scripts/step3_ABLUP_64.R
In arfesta/SimBreeder: Forward population breeding simulator
# Run this on Domino ####
source('/mnt/simulator/calc_TGV.R')
source('/mnt/simulator/sim_phenos.R')
source('/mnt/simulator/create_cross_design.R')
source('/mnt/simulator/make_crosses.R')
source('/mnt/simulator/calc_TGV.R')
source('/mnt/extract_selections.new3.R')
## Run Values ####
NumParents=64
prog.per.cross = 60
af.selection = "ABLUP"
rel.mat.cross = "pedigree"
num.sel.af = 64
num.of.cores = 1
#Values for alleles and trait variation
Major.value =1
Minor.value =-100
Dominance.Coeff=0
indiv.tree.h2 = .3
load("./all_seeds.RData")
for(pop.num in 2:10){
this.gen <- all_seeds[[pop.num]]
load.pop <- paste0("/mnt/data/base_population_op_testing/base_population_op_test_",pop.num,".RData")
load(load.pop)
###Create matrices to hold outputs####
simulations=1; generations=10
genetic.gain.mine <- matrix(data=NA,nrow=simulations,ncol=generations)
genotypic.variance <- matrix(data=NA,nrow=simulations,ncol=generations)
phenotypic.gain.mine <- matrix(data=NA,nrow=simulations,ncol=generations)
mean.select.inbreeding<- matrix(data=NA,nrow=simulations,ncol=generations)
mean.pop.inbreeding<- matrix(data=NA,nrow=simulations,ncol=generations)
bulmer.effect <- matrix(data=NA,nrow=simulations,ncol=generations)
coancest.threshold <- matrix(data=NA,nrow=simulations,ncol=generations)
delt.alleles <- matrix(data=NA,nrow=simulations,ncol=generations)
max.delt.alleles <- matrix(data=NA,nrow=simulations,ncol=generations)
min.delt.alleles <- matrix(data=NA,nrow=simulations,ncol=generations)
###Subset the open pollinated parent output to use as input for 1st Generation####
op.test$mean.parent.phenos <- op.test$mean.parent.phenos[1:NumParents]
op.test$mean.parent.tgv <- op.test$mean.parent.tgv[1:NumParents]
op.test$delt.alleles <- op.test$delt.alleles[1:NumParents]
op.test$genetic.values <- op.test$genetic.values[1:NumParents]
op.test$phenos <- op.test$phenos[1:NumParents]
op.test$genos.3d <- op.test$genos.3d[,1:NumParents,]
op.test$marker.matrix <- op.test$marker.matrix[1:NumParents,]
#### Create 1st gen progeny#######
set.seed(this.gen[1]+1)
cross.design <- create_cross_design(parent.info = op.test,prog.percross = prog.per.cross,generation = 1,use.op.par.phenos = T)
set.seed(this.gen[1]+2)
progeny1 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = base_pop_data$parents,num.cores = num.of.cores)
progeny1.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny1,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[1]); progeny1.phenos <- sim_phenos(TGV.object = progeny1.TGV,h2 = indiv.tree.h2)
progeny1.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = base_pop_data$parents.TGV,
past.phenos = base_pop_data$parents.phenos,parent.info = op.test,progeny.info = progeny1,
progeny.TGV = progeny1.TGV,progeny.phenos = progeny1.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,1] <- mean(progeny1.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,1] <- var(progeny1.extractions$select.genval)
phenotypic.gain.mine[1,1] <-mean(progeny1.extractions$selection.phenos)-mean(op.test$phenos)
mean.select.inbreeding[1,1] <- mean(progeny1.extractions$select.inbred.level)
mean.pop.inbreeding[1,1]<- mean(progeny1.extractions$prog.inbred.level)
bulmer.effect[1,1] <- progeny1.extractions$bulmer.effect
coancest.threshold[1,1] <- 0
delt.alleles[1,1] <- sum(progeny1.extractions$delt.alleles)/NumParents
max.delt.alleles[1,1] <- max(progeny1.extractions$delt.allele)
min.delt.alleles[1,1] <- min(progeny1.extractions$delt.allele)
rm(progeny1); gc()
####Generation 2#####
set.seed(this.gen[2]+1);
cross.design <- create_cross_design(parent.info = progeny1.extractions,prog.percross = prog.per.cross,generation = 2)
set.seed(this.gen[2]+2);progeny2 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny1.extractions,num.cores = num.of.cores)
progeny2.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny2,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[2]); progeny2.phenos <- sim_phenos(TGV.object = progeny2.TGV,h2 = indiv.tree.h2)
progeny2.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny1.TGV,
past.phenos = progeny1.phenos,parent.info = progeny1.extractions,progeny.info = progeny2,
progeny.TGV = progeny2.TGV,progeny.phenos = progeny2.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,2] <- mean(progeny2.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,2]<- var(progeny2.extractions$select.genval)
phenotypic.gain.mine[1,2] <-mean(progeny2.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,2]<- mean(progeny2.extractions$select.inbred.level)
mean.pop.inbreeding[1,2]<- mean(progeny2.extractions$prog.inbred.level)
bulmer.effect[1,2]<- progeny2.extractions$bulmer.effect
coancest.threshold[1,2] <- cross.design$coancestry.threshold
delt.alleles[1,2] <- sum(progeny2.extractions$delt.alleles)/NumParents
max.delt.alleles[1,2] <- max(progeny2.extractions$delt.allele)
min.delt.alleles[1,2] <- min(progeny2.extractions$delt.allele)
rm(progeny1.TGV,progeny1.extractions, progeny1.phenos,progeny2);gc()
####Generations 3####
set.seed(this.gen[3]+1); cross.design <- create_cross_design(parent.info = progeny2.extractions,prog.percross = prog.per.cross,generation = 3)
set.seed(this.gen[3]+2); progeny3 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny2.extractions,num.cores = num.of.cores)
progeny3.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny3,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[3]); progeny3.phenos <- sim_phenos(TGV.object = progeny3.TGV,h2 = indiv.tree.h2)
progeny3.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny2.TGV,
past.phenos = progeny2.phenos,parent.info = progeny2.extractions,progeny.info = progeny3,
progeny.TGV = progeny3.TGV,progeny.phenos = progeny3.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,3] <- mean(progeny3.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,3]<- var(progeny3.extractions$select.genval)
phenotypic.gain.mine[1,3] <-mean(progeny3.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,3]<- mean(progeny3.extractions$select.inbred.level)
mean.pop.inbreeding[1,3]<- mean(progeny3.extractions$prog.inbred.level)
bulmer.effect[1,3]<- progeny3.extractions$bulmer.effect
coancest.threshold[1,3] <- cross.design$coancestry.threshold
delt.alleles[1,3] <- sum(progeny3.extractions$delt.alleles)/NumParents
max.delt.alleles[1,3] <- max(progeny3.extractions$delt.allele)
min.delt.alleles[1,3] <- min(progeny3.extractions$delt.allele)
rm(progeny2.TGV,progeny2.extractions, progeny2.phenos,progeny3);gc()
####Generations 4####
set.seed(this.gen[4]+1); cross.design <- create_cross_design(parent.info = progeny3.extractions,prog.percross = prog.per.cross,generation = 4)
set.seed(this.gen[4]+2); progeny4 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny3.extractions,num.cores = num.of.cores)
progeny4.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny4,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[4]); progeny4.phenos <- sim_phenos(TGV.object = progeny4.TGV,h2 = indiv.tree.h2)
progeny4.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny3.TGV,
past.phenos = progeny3.phenos,parent.info = progeny3.extractions,progeny.info = progeny4,
progeny.TGV = progeny4.TGV,progeny.phenos = progeny4.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,4] <- mean(progeny4.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,4]<- var(progeny4.extractions$select.genval)
phenotypic.gain.mine[1,4] <-mean(progeny4.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,4]<- mean(progeny4.extractions$select.inbred.level)
mean.pop.inbreeding[1,4]<- mean(progeny4.extractions$prog.inbred.level)
bulmer.effect[1,4]<- progeny4.extractions$bulmer.effect
coancest.threshold[1,4] <- cross.design$coancestry.threshold
delt.alleles[1,4] <- sum(progeny4.extractions$delt.alleles)/NumParents
max.delt.alleles[1,4] <- max(progeny4.extractions$delt.allele)
min.delt.alleles[1,4] <- min(progeny4.extractions$delt.allele)
rm(progeny3.TGV,progeny3.extractions, progeny3.phenos,progeny4);gc()
####Generations 5####
set.seed(this.gen[5]+1); cross.design <- create_cross_design(parent.info = progeny4.extractions,prog.percross = prog.per.cross,generation = 5)
set.seed(this.gen[5]+2); progeny5 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny4.extractions,num.cores = num.of.cores)
progeny5.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny5,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[5]); progeny5.phenos <- sim_phenos(TGV.object = progeny5.TGV,h2 = indiv.tree.h2)
progeny5.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny4.TGV,
past.phenos = progeny4.phenos,parent.info = progeny4.extractions,progeny.info = progeny5,
progeny.TGV = progeny5.TGV,progeny.phenos = progeny5.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,5] <- mean(progeny5.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,5]<- var(progeny5.extractions$select.genval)
phenotypic.gain.mine[1,5] <-mean(progeny5.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,5]<- mean(progeny5.extractions$select.inbred.level)
mean.pop.inbreeding[1,5]<- mean(progeny5.extractions$prog.inbred.level)
bulmer.effect[1,5]<- progeny5.extractions$bulmer.effect
coancest.threshold[1,5] <- cross.design$coancestry.threshold
delt.alleles[1,5] <- sum(progeny5.extractions$delt.alleles)/NumParents
max.delt.alleles[1,5] <- max(progeny5.extractions$delt.allele)
min.delt.alleles[1,5] <- min(progeny5.extractions$delt.allele)
rm(progeny4.TGV,progeny4.extractions, progeny4.phenos,progeny5);gc()
####Generations 6####
set.seed(this.gen[6]+1); cross.design <- create_cross_design(parent.info = progeny5.extractions,prog.percross = prog.per.cross,generation = 6)
set.seed(this.gen[6]+2); progeny6 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny5.extractions,num.cores = num.of.cores)
progeny6.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny6,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[6]); progeny6.phenos <- sim_phenos(TGV.object = progeny6.TGV,h2 = indiv.tree.h2)
progeny6.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny5.TGV,
past.phenos = progeny5.phenos,parent.info = progeny5.extractions,progeny.info = progeny6,
progeny.TGV = progeny6.TGV,progeny.phenos = progeny6.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,6] <- mean(progeny6.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,6]<- var(progeny6.extractions$select.genval)
phenotypic.gain.mine[1,6] <-mean(progeny6.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,6]<- mean(progeny6.extractions$select.inbred.level)
mean.pop.inbreeding[1,6]<- mean(progeny6.extractions$prog.inbred.level)
bulmer.effect[1,6]<- progeny6.extractions$bulmer.effect
coancest.threshold[1,6] <- cross.design$coancestry.threshold
delt.alleles[1,6] <- sum(progeny6.extractions$delt.alleles)/NumParents
max.delt.alleles[1,6] <- max(progeny6.extractions$delt.allele)
min.delt.alleles[1,6] <- min(progeny6.extractions$delt.allele)
rm(progeny5.TGV,progeny5.extractions, progeny5.phenos,progeny6);gc()
####Generations 7####
set.seed(this.gen[7]+1); cross.design <- create_cross_design(parent.info = progeny6.extractions,prog.percross = prog.per.cross,generation = 7)
set.seed(this.gen[7]+2); progeny7 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny6.extractions,num.cores = num.of.cores)
progeny7.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny7,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[7]); progeny7.phenos <- sim_phenos(TGV.object = progeny7.TGV,h2 = indiv.tree.h2)
progeny7.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny6.TGV,
past.phenos = progeny6.phenos,parent.info = progeny6.extractions,progeny.info = progeny7,
progeny.TGV = progeny7.TGV,progeny.phenos = progeny7.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,7] <- mean(progeny7.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,7]<- var(progeny7.extractions$select.genval)
phenotypic.gain.mine[1,7] <-mean(progeny7.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,7]<- mean(progeny7.extractions$select.inbred.level)
mean.pop.inbreeding[1,7]<- mean(progeny7.extractions$prog.inbred.level)
bulmer.effect[1,7]<- progeny7.extractions$bulmer.effect
coancest.threshold[1,7] <- cross.design$coancestry.threshold
delt.alleles[1,7] <- sum(progeny7.extractions$delt.alleles)/NumParents
max.delt.alleles[1,7] <- max(progeny7.extractions$delt.allele)
min.delt.alleles[1,7] <- min(progeny7.extractions$delt.allele)
rm(progeny6.TGV,progeny6.extractions, progeny6.phenos,progeny7);gc()
####Generations 8####
set.seed(this.gen[8]+1); cross.design <- create_cross_design(parent.info = progeny7.extractions,prog.percross = prog.per.cross,generation = 8)
set.seed(this.gen[8]+2); progeny8 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny7.extractions,num.cores = num.of.cores)
progeny8.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny8,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[8]); progeny8.phenos <- sim_phenos(TGV.object = progeny8.TGV,h2 = indiv.tree.h2)
progeny8.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny7.TGV,
past.phenos = progeny7.phenos,parent.info = progeny7.extractions,progeny.info = progeny8,
progeny.TGV = progeny8.TGV,progeny.phenos = progeny8.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,8] <- mean(progeny8.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,8]<- var(progeny8.extractions$select.genval)
phenotypic.gain.mine[1,8] <-mean(progeny8.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,8]<- mean(progeny8.extractions$select.inbred.level)
mean.pop.inbreeding[1,8]<- mean(progeny8.extractions$prog.inbred.level)
bulmer.effect[1,8]<- progeny8.extractions$bulmer.effect
coancest.threshold[1,8] <- cross.design$coancestry.threshold
delt.alleles[1,8] <- sum(progeny8.extractions$delt.alleles)/NumParents
max.delt.alleles[1,8] <- max(progeny8.extractions$delt.allele)
min.delt.alleles[1,8] <- min(progeny8.extractions$delt.allele)
rm(progeny7.TGV,progeny7.extractions, progeny7.phenos,progeny8);gc()
####Generations 9####
set.seed(this.gen[9]+1); cross.design <- create_cross_design(parent.info = progeny8.extractions,prog.percross = prog.per.cross,generation = 9)
set.seed(this.gen[9]+2); progeny9 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny8.extractions,num.cores = num.of.cores)
progeny9.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny9,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[9]); progeny9.phenos <- sim_phenos(TGV.object = progeny9.TGV,h2 = indiv.tree.h2)
progeny9.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny8.TGV,
past.phenos = progeny8.phenos,parent.info = progeny8.extractions,progeny.info = progeny9,
progeny.TGV = progeny9.TGV,progeny.phenos = progeny9.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,9] <- mean(progeny9.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,9]<- var(progeny9.extractions$select.genval)
phenotypic.gain.mine[1,9] <-mean(progeny9.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,9]<- mean(progeny9.extractions$select.inbred.level)
mean.pop.inbreeding[1,9]<- mean(progeny9.extractions$prog.inbred.level)
bulmer.effect[1,9]<- progeny9.extractions$bulmer.effect
coancest.threshold[1,9] <- cross.design$coancestry.threshold
delt.alleles[1,9] <- sum(progeny9.extractions$delt.alleles)/NumParents
max.delt.alleles[1,9] <- max(progeny9.extractions$delt.allele)
min.delt.alleles[1,9] <- min(progeny9.extractions$delt.allele)
rm(progeny8.TGV,progeny8.extractions, progeny8.phenos,progeny9);gc()
####Generations 10####
set.seed(this.gen[10]+1); cross.design <- create_cross_design(parent.info = progeny9.extractions,prog.percross = prog.per.cross,generation = 10)
set.seed(this.gen[10]+2); progeny10 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny9.extractions,num.cores = num.of.cores)
progeny10.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny10,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[10]); progeny10.phenos <- sim_phenos(TGV.object = progeny10.TGV,h2 = indiv.tree.h2)
progeny10.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny9.TGV,
past.phenos = progeny9.phenos,parent.info = progeny9.extractions,progeny.info = progeny10,
progeny.TGV = progeny10.TGV,progeny.phenos = progeny10.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,10] <- mean(progeny10.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,10]<- var(progeny10.extractions$select.genval)
phenotypic.gain.mine[1,10] <-mean(progeny10.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,10]<- mean(progeny10.extractions$select.inbred.level)
mean.pop.inbreeding[1,10]<- mean(progeny10.extractions$prog.inbred.level)
bulmer.effect[1,10]<- progeny10.extractions$bulmer.effect
coancest.threshold[1,10] <- cross.design$coancestry.threshold
delt.alleles[1,10] <- sum(progeny10.extractions$delt.alleles)/NumParents
max.delt.alleles[1,10] <- max(progeny10.extractions$delt.allele)
min.delt.alleles[1,10] <- min(progeny10.extractions$delt.allele)
rm(progeny9.TGV,progeny9.extractions, progeny9.phenos,progeny10,progeny10.extractions,progeny10.phenos,progeny10.TGV);gc()
#### List for output objects####
newList <- list("g"=genetic.gain.mine,"gv"=genotypic.variance, "pg"=phenotypic.gain.mine,"si"=mean.select.inbreeding,
"pi"=mean.pop.inbreeding,"be"=bulmer.effect, "ct"=coancest.threshold, "maxd"=max.delt.alleles,"mind"=min.delt.alleles,
"da"=delt.alleles)
the.name <- paste("ABLUP.nodom.64.OP.pop", pop.num, sep="")
assign(x=the.name,value=newList)
save(list=the.name,file=paste("./data/results3/64/ABLUP/",the.name,".RData",sep=""))
}
arfesta/SimBreeder documentation built on Dec. 19, 2021, 4:37 a.m.
R Package Documentation
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# Run this on Domino ####
source('/mnt/simulator/calc_TGV.R')
source('/mnt/simulator/sim_phenos.R')
source('/mnt/simulator/create_cross_design.R')
source('/mnt/simulator/make_crosses.R')
source('/mnt/simulator/calc_TGV.R')
source('/mnt/extract_selections.new3.R')
## Run Values ####
NumParents=64
prog.per.cross = 60
af.selection = "ABLUP"
rel.mat.cross = "pedigree"
num.sel.af = 64
num.of.cores = 1
#Values for alleles and trait variation
Major.value =1
Minor.value =-100
Dominance.Coeff=0
indiv.tree.h2 = .3
load("./all_seeds.RData")
for(pop.num in 2:10){
this.gen <- all_seeds[[pop.num]]
load.pop <- paste0("/mnt/data/base_population_op_testing/base_population_op_test_",pop.num,".RData")
load(load.pop)
###Create matrices to hold outputs####
simulations=1; generations=10
genetic.gain.mine <- matrix(data=NA,nrow=simulations,ncol=generations)
genotypic.variance <- matrix(data=NA,nrow=simulations,ncol=generations)
phenotypic.gain.mine <- matrix(data=NA,nrow=simulations,ncol=generations)
mean.select.inbreeding<- matrix(data=NA,nrow=simulations,ncol=generations)
mean.pop.inbreeding<- matrix(data=NA,nrow=simulations,ncol=generations)
bulmer.effect <- matrix(data=NA,nrow=simulations,ncol=generations)
coancest.threshold <- matrix(data=NA,nrow=simulations,ncol=generations)
delt.alleles <- matrix(data=NA,nrow=simulations,ncol=generations)
max.delt.alleles <- matrix(data=NA,nrow=simulations,ncol=generations)
min.delt.alleles <- matrix(data=NA,nrow=simulations,ncol=generations)
###Subset the open pollinated parent output to use as input for 1st Generation####
op.test$mean.parent.phenos <- op.test$mean.parent.phenos[1:NumParents]
op.test$mean.parent.tgv <- op.test$mean.parent.tgv[1:NumParents]
op.test$delt.alleles <- op.test$delt.alleles[1:NumParents]
op.test$genetic.values <- op.test$genetic.values[1:NumParents]
op.test$phenos <- op.test$phenos[1:NumParents]
op.test$genos.3d <- op.test$genos.3d[,1:NumParents,]
op.test$marker.matrix <- op.test$marker.matrix[1:NumParents,]
#### Create 1st gen progeny#######
set.seed(this.gen[1]+1)
cross.design <- create_cross_design(parent.info = op.test,prog.percross = prog.per.cross,generation = 1,use.op.par.phenos = T)
set.seed(this.gen[1]+2)
progeny1 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = base_pop_data$parents,num.cores = num.of.cores)
progeny1.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny1,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[1]); progeny1.phenos <- sim_phenos(TGV.object = progeny1.TGV,h2 = indiv.tree.h2)
progeny1.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = base_pop_data$parents.TGV,
past.phenos = base_pop_data$parents.phenos,parent.info = op.test,progeny.info = progeny1,
progeny.TGV = progeny1.TGV,progeny.phenos = progeny1.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,1] <- mean(progeny1.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,1] <- var(progeny1.extractions$select.genval)
phenotypic.gain.mine[1,1] <-mean(progeny1.extractions$selection.phenos)-mean(op.test$phenos)
mean.select.inbreeding[1,1] <- mean(progeny1.extractions$select.inbred.level)
mean.pop.inbreeding[1,1]<- mean(progeny1.extractions$prog.inbred.level)
bulmer.effect[1,1] <- progeny1.extractions$bulmer.effect
coancest.threshold[1,1] <- 0
delt.alleles[1,1] <- sum(progeny1.extractions$delt.alleles)/NumParents
max.delt.alleles[1,1] <- max(progeny1.extractions$delt.allele)
min.delt.alleles[1,1] <- min(progeny1.extractions$delt.allele)
rm(progeny1); gc()
####Generation 2#####
set.seed(this.gen[2]+1);
cross.design <- create_cross_design(parent.info = progeny1.extractions,prog.percross = prog.per.cross,generation = 2)
set.seed(this.gen[2]+2);progeny2 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny1.extractions,num.cores = num.of.cores)
progeny2.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny2,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[2]); progeny2.phenos <- sim_phenos(TGV.object = progeny2.TGV,h2 = indiv.tree.h2)
progeny2.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny1.TGV,
past.phenos = progeny1.phenos,parent.info = progeny1.extractions,progeny.info = progeny2,
progeny.TGV = progeny2.TGV,progeny.phenos = progeny2.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,2] <- mean(progeny2.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,2]<- var(progeny2.extractions$select.genval)
phenotypic.gain.mine[1,2] <-mean(progeny2.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,2]<- mean(progeny2.extractions$select.inbred.level)
mean.pop.inbreeding[1,2]<- mean(progeny2.extractions$prog.inbred.level)
bulmer.effect[1,2]<- progeny2.extractions$bulmer.effect
coancest.threshold[1,2] <- cross.design$coancestry.threshold
delt.alleles[1,2] <- sum(progeny2.extractions$delt.alleles)/NumParents
max.delt.alleles[1,2] <- max(progeny2.extractions$delt.allele)
min.delt.alleles[1,2] <- min(progeny2.extractions$delt.allele)
rm(progeny1.TGV,progeny1.extractions, progeny1.phenos,progeny2);gc()
####Generations 3####
set.seed(this.gen[3]+1); cross.design <- create_cross_design(parent.info = progeny2.extractions,prog.percross = prog.per.cross,generation = 3)
set.seed(this.gen[3]+2); progeny3 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny2.extractions,num.cores = num.of.cores)
progeny3.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny3,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[3]); progeny3.phenos <- sim_phenos(TGV.object = progeny3.TGV,h2 = indiv.tree.h2)
progeny3.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny2.TGV,
past.phenos = progeny2.phenos,parent.info = progeny2.extractions,progeny.info = progeny3,
progeny.TGV = progeny3.TGV,progeny.phenos = progeny3.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,3] <- mean(progeny3.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,3]<- var(progeny3.extractions$select.genval)
phenotypic.gain.mine[1,3] <-mean(progeny3.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,3]<- mean(progeny3.extractions$select.inbred.level)
mean.pop.inbreeding[1,3]<- mean(progeny3.extractions$prog.inbred.level)
bulmer.effect[1,3]<- progeny3.extractions$bulmer.effect
coancest.threshold[1,3] <- cross.design$coancestry.threshold
delt.alleles[1,3] <- sum(progeny3.extractions$delt.alleles)/NumParents
max.delt.alleles[1,3] <- max(progeny3.extractions$delt.allele)
min.delt.alleles[1,3] <- min(progeny3.extractions$delt.allele)
rm(progeny2.TGV,progeny2.extractions, progeny2.phenos,progeny3);gc()
####Generations 4####
set.seed(this.gen[4]+1); cross.design <- create_cross_design(parent.info = progeny3.extractions,prog.percross = prog.per.cross,generation = 4)
set.seed(this.gen[4]+2); progeny4 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny3.extractions,num.cores = num.of.cores)
progeny4.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny4,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[4]); progeny4.phenos <- sim_phenos(TGV.object = progeny4.TGV,h2 = indiv.tree.h2)
progeny4.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny3.TGV,
past.phenos = progeny3.phenos,parent.info = progeny3.extractions,progeny.info = progeny4,
progeny.TGV = progeny4.TGV,progeny.phenos = progeny4.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,4] <- mean(progeny4.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,4]<- var(progeny4.extractions$select.genval)
phenotypic.gain.mine[1,4] <-mean(progeny4.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,4]<- mean(progeny4.extractions$select.inbred.level)
mean.pop.inbreeding[1,4]<- mean(progeny4.extractions$prog.inbred.level)
bulmer.effect[1,4]<- progeny4.extractions$bulmer.effect
coancest.threshold[1,4] <- cross.design$coancestry.threshold
delt.alleles[1,4] <- sum(progeny4.extractions$delt.alleles)/NumParents
max.delt.alleles[1,4] <- max(progeny4.extractions$delt.allele)
min.delt.alleles[1,4] <- min(progeny4.extractions$delt.allele)
rm(progeny3.TGV,progeny3.extractions, progeny3.phenos,progeny4);gc()
####Generations 5####
set.seed(this.gen[5]+1); cross.design <- create_cross_design(parent.info = progeny4.extractions,prog.percross = prog.per.cross,generation = 5)
set.seed(this.gen[5]+2); progeny5 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny4.extractions,num.cores = num.of.cores)
progeny5.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny5,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[5]); progeny5.phenos <- sim_phenos(TGV.object = progeny5.TGV,h2 = indiv.tree.h2)
progeny5.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny4.TGV,
past.phenos = progeny4.phenos,parent.info = progeny4.extractions,progeny.info = progeny5,
progeny.TGV = progeny5.TGV,progeny.phenos = progeny5.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,5] <- mean(progeny5.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,5]<- var(progeny5.extractions$select.genval)
phenotypic.gain.mine[1,5] <-mean(progeny5.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,5]<- mean(progeny5.extractions$select.inbred.level)
mean.pop.inbreeding[1,5]<- mean(progeny5.extractions$prog.inbred.level)
bulmer.effect[1,5]<- progeny5.extractions$bulmer.effect
coancest.threshold[1,5] <- cross.design$coancestry.threshold
delt.alleles[1,5] <- sum(progeny5.extractions$delt.alleles)/NumParents
max.delt.alleles[1,5] <- max(progeny5.extractions$delt.allele)
min.delt.alleles[1,5] <- min(progeny5.extractions$delt.allele)
rm(progeny4.TGV,progeny4.extractions, progeny4.phenos,progeny5);gc()
####Generations 6####
set.seed(this.gen[6]+1); cross.design <- create_cross_design(parent.info = progeny5.extractions,prog.percross = prog.per.cross,generation = 6)
set.seed(this.gen[6]+2); progeny6 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny5.extractions,num.cores = num.of.cores)
progeny6.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny6,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[6]); progeny6.phenos <- sim_phenos(TGV.object = progeny6.TGV,h2 = indiv.tree.h2)
progeny6.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny5.TGV,
past.phenos = progeny5.phenos,parent.info = progeny5.extractions,progeny.info = progeny6,
progeny.TGV = progeny6.TGV,progeny.phenos = progeny6.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,6] <- mean(progeny6.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,6]<- var(progeny6.extractions$select.genval)
phenotypic.gain.mine[1,6] <-mean(progeny6.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,6]<- mean(progeny6.extractions$select.inbred.level)
mean.pop.inbreeding[1,6]<- mean(progeny6.extractions$prog.inbred.level)
bulmer.effect[1,6]<- progeny6.extractions$bulmer.effect
coancest.threshold[1,6] <- cross.design$coancestry.threshold
delt.alleles[1,6] <- sum(progeny6.extractions$delt.alleles)/NumParents
max.delt.alleles[1,6] <- max(progeny6.extractions$delt.allele)
min.delt.alleles[1,6] <- min(progeny6.extractions$delt.allele)
rm(progeny5.TGV,progeny5.extractions, progeny5.phenos,progeny6);gc()
####Generations 7####
set.seed(this.gen[7]+1); cross.design <- create_cross_design(parent.info = progeny6.extractions,prog.percross = prog.per.cross,generation = 7)
set.seed(this.gen[7]+2); progeny7 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny6.extractions,num.cores = num.of.cores)
progeny7.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny7,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[7]); progeny7.phenos <- sim_phenos(TGV.object = progeny7.TGV,h2 = indiv.tree.h2)
progeny7.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny6.TGV,
past.phenos = progeny6.phenos,parent.info = progeny6.extractions,progeny.info = progeny7,
progeny.TGV = progeny7.TGV,progeny.phenos = progeny7.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,7] <- mean(progeny7.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,7]<- var(progeny7.extractions$select.genval)
phenotypic.gain.mine[1,7] <-mean(progeny7.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,7]<- mean(progeny7.extractions$select.inbred.level)
mean.pop.inbreeding[1,7]<- mean(progeny7.extractions$prog.inbred.level)
bulmer.effect[1,7]<- progeny7.extractions$bulmer.effect
coancest.threshold[1,7] <- cross.design$coancestry.threshold
delt.alleles[1,7] <- sum(progeny7.extractions$delt.alleles)/NumParents
max.delt.alleles[1,7] <- max(progeny7.extractions$delt.allele)
min.delt.alleles[1,7] <- min(progeny7.extractions$delt.allele)
rm(progeny6.TGV,progeny6.extractions, progeny6.phenos,progeny7);gc()
####Generations 8####
set.seed(this.gen[8]+1); cross.design <- create_cross_design(parent.info = progeny7.extractions,prog.percross = prog.per.cross,generation = 8)
set.seed(this.gen[8]+2); progeny8 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny7.extractions,num.cores = num.of.cores)
progeny8.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny8,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[8]); progeny8.phenos <- sim_phenos(TGV.object = progeny8.TGV,h2 = indiv.tree.h2)
progeny8.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny7.TGV,
past.phenos = progeny7.phenos,parent.info = progeny7.extractions,progeny.info = progeny8,
progeny.TGV = progeny8.TGV,progeny.phenos = progeny8.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,8] <- mean(progeny8.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,8]<- var(progeny8.extractions$select.genval)
phenotypic.gain.mine[1,8] <-mean(progeny8.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,8]<- mean(progeny8.extractions$select.inbred.level)
mean.pop.inbreeding[1,8]<- mean(progeny8.extractions$prog.inbred.level)
bulmer.effect[1,8]<- progeny8.extractions$bulmer.effect
coancest.threshold[1,8] <- cross.design$coancestry.threshold
delt.alleles[1,8] <- sum(progeny8.extractions$delt.alleles)/NumParents
max.delt.alleles[1,8] <- max(progeny8.extractions$delt.allele)
min.delt.alleles[1,8] <- min(progeny8.extractions$delt.allele)
rm(progeny7.TGV,progeny7.extractions, progeny7.phenos,progeny8);gc()
####Generations 9####
set.seed(this.gen[9]+1); cross.design <- create_cross_design(parent.info = progeny8.extractions,prog.percross = prog.per.cross,generation = 9)
set.seed(this.gen[9]+2); progeny9 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny8.extractions,num.cores = num.of.cores)
progeny9.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny9,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[9]); progeny9.phenos <- sim_phenos(TGV.object = progeny9.TGV,h2 = indiv.tree.h2)
progeny9.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny8.TGV,
past.phenos = progeny8.phenos,parent.info = progeny8.extractions,progeny.info = progeny9,
progeny.TGV = progeny9.TGV,progeny.phenos = progeny9.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,9] <- mean(progeny9.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,9]<- var(progeny9.extractions$select.genval)
phenotypic.gain.mine[1,9] <-mean(progeny9.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,9]<- mean(progeny9.extractions$select.inbred.level)
mean.pop.inbreeding[1,9]<- mean(progeny9.extractions$prog.inbred.level)
bulmer.effect[1,9]<- progeny9.extractions$bulmer.effect
coancest.threshold[1,9] <- cross.design$coancestry.threshold
delt.alleles[1,9] <- sum(progeny9.extractions$delt.alleles)/NumParents
max.delt.alleles[1,9] <- max(progeny9.extractions$delt.allele)
min.delt.alleles[1,9] <- min(progeny9.extractions$delt.allele)
rm(progeny8.TGV,progeny8.extractions, progeny8.phenos,progeny9);gc()
####Generations 10####
set.seed(this.gen[10]+1); cross.design <- create_cross_design(parent.info = progeny9.extractions,prog.percross = prog.per.cross,generation = 10)
set.seed(this.gen[10]+2); progeny10 <- make_crosses(map.info=base_pop_data$genetic.map, cross.design = cross.design, parent.info = progeny9.extractions,num.cores = num.of.cores)
progeny10.TGV <- calc_TGV(map.info = base_pop_data$genetic.map,geno.info = progeny10,cross.design = cross.design,A = Major.value,a = Minor.value,dom.coeff = Dominance.Coeff)
set.seed(this.gen[10]); progeny10.phenos <- sim_phenos(TGV.object = progeny10.TGV,h2 = indiv.tree.h2)
progeny10.extractions <- extract_selections(map.info = base_pop_data$genetic.map,cross.design = cross.design,past.tgv = progeny9.TGV,
past.phenos = progeny9.phenos,parent.info = progeny9.extractions,progeny.info = progeny10,
progeny.TGV = progeny10.TGV,progeny.phenos = progeny10.phenos,among.family.selection = af.selection,
num.selections.among.family = num.sel.af,reduced = T,num.cores = num.of.cores)
genetic.gain.mine[1,10] <- mean(progeny10.extractions$select.genval) - mean(op.test$genetic.values)
genotypic.variance[1,10]<- var(progeny10.extractions$select.genval)
phenotypic.gain.mine[1,10] <-mean(progeny10.extractions$selection.phenos) - mean(op.test$phenos)
mean.select.inbreeding[1,10]<- mean(progeny10.extractions$select.inbred.level)
mean.pop.inbreeding[1,10]<- mean(progeny10.extractions$prog.inbred.level)
bulmer.effect[1,10]<- progeny10.extractions$bulmer.effect
coancest.threshold[1,10] <- cross.design$coancestry.threshold
delt.alleles[1,10] <- sum(progeny10.extractions$delt.alleles)/NumParents
max.delt.alleles[1,10] <- max(progeny10.extractions$delt.allele)
min.delt.alleles[1,10] <- min(progeny10.extractions$delt.allele)
rm(progeny9.TGV,progeny9.extractions, progeny9.phenos,progeny10,progeny10.extractions,progeny10.phenos,progeny10.TGV);gc()
#### List for output objects####
newList <- list("g"=genetic.gain.mine,"gv"=genotypic.variance, "pg"=phenotypic.gain.mine,"si"=mean.select.inbreeding,
"pi"=mean.pop.inbreeding,"be"=bulmer.effect, "ct"=coancest.threshold, "maxd"=max.delt.alleles,"mind"=min.delt.alleles,
"da"=delt.alleles)
the.name <- paste("ABLUP.nodom.64.OP.pop", pop.num, sep="")
assign(x=the.name,value=newList)
save(list=the.name,file=paste("./data/results3/64/ABLUP/",the.name,".RData",sep=""))
}
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