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562
src/beefblup.jl
Normal file → Executable file
562
src/beefblup.jl
Normal file → Executable file
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@ -1,281 +1,281 @@
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#!/bin/bash
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#=
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exec julia --project=$(realpath $(dirname $(dirname "${BASH_SOURCE[0]}"))) "${BASH_SOURCE[0]}" "$@"
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=#
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# beefblup
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# Main script for performing single-variate BLUP to find beef cattle
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# breeding values
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# Usage: julia beefblup.jl
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# (C) 2020 Thomas A. Christensen II
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# Licensed under BSD-3-Clause License
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# cSpell:includeRegExp #.*
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# cSpell:includeRegExp ("""|''')[^\1]*\1
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# Import the required packages
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using CSV
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using DataFrames
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using LinearAlgebra
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using Dates
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using Gtk
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# Display stuff
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println("beefblup v 0.1")
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println("(C) 2020 Thomas A. Christensen II")
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println("https://github.com/millironx/beefblup")
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print("\n")
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### Prompt User
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# Ask for an input spreadsheet
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path = open_dialog_native(
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"Select a beefblup worksheet",
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GtkNullContainer(),
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("*.csv", GtkFileFilter("*.csv", name="beefblup worksheet"))
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)
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# Ask for an output text filename
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savepath = save_dialog_native(
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"Save your beefblup results",
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GtkNullContainer(),
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(GtkFileFilter("*.txt", name="Results file"),
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"*.txt")
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)
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# Ask for heritability
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print("What is the heritability for this trait?> ")
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h2 = parse(Float64, readline(stdin))
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### Import input filename
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print("[🐮]: Importing data file...")
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# Import data from a suitable spreadsheet
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data = CSV.File(path) |> DataFrame
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print("Done!\n")
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### Process input file
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print("[🐮]: Processing and formatting data...")
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# Sort the array by date
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sort!(data, :birthdate)
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# Define fields to hold id values for animals and their parents
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numanimals = length(data.id)
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# Find the index values for animals and their parents
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dam = indexin(data.dam, data.id)
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sire = indexin(data.sire, data.id)
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# Extract all of the fixed effects
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fixedfx = select(data, Not([:id, :birthdate, :sire, :dam]))[:,1:end-1]
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# Find any columns that need to be deleted
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for i in 1:ncol(fixedfx)
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if length(unique(fixedfx[:,i])) <= 1
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colname = names(fixedfx)[i]
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print("Column '")
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print(colname)
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print("' does not have any unique animals and will be removed from this analysis\n")
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deletecols!(fixedfx,i)
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end
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end
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# Determine how many contemporary groups there are
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numtraits = ncol(fixedfx)
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numgroups = ones(1, numtraits)
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for i in 1:numtraits
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numgroups[i] = length(unique(fixedfx[:,i]))
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end
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# If there are more groups than animals, then the analysis cannot continue
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if sum(numgroups) >= numanimals
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println("There are more contemporary groups than animals. The analysis will
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now abort.")
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exit()
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end
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# Define a "normal" animal as one of the last in the groups, provided that
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# all traits do not have null values
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normal = Array{String}(undef,1,numtraits)
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for i in 1:numtraits
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for j in numanimals:-1:1
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if !ismissing(fixedfx[j,i])
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normal[i] = string(fixedfx[j,i])
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break
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end
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end
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end
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print("Done!\n")
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### Create the fixed-effect matrix
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print("[🐮]: Creating the fixed-effect matrix...")
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# Form the fixed-effect matrix
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X = zeros(Int8, numanimals, floor(Int,sum(numgroups))-length(numgroups)+1)
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X[:,1] = ones(Int8, 1, numanimals)
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# Create an external counter that will increment through both loops
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counter = 2
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# Store the traits in a string array
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adjustedtraits =
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Array{String}(undef,floor(Int,sum(numgroups))-length(numgroups))
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# Iterate through each group
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for i in 1:length(normal)
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# Find the traits that are present in this trait
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localdata = string.(fixedfx[:,i])
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traits = unique(localdata)
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# Remove the normal version from the analysis
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effecttraits = traits[findall(x -> x != normal[i], traits)]
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# Iterate inside of the group
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for j in 1:(length(effecttraits) - 1)
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matchedindex = findall(x -> x != effecttraits[j], localdata)
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X[matchedindex, counter] .= 1
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# Add this trait to the string
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adjustedtraits[counter - 1] = traits[j]
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# Increment the big counter
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global counter = counter + 1
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end
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end
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print("Done!\n")
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### Additive relationship matrix
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print("[🐮]: Creating additive relationship matrix...")
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# Create an empty matrix for the additive relationship matrix
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A = zeros(numanimals, numanimals)
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# Create the additive relationship matrix by the FORTRAN method presented by
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# Henderson
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for i in 1:numanimals
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if !isnothing(dam[i]) && !isnothing(sire[i])
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for j in 1:(i-1)
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A[j,i] = 0.5*(A[j,sire[i]] + A[j,dam[i]])
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A[i,j] = A[j,i]
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end
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A[i,i] = 1 + 0.5*A[sire[i], dam[i]]
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elseif !isnothing(dam[i]) && isnothing(sire[i])
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for j in 1:(i-1)
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A[j,i] = 0.5*A[j,dam[i]]
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A[i,j] = A[j,i]
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end
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A[i,i] = 1
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elseif isnothing(dam[i]) && !isnothing(sire[i])
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for j in 1:(i-1)
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A[j,i] = 0.5*A[j,sire[i]]
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A[i,j] = A[j,i]
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end
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A[i,i] = 1
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else
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for j in 1:(i-1)
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A[j,i] = 0
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A[i,j] = 0
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end
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A[i,i] = 1
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end
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end
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print("Done!\n")
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### Perform BLUP
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print("[🐮]: Solving the mixed-model equations...")
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# Extract the observed data
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Y = convert(Array{Float64}, data[:,end])
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# The random effects matrix
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Z = Matrix{Int}(I, numanimals, numanimals)
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# Remove items where there is no data
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nullobs = findall(isnothing, Y)
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Z[nullobs, nullobs] .= 0
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# Calculate heritability
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λ = (1-h2)/h2
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# Use the mixed-model equations
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MME = [X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*λ)]
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MMY = [X'*Y; Z'*Y]
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solutions = MME\MMY
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# Find the accuracies
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diaginv = diag(inv(MME))
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reliability = ones(Float64, length(diaginv)) - diaginv.*λ
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print("Done!\n")
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### Output the results
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print("[🐮]: Saving results...")
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# Find how many traits we found BLUE for
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numgroups = numgroups .- 1
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# Start printing results to output
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fileID = open(savepath, "w")
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write(fileID, "beefblup Results Report\n")
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write(fileID, "Produced using beefblup for Julia (")
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write(fileID, "https://github.com/millironx/beefblup")
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write(fileID, ")\n\n")
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write(fileID, "Input:\t")
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write(fileID, path)
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write(fileID, "\nAnalysis performed:\t")
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write(fileID, string(Dates.today()))
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write(fileID, "\nTrait examined:\t")
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write(fileID, headers[5])
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write(fileID, "\n\n")
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# Print base population stats
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write(fileID, "Base Population:\n")
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for i in 1:length(numgroups)
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write(fileID, "\t")
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write(fileID, headers[i+5])
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write(fileID, ":\t")
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write(fileID, normal[i])
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write(fileID, "\n")
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end
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write(fileID, "\tMean ")
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write(fileID, headers[5])
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write(fileID, ":\t")
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write(fileID, string(solutions[1]))
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write(fileID, "\n\n")
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# Contemporary group adjustments
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counter = 2
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write(fileID, "Contemporary Group Effects:\n")
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for i in 1:length(numgroups)
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write(fileID, "\t")
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write(fileID, headers[i+5])
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write(fileID, "\tEffect\tReliability\n")
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for j in 1:numgroups[i]
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write(fileID, "\t")
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write(fileID, adjustedtraits[counter - 1])
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write(fileID, "\t")
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write(fileID, string(solutions[counter]))
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write(fileID, "\t")
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write(fileID, string(reliability[counter]))
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write(fileID, "\n")
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global counter = counter + 1
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end
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write(fileID, "\n")
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end
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write(fileID, "\n")
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# Expected breeding values
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write(fileID, "Expected Breeding Values:\n")
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write(fileID, "\tID\tEBV\tReliability\n")
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for i in 1:numanimals
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write(fileID, "\t")
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write(fileID, data.id[i])
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write(fileID, "\t")
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write(fileID, string(solutions[i+counter-1]))
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write(fileID, "\t")
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write(fileID, string(reliability[i+counter-1]))
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write(fileID, "\n")
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end
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write(fileID, "\n - END REPORT -")
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close(fileID)
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print("Done!\n")
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#!/bin/bash
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#=
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exec julia --project=$(realpath $(dirname $(dirname "${BASH_SOURCE[0]}"))) "${BASH_SOURCE[0]}" "$@"
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=#
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# beefblup
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# Main script for performing single-variate BLUP to find beef cattle
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# breeding values
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# Usage: julia beefblup.jl
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# (C) 2020 Thomas A. Christensen II
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# Licensed under BSD-3-Clause License
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# cSpell:includeRegExp #.*
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# cSpell:includeRegExp ("""|''')[^\1]*\1
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# Import the required packages
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using CSV
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using DataFrames
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using LinearAlgebra
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using Dates
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using Gtk
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# Display stuff
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println("beefblup v 0.1")
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println("(C) 2020 Thomas A. Christensen II")
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println("https://github.com/millironx/beefblup")
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print("\n")
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### Prompt User
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# Ask for an input spreadsheet
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path = open_dialog_native(
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"Select a beefblup worksheet",
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GtkNullContainer(),
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("*.csv", GtkFileFilter("*.csv", name="beefblup worksheet"))
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)
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# Ask for an output text filename
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savepath = save_dialog_native(
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"Save your beefblup results",
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GtkNullContainer(),
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(GtkFileFilter("*.txt", name="Results file"),
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"*.txt")
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)
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# Ask for heritability
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print("What is the heritability for this trait?> ")
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h2 = parse(Float64, readline(stdin))
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|
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### Import input filename
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print("[🐮]: Importing data file...")
|
||||
|
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# Import data from a suitable spreadsheet
|
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data = CSV.File(path) |> DataFrame
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print("Done!\n")
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|
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### Process input file
|
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print("[🐮]: Processing and formatting data...")
|
||||
|
||||
# Sort the array by date
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sort!(data, :birthdate)
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|
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# Define fields to hold id values for animals and their parents
|
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numanimals = length(data.id)
|
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|
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# Find the index values for animals and their parents
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dam = indexin(data.dam, data.id)
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sire = indexin(data.sire, data.id)
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# Extract all of the fixed effects
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fixedfx = select(data, Not([:id, :birthdate, :sire, :dam]))[:,1:end-1]
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|
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# Find any columns that need to be deleted
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for i in 1:ncol(fixedfx)
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if length(unique(fixedfx[:,i])) <= 1
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colname = names(fixedfx)[i]
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print("Column '")
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print(colname)
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print("' does not have any unique animals and will be removed from this analysis\n")
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deletecols!(fixedfx,i)
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end
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end
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# Determine how many contemporary groups there are
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numtraits = ncol(fixedfx)
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numgroups = ones(1, numtraits)
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for i in 1:numtraits
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numgroups[i] = length(unique(fixedfx[:,i]))
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end
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# If there are more groups than animals, then the analysis cannot continue
|
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if sum(numgroups) >= numanimals
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println("There are more contemporary groups than animals. The analysis will
|
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now abort.")
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exit()
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end
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||||
|
||||
# Define a "normal" animal as one of the last in the groups, provided that
|
||||
# all traits do not have null values
|
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normal = Array{String}(undef,1,numtraits)
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for i in 1:numtraits
|
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for j in numanimals:-1:1
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if !ismissing(fixedfx[j,i])
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normal[i] = string(fixedfx[j,i])
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break
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end
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end
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end
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print("Done!\n")
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||||
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||||
### Create the fixed-effect matrix
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print("[🐮]: Creating the fixed-effect matrix...")
|
||||
|
||||
# Form the fixed-effect matrix
|
||||
X = zeros(Int8, numanimals, floor(Int,sum(numgroups))-length(numgroups)+1)
|
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X[:,1] = ones(Int8, 1, numanimals)
|
||||
|
||||
# Create an external counter that will increment through both loops
|
||||
counter = 2
|
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|
||||
# Store the traits in a string array
|
||||
adjustedtraits =
|
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Array{String}(undef,floor(Int,sum(numgroups))-length(numgroups))
|
||||
# Iterate through each group
|
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for i in 1:length(normal)
|
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# Find the traits that are present in this trait
|
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localdata = string.(fixedfx[:,i])
|
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traits = unique(localdata)
|
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# Remove the normal version from the analysis
|
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effecttraits = traits[findall(x -> x != normal[i], traits)]
|
||||
# Iterate inside of the group
|
||||
for j in 1:(length(effecttraits) - 1)
|
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matchedindex = findall(x -> x != effecttraits[j], localdata)
|
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X[matchedindex, counter] .= 1
|
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# Add this trait to the string
|
||||
adjustedtraits[counter - 1] = traits[j]
|
||||
# Increment the big counter
|
||||
global counter = counter + 1
|
||||
end
|
||||
end
|
||||
|
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print("Done!\n")
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||||
|
||||
### Additive relationship matrix
|
||||
print("[🐮]: Creating additive relationship matrix...")
|
||||
|
||||
# Create an empty matrix for the additive relationship matrix
|
||||
A = zeros(numanimals, numanimals)
|
||||
|
||||
# Create the additive relationship matrix by the FORTRAN method presented by
|
||||
# Henderson
|
||||
for i in 1:numanimals
|
||||
if !isnothing(dam[i]) && !isnothing(sire[i])
|
||||
for j in 1:(i-1)
|
||||
A[j,i] = 0.5*(A[j,sire[i]] + A[j,dam[i]])
|
||||
A[i,j] = A[j,i]
|
||||
end
|
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A[i,i] = 1 + 0.5*A[sire[i], dam[i]]
|
||||
elseif !isnothing(dam[i]) && isnothing(sire[i])
|
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for j in 1:(i-1)
|
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A[j,i] = 0.5*A[j,dam[i]]
|
||||
A[i,j] = A[j,i]
|
||||
end
|
||||
A[i,i] = 1
|
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elseif isnothing(dam[i]) && !isnothing(sire[i])
|
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for j in 1:(i-1)
|
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A[j,i] = 0.5*A[j,sire[i]]
|
||||
A[i,j] = A[j,i]
|
||||
end
|
||||
A[i,i] = 1
|
||||
else
|
||||
for j in 1:(i-1)
|
||||
A[j,i] = 0
|
||||
A[i,j] = 0
|
||||
end
|
||||
A[i,i] = 1
|
||||
end
|
||||
end
|
||||
|
||||
print("Done!\n")
|
||||
|
||||
### Perform BLUP
|
||||
print("[🐮]: Solving the mixed-model equations...")
|
||||
|
||||
# Extract the observed data
|
||||
Y = convert(Array{Float64}, data[:,end])
|
||||
|
||||
# The random effects matrix
|
||||
Z = Matrix{Int}(I, numanimals, numanimals)
|
||||
|
||||
# Remove items where there is no data
|
||||
nullobs = findall(isnothing, Y)
|
||||
Z[nullobs, nullobs] .= 0
|
||||
|
||||
# Calculate heritability
|
||||
λ = (1-h2)/h2
|
||||
|
||||
# Use the mixed-model equations
|
||||
MME = [X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*λ)]
|
||||
MMY = [X'*Y; Z'*Y]
|
||||
solutions = MME\MMY
|
||||
|
||||
# Find the accuracies
|
||||
diaginv = diag(inv(MME))
|
||||
reliability = ones(Float64, length(diaginv)) - diaginv.*λ
|
||||
|
||||
print("Done!\n")
|
||||
|
||||
### Output the results
|
||||
print("[🐮]: Saving results...")
|
||||
|
||||
# Find how many traits we found BLUE for
|
||||
numgroups = numgroups .- 1
|
||||
|
||||
# Start printing results to output
|
||||
fileID = open(savepath, "w")
|
||||
write(fileID, "beefblup Results Report\n")
|
||||
write(fileID, "Produced using beefblup for Julia (")
|
||||
write(fileID, "https://github.com/millironx/beefblup")
|
||||
write(fileID, ")\n\n")
|
||||
write(fileID, "Input:\t")
|
||||
write(fileID, path)
|
||||
write(fileID, "\nAnalysis performed:\t")
|
||||
write(fileID, string(Dates.today()))
|
||||
write(fileID, "\nTrait examined:\t")
|
||||
write(fileID, headers[5])
|
||||
write(fileID, "\n\n")
|
||||
|
||||
# Print base population stats
|
||||
write(fileID, "Base Population:\n")
|
||||
for i in 1:length(numgroups)
|
||||
write(fileID, "\t")
|
||||
write(fileID, headers[i+5])
|
||||
write(fileID, ":\t")
|
||||
write(fileID, normal[i])
|
||||
write(fileID, "\n")
|
||||
end
|
||||
write(fileID, "\tMean ")
|
||||
write(fileID, headers[5])
|
||||
write(fileID, ":\t")
|
||||
write(fileID, string(solutions[1]))
|
||||
write(fileID, "\n\n")
|
||||
|
||||
# Contemporary group adjustments
|
||||
counter = 2
|
||||
write(fileID, "Contemporary Group Effects:\n")
|
||||
for i in 1:length(numgroups)
|
||||
write(fileID, "\t")
|
||||
write(fileID, headers[i+5])
|
||||
write(fileID, "\tEffect\tReliability\n")
|
||||
for j in 1:numgroups[i]
|
||||
write(fileID, "\t")
|
||||
write(fileID, adjustedtraits[counter - 1])
|
||||
write(fileID, "\t")
|
||||
write(fileID, string(solutions[counter]))
|
||||
write(fileID, "\t")
|
||||
write(fileID, string(reliability[counter]))
|
||||
write(fileID, "\n")
|
||||
|
||||
global counter = counter + 1
|
||||
end
|
||||
write(fileID, "\n")
|
||||
end
|
||||
write(fileID, "\n")
|
||||
|
||||
# Expected breeding values
|
||||
write(fileID, "Expected Breeding Values:\n")
|
||||
write(fileID, "\tID\tEBV\tReliability\n")
|
||||
for i in 1:numanimals
|
||||
write(fileID, "\t")
|
||||
write(fileID, data.id[i])
|
||||
write(fileID, "\t")
|
||||
write(fileID, string(solutions[i+counter-1]))
|
||||
write(fileID, "\t")
|
||||
write(fileID, string(reliability[i+counter-1]))
|
||||
write(fileID, "\n")
|
||||
end
|
||||
|
||||
write(fileID, "\n - END REPORT -")
|
||||
close(fileID)
|
||||
|
||||
print("Done!\n")
|
||||
|
|
|
|||
Loading…
Reference in a new issue