R/WHEAT.R
In lfmerrick21/WhEATBreeders: Wrappers for Genomic Selection
Defines functions
WHEAT
WHEAT<-function(Phenotype,
Genotype,
QC=TRUE,
GS=TRUE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
Filter_Ind=TRUE,
Missing_Rate_Ind=0.80,
#If QC Info is FALSE
GDre=NULL,
GT=NULL,
GIre=NULL,
GBS_Train=NULL,
GBS_Predict=NULL,
Matrix=NULL,
#GS Info
Type="Regression",
Replications=1,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Tested", #Tested or Untested
Trial=c("F5_2015","DH_2020","BL_2015_2020"),
Scheme="K-Fold",
Method="Two-Step",
Package="rrBLUP",
model="rrBLUP",
Kernel="Markers",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE){
if(QC==TRUE){
colnames(Phenotype)[1:2]<-c("Genotype","Env")
Phenotype=Phenotype %>% filter(Env %in% c(Trial))
#############PandG#########################
###############################################################################
#Get Taxa
#Phenotype=Phenotype1
Pheno_Names=Phenotype
names(Pheno_Names)[1]<-"Taxa"
Pheno_Names$Taxa=as.character(Pheno_Names$Taxa)
gname=as.vector(Pheno_Names$Taxa)
gname<-clean_names(gname)
#Filter Hapmap for Taxa
if(Geno_Type=="VCF"){
#Genotype1=Genotype
Genotype1=data.frame(Genotype@fix,Genotype@gt)
genotypes_num<- VCF_2_numeric(Genotype1)
GD=genotypes_num$genotypes
rownames(GD)<-clean_names_op(rownames(GD))
names.use <- rownames(GD)[(rownames(GD) %in% gname)]
GD <-GD[row.names(GD) %in% c(names.use),]
#GD <- GD[, names.use, with = FALSE]
GI=genotypes_num$marker_map
#setnames(GI, old = c('rs','chr','pos'),
#new = c('SNP','Chromosome','Position'))
GI <- GI %>%
rename(SNP=rs,
Chromosome=chr,
Position=pos)
GT=data.frame(V1=rownames(GD))
#Save Raw Files
fwrite(GT,paste0(Study,"_GT_taxa.csv"))
fwrite(GD,paste0(Study,"_GD_numeric_Raw.csv"))
fwrite(GI,paste0(Study,"_GI_map_Raw.csv"))
save(GD,GI,GT,file=paste0(Study,"_Raw.RData"))
}
if(Geno_Type=="Hapmap"){
names.use <- names(Genotype)[(names(Genotype) %in% gname)]
hapmap <- Genotype[, names.use, with = FALSE]
hapmap=cbind(Genotype[,1:11],hapmap)
#str(output)
hapmap[hapmap=="NA"]<-"NA"
hapmap$`assembly#`=as.character(hapmap$`assembly#`)
hapmap$center=as.character(hapmap$center)
hapmap$protLSID=as.character(hapmap$protLSID)
hapmap$assayLSID=as.character(hapmap$assayLSID)
hapmap$panelLSID=as.character(hapmap$panelLSID)
hapmap$QCcode=as.character(hapmap$QCcode)
dim(hapmap)
#Save filtered hapmap
save(hapmap,file=paste0(Study,"_Filtered_Hapmap.RData"))
fwrite(hapmap,paste0(Study,"_Filtered_Hapmap.hmp.txt"),sep="\t",row.names = FALSE,col.names = TRUE)
#Convert to Numeric
if(Imputation=="Middle"){
outG=GAPIT.HapMap(hapmap,SNP.impute="Middle")
}else{
outG=GAPIT.HapMap(hapmap,SNP.impute="None")
}
GT=outG$GT
GT=data.frame(V1=rownames(GT),GT)
GD=outG$GD
GI=outG$GI
rownames(GD)=rownames(GT)
colnames(GD)=GI$SNP
#Save Raw Files
fwrite(GT,paste0(Study,"_GT_taxa.csv"))
fwrite(GD,paste0(Study,"_GD_numeric_Raw.csv"))
fwrite(GI,paste0(Study,"_GI_map_Raw.csv"))
save(GD,GI,GT,file=paste0(Study,"_Raw.RData"))
}
#### Filter
##### Remove makers based on missing data
if(Filter==FALSE){
GDmf = GD
GImf = GI
}else{
mr=calc_missrate(GD)
mr_indices <- which(mr > Missing_Rate)
if(length(mr_indices)!=0){
GDmr = GD[,-mr_indices]
GImr = GI[-mr_indices,]
}else{
GDmr = GD
GImr = GI
}
dim(GDmr)
dim(GImr)
##### Remove Monomorphic Markers
maf <- calc_maf_apply(GDmr, encoding = c(0, 1, 2))
mono_indices <- which(maf ==0)
if(length(mono_indices)!=0){
GDmo = GDmr[,-mono_indices]
GImo = GImr[-mono_indices,]
}else{
GDmo = GDmr
GImo = GImr
}
dim(GDmo)
dim(GImo)
##### Remove MAF
maf <- calc_maf_apply(GDmo, encoding = c(0, 1, 2))
mono_indices <- which(maf < MAF)
if(length(mono_indices)!=0){
GDmf = GDmo[,-mono_indices]
GImf = GImo[-mono_indices,]
}else{
GDmf = GDmo
GImf = GImo
}
dim(GDmf)
dim(GImf)
}
if(Filter_Ind==FALSE){
GDmf = GDmf
}else{
mr=calc_missrate(t(GDmf))
mr_indices <- which(mr > Missing_Rate_Ind)
if(length(mr_indices)!=0){
GDmf = GDmf[-mr_indices,]
GT=GT[-mr_indices,]
}else{
GDmf = GDmf
GT=GT
}
dim(GDmf)
dim(GImf)
}
#### Imputation
##### Imputation Using BEAGLE
if(Imputation=="None"){
GDEre = GDmf
GIEre = GImf
dim(GDEre)
dim(GIEre)
fwrite(GDre,paste0(Study,"_GD_Filt_Imputed.csv"))
fwrite(GIre[,1:3],paste0(Study,"_GI_Filt_Imputed.csv"))
save(GDre,GIre,GT,file=paste0(Study,"_Filt_Imputed.RData"))
}else{
if(Imputation=="Beagle"){
GImf$chr <- GImf$Chromosome
GImf$rs <- GImf$SNP
GImf$pos <- GImf$Position
LD_file <- paste0(Study)
vcf_LD <- numeric_2_VCF(GDmf, GImf)
write_vcf(vcf_LD, outfile = LD_file)#Exports vcf
# Assign parameters
genotype_file = paste0(Study,".vcf")
outfile = paste0(Study,"_imp")
# Define a system command
command1_prefix <- "java -jar beagle.25Nov19.28d.jar"
command_args <- paste(" gt=", genotype_file, " out=", outfile, sep = "")
command1 <- paste(command1_prefix, command_args)
test <- system(command1)
# Run BEAGLE using the system function, this will produce a gzip .vcf file
Xvcf=read.vcfR(paste0(Study,"_imp",".vcf.gz"))
Xbgl=data.frame(Xvcf@fix,Xvcf@gt)
genotypes_imp <- VCF_2_numeric(Xbgl)[[1]]
#Pre-imputed genotype matrix
dim(GDmf)
sum(is.na(GDmf))
#Imputed genotype matrix
dim(genotypes_imp)
sum(is.na(genotypes_imp))
#Remove MAF <5%
maf <- calc_maf_apply(genotypes_imp, encoding = c(0, 1, 2))
mono_indices <- which(maf < MAF)
if(length(mono_indices)!=0){
GDre = genotypes_imp[,-mono_indices]
GIre = GImf[-mono_indices,]
}else{
GDre = genotypes_imp
GIre = GImf
}
GIre=GIre[,1:3]
dim(GDre)
dim(GIre)
fwrite(GDre,paste0(Study,"_GD_Filt_Imputed.csv"))
fwrite(GIre[,1:3],paste0(Study,"_GI_Filt_Imputed.csv"))
save(GDre,GIre,GT,file=paste0(Study,"_Filt_Imputed.RData"))
dim(GDre)
dim(GIre)
dim(GT)
}
if(Imputation=="KNN"){
#Section for downloading package
#if (!requireNamespace("BiocManager", quietly = TRUE))
#install.packages("BiocManager")
#BiocManager::install("impute")
x=impute::impute.knn(as.matrix(t(GDmf)))
myGD_imp=t(x$data)
myGD_imp<-round(myGD_imp,0)
sum(is.na(myGD_imp))
#Filter again
#Remove MAF <5%
maf <- calc_maf_apply(myGD_imp, encoding = c(0, 1, 2))
mono_indices <- which(maf < MAF)
if(length(mono_indices)!=0){
GDre = myGD_imp[,-mono_indices]
GIre = GImf[-mono_indices,]
}else{
GDre = myGD_imp
GIre = GImf
}
dim(GDre)
dim(GIre)
GIre=GIre[,1:3]
fwrite(GDre,paste0(Study,"_GD_Filt_Imputed.csv"))
fwrite(GIre[,1:3],paste0(Study,"_GI_Filt_Imputed.csv"))
save(GDre,GIre,GT,file=paste0(Study,"_Filt_Imputed.RData"))
}
}
##########PG################################
if(Method=="Two-Step"){
if(Outcome=="Tested"){
if(!is.null(CV)){
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),]
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre,CV)
#mv(from = "GBS", to = paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait)],GDre,GT,GIre,CV)
GBS<<-PGandCV(Pheno[,Namescole],GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_2_CV_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_CV_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_CV_",Trial[i]))
#save(list=paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_CV_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_CV_",Study,".RData"))
}else{
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),]
#GBS<<-PandG(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre)
#mv(from = "GBS", to = paste0("GBS_2_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PandG(Pheno[,c("Genotype",Trait)],GDre,GT,GIre)
GBS<<-PandG(Pheno[,Namescole],GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_2_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_",Trial[i]))
#save(list=paste0("GBS_2_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_",Study,".RData"))
}
}
if(Outcome=="Untested"){
if(!is.null(CV)){
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),c(1,Trait[j])]
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre,CV)
#mv(from = "GBS", to = paste0("GBS_2_CV_Untested_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait)],GDre,GT,GIre,CV)
GBS<<-PGandCV(Pheno[,Namescole],GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_2_CV_Untested_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_CV_Untested_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_CV_Untested_",Trial[i]))
#save(list=paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_CV_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_CV_Untested_",Study,".RData"))
}else{
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),c(1,Trait[j])]
#GBS<<-PandG(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre)
#mv(from = "GBS", to = paste0("GBS_2_Untested_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PandG(Pheno[,c("Genotype",Trait)],GDre,GT,GIre)
GBS<<-PandG(Pheno[,Namescole],GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_2_Untested_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_Untested_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_Untested_",Trial[i]))
#save(list=paste0("GBS_2_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_Untested_",Study,".RData"))
}
}
}
if(Method=="One-Step"){
if(!is.null(CV)){
Pheno=Phenotype %>% filter(Env %in% c(Trial))
Pheno=Phenotype[,c("Genotype","Env",Trait)]
GBS<<-PGEandCV(Pheno,GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_1_CV_",Study),envir = globalenv())
save(list=paste0("GBS_1_CV_",Study),file=paste0("GBS_1_CV_",Study,".RData"))
}else{
Pheno=Phenotype %>% filter(Env %in% c(Trial))
Pheno=Phenotype[,c("Genotype","Env",Trait)]
GBS<<-PGandE(Pheno,GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_1_",Study),envir = globalenv())
save(list=paste0("GBS_1_",Study),file=paste0("GBS_1_",Study,".RData"))
}
}
if(Method=="One-Step"){
if(Scheme=="K-Fold"){
Matrix<<-GE_Matrix_IND(genotypes=get(paste0("GBS_1_",Study))$geno, phenotype=get(paste0("GBS_1_",Study))$pheno,trait=Trait,Kernel=Kernel,GE=GE,model=GE_model,Sparse=Sparse,m=m,degree=degree, nL=nL)
mv(from = "Matrix", to = paste0("Matrix_",Study,GE_model),envir = globalenv())
save(list=paste0("Matrix_",Study),file=paste0("Matrix_",Study,".RData"))
}else{
Matrix<<-GE_Matrix_IND(train_genotypes=get(paste0("GBS_1_",Study))$geno, train_phenotype=get(paste0("GBS_1_",Study))$pheno,test_genotypes=get(paste0("GBS_1_",Study))$geno, test_phenotype=get(paste0("GBS_1_",Study))$pheno,trait=Trait,Kernel=Kernel,GE=GE,model=GE_model,Sparse=Sparse,m=m,degree=degree, nL=nL)
mv(from = "Matrix", to = paste0("Matrix_",Study,GE_model),envir = globalenv())
save(list=paste0("Matrix_",Study),file=paste0("Matrix_",Study,".RData"))
}
}
#############GS#########################
#Load files
#if(Method=="Two-Step"){
#if(!is.null(CV)){
#load(file=paste0("GBS_2_CV_",Study,".RData"))
#}else{
#load(file=paste0("GBS_2_",Study,".RData"))
#}
#}
#if(Method=="One-Step"){
#if(!is.null(CV)){
#load(file=paste0("GBS_1_CV_",Study,".RData"))
#}else{
#load(file=paste0("GBS_1_",Study,".RData"))
#}
#}
}
if(GS==TRUE){
#####################GS######################################################
if(QC==FALSE){
if(is.null(GBS_Train)){
colnames(Phenotype)[1:2]<-c("Genotype","Env")
Phenotype=Phenotype %>% filter(Env %in% c(Trial))
if(Method=="Two-Step"){
if(Outcome=="Tested"){
if(!is.null(CV)){
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),]
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre,CV)
#mv(from = "GBS", to = paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait)],GDre,GT,GIre,CV)
GBS<<-PGandCV(Pheno[,Namescole],GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_2_CV_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_CV_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_CV_",Trial[i]))
#save(list=paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_CV_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_CV_",Study,".RData"))
}else{
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),]
#GBS<<-PandG(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre)
#mv(from = "GBS", to = paste0("GBS_2_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PandG(Pheno[,c("Genotype",Trait)],GDre,GT,GIre)
GBS<<-PandG(Pheno[,Namescole],GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_2_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_",Trial[i]))
#save(list=paste0("GBS_2_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_",Study,".RData"))
}}
if(Outcome=="Untested"){
if(!is.null(CV)){
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),c(1,Trait[j])]
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre,CV)
#mv(from = "GBS", to = paste0("GBS_2_CV_Untested_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait)],GDre,GT,GIre,CV)
GBS<<-PGandCV(Pheno[,Namescole],GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_2_CV_Untested_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_CV_Untested_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_CV_Untested_",Trial[i]))
#save(list=paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_CV_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_CV_Untested_",Study,".RData"))
}else{
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
#GBS<<-PandG(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre)
#mv(from = "GBS", to = paste0("GBS_2_Untested_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PandG(Pheno[,c("Genotype",Trait)],GDre,GT,GIre)
GBS<<-PandG(Pheno[,Namescole],GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_2_Untested_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_Untested_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_Untested_",Trial[i]))
#save(list=paste0("GBS_2_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_Untested_",Study,".RData"))
}
}
}
if(Method=="One-Step"){
if(!is.null(CV)){
Pheno=Phenotype %>% filter(Env %in% c(Trial))
Pheno=Phenotype[,c("Genotype","Env",Trait)]
GBS<<-PGEandCV(Pheno,GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_1_CV_",Study),envir = globalenv())
save(list=paste0("GBS_1_CV_",Study),file=paste0("GBS_1_CV_",Study,".RData"))
}else{
Pheno=Phenotype %>% filter(Env %in% c(Trial))
Pheno=Phenotype[,c("Genotype","Env",Trait)]
GBS<<-PGandE(Pheno,GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_1_",Study),envir = globalenv())
save(list=paste0("GBS_1_",Study),file=paste0("GBS_1_",Study,".RData"))
}
}
if(Method=="One-Step"){
if(Scheme=="K-Fold"){
Matrix<<-GE_Matrix_IND(genotypes=get(paste0("GBS_1_",Study))$geno, phenotype=get(paste0("GBS_1_",Study))$pheno,trait=Trait,Kernel=Kernel,GE=GE,model=GE_model,Sparse=Sparse,m=m,degree=degree, nL=nL)
mv(from = "Matrix", to = paste0("Matrix_",Study,GE_model),envir = globalenv())
save(list=paste0("Matrix_",Study),file=paste0("Matrix_",Study,".RData"))
}else{
Matrix<<-GE_Matrix_IND(train_genotypes=get(paste0("GBS_1_",Study))$geno, train_phenotype=get(paste0("GBS_1_",Study))$pheno,test_genotypes=get(paste0("GBS_1_",Study))$geno, test_phenotype=get(paste0("GBS_1_",Study))$pheno,trait=Trait,Kernel=Kernel,GE=GE,model=GE_model,Sparse=Sparse,m=m,degree=degree, nL=nL)
mv(from = "Matrix", to = paste0("Matrix_",Study,GE_model),envir = globalenv())
save(list=paste0("Matrix_",Study),file=paste0("Matrix_",Study,".RData"))
}
}
}
}
if(Method=="Two-Step"){
Results_All=list()
for(j in 1:length(Trait)){
if(Outcome=="Tested"){
if(Scheme=="K-Fold"){
if(is.null(GBS_Train)){
#GBS_Train=get(paste0("GBS_2_",Training,"_",Trait[i]))
GBS_Train=get(paste0("GBS_2_",Training))
}
if(Package=="rrBLUP"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=PC,
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
if(Messages==TRUE){
print(paste0("Peforming ",Method," ",Scheme," ",Type," using the package ",Package," with the model ",model," and ",Kernel," on ",Trait[j]," using ",Training,"."))
}
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
if(Package=="MAS"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
PCA=GBS_Train$PC[,1:PC],
CV=CV,
folds = folds,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
PCA=PC,
CV=CV,
folds = folds,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=MAS_CV(phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
PCA=GBS_Train$PC[,1:PC],
CV=GBS_Train$CV[,-1],
folds = folds,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_CV(phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
PCA=PC,
CV=GBS_Train$CV[,-1],
folds = folds,
transformation=transformation
)})
}
}
}
}
if(Package=="BGLR"){
if(Type=="Ordinal"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
}else{
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
}
if(Package=="GAPIT"){
if(!is.null(CV)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_CV(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
myGD=GBS_Train$map,
model=model,
PCA.total=PC,
CV=GBS_Train$CV[,-1],
kinship=kinship,
markers=markers,
folds = folds,
transformation=transformation
)})
}else{
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_CV(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
myGD=GBS_Train$map,
model=model,
PCA.total=PC,
CV=CV,
kinship=kinship,
markers=markers,
folds = folds,
transformation=transformation
)})
}
}
if(Package=="caret"){
Results=sapply(1:Replications, function(i,...){Results=Caret_Models_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
type=type,
model=model,
Kernel=Kernel,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
sampling=sampling,
repeats=repeats,
method=method
)})
}
if(Package=="GLM"){
Results=sapply(1:Replications, function(i,...){Results=GLM_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
fam=fam,
Kernel=Kernel,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
if(Scheme=="VS"){
if(is.null(GBS_Train)){
#GBS_Train=get(paste0("GBS_2_",Training,"_",Trait[j]))
#GBS_Predict=get(paste0("GBS_2_",Prediction,"_",Trait[j]))
GBS_Train=get(paste0("GBS_2_",Training))
GBS_Predict=get(paste0("GBS_2_",Prediction))
}
if(Package=="rrBLUP"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
if(Package=="MAS"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
markers=markers,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
markers=markers,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=MAS_VS(train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_VS(train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
transformation=transformation
)})
}
}
}
}
if(Package=="BGLR"){
if(Type=="Ordinal"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
}else{
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
}
if(Package=="caret"){
Results=sapply(1:Replications, function(i,...){Results=Caret_Models_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
type=type,
model=model,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
sampling=sampling,
repeats=repeats,
method=method
)})
}
if(Package=="GAPIT"){
if(!is.null(CV)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_VS(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$numeric,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_GM=GBS_Predict$map,
test_CV=GBS_Predict$CV[,-1],
model=model,
PCA.total=PC,
kinship=kinship,
markers=markers,
transformation=transformation
)})
}else{
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_VS(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_CV=CV,
test_genotypes= GBS_Predict$numeric,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_GM=GBS_Predict$map,
test_CV=CV,
model=model,
PCA.total=PC,
kinship=kinship,
markers=markers,
transformation=transformation
)})
}
}
if(Package=="GLM"){
Results=sapply(1:Replications, function(i,...){Results=GLM_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
fam=fam,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
if(Outcome=="Untested"){
if(Scheme=="K-Fold"){
if(is.null(GBS_Train)){
GBS_Train=get(paste0("GBS_2_",Training,"_",Trait[j]))
}
#Is there even a point for K-fold for untested?
#We can do this if we include blank values, but not in rrBLUP or DL
if(package=="rrBLUP"){
if(Kernel=="Markers"){
NULL
}else{}
}
}
if(Scheme=="VS"){
if(is.null(GBS_Train)){
#GBS_Train=get(paste0("GBS_2_Untested_",Training,"_",Trait[j]))
#GBS_Predict=get(paste0("GBS_2_Untested_",Prediction,"_",Trait[j]))
GBS_Train=get(paste0("GBS_2_Untested_",Training))
GBS_Predict=get(paste0("GBS_2_Untested_",Prediction))
}
if(Package=="rrBLUP"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
if(Messages==TRUE){
print(paste0("Peforming ",Method," ",Scheme," ",Type," using the package ",Package," with the model ",model," and ",Kernel," on ",Trait[j]," using ",Training,"."))
}
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
if(Package=="MAS"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
markers=markers,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
markers=markers,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=MAS_VS_UT(train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_VS_UT(train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
transformation=transformation
)})
}
}
}
}
if(Package=="BGLR"){
if(Type=="Ordinal"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
}else{
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
}
if(Package=="caret"){
Results=sapply(1:Replications, function(i,...){Results=Caret_Models_CV_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
type=type,
model=model,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
sampling="up",
repeats=5,
method="repeatedcv"
)})
}
if(Package=="GAPIT"){
if(!is.null(CV)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_VS_UT(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$numeric,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_GM=GBS_Predict$map,
test_CV=GBS_Predict$CV[,-1],
model=model,
PCA.total=PC,
kinship=kinship,
markers=markers,
transformation=transformation
)})
}else{
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_VS_UT(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_CV=CV,
test_genotypes= GBS_Predict$numeric,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_GM=GBS_Predict$map,
test_CV=CV,
model=model,
PCA.total=PC,
kinship=kinship,
markers=markers,
transformation=transformation
)})
}
}
if(Package=="GLM"){
Results=sapply(1:Replications, function(i,...){Results=GLM_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
fam=fam,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
if(!is.null(CV)){
CV_Message=TRUE
}else{
CV_Message=NULL
}
if(!is.null(PC)){
PCA_Message=TRUE
}else{
PCA_Message=NULL
}
if(GAGS==TRUE){
GAGS_Message=TRUE
}else{
GAGS_Message=NULL
}
if(Outcome=="Tested"){
Results_Accuracy=Extract_ACC(Results,Replications,Training,model,Kernel,CV_Message,Trait[j])
Results_Predictions=Extract_Pred(Results,Replications,Training,model,Kernel,CV_Message,Trait[j])
Results_Both=list(Accuracy=Results_Accuracy,Predictions=Results_Predictions)
#Results_All[[j]]=Results_Both
Results_All[[Trait[j]]]=Results_Both
#names(Results_All[[j]])<-Trait[j]
}
if(Outcome=="Untested"){
Results_Predictions=Extract_Pred_UT(Results,Replications,Prediction,model,Kernel,CV_Message,Trait[j])
Results_Both=list(Predictions=Results_Predictions)
#Results_All[[j]]=Results_Both
Results_All[[Trait[j]]]=Results_Both
#names(Results_All[[j]])<-Trait[j]
}
}
save(Results_All, file=paste0(Study,"_Results",".RData"))
return(Results_All)
}
if(Method=="One-Step"){
if(is.null(Matrix)){
Matrix=get(paste0("Matrix_",Study))
}
#We can do this with rrBLUP if we use Kernel/kin.blup
if(Outcome=="Tested"){
if(Scheme=="K-Fold"){
if(Package=="rrBLUP"){
}
if(Package=="BGLR"){
if(UN==TRUE){
Results=sapply(1:Replications, function(i,...){MTME(Matrix=Matrix,
trait=trait,
model=GE_model,
nIter = nIter,
burnIn = burnIn,
folds = folds,
UN=UN
)})
}else{
Results=sapply(1:Replications, function(i,...){MTME_UN(Matrix=Matrix,
trait=trait,
model=GE_model,
nIter = nIter,
burnIn = burnIn,
folds = folds
)})
}
}
if(Package=="tensorflow"){
if(UN==TRUE){
Results=sapply(1:Replications, function(i,...){MLP_CV(Matrix=Matrix,
trait=trait,
model=GE_model,
digits=digits,
nCVI=nCVI,
K=folds,
Sparse=Sparse,
folds = folds,
UN=UN
)})
}else{
Results=sapply(1:Replications, function(i,...){MLP_CV_UN(Matrix=Matrix,
trait=trait,
model=GE_model,
digits=digits,
nCVI=nCVI,
K=folds,
Sparse=Sparse,
folds = folds
)})
}
}
}
if(Scheme=="VS"){
}
}
if(Outcome=="Untested"){
if(Scheme=="K-Fold"){
#We can do this if we include blank values, but not in rrBLUP or DL
if(package=="rrBLUP"){
if(Kernel=="Markers"){
}else{
}
}
}
if(Scheme=="VS"){
}
}
}
}
}
lfmerrick21/WhEATBreeders documentation built on Jan. 1, 2023, 7:12 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions WHEAT
WHEAT<-function(Phenotype,
Genotype,
QC=TRUE,
GS=TRUE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
Filter_Ind=TRUE,
Missing_Rate_Ind=0.80,
#If QC Info is FALSE
GDre=NULL,
GT=NULL,
GIre=NULL,
GBS_Train=NULL,
GBS_Predict=NULL,
Matrix=NULL,
#GS Info
Type="Regression",
Replications=1,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Tested", #Tested or Untested
Trial=c("F5_2015","DH_2020","BL_2015_2020"),
Scheme="K-Fold",
Method="Two-Step",
Package="rrBLUP",
model="rrBLUP",
Kernel="Markers",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE){
if(QC==TRUE){
colnames(Phenotype)[1:2]<-c("Genotype","Env")
Phenotype=Phenotype %>% filter(Env %in% c(Trial))
#############PandG#########################
###############################################################################
#Get Taxa
#Phenotype=Phenotype1
Pheno_Names=Phenotype
names(Pheno_Names)[1]<-"Taxa"
Pheno_Names$Taxa=as.character(Pheno_Names$Taxa)
gname=as.vector(Pheno_Names$Taxa)
gname<-clean_names(gname)
#Filter Hapmap for Taxa
if(Geno_Type=="VCF"){
#Genotype1=Genotype
Genotype1=data.frame(Genotype@fix,Genotype@gt)
genotypes_num<- VCF_2_numeric(Genotype1)
GD=genotypes_num$genotypes
rownames(GD)<-clean_names_op(rownames(GD))
names.use <- rownames(GD)[(rownames(GD) %in% gname)]
GD <-GD[row.names(GD) %in% c(names.use),]
#GD <- GD[, names.use, with = FALSE]
GI=genotypes_num$marker_map
#setnames(GI, old = c('rs','chr','pos'),
#new = c('SNP','Chromosome','Position'))
GI <- GI %>%
rename(SNP=rs,
Chromosome=chr,
Position=pos)
GT=data.frame(V1=rownames(GD))
#Save Raw Files
fwrite(GT,paste0(Study,"_GT_taxa.csv"))
fwrite(GD,paste0(Study,"_GD_numeric_Raw.csv"))
fwrite(GI,paste0(Study,"_GI_map_Raw.csv"))
save(GD,GI,GT,file=paste0(Study,"_Raw.RData"))
}
if(Geno_Type=="Hapmap"){
names.use <- names(Genotype)[(names(Genotype) %in% gname)]
hapmap <- Genotype[, names.use, with = FALSE]
hapmap=cbind(Genotype[,1:11],hapmap)
#str(output)
hapmap[hapmap=="NA"]<-"NA"
hapmap$`assembly#`=as.character(hapmap$`assembly#`)
hapmap$center=as.character(hapmap$center)
hapmap$protLSID=as.character(hapmap$protLSID)
hapmap$assayLSID=as.character(hapmap$assayLSID)
hapmap$panelLSID=as.character(hapmap$panelLSID)
hapmap$QCcode=as.character(hapmap$QCcode)
dim(hapmap)
#Save filtered hapmap
save(hapmap,file=paste0(Study,"_Filtered_Hapmap.RData"))
fwrite(hapmap,paste0(Study,"_Filtered_Hapmap.hmp.txt"),sep="\t",row.names = FALSE,col.names = TRUE)
#Convert to Numeric
if(Imputation=="Middle"){
outG=GAPIT.HapMap(hapmap,SNP.impute="Middle")
}else{
outG=GAPIT.HapMap(hapmap,SNP.impute="None")
}
GT=outG$GT
GT=data.frame(V1=rownames(GT),GT)
GD=outG$GD
GI=outG$GI
rownames(GD)=rownames(GT)
colnames(GD)=GI$SNP
#Save Raw Files
fwrite(GT,paste0(Study,"_GT_taxa.csv"))
fwrite(GD,paste0(Study,"_GD_numeric_Raw.csv"))
fwrite(GI,paste0(Study,"_GI_map_Raw.csv"))
save(GD,GI,GT,file=paste0(Study,"_Raw.RData"))
}
#### Filter
##### Remove makers based on missing data
if(Filter==FALSE){
GDmf = GD
GImf = GI
}else{
mr=calc_missrate(GD)
mr_indices <- which(mr > Missing_Rate)
if(length(mr_indices)!=0){
GDmr = GD[,-mr_indices]
GImr = GI[-mr_indices,]
}else{
GDmr = GD
GImr = GI
}
dim(GDmr)
dim(GImr)
##### Remove Monomorphic Markers
maf <- calc_maf_apply(GDmr, encoding = c(0, 1, 2))
mono_indices <- which(maf ==0)
if(length(mono_indices)!=0){
GDmo = GDmr[,-mono_indices]
GImo = GImr[-mono_indices,]
}else{
GDmo = GDmr
GImo = GImr
}
dim(GDmo)
dim(GImo)
##### Remove MAF
maf <- calc_maf_apply(GDmo, encoding = c(0, 1, 2))
mono_indices <- which(maf < MAF)
if(length(mono_indices)!=0){
GDmf = GDmo[,-mono_indices]
GImf = GImo[-mono_indices,]
}else{
GDmf = GDmo
GImf = GImo
}
dim(GDmf)
dim(GImf)
}
if(Filter_Ind==FALSE){
GDmf = GDmf
}else{
mr=calc_missrate(t(GDmf))
mr_indices <- which(mr > Missing_Rate_Ind)
if(length(mr_indices)!=0){
GDmf = GDmf[-mr_indices,]
GT=GT[-mr_indices,]
}else{
GDmf = GDmf
GT=GT
}
dim(GDmf)
dim(GImf)
}
#### Imputation
##### Imputation Using BEAGLE
if(Imputation=="None"){
GDEre = GDmf
GIEre = GImf
dim(GDEre)
dim(GIEre)
fwrite(GDre,paste0(Study,"_GD_Filt_Imputed.csv"))
fwrite(GIre[,1:3],paste0(Study,"_GI_Filt_Imputed.csv"))
save(GDre,GIre,GT,file=paste0(Study,"_Filt_Imputed.RData"))
}else{
if(Imputation=="Beagle"){
GImf$chr <- GImf$Chromosome
GImf$rs <- GImf$SNP
GImf$pos <- GImf$Position
LD_file <- paste0(Study)
vcf_LD <- numeric_2_VCF(GDmf, GImf)
write_vcf(vcf_LD, outfile = LD_file)#Exports vcf
# Assign parameters
genotype_file = paste0(Study,".vcf")
outfile = paste0(Study,"_imp")
# Define a system command
command1_prefix <- "java -jar beagle.25Nov19.28d.jar"
command_args <- paste(" gt=", genotype_file, " out=", outfile, sep = "")
command1 <- paste(command1_prefix, command_args)
test <- system(command1)
# Run BEAGLE using the system function, this will produce a gzip .vcf file
Xvcf=read.vcfR(paste0(Study,"_imp",".vcf.gz"))
Xbgl=data.frame(Xvcf@fix,Xvcf@gt)
genotypes_imp <- VCF_2_numeric(Xbgl)[[1]]
#Pre-imputed genotype matrix
dim(GDmf)
sum(is.na(GDmf))
#Imputed genotype matrix
dim(genotypes_imp)
sum(is.na(genotypes_imp))
#Remove MAF <5%
maf <- calc_maf_apply(genotypes_imp, encoding = c(0, 1, 2))
mono_indices <- which(maf < MAF)
if(length(mono_indices)!=0){
GDre = genotypes_imp[,-mono_indices]
GIre = GImf[-mono_indices,]
}else{
GDre = genotypes_imp
GIre = GImf
}
GIre=GIre[,1:3]
dim(GDre)
dim(GIre)
fwrite(GDre,paste0(Study,"_GD_Filt_Imputed.csv"))
fwrite(GIre[,1:3],paste0(Study,"_GI_Filt_Imputed.csv"))
save(GDre,GIre,GT,file=paste0(Study,"_Filt_Imputed.RData"))
dim(GDre)
dim(GIre)
dim(GT)
}
if(Imputation=="KNN"){
#Section for downloading package
#if (!requireNamespace("BiocManager", quietly = TRUE))
#install.packages("BiocManager")
#BiocManager::install("impute")
x=impute::impute.knn(as.matrix(t(GDmf)))
myGD_imp=t(x$data)
myGD_imp<-round(myGD_imp,0)
sum(is.na(myGD_imp))
#Filter again
#Remove MAF <5%
maf <- calc_maf_apply(myGD_imp, encoding = c(0, 1, 2))
mono_indices <- which(maf < MAF)
if(length(mono_indices)!=0){
GDre = myGD_imp[,-mono_indices]
GIre = GImf[-mono_indices,]
}else{
GDre = myGD_imp
GIre = GImf
}
dim(GDre)
dim(GIre)
GIre=GIre[,1:3]
fwrite(GDre,paste0(Study,"_GD_Filt_Imputed.csv"))
fwrite(GIre[,1:3],paste0(Study,"_GI_Filt_Imputed.csv"))
save(GDre,GIre,GT,file=paste0(Study,"_Filt_Imputed.RData"))
}
}
##########PG################################
if(Method=="Two-Step"){
if(Outcome=="Tested"){
if(!is.null(CV)){
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),]
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre,CV)
#mv(from = "GBS", to = paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait)],GDre,GT,GIre,CV)
GBS<<-PGandCV(Pheno[,Namescole],GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_2_CV_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_CV_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_CV_",Trial[i]))
#save(list=paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_CV_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_CV_",Study,".RData"))
}else{
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),]
#GBS<<-PandG(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre)
#mv(from = "GBS", to = paste0("GBS_2_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PandG(Pheno[,c("Genotype",Trait)],GDre,GT,GIre)
GBS<<-PandG(Pheno[,Namescole],GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_2_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_",Trial[i]))
#save(list=paste0("GBS_2_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_",Study,".RData"))
}
}
if(Outcome=="Untested"){
if(!is.null(CV)){
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),c(1,Trait[j])]
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre,CV)
#mv(from = "GBS", to = paste0("GBS_2_CV_Untested_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait)],GDre,GT,GIre,CV)
GBS<<-PGandCV(Pheno[,Namescole],GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_2_CV_Untested_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_CV_Untested_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_CV_Untested_",Trial[i]))
#save(list=paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_CV_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_CV_Untested_",Study,".RData"))
}else{
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),c(1,Trait[j])]
#GBS<<-PandG(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre)
#mv(from = "GBS", to = paste0("GBS_2_Untested_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PandG(Pheno[,c("Genotype",Trait)],GDre,GT,GIre)
GBS<<-PandG(Pheno[,Namescole],GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_2_Untested_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_Untested_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_Untested_",Trial[i]))
#save(list=paste0("GBS_2_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_Untested_",Study,".RData"))
}
}
}
if(Method=="One-Step"){
if(!is.null(CV)){
Pheno=Phenotype %>% filter(Env %in% c(Trial))
Pheno=Phenotype[,c("Genotype","Env",Trait)]
GBS<<-PGEandCV(Pheno,GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_1_CV_",Study),envir = globalenv())
save(list=paste0("GBS_1_CV_",Study),file=paste0("GBS_1_CV_",Study,".RData"))
}else{
Pheno=Phenotype %>% filter(Env %in% c(Trial))
Pheno=Phenotype[,c("Genotype","Env",Trait)]
GBS<<-PGandE(Pheno,GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_1_",Study),envir = globalenv())
save(list=paste0("GBS_1_",Study),file=paste0("GBS_1_",Study,".RData"))
}
}
if(Method=="One-Step"){
if(Scheme=="K-Fold"){
Matrix<<-GE_Matrix_IND(genotypes=get(paste0("GBS_1_",Study))$geno, phenotype=get(paste0("GBS_1_",Study))$pheno,trait=Trait,Kernel=Kernel,GE=GE,model=GE_model,Sparse=Sparse,m=m,degree=degree, nL=nL)
mv(from = "Matrix", to = paste0("Matrix_",Study,GE_model),envir = globalenv())
save(list=paste0("Matrix_",Study),file=paste0("Matrix_",Study,".RData"))
}else{
Matrix<<-GE_Matrix_IND(train_genotypes=get(paste0("GBS_1_",Study))$geno, train_phenotype=get(paste0("GBS_1_",Study))$pheno,test_genotypes=get(paste0("GBS_1_",Study))$geno, test_phenotype=get(paste0("GBS_1_",Study))$pheno,trait=Trait,Kernel=Kernel,GE=GE,model=GE_model,Sparse=Sparse,m=m,degree=degree, nL=nL)
mv(from = "Matrix", to = paste0("Matrix_",Study,GE_model),envir = globalenv())
save(list=paste0("Matrix_",Study),file=paste0("Matrix_",Study,".RData"))
}
}
#############GS#########################
#Load files
#if(Method=="Two-Step"){
#if(!is.null(CV)){
#load(file=paste0("GBS_2_CV_",Study,".RData"))
#}else{
#load(file=paste0("GBS_2_",Study,".RData"))
#}
#}
#if(Method=="One-Step"){
#if(!is.null(CV)){
#load(file=paste0("GBS_1_CV_",Study,".RData"))
#}else{
#load(file=paste0("GBS_1_",Study,".RData"))
#}
#}
}
if(GS==TRUE){
#####################GS######################################################
if(QC==FALSE){
if(is.null(GBS_Train)){
colnames(Phenotype)[1:2]<-c("Genotype","Env")
Phenotype=Phenotype %>% filter(Env %in% c(Trial))
if(Method=="Two-Step"){
if(Outcome=="Tested"){
if(!is.null(CV)){
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),]
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre,CV)
#mv(from = "GBS", to = paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait)],GDre,GT,GIre,CV)
GBS<<-PGandCV(Pheno[,Namescole],GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_2_CV_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_CV_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_CV_",Trial[i]))
#save(list=paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_CV_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_CV_",Study,".RData"))
}else{
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),]
#GBS<<-PandG(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre)
#mv(from = "GBS", to = paste0("GBS_2_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PandG(Pheno[,c("Genotype",Trait)],GDre,GT,GIre)
GBS<<-PandG(Pheno[,Namescole],GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_2_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_",Trial[i]))
#save(list=paste0("GBS_2_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_",Study,".RData"))
}}
if(Outcome=="Untested"){
if(!is.null(CV)){
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno<-Pheno[complete.cases(Pheno[,Trait[j]]),c(1,Trait[j])]
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre,CV)
#mv(from = "GBS", to = paste0("GBS_2_CV_Untested_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PGandCV(Pheno[,c("Genotype",Trait)],GDre,GT,GIre,CV)
GBS<<-PGandCV(Pheno[,Namescole],GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_2_CV_Untested_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_CV_Untested_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_CV_Untested_",Trial[i]))
#save(list=paste0("GBS_2_CV_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_CV_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_CV_Untested_",Study,".RData"))
}else{
GBS.list=c()
for(i in 1:length(Trial)){
Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
Pheno=data.frame(Pheno)
Namescol=c("Genotype","Env",Trait)
Pheno=Pheno[,Namescol]
Namescole=c("Genotype",Trait)
#for(j in 1:length(Trait)){
#Pheno=Phenotype %>% filter(Env %in% c(Trial[i]))
#GBS<<-PandG(Pheno[,c("Genotype",Trait[j])],GDre,GT,GIre)
#mv(from = "GBS", to = paste0("GBS_2_Untested_",Trial[i],"_",Trait[j]),envir = globalenv())
#GBS<<-PandG(Pheno[,c("Genotype",Trait)],GDre,GT,GIre)
GBS<<-PandG(Pheno[,Namescole],GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_2_Untested_",Trial[i]),envir = globalenv())
#GBS.list=c(GBS.list,paste0("GBS_2_Untested_",Trial[i],"_",Trait[j]))
GBS.list=c(GBS.list,paste0("GBS_2_Untested_",Trial[i]))
#save(list=paste0("GBS_2_",Trial[i],"_",Trait[j]),file=paste0("GBS_2_",Trial[i],"_",Trait[j],".RData"))
#}
}
save(list = GBS.list, file=paste0("GBS_2_Untested_",Study,".RData"))
}
}
}
if(Method=="One-Step"){
if(!is.null(CV)){
Pheno=Phenotype %>% filter(Env %in% c(Trial))
Pheno=Phenotype[,c("Genotype","Env",Trait)]
GBS<<-PGEandCV(Pheno,GDre,GT,GIre,CV)
mv(from = "GBS", to = paste0("GBS_1_CV_",Study),envir = globalenv())
save(list=paste0("GBS_1_CV_",Study),file=paste0("GBS_1_CV_",Study,".RData"))
}else{
Pheno=Phenotype %>% filter(Env %in% c(Trial))
Pheno=Phenotype[,c("Genotype","Env",Trait)]
GBS<<-PGandE(Pheno,GDre,GT,GIre)
mv(from = "GBS", to = paste0("GBS_1_",Study),envir = globalenv())
save(list=paste0("GBS_1_",Study),file=paste0("GBS_1_",Study,".RData"))
}
}
if(Method=="One-Step"){
if(Scheme=="K-Fold"){
Matrix<<-GE_Matrix_IND(genotypes=get(paste0("GBS_1_",Study))$geno, phenotype=get(paste0("GBS_1_",Study))$pheno,trait=Trait,Kernel=Kernel,GE=GE,model=GE_model,Sparse=Sparse,m=m,degree=degree, nL=nL)
mv(from = "Matrix", to = paste0("Matrix_",Study,GE_model),envir = globalenv())
save(list=paste0("Matrix_",Study),file=paste0("Matrix_",Study,".RData"))
}else{
Matrix<<-GE_Matrix_IND(train_genotypes=get(paste0("GBS_1_",Study))$geno, train_phenotype=get(paste0("GBS_1_",Study))$pheno,test_genotypes=get(paste0("GBS_1_",Study))$geno, test_phenotype=get(paste0("GBS_1_",Study))$pheno,trait=Trait,Kernel=Kernel,GE=GE,model=GE_model,Sparse=Sparse,m=m,degree=degree, nL=nL)
mv(from = "Matrix", to = paste0("Matrix_",Study,GE_model),envir = globalenv())
save(list=paste0("Matrix_",Study),file=paste0("Matrix_",Study,".RData"))
}
}
}
}
if(Method=="Two-Step"){
Results_All=list()
for(j in 1:length(Trait)){
if(Outcome=="Tested"){
if(Scheme=="K-Fold"){
if(is.null(GBS_Train)){
#GBS_Train=get(paste0("GBS_2_",Training,"_",Trait[i]))
GBS_Train=get(paste0("GBS_2_",Training))
}
if(Package=="rrBLUP"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=PC,
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
if(Messages==TRUE){
print(paste0("Peforming ",Method," ",Scheme," ",Type," using the package ",Package," with the model ",model," and ",Kernel," on ",Trait[j]," using ",Training,"."))
}
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
if(Package=="MAS"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
PCA=GBS_Train$PC[,1:PC],
CV=CV,
folds = folds,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
PCA=PC,
CV=CV,
folds = folds,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=MAS_CV(phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
PCA=GBS_Train$PC[,1:PC],
CV=GBS_Train$CV[,-1],
folds = folds,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_CV(phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
PCA=PC,
CV=GBS_Train$CV[,-1],
folds = folds,
transformation=transformation
)})
}
}
}
}
if(Package=="BGLR"){
if(Type=="Ordinal"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
}else{
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
Y=GBS_Train$pheno,
GM=GBS_Train$map,
GD=GBS_Train$numeric,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=GBS_Train$CV[,-1],
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=GBS_Train$PC[,1:PC],
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
model=model,
Kernel=Kernel,
PCA=PC,
CV=CV,
markers=markers,
folds = folds,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
}
if(Package=="GAPIT"){
if(!is.null(CV)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_CV(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
myGD=GBS_Train$map,
model=model,
PCA.total=PC,
CV=GBS_Train$CV[,-1],
kinship=kinship,
markers=markers,
folds = folds,
transformation=transformation
)})
}else{
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_CV(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
myGD=GBS_Train$map,
model=model,
PCA.total=PC,
CV=CV,
kinship=kinship,
markers=markers,
folds = folds,
transformation=transformation
)})
}
}
if(Package=="caret"){
Results=sapply(1:Replications, function(i,...){Results=Caret_Models_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
type=type,
model=model,
Kernel=Kernel,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
sampling=sampling,
repeats=repeats,
method=method
)})
}
if(Package=="GLM"){
Results=sapply(1:Replications, function(i,...){Results=GLM_CV(genotypes = GBS_Train$geno,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
fam=fam,
Kernel=Kernel,
markers=markers,
folds = folds,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
if(Scheme=="VS"){
if(is.null(GBS_Train)){
#GBS_Train=get(paste0("GBS_2_",Training,"_",Trait[j]))
#GBS_Predict=get(paste0("GBS_2_",Prediction,"_",Trait[j]))
GBS_Train=get(paste0("GBS_2_",Training))
GBS_Predict=get(paste0("GBS_2_",Prediction))
}
if(Package=="rrBLUP"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
if(Package=="MAS"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
markers=markers,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
markers=markers,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=MAS_VS(train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_VS(train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
transformation=transformation
)})
}
}
}
}
if(Package=="BGLR"){
if(Type=="Ordinal"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
}else{
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
}
if(Package=="caret"){
Results=sapply(1:Replications, function(i,...){Results=Caret_Models_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
type=type,
model=model,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
sampling=sampling,
repeats=repeats,
method=method
)})
}
if(Package=="GAPIT"){
if(!is.null(CV)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_VS(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$numeric,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_GM=GBS_Predict$map,
test_CV=GBS_Predict$CV[,-1],
model=model,
PCA.total=PC,
kinship=kinship,
markers=markers,
transformation=transformation
)})
}else{
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_VS(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_CV=CV,
test_genotypes= GBS_Predict$numeric,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_GM=GBS_Predict$map,
test_CV=CV,
model=model,
PCA.total=PC,
kinship=kinship,
markers=markers,
transformation=transformation
)})
}
}
if(Package=="GLM"){
Results=sapply(1:Replications, function(i,...){Results=GLM_VS(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
fam=fam,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
if(Outcome=="Untested"){
if(Scheme=="K-Fold"){
if(is.null(GBS_Train)){
GBS_Train=get(paste0("GBS_2_",Training,"_",Trait[j]))
}
#Is there even a point for K-fold for untested?
#We can do this if we include blank values, but not in rrBLUP or DL
if(package=="rrBLUP"){
if(Kernel=="Markers"){
NULL
}else{}
}
}
if(Scheme=="VS"){
if(is.null(GBS_Train)){
#GBS_Train=get(paste0("GBS_2_Untested_",Training,"_",Trait[j]))
#GBS_Predict=get(paste0("GBS_2_Untested_",Prediction,"_",Trait[j]))
GBS_Train=get(paste0("GBS_2_Untested_",Training))
GBS_Predict=get(paste0("GBS_2_Untested_",Prediction))
}
if(Package=="rrBLUP"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
if(Messages==TRUE){
print(paste0("Peforming ",Method," ",Scheme," ",Type," using the package ",Package," with the model ",model," and ",Kernel," on ",Trait[j]," using ",Training,"."))
}
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=rrBLUP_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
if(Package=="MAS"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
markers=markers,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
markers=markers,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total,
transformation=transformation
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=MAS_VS_UT(train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=MAS_VS_UT(train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
transformation=transformation
)})
}
}
}
}
if(Package=="BGLR"){
if(Type=="Ordinal"){
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_Ordinal_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
}else{
if(GAGS==TRUE){
if(!is.null(PC)){
#No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=GBS_Train$PC[,1:PC],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}else{
#No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_GAGS_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_GD=GBS_Train$numeric,
train_PCA=PC,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
GWAS=GWAS,
alpha=alpha,
threshold=threshold,
QTN=QTN,
PCA.total=PCA.total
)})
}
}else{
if(!is.null(CV)){
if(!is.null(PC)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=GBS_Predict$CV[,-1],
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}else{
if(!is.null(PC)){
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=GBS_Train$PC[,1:PC],
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=GBS_Predict$PC[,1:PC],
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}else{
#No GAGS, No CV, No PC
Results=sapply(1:Replications, function(i,...){Results=BGLR_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_PCA=PC,
train_CV=CV,
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_PCA=PC,
test_CV=CV,
model=model,
Kernel=Kernel,
markers=markers,
nIter = nIter,
burnIn = burnIn,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation
)})
}
}
}
}
}
if(Package=="caret"){
Results=sapply(1:Replications, function(i,...){Results=Caret_Models_CV_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
type=type,
model=model,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL,
transformation=transformation,
sampling="up",
repeats=5,
method="repeatedcv"
)})
}
if(Package=="GAPIT"){
if(!is.null(CV)){
#No GAGS
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_VS_UT(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_CV=GBS_Train$CV[,-1],
test_genotypes= GBS_Predict$numeric,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_GM=GBS_Predict$map,
test_CV=GBS_Predict$CV[,-1],
model=model,
PCA.total=PC,
kinship=kinship,
markers=markers,
transformation=transformation
)})
}else{
#No GAGS, No CV
Results=sapply(1:Replications, function(i,...){Results=GAPIT_GS_VS_UT(genotypes = GBS_Train$numeric,
phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
train_GM=GBS_Train$map,
train_CV=CV,
test_genotypes= GBS_Predict$numeric,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
test_GM=GBS_Predict$map,
test_CV=CV,
model=model,
PCA.total=PC,
kinship=kinship,
markers=markers,
transformation=transformation
)})
}
}
if(Package=="GLM"){
Results=sapply(1:Replications, function(i,...){Results=GLM_VS_UT(train_genotypes = GBS_Train$geno,
train_phenotype = GBS_Train$pheno[,c("Genotype",Trait[j])],
test_genotypes= GBS_Predict$geno,
test_phenotype= GBS_Predict$pheno[,c("Genotype",Trait[j])],
fam=fam,
Kernel=Kernel,
markers=markers,
Sparse=Sparse,
m=m,
degree=degree,
nL=nL
)})
}
}
}
if(!is.null(CV)){
CV_Message=TRUE
}else{
CV_Message=NULL
}
if(!is.null(PC)){
PCA_Message=TRUE
}else{
PCA_Message=NULL
}
if(GAGS==TRUE){
GAGS_Message=TRUE
}else{
GAGS_Message=NULL
}
if(Outcome=="Tested"){
Results_Accuracy=Extract_ACC(Results,Replications,Training,model,Kernel,CV_Message,Trait[j])
Results_Predictions=Extract_Pred(Results,Replications,Training,model,Kernel,CV_Message,Trait[j])
Results_Both=list(Accuracy=Results_Accuracy,Predictions=Results_Predictions)
#Results_All[[j]]=Results_Both
Results_All[[Trait[j]]]=Results_Both
#names(Results_All[[j]])<-Trait[j]
}
if(Outcome=="Untested"){
Results_Predictions=Extract_Pred_UT(Results,Replications,Prediction,model,Kernel,CV_Message,Trait[j])
Results_Both=list(Predictions=Results_Predictions)
#Results_All[[j]]=Results_Both
Results_All[[Trait[j]]]=Results_Both
#names(Results_All[[j]])<-Trait[j]
}
}
save(Results_All, file=paste0(Study,"_Results",".RData"))
return(Results_All)
}
if(Method=="One-Step"){
if(is.null(Matrix)){
Matrix=get(paste0("Matrix_",Study))
}
#We can do this with rrBLUP if we use Kernel/kin.blup
if(Outcome=="Tested"){
if(Scheme=="K-Fold"){
if(Package=="rrBLUP"){
}
if(Package=="BGLR"){
if(UN==TRUE){
Results=sapply(1:Replications, function(i,...){MTME(Matrix=Matrix,
trait=trait,
model=GE_model,
nIter = nIter,
burnIn = burnIn,
folds = folds,
UN=UN
)})
}else{
Results=sapply(1:Replications, function(i,...){MTME_UN(Matrix=Matrix,
trait=trait,
model=GE_model,
nIter = nIter,
burnIn = burnIn,
folds = folds
)})
}
}
if(Package=="tensorflow"){
if(UN==TRUE){
Results=sapply(1:Replications, function(i,...){MLP_CV(Matrix=Matrix,
trait=trait,
model=GE_model,
digits=digits,
nCVI=nCVI,
K=folds,
Sparse=Sparse,
folds = folds,
UN=UN
)})
}else{
Results=sapply(1:Replications, function(i,...){MLP_CV_UN(Matrix=Matrix,
trait=trait,
model=GE_model,
digits=digits,
nCVI=nCVI,
K=folds,
Sparse=Sparse,
folds = folds
)})
}
}
}
if(Scheme=="VS"){
}
}
if(Outcome=="Untested"){
if(Scheme=="K-Fold"){
#We can do this if we include blank values, but not in rrBLUP or DL
if(package=="rrBLUP"){
if(Kernel=="Markers"){
}else{
}
}
}
if(Scheme=="VS"){
}
}
}
}
}
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