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functions.R
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# Long-term selection in layers (support functions)
# Ivan Pocrnic
RecSysMale <- function(datafile, popname) {
datafile = rbind(datafile,
tibble(Generation = gen,
IId = popname@id,
FId = popname@father,
MId = popname@mother,
Sex = popname@sex,
Year = year,
Program = Program,
SelCand = candidatesgroup,
PhenoT1 = NA,
PhenoT2 = NA,
PhenoT3 = NA,
EbvT1 = NA,
EbvT2 = NA,
EbvT3 = NA,
TbvT1 = popname@gv[, 1],
TbvT2 = popname@gv[, 2],
TbvT3 = popname@gv[, 3]))
#return(datafile)
}
RecSysFemale <- function(datafile, popname) {
datafile = rbind(datafile,
tibble(Generation = gen,
IId = popname@id,
FId = popname@father,
MId = popname@mother,
Sex = popname@sex,
Year = year,
Program = Program,
SelCand = candidatesgroup,
PhenoT1 = popname@pheno[,1],
PhenoT2 = popname@pheno[,2],
PhenoT3 = popname@pheno[,3],
EbvT1 = NA,
EbvT2 = NA,
EbvT3 = NA,
TbvT1 = popname@gv[, 1],
TbvT2 = popname@gv[, 2],
TbvT3 = popname@gv[, 3]))
#return(datafile)
}
run_blup <- function(datafile) {
system(command = "cp ../renum.par .")
system(command = "cp ../extractsol.sh .")
system(command = "echo renum.par | $HOME/bin/renumf90 | tee renum.log")
system(command = "echo renf90.par | $HOME/bin/blupf90 | tee blup.log")
system(command = "sh extractsol.sh")
# Update breeding values in database:
sol1 <- read.table("sl3.tmp")
sol1 <- sol1[sol1$V2 %in% datafile$IId,]
sol1 <- sol1[order(match(sol1$V2, datafile$IId)),]
datafile$EbvT1 <- sol1$V3
datafile$EbvT2 <- sol1$V4
datafile$EbvT3 <- sol1$V5
return(datafile)
}
run_gblup <- function(datafile) {
system(command = "cp ../renumGS.par .")
system(command = "cp ../extractsol.sh .")
system(command = "echo renumGS.par | $HOME/bin/renumf90 | tee renum.log")
system(command = "echo renf90.par | $HOME/bin/blupf90 | tee blup.log")
system(command = "sh extractsol.sh")
# Update breeding values in database:
sol1 <- read_table2("sl3.tmp",col_names=FALSE,
col_types = cols(.default = col_character(),
X1 = col_character(),
X2 = col_character(),
X3 = col_double(),
X4 = col_double(),
X5 = col_double()))
sol1_1 <- sol1[sol1$X2 %in% datafile$IId,]
sol1_1 <- sol1_1[order(match(sol1_1$X2, datafile$IId)),]
datafile$EbvT1 <- sol1_1$X3
datafile$EbvT2 <- sol1_1$X4
datafile$EbvT3 <- sol1_1$X5
return(datafile)
}
# Input: candidates_plan & RecSys
run_OCS_NOMP <- function(ocspop, datafile, deg, sires, dams) {
# Create index of EBV for criterion:
tmp = data.frame(id=ocspop@id, crit=(0.2*ocspop@ebv[,1]+0.35*ocspop@ebv[,2])+0.45*ocspop@ebv[,3])
write.table(x=tmp, file="ebv.txt", col.names=FALSE, row.names=FALSE, quote=FALSE)
# Assign sex:
tmp = data.frame(id=ocspop@id, genderRole=ocspop@sex)
tmp$genderRole = as.numeric(factor(ocspop@sex, levels=c("M", "F")))
write.table(x=tmp, file="GenderRole.txt", col.names=FALSE, row.names=FALSE, quote=FALSE)
# Full (All Animals) Pedigree:
tmp <- data.frame(datafile[,c("IId", "FId", "MId")])
tmp$FId[is.na(tmp$FId)] <- 0
tmp$MId[is.na(tmp$MId)] <- 0
write.table(tmp, file="Blupf90.ped", quote=FALSE, row.names=FALSE, col.names=FALSE, sep=" ", na = "0")
# ID of the OCS candidates:
idcand=ocspop@id
write.table(idcand, file="PedigreeSet.ped", quote=FALSE, row.names=FALSE, col.names=FALSE, sep=" ", na = "0")
# AlphaRelate parameter file:
sink(file = "AlphaRelateSpec.txt")
cat("PedigreeFile , Blupf90.ped\n")
cat("PedInbreeding , Yes\n")
cat("PedNrm , Yes\n")
cat("PedNrmSubsetFile , PedigreeSet.ped\n")
cat("OutputFormat , f7.4\n")
cat("Stop\n")
sink()
# Run AlphaRelate:
system(command="$HOME/bin/AlphaRelate | tee AlphaRelate.log")
# AlphaMate parameter file:
sink(file = "AlphaMateSpec.txt")
cat("GenderFile , GenderRole.txt\n")
cat("NrmMatrixFile , PedigreeNrm.txt\n")
cat("SelCriterionFile , ebv.txt\n")
cat("MateAllocation , No\n")
cat("EvolAlgStopTolerance , 0.01\n")
cat("NumberOfMatings , ",dams,"\n", sep="")
cat("NumberOfMaleParents , ",sires,"\n", sep="")
cat("EqualizeMaleContributions , Yes\n")
cat("NumberOfFemaleParents , ",dams,"\n", sep="")
cat("EqualizeFemaleContributions , Yes\n")
cat("TargetDegree , ",deg,"\n", sep="")
cat("Stop\n")
sink()
# Run AlphaMate:
system(command="$HOME/bin/AlphaMate | tee AlphaMate.log")
#return()
}
# Input: candidates_plan & RecSys
run_OCS_EASED <- function(ocspop, datafile, deg, dams) {
# Create index of EBV for criterion:
tmp = data.frame(id = ocspop@id, crit = (0.2*ocspop@ebv[,1]+0.35*ocspop@ebv[,2])+0.45*ocspop@ebv[,3])
write.table(x = tmp, file = "ebv.txt", col.names = FALSE, row.names = FALSE, quote = FALSE)
# Assign sex:
tmp = data.frame(id = ocspop@id, genderRole = ocspop@sex)
tmp$genderRole = as.numeric(factor(ocspop@sex, levels=c("M", "F")))
write.table(x = tmp, file = "GenderRole.txt", col.names = FALSE, row.names = FALSE, quote = FALSE)
# Full (All Animals) Pedigree:
tmp <- data.frame(datafile[,c("IId", "FId", "MId")])
tmp$FId[is.na(tmp$FId)] <- 0
tmp$MId[is.na(tmp$MId)] <- 0
write.table(tmp, file = "Blupf90.ped", quote = FALSE, row.names = FALSE, col.names = FALSE, sep = " ", na = "0")
# ID of the OCS candidates:
idcand = ocspop@id
write.table(idcand, file = "PedigreeSet.ped", quote = FALSE, row.names = FALSE, col.names = FALSE, sep = " ", na = "0")
# AlphaRelate parameter file:
sink(file = "AlphaRelateSpec.txt")
cat("PedigreeFile , Blupf90.ped\n")
cat("PedInbreeding , Yes\n")
cat("PedNrm , Yes\n")
cat("PedNrmSubsetFile , PedigreeSet.ped\n")
cat("OutputFormat , f7.4\n")
cat("Stop\n")
sink()
# Run AlphaRelate:
system(command="$HOME/bin/AlphaRelate | tee AlphaRelate.log")
# AlphaMate parameter file:
sink(file = "AlphaMateSpec.txt")
cat("GenderFile , GenderRole.txt\n")
cat("NrmMatrixFile , PedigreeNrm.txt\n")
cat("SelCriterionFile , ebv.txt\n")
cat("MateAllocation , No\n")
cat("EvolAlgStopTolerance , 0.01\n")
cat("NumberOfMatings , ",dams,"\n", sep="")
cat("NumberOfFemaleParents , ",dams,"\n", sep="")
cat("EqualizeFemaleContributions , Yes\n")
cat("TargetDegree , ",deg,"\n", sep="")
cat("Stop\n")
sink()
# Run AlphaMate:
system(command="$HOME/bin/AlphaMate | tee AlphaMate.log")
#return()
}
# Input: candidates_plan & RecSys
run_MinF <- function(ocspop, datafile, sires, dams) {
# Create index of EBV for criterion:
tmp = data.frame(id=ocspop@id, crit=(0.2*ocspop@ebv[,1]+0.35*ocspop@ebv[,2])+0.45*ocspop@ebv[,3])
write.table(x=tmp, file="ebv.txt", col.names=FALSE, row.names=FALSE, quote=FALSE)
# Assign sex:
tmp = data.frame(id=ocspop@id, genderRole=ocspop@sex)
tmp$genderRole = as.numeric(factor(ocspop@sex, levels=c("M", "F")))
write.table(x=tmp, file="GenderRole.txt", col.names=FALSE, row.names=FALSE, quote=FALSE)
# Full (All Animals) Pedigree:
tmp <- data.frame(datafile[,c("IId", "FId", "MId")])
tmp$FId[is.na(tmp$FId)] <- 0
tmp$MId[is.na(tmp$MId)] <- 0
write.table(tmp, file="Blupf90.ped", quote=FALSE, row.names=FALSE, col.names=FALSE, sep=" ", na = "0")
# ID of the OCS candidates:
idcand=ocspop@id
write.table(idcand, file="PedigreeSet.ped", quote=FALSE, row.names=FALSE, col.names=FALSE, sep=" ", na = "0")
# AlphaRelate parameter file:
sink(file = "AlphaRelateSpec.txt")
cat("PedigreeFile , Blupf90.ped\n")
cat("PedInbreeding , Yes\n")
cat("PedNrm , Yes\n")
cat("PedNrmSubsetFile , PedigreeSet.ped\n")
cat("OutputFormat , f7.4\n")
cat("Stop\n")
sink()
# Run AlphaRelate:
system(command="$HOME/bin/AlphaRelate | tee AlphaRelate.log")
# AlphaMate parameter file:
sink(file = "AlphaMateSpec.txt")
cat("GenderFile , GenderRole.txt\n")
cat("NrmMatrixFile , PedigreeNrm.txt\n")
cat("SelCriterionFile , ebv.txt\n")
cat("NumberOfMatings , ",dams,"\n", sep="")
cat("NumberOfMaleParents , ",sires,"\n", sep="")
cat("EqualizeMaleContributions , Yes\n")
cat("NumberOfFemaleParents , ",dams,"\n", sep="")
cat("EqualizeFemaleContributions , Yes\n")
cat("ModeMinInbreeding , Yes\n")
cat("Stop\n")
sink()
# Run AlphaMate:
system(command="$HOME/bin/AlphaMate | tee AlphaMate.log")
#return()
}
# N.B. This function is redundant with new versions of AlphaSimR, but main code should be updated as well
run_expandCrossPlan <- function(nProgenyPerMat, CrossPlanFile) {
for (set in 1:length(nProgenyPerMat)) {
crp = matrix(ncol=2, nrow=nrow(CrossPlanFile) * nProgenyPerMat[set])
k = 0
for (i in 1:nrow(CrossPlanFile)) {
for (j in 1:nProgenyPerMat[set]) {
k = k + 1
crp[k, 1] = CrossPlanFile[i, 2]
crp[k, 2] = CrossPlanFile[i, 3]
}
}
}
crp=data.frame(crp)
tmp = crp %>%
mutate(sm=as.character(crp$X1)) %>%
mutate(sf=as.character(crp$X2))
crossPlanM=as.matrix(cbind(tmp$sf,tmp$sm))
return(crossPlanM)
}
run_allocate_matings <- function(nProgenyPerMat, CrossPlanFile) {
dfF = CrossPlanFile %>%
filter(Gender == 2) %>%
select("Id") %>%
rename(Id_F = Id) %>%
dplyr::mutate(Id_F = as.character(Id_F))
dfM = CrossPlanFile %>%
filter(Gender == 1) %>%
select("Id", "nContribution") %>%
rename(Id_M = Id) %>%
slice(rep(1:n(), times = nContribution) ) %>%
dplyr::mutate(Id_M = as.character(Id_M))
crossPlanMF = cbind( slice(dfF, sample(1:n())),
slice(dfM, sample(1:n())) )
crossPlanMF_expanded = crossPlanMF %>%
slice(rep(1:n(), each = nProgenyPerMat))
crossPlanMF_final = as.matrix(cbind(crossPlanMF_expanded$Id_F, crossPlanMF_expanded$Id_M))
return(crossPlanMF_final)
}
run_prepare <- function(datafile) {
# Create pedigree file:
blupPed <- datafile[,c("IId", "FId", "MId")]
blupPed$FId[is.na(blupPed$FId)] <- 0
blupPed$MId[is.na(blupPed$MId)] <- 0
# blupPed <- blupPed[order(blupPed$IId),]
# This ordering might not be correct, but doesn't matter since we run renumf90 later anyways
write.table(blupPed, "Blupf90.ped", quote=FALSE, row.names=FALSE, col.names=FALSE, sep=" ", na = "0")
# Delete pedigree - each time create a new pedigree:
rm(blupPed)
# Create phenotype file:
# Add the overall mean vector for blupf90: rep(1,nrow(Pheno))
# Only females have phenotypes, and animals in Generation 0 have no phenotypes
# Females from the current selection group don't have Phenotype T3
# gen and candidatesgroup
Pheno = datafile %>%
filter(Sex == "F" & Generation >= 1)
Pheno = Pheno %>%
dplyr::mutate(PhenoT3 = replace(PhenoT3, Generation == gen & SelCand == candidatesgroup, NA))
write.table(cbind(Pheno[,c("IId", "PhenoT1", "PhenoT2", "PhenoT3", "Generation")], rep(1,nrow(Pheno))), "Blupf901.dat",
quote=FALSE, row.names=FALSE, col.names=FALSE, sep=" ", na = "0")
# Delete datafile - each time create a new datafile:
rm(Pheno)
}
# Prepare genotype file in blupf90 format
# Extremely inefficient (copying) !!!
run_prepareGENO <- function(popname) {
idg = popname@id
pr=pullSnpGeno(popname)
prt = apply(pr[,1:ncol(pr)],1,paste,sep ="",collapse="")
idgpr=cbind(idg, prt)
write.table(idgpr,"mrk.tmp",col.names=FALSE,row.names=FALSE,quote=FALSE,append=TRUE,sep=" ")
system(command = "sh prepgeno.sh")
}
# Create summary report table
PullSumm <- function(datafile, popname, sex) {
gePa = genParam(popname)
datafile = rbind(datafile,
tibble(Replicate = rep,
Generation = gen,
Program = Program,
SelCand = candidatesgroup,
Sex = sex,
Year = year,
NumberID = popname@nInd,
MeanPhenoT1 = (meanP(popname))[1],
MeanPhenoT2 = (meanP(popname))[2],
MeanPhenoT3 = (meanP(popname))[3],
MeanGenoT1 = gePa$mu[1],
MeanGenoT2 = gePa$mu[2],
MeanGenoT3 = gePa$mu[3],
MeanA_T1 = mean(gePa$gv_a[, 1]),
MeanA_T2 = mean(gePa$gv_a[, 2]),
MeanA_T3 = mean(gePa$gv_a[, 3]),
MeanD_T1 = mean(gePa$gv_d[, 1]),
MeanD_T2 = mean(gePa$gv_d[, 2]),
MeanD_T3 = mean(gePa$gv_d[, 3]),
VarA_T1 = gePa$varA[1,1],
VarA_T2 = gePa$varA[2,2],
VarA_T3 = gePa$varA[3,3],
VarD_T1 = gePa$varD[1,1],
VarD_T2 = gePa$varD[2,2],
VarD_T3 = gePa$varD[3,3],
VarG_T1 = gePa$varG[1,1],
VarG_T2 = gePa$varG[2,2],
VarG_T3 = gePa$varG[3,3],
GenicVA_T1 = gePa$genicVarA[1],
GenicVA_T2 = gePa$genicVarA[2],
GenicVA_T3 = gePa$genicVarA[3],
GenicVD_T1 = gePa$genicVarD[1],
GenicVD_T2 = gePa$genicVarD[2],
GenicVD_T3 = gePa$genicVarD[3],
GenicVG_T1 = gePa$genicVarG[1],
GenicVG_T2 = gePa$genicVarG[2],
GenicVG_T3 = gePa$genicVarG[3]
))
#return(datafile)
}
# N.B. Huge function creating several metrics in summary report table
PullSummTogether <- function(datafile, popname) {
# Calculate population statistics
gePa = genParam(popname)
# Get the True Index
true_index = selIndex(popname@gv, b = iweight)
ebv_index = selIndex(popname@ebv, b = iweight)
# Extract Genotypes
M = pullSnpGeno(popname)
N = pullSnpGeno(popname, snpChip = 2)
QT1 = pullQtlGeno(popname, trait = 1)
QT2 = pullQtlGeno(popname, trait = 2)
QT3 = pullQtlGeno(popname, trait = 3)
# Calculate Observed and Expected Heterozygosity
# For selected SNP
# het_snp = sum(apply(X = M, MARGIN = 2, FUN = function(X) (sum(X==1)/length(X)) ) )
het_snp = apply(X = M, MARGIN = 2, FUN = function(X) (sum(X==1)/length(X)) )
p_snp = colMeans(M)/2
# For neutral SNP
het_neutral = apply(X = N, MARGIN = 2, FUN = function(X) (sum(X==1)/length(X)) )
p_neutral = colMeans(N)/2
# For QTL
het_qtl1 = apply(X = QT1, MARGIN = 2, FUN = function(X) (sum(X==1)/length(X)) )
het_qtl2 = apply(X = QT2, MARGIN = 2, FUN = function(X) (sum(X==1)/length(X)) )
het_qtl3 = apply(X = QT3, MARGIN = 2, FUN = function(X) (sum(X==1)/length(X)) )
p_qtl1 = colMeans(QT1)/2
p_qtl2 = colMeans(QT2)/2
p_qtl3 = colMeans(QT3)/2
# Param.
nSNP = ncol(M)
nQTL = ncol(QT1)
LDB = NULL
LDW = NULL
for (traitN in 1:3) {
ChrN = SP$nChr
QtlN = unique(SP$traits[[traitN]]@lociPerChr)
QTL_genos = sweep(pullQtlGeno(popname), 2,
SP$traits[[traitN]]@addEff,
"*")
QTL.matrix = popVar(QTL_genos)
# chr.genetic = NULL
# chr.genic = NULL
chr.LD.without = NULL
chr.LD = NULL
for(c in 1:ChrN){
i = ((c * QtlN) - QtlN) + 1
j = c * QtlN
# genetic.value <- sum(QTL.matrix[i:j, i:j])
# genic.value <- sum(diag(QTL.matrix[i:j, i:j]))
LD.without <- (sum(QTL.matrix[ ,i:j]) - sum(QTL.matrix[i:j, i:j]))
matrix.chr <- popVar(QTL_genos[ ,i:j])
LD.chr <- (sum(matrix.chr) - sum(diag(matrix.chr)))
# chr.genetic[c] <- list(genetic.value)
# chr.genic[c] <- list(genic.value)
chr.LD.without[c] <- list(LD.without)
chr.LD[c] <- list(LD.chr)
}
LDB[traitN] = list(sum(unlist(chr.LD)) )
LDW[traitN] = list(sum(unlist(chr.LD.without)) )
}
# Accumulate all info into datafile
datafile = rbind(datafile,
tibble(Replicate = rep,
Generation = gen,
Program = Program,
SelCand = candidatesgroup,
Year = year,
NumberID = popname@nInd,
MeanPhenoT1 = (meanP(popname))[1],
MeanPhenoT2 = (meanP(popname))[2],
MeanPhenoT3 = (meanP(popname))[3],
MeanGenoT1 = gePa$mu[1],
MeanGenoT2 = gePa$mu[2],
MeanGenoT3 = gePa$mu[3],
Accuracy_T1 = cor(gv(popname)[,1], ebv(popname)[,1], use = "pairwise.complete.obs"),
Accuracy_T2 = cor(gv(popname)[,2], ebv(popname)[,2], use = "pairwise.complete.obs"),
Accuracy_T3 = cor(gv(popname)[,3], ebv(popname)[,3], use = "pairwise.complete.obs"),
Accuracy_index = cor(true_index, ebv_index, use = "pairwise.complete.obs"),
MeanA_T1 = mean(gePa$gv_a[, 1]),
MeanA_T2 = mean(gePa$gv_a[, 2]),
MeanA_T3 = mean(gePa$gv_a[, 3]),
MeanD_T1 = mean(gePa$gv_d[, 1]),
MeanD_T2 = mean(gePa$gv_d[, 2]),
MeanD_T3 = mean(gePa$gv_d[, 3]),
VarA_T1 = gePa$varA[1,1],
VarA_T2 = gePa$varA[2,2],
VarA_T3 = gePa$varA[3,3],
VarD_T1 = gePa$varD[1,1],
VarD_T2 = gePa$varD[2,2],
VarD_T3 = gePa$varD[3,3],
VarG_T1 = gePa$varG[1,1],
VarG_T2 = gePa$varG[2,2],
VarG_T3 = gePa$varG[3,3],
GenicVA_T1 = gePa$genicVarA[1],
GenicVA_T2 = gePa$genicVarA[2],
GenicVA_T3 = gePa$genicVarA[3],
GenicVD_T1 = gePa$genicVarD[1],
GenicVD_T2 = gePa$genicVarD[2],
GenicVD_T3 = gePa$genicVarD[3],
GenicVG_T1 = gePa$genicVarG[1],
GenicVG_T2 = gePa$genicVarG[2],
GenicVG_T3 = gePa$genicVarG[3],
LDB_T1 = LDB[1],
LDB_T2 = LDB[2],
LDB_T3 = LDB[3],
LDW_T1 = LDW[1],
LDW_T2 = LDW[2],
LDW_T3 = LDW[3],
LDT_T1 = LDB[[1]] + LDW[[1]],
LDT_T2 = LDB[[2]] + LDW[[2]],
LDT_T3 = LDB[[3]] + LDW[[3]],
covG_L_T1 = gePa$covG_L[1],
covG_L_T2 = gePa$covG_L[2],
covG_L_T3 = gePa$covG_L[3],
LD_T1 = gePa$varG[1,1] - gePa$genicVarG[1],
LD_T2 = gePa$varG[2,2] - gePa$genicVarG[2],
LD_T3 = gePa$varG[3,3] - gePa$genicVarG[3],
covG_HW_T1 = gePa$covG_HW[1],
covG_HW_T2 = gePa$covG_HW[2],
covG_HW_T3 = gePa$covG_HW[3],
Mean_index = mean(true_index),
Var_index = var(true_index) * ( (length(true_index) - 1) / length(true_index) ),
MarkerHet = sum(het_snp),
NeutralHet = sum(het_neutral),
QTLHet_T1 = sum(het_qtl1),
QTLHet_T2 = sum(het_qtl2),
QTLHet_T3 = sum(het_qtl3),
F_snp_obs = 1 - ( sum(het_snp) / nSNP) ,
F_snp_exp = 1 - (sum(2*p_snp*(1-p_snp)) / nSNP),
F_neutral_obs = 1 - ( sum(het_neutral) / nSNP) ,
F_neutral_exp = 1 - (sum(2*p_neutral*(1-p_neutral)) / nSNP),
F_QTL1_obs = 1 - ( sum(het_qtl1) / nQTL),
F_QTL2_obs = 1 - ( sum(het_qtl2) / nQTL),
F_QTL3_obs = 1 - ( sum(het_qtl3) / nQTL),
F_QTL1_exp = 1 - (sum(2*p_qtl1*(1-p_qtl1)) / nQTL),
F_QTL2_exp = 1 - (sum(2*p_qtl2*(1-p_qtl2)) / nQTL),
F_QTL3_exp = 1 - (sum(2*p_qtl3*(1-p_qtl3)) / nQTL)
))
# return(datafile)
}
PullSireContribution <- function(datafile, CrossPlanFile) {
dfM = CrossPlanFile %>%
filter(Gender == 1) %>%
select("Id", "nContribution")
datafile = rbind(datafile,
tibble(Replicate = rep,
Generation = gen,
Program = Program,
SelCand = candidatesgroup,
Year = year,
N_Sires = nrow(dfM),
Mean_Contri = mean(dfM$nContribution),
Min_Contri = min(dfM$nContribution),
Max_Contri = max(dfM$nContribution)
))
#return(datafile)
}
prepare_par <- function() {
# extractsol.sh can be rather replaced by R-function
sink("extractsol.sh", type="output")
writeLines("#!/bin/bash
awk '{if ($1==1 && $2==2) print $3,$4}' solutions | sort +0 -1 > sol1.tmp
awk '{if ($1==2 && $2==2) print $3,$4}' solutions | sort +0 -1 > sol2.tmp
awk '{if ($1==3 && $2==2) print $3,$4}' solutions | sort +0 -1 > sol3.tmp
awk '{print $1, $10}' renadd02.ped | sort +0 -1 > id.tmp
join -1 +1 -2 +1 id.tmp sol1.tmp > sl1.tmp
join -1 +1 -2 +1 sl1.tmp sol2.tmp > sl2.tmp
join -1 +1 -2 +1 sl2.tmp sol3.tmp > sl3.tmp
# rm *.tmp
")
sink()
sink("prepgeno.sh", type="output")
writeLines("#!/bin/bash
# Prepare Marker Genotypes file for blupf90 format
# And automatically cut old markers for blupf90 limit (25k)
# Don't forget escaping quotes and backslashes when in R :)
# Blupf90 fixed format
awk '{printf(\"%-8s %s\\n\", $1,i$2)}' mrk.tmp > mrk2.tmp
gen=$(< mrk2.tmp wc -l)
# hard limit = 25000
limit=10200
if [ \"$gen\" -gt \"$limit\" ]
then
cold=(`expr $gen - $limit + 1`)
tail -n +$cold mrk2.tmp > markeri.dat
else
cp mrk2.tmp markeri.dat
fi
")
sink()
sink("renum.par", type="output")
writeLines("#renumf90 parametar file
DATAFILE
Blupf901.dat
TRAITS
2 3 4
#Phen
FIELDS_PASSED TO OUTPUT
1 5
#id generation
WEIGHT(S)
RESIDUAL_VARIANCE
4.5556 0 0
0 3.5455 0
0 0 3.0
EFFECT
6 6 6 cross alpha
#mean
EFFECT
1 1 1 cross alpha
#animal
RANDOM
animal
OPTIONAL
FILE
Blupf90.ped
PED_DEPTH
0
INBREEDING
pedigree
(CO)VARIANCES
1.00 0.75 0.60
0.75 1.00 0.70
0.60 0.70 1.00
OPTION blksize 3
")
sink()
sink("renumGS.par", type="output")
writeLines("#renumf90 parametar file
DATAFILE
Blupf901.dat
TRAITS
2 3 4
#Phen
FIELDS_PASSED TO OUTPUT
1 5
#id generation
WEIGHT(S)
RESIDUAL_VARIANCE
4.5556 0 0
0 3.5455 0
0 0 3.0
EFFECT
6 6 6 cross alpha
#mean
EFFECT
1 1 1 cross alpha
#animal
RANDOM
animal
OPTIONAL
FILE
Blupf90.ped
SNP_FILE
../markeri.dat
PED_DEPTH
0
INBREEDING
pedigree
(CO)VARIANCES
1.00 0.75 0.60
0.75 1.00 0.70
0.60 0.70 1.00
OPTION blksize 3
OPTION msg 1
OPTION no_quality_control
OPTION use_yams
OPTION thrStopCorAG 0
")
sink()
}