (P): [-1.19]
[ Info: Selection response (G): [-0.89]
┌ Info:
│ Residual_Variance =
│ 1×1 Array{Float64,2}:
└ 1.0
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (30 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [-0.24]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.566]Random Selection
julia> progenies = select(cohort, 30, criteria="random")
[ Info: --------- Selection Summary ---------
[ Info: Select 30 individuals out of 100 individuals
[ Info: Selection differential (P): [-0.06]
[ Info: Selection response (G): [-0.191]
┌ Info:
│ Residual_Variance =
│ 1×1 Array{Float64,2}:
└ 1.0
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (30 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [0.441]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.946]Selection wiht Multiple Parameters
It's possible specify multiple parameters described above in one selection. User can either enclose parameters as keyword arguments, or pass them through a dictionary object.
# Keyword args
julia> progenies = select(cohort, 30, h2=0.3, weights=[-1])
# Equivalent
julia> args = Dict(:h2=>0.3,
:weights=>[-1])
julia> progenies = select(cohort, 30; args...)
[ Info: --------- Selection Summary ---------
[ Info: Select 30 individuals out of 100 individuals
[ Info: Selection differential (P): [-1.086]
[ Info: Selection response (G): [-0.486]
┌ Info:
│ Residual_Variance =
│ 1×1 Array{Float64,2}:
└ 2.3333333333333335
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (30 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [0.154]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.818]Multi-Trait Selection
Set demo genome and phenome with single traits controlled by 50 QTLs.
julia> build_demo()
julia> summary()
[ Info: --------- Genome Summary ---------
[ Info: Number of Chromosome : 10
[ Info:
[ Info: Chromosome Length (cM): 1500.0
[ Info: [150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0]
[ 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:
[ 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]Initialize a cohort with 100 individuals
julia> cohort = Cohort(100)Assign Heritabilities for Multiple Traits
julia> progenies = select(cohort, 30, h2=[0.9, 0.3])
[ Info: --------- Selection Summary ---------
[ Info: Select 30 individuals out of 100 individuals
[ Info: Selection differential (P): [0.468 1.028]
[ Info: Selection response (G): [0.383 0.636]
┌ Info:
│ Residual_Variance =
│ 2×2 Array{Float64,2}:
│ 0.111111 0.0
└ 0.0 2.33333
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (30 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [-0.889 0.28]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.947 0.625]Assign Trait Correlations via Residual Covariance
julia> progenies = select(cohort, 30, ve=[1 0.3
0.3 1])
[ Info: --------- Selection Summary ---------
[ Info: Select 30 individuals out of 100 individuals
[ Info: Selection differential (P): [0.866 0.925]
[ Info: Selection response (G): [0.662 0.762]
┌ Info:
│ Residual_Variance =
│ 2×2 Array{Float64,2}:
│ 1.0 0.3
└ 0.3 1.0
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (30 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [-0.608 0.406]
[ Info:
[ Info: Variance of breeding values:
[ Info: [0.549 0.476]Derive Selection Index for Multiple Traits
Assigning a vector to the parameter weights to derive a selection index which is a linear combintation of the weights and the phenotypes. In this example, we demonstrate two traits with the heritability of 0.3 and 0.8, respectively. And we can select traits with more weight on the second trait which is more heritable, and negatively select the first trait.
julia> progenies = select(cohort, 30, h2=[.3, .8], weights=[-0.1, 0.9])
[ Info: --------- Selection Summary ---------
[ Info: Select 30 individuals out of 100 individuals
[ Info: Selection differential (P): [-0.318 1.027]
[ Info: Selection response (G): [-0.233 0.869]
┌ Info:
│ Residual_Variance =
│ 2×2 Array{Float64,2}:
│ 2.33333 0.0
└ 0.0 0.25
[ Info: --------- Offsprings Summary ---------
[ Info: Cohort (30 individuals)
[ Info:
[ Info: Mean of breeding values:
[ Info: [-1.508 0.513]
[ Info:
[ Info: Variance of breeding values:
[ Info: [1.053 0.458]