Last updated: 2021-03-24

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Unstaged changes:
    Modified:   analysis/NGCleadersCall.Rmd
    Modified:   code/fitDirectionalDomMtBRR.R
    Modified:   code/fitmtBRR.R
    Modified:   code/getDirectionalDomGenomicBLUPs.R
    Modified:   code/getDirectionalDomMtCrossMeanPreds.R
    Modified:   code/getDirectionalDomMtCrossVarBVpreds.R
    Modified:   code/getDirectionalDomMtCrossVarTGVpreds.R
    Modified:   code/getDirectionalDomVarComps.R
    Modified:   code/getGenomicBLUPs.R
    Modified:   code/getMtCrossMeanPreds.R
    Modified:   code/getMtCrossVarPreds.R
    Modified:   code/getUntestedMtCrossVarPreds.R
    Modified:   code/getVarComps.R
    Modified:   data/blups_forawcdata.rds
    Modified:   data/genmap_awc_May2020.rds
    Modified:   data/parentwise_crossVal_folds.rds
    Modified:   data/ped_awc.rds
    Modified:   data/selection_index_weights_4traits.rds
    Modified:   output/CrossesToPredict_top100stdSI_and_209originalParents.rds
    Modified:   output/accuraciesMeans.rds
    Modified:   output/accuraciesUC.rds
    Modified:   output/accuraciesVars.rds
    Modified:   output/crossRealizations/realizedCrossMeans.rds
    Modified:   output/crossRealizations/realizedCrossMeans_BLUPs.rds
    Modified:   output/crossRealizations/realizedCrossMetrics.rds
    Modified:   output/crossRealizations/realizedCrossVars.rds
    Modified:   output/crossRealizations/realizedCrossVars_BLUPs.rds
    Modified:   output/crossRealizations/realized_cross_means_and_covs_traits.rds
    Modified:   output/crossRealizations/realized_cross_means_and_vars_selindices.rds
    Modified:   output/ddEffects.rds
    Modified:   output/gebvs_ModelA_GroupAll_stdSI.rds
    Modified:   output/obsVSpredMeans.rds
    Modified:   output/obsVSpredUC.rds
    Modified:   output/obsVSpredVars.rds
    Modified:   output/pmv_DirectionalDom_varcomps_geneticgroups.rds
    Modified:   output/pmv_varcomps_geneticgroups.rds
    Modified:   output/pmv_varcomps_geneticgroups_tidy_includingSIvars.rds
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# devtools::install_github("wolfemd/predCrossVar", ref = 'master', force=T)

Crosses to predict

The 462 crosses actually made
The 100 clones with top rank on the StdSI
The 209 clones already used as parents

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)
parents<-union(ped$sireID,ped$damID)

# Selection weights -----------
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))

# GEBVs --------------
gebvs<-readRDS(here::here("output","gblups_geneticgroups.rds")) %>% 
  filter(Group=="All",Model=="A") %>% 
  unnest(GBLUPs) %>% 
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART")))
gebvMat<-gebvs %>% 
  column_to_rownames(var = "germplasmName") %>% 
  as.matrix
## GEBVs on the "standard" selection index
gebvs %<>% 
  mutate(stdSI=as.numeric(gebvMat%*%indices$stdSI))
saveRDS(gebvs,here::here("output","gebvs_ModelA_GroupAll_stdSI.rds"))
## The top 100 on the index
top100stdSI<-gebvs %>% 
  arrange(desc(stdSI)) %>% 
  slice(1:100) %$% germplasmName
saveRDS(top100stdSI,here::here("output","top100stdSI.rds"))

# table(top100stdSI %in% parents) # only 3
# length(grep("TMS13|TMS14|TMS15",top100stdSI, invert = T)) # 2
# length(grep("TMS13",top100stdSI)) # 52
# length(grep("TMS14",top100stdSI)) # 31
# length(grep("TMS15",top100stdSI)) # 15

## Highest BV -------------
# gebvs %>% 
#   slice_max(order_by = stdSI, n=1) # TMS13F1095P0013
## Lowest BV
# gebvs %>% 
#   filter(germplasmName %in% union(parents,top100stdSI)) %>% 
#   slice_min(order_by = stdSI, n=1) # IITA-TMS-IBA011371

# Crosses To Predict -------------
CrossesToPredict<-crosses2predict(union(parents,top100stdSI)) %>%  # makes df of pairwise non-recprical, selfs-included crosses
  bind_rows(ped %>% # add the crosses already made (for convenience)
              distinct(sireID,damID)) %>% 
  distinct # avoid duplication
# nrow(CrossesToPredict) # [1] 47083
saveRDS(CrossesToPredict,here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))

Preducted untested cross variances

Model A and AD

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88;

library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# Crosses To Predict -------------
CrossesToPredict<-readRDS(here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)
parents<-union(ped$sireID,ped$damID)
top100stdSI<-readRDS(here::here("output","top100stdSI.rds"))

# Recomb frequency matrix ------------
recombFreqMat<-readRDS(here::here("data","recombFreqMat_1minus2c_awcmap_May2020.rds"))

# Haplotype Matrix ------------
haploMat<-readRDS(file=here::here("data","haps_awc.rds"))
parenthaps<-sort(c(paste0(union(parents,top100stdSI),"_HapA"),
                   paste0(union(parents,top100stdSI),"_HapB")))
haploMat<-haploMat[parenthaps,colnames(recombFreqMat)]; dim(haploMat)

# Parallelization specs ---------
require(furrr); 
options(future.globals.maxSize=50000*1024^2)
ncores<-10; 

# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5

# Path for output ----------
outpath<-"output/crossPredictions"

# getUntestedMtCrossVarPreds function -------------
## Function to run for each rep-fold-Model (==unique set of marker effects), predict the relevant cross variances.
source(here::here("code","getUntestedMtCrossVarPreds.R"))

# Divide CrossesToPredict into chunks for each server ------------
nchunks<-5
CrossesToPredict %<>% 
  mutate(Chunk=rep(1:nchunks, each=ceiling(nrow(.)/nchunks), length.out=nrow(.))) %>% 
  nest(data=c(sireID,damID))

Run Var(TGV) predictions (Model AD)

# cbsulm13 - done
chunk<-1;
# cbsulm15 - done
chunk<-2;
# cbsulm20 - done
chunk<-3;
# cbsulm22 - done
chunk<-4;
# cbsulm23 - done
chunk<-5;

# Start run on each server / chunk:  Aug 03 at 6:50AM
getUntestedMtCrossVarPreds(inprefix = "mt_All_AD",
                           outpath = "output/crossPredictions",
                           outprefix = paste0("predUntestedCrossTGVs_chunk",chunk,"_AD"),
                           predType="VPM", Model = "AD", nIter=30000, burnIn=5000,thin=5,
                           CrossesToPredict=CrossesToPredict$data[[chunk]],
                           recombFreqMat=recombFreqMat,haploMat=haploMat,ncores=ncores)

Run Var(BV) predictions (Model A)

# cbsulm13 - aug 4, 10pm - done
chunk<-1;
# cbsulm15 - aug 4, 10pm - done
chunk<-2;
# cbsulm20 - aug 4, 6:55am - done
chunk<-3;
# cbsulm22 - aug 4, 10pm - done 
chunk<-4;
# cbsulm23 - aug 4, 6:55am - done
chunk<-5;

# Start run on each server / chunk: 
getUntestedMtCrossVarPreds(inprefix = "mt_All_A",
                           outpath = "output/crossPredictions",
                           outprefix = paste0("predUntestedCrossBVs_chunk",chunk,"_A"),
                           predType="VPM", Model = "A", nIter=30000, burnIn=5000,thin=5,
                           CrossesToPredict=CrossesToPredict$data[[chunk]],
                           recombFreqMat=recombFreqMat,haploMat=haploMat,ncores=ncores)

Model DirDom

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=112;

library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# Crosses To Predict -------------
CrossesToPredict<-readRDS(here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)
parents<-union(ped$sireID,ped$damID)
top100stdSI<-readRDS(here::here("output","top100stdSI.rds"))

# Recomb frequency matrix ------------
recombFreqMat<-readRDS(here::here("data","recombFreqMat_1minus2c_awcmap_May2020.rds"))

# Haplotype Matrix ------------
haploMat<-readRDS(file=here::here("data","haps_awc.rds"))
parenthaps<-sort(c(paste0(union(parents,top100stdSI),"_HapA"),
                   paste0(union(parents,top100stdSI),"_HapB")))
haploMat<-haploMat[parenthaps,colnames(recombFreqMat)]; dim(haploMat)

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); 

# Parallelization specs ---------
require(furrr); 
options(future.globals.maxSize=50000*1024^2)
ncores<-10; 

# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5

# Path for output ----------
outpath<-"output/crossPredictions"

# getUntestedMtCrossVarPreds function -------------
## Function to run for each rep-fold-Model (==unique set of marker effects), predict the relevant cross variances.
source(here::here("code","getUntestedMtCrossVarPreds.R"))

# Divide CrossesToPredict into chunks for each server ------------
nchunks<-4
CrossesToPredict %<>% 
  mutate(Chunk=rep(1:nchunks, each=ceiling(nrow(.)/nchunks), length.out=nrow(.))) %>% 
  nest(data=c(sireID,damID))

Run Var(TGV) predictions

# cbsulm13 - Done!
chunk<-1;
# cbsulm17 - Done!
chunk<-2;
# cbsulm12 - Done!
chunk<-3;
# cbsulm16 - Done!
chunk<-4;

# Start run on each server / chunk: 
getDirDomUntestedMtCrossVarTGVpreds(inprefix = "mt_All_DirectionalDom",
                                    outpath = "output/crossPredictions",
                                    outprefix = paste0("predUntestedCrossTGVs_chunk",chunk,"_DirDom"),
                                    predType="VPM", nIter=30000, burnIn=5000,thin=5,
                                    CrossesToPredict=CrossesToPredict$data[[chunk]],
                                    recombFreqMat=recombFreqMat,haploMat=haploMat,ncores=ncores)

Run Var(BV) predictions

# cbsulm26 - Done! (~32hrs)
chunk<-1;
# cbsulm15 - Done!
chunk<-2;
# cbsulm26 - Done!
chunk<-3;
# cbsulm17 - Done!
chunk<-4;

# Start run on each server / chunk: 
getDirDomUntestedMtCrossVarBVpreds(inprefix = "mt_All_DirectionalDom",
                                   outpath = "output/crossPredictions",
                                   outprefix = paste0("predUntestedCrossBVs_chunk",chunk,"_DirDom"),
                                   predType="VPM", nIter=30000, burnIn=5000,thin=5,
                                   CrossesToPredict=CrossesToPredict$data[[chunk]],
                                   recombFreqMat=recombFreqMat,haploMat=haploMat,doseMat=snps,ncores=ncores)

Re-predict self-crosses

Discovered a bug effecting self-crosses. The original version of predCrossVar run (circa July 2020) incorrectly calculated gametic LD matrices with duplicated haplotypes for cases where sireID==damID. Fixed the bug in the package, archived the original version via Git/GitHub. Re-install predCrossVar on server and re-do predictions of selfs.

Model A and AD

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88;

library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# Crosses To Predict -------------
CrossesToPredict<-readRDS(here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))
# just the selfs
CrossesToPredict %<>% filter(sireID==damID)
# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)
parents<-union(ped$sireID,ped$damID)
top100stdSI<-readRDS(here::here("output","top100stdSI.rds"))

# Recomb frequency matrix ------------
recombFreqMat<-readRDS(here::here("data","recombFreqMat_1minus2c_awcmap_May2020.rds"))

# Haplotype Matrix ------------
haploMat<-readRDS(file=here::here("data","haps_awc.rds"))
parenthaps<-sort(c(paste0(union(parents,top100stdSI),"_HapA"),
                   paste0(union(parents,top100stdSI),"_HapB")))
haploMat<-haploMat[parenthaps,colnames(recombFreqMat)]; dim(haploMat)

# Parallelization specs ---------
require(furrr); 
options(future.globals.maxSize=50000*1024^2)
ncores<-10; 

# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5

# Path for output ----------
outpath<-"output/crossPredictions"

# getUntestedMtCrossVarPreds function -------------
## Function to run for each rep-fold-Model (==unique set of marker effects), predict the relevant cross variances.
source(here::here("code","getUntestedMtCrossVarPreds.R"))

Run Var(TGV) predictions (Model AD)

# Start run on each server / chunk:  
getUntestedMtCrossVarPreds(inprefix = "mt_All_AD",
                           outpath = "output/crossPredictions",
                           outprefix = paste0("predUntestedCrossTGVs_ReDoSelfs_AD"),
                           predType="VPM", Model = "AD", 
                           nIter=30000, burnIn=5000,thin=5,
                           CrossesToPredict=CrossesToPredict,
                           recombFreqMat=recombFreqMat,
                           haploMat=haploMat,ncores=ncores)

Run Var(BV) predictions (Model A)

# Start run on each server / chunk: 
getUntestedMtCrossVarPreds(inprefix = "mt_All_A",
                           outpath = "output/crossPredictions",
                           outprefix = paste0("predUntestedCrossBVs_ReDoSelfs_A"),
                           predType="VPM", Model = "A", 
                           nIter=30000, burnIn=5000,thin=5,
                           CrossesToPredict=CrossesToPredict,
                           recombFreqMat=recombFreqMat,
                           haploMat=haploMat,ncores=ncores)

Predict untested cross means

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=112;

library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# Crosses To Predict -------------
CrossesToPredict<-readRDS(here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)
parents<-union(ped$sireID,ped$damID)
top100stdSI<-readRDS(here::here("output","top100stdSI.rds"))

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); 

# Parallelization specs ---------
require(furrr); 
options(future.globals.maxSize=50000*1024^2)
ncores<-10; 

# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5

# Path for output ----------
outpath<-"output/crossPredictions"

# getMtCrossMeanPreds function -------------
source(here::here("code","getMtCrossMeanPreds.R"))
# getDirectionalDomMtCrossMeanPreds function -------------
source(here::here("code","getDirectionalDomMtCrossMeanPreds.R"))

# sampleIDs -------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>% 
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order
sampleIDs<-blups$germplasmName[blups$germplasmName %in% rownames(snps)]
rm(blups)

Run mean predictions (Model A and AD)

# Done Aug 8
predictedUntestedCrossMeansBV<-getMtCrossMeanPreds(outprefix="mt_All_A",
                                                   Model="A",
                                                   CrossesToPredict=CrossesToPredict,
                                                   doseMat=snps,
                                                   sampleIDs = sampleIDs)
predictedUntestedCrossMeansTGV<-getMtCrossMeanPreds(outprefix="mt_All_AD",
                                                    Model="AD",
                                                    CrossesToPredict=CrossesToPredict,
                                                    doseMat=snps,
                                                    sampleIDs = sampleIDs)

saveRDS(predictedUntestedCrossMeansBV,file=here::here("output/crossPredictions","predictedUntestedCrossMeansBV.rds"))
saveRDS(predictedUntestedCrossMeansTGV,file=here::here("output/crossPredictions","predictedUntestedCrossMeansTGV.rds"))

Run mean predictions (Model DirDom)

predictedUntestedCrossMeansDirDom<-getDirectionalDomMtCrossMeanPreds(outprefix="mt_All_DirectionalDom",
                                                                     CrossesToPredict=CrossesToPredict,
                                                                     doseMat=snps,
                                                                     sampleIDs = sampleIDs)

saveRDS(predictedUntestedCrossMeansDirDom,
        file=here::here("output/crossPredictions","predictedUntestedCrossMeansDirDom.rds"))

Process prediction results

Tidy predicted variances

Next step, used a server, unpacking output for 10 varcomps x ~47K crosses.

Keep the dom. variance in the “tidied” output long enough to calculate dom. variance on the sel. indices. This was previously not done.

Calc. VarTGV=VarA + VarD only for the sup. tables (Tables S17 and S18), which are used for plots and summaries in the manuscript.

library(tidyverse); library(magrittr); library(predCrossVar) 
# Model A
predUntestedCrossVarBVs<-list.files(here::here("output/crossPredictions")) %>% 
  grep("predUntestedCrossBVs",.,value = T) %>% 
  tibble(File=.) %>% 
  mutate(Model=ifelse(grepl("_A_",File),"A","DirDomBV"),
         crossPredictions=map(File,~readRDS(here::here("output/crossPredictions",.)))) %>% 
  unnest_wider(crossPredictions) %>% 
  unnest(varcovars) %>% 
  select(-totcomputetime) %>% 
  unnest_wider(varcomps) # 10 variances/covariances x 5 chunks x 1 models = 50 blocks of ~10K crosses chunk per varcomp

require(furrr); options(mc.cores=50); plan(multiprocess)
predUntestedCrossVarBVs %<>% # in parallel across the 50 chunks
  mutate(predictedfamvars=future_map(predictedfamvars,~unnest(.,predVars) %>% select(-PMV))) %>% 
  select(-totcomputetime) %>% 
  unnest(predictedfamvars) %>% 
  rename(predVar=VPM) %>% 
  mutate(predOf="VarBV")
predUntestedCrossVarBVs %<>% select(-VarComp,-totcomputetime)

library(tidyverse); library(magrittr); library(predCrossVar) 

# Model AD
predUntestedCrossVarTGVs<-list.files(here::here("output/crossPredictions")) %>% 
  grep("predUntestedCrossTGVs",.,value = T) %>% 
  tibble(File=.) %>% 
  mutate(Model=ifelse(grepl("_AD_",File),"AD","DirDomAD"),
         crossPredictions=map(File,~readRDS(here::here("output/crossPredictions",.)))) %>% 
  unnest_wider(crossPredictions) %>% 
  unnest(varcovars) %>% 
  select(-totcomputetime) %>% 
  unnest_wider(varcomps) # 10 variances/covariances x 5 chunks x 2 models = 100 blocks of ~10K crosses chunk per varcomp
predUntestedCrossVarTGVs$predictedfamvars[[1]] %>% 
  mutate(Nsegsnps=map_dbl(predVars,~.$Nsegsnps[1]))
require(furrr); options(mc.cores=50); plan(multiprocess)
predUntestedCrossVarTGVs %<>% # in parallel across the 100 chunks
  mutate(predictedfamvars=future_map(predictedfamvars,function(predictedfamvars){
    return(predictedfamvars %<>% 
             # format each families output in serial 
             mutate(Nsegsnps=map_dbl(predVars,~.$Nsegsnps[1]),
                    predVars=map(predVars,function(predVars){ 
                      return(predVars %>%
                               select(VarComp,VPM)) })) %>% 
             unnest(predVars))})) %>% 
  select(-totcomputetime) %>% 
  unnest(predictedfamvars) %>% 
  rename(predVar=VPM) %>% 
  rename(predOf=VarComp)

Remove the original predictions for selfs and replace with the correct predictions.

# verify nrow before removing "re-do's"
predUntestedCrossVarBVs %>% dim() # [1] 947780      9
# df of re-predicted selfs
## should only be from the ClassicAD model
redoBVs<-predUntestedCrossVarBVs %>% 
  filter(grepl("_ReDoSelfs_",File))
dim(redoBVs) # [1] 6120    9
redoBVs %>% count(Model,predOf)
#   Model predOf     n
#   <chr> <chr>  <int>
# 1 A     VarBV   3060

# df of original predictions (selfs and outcrosses)
# predUntestedCrossVarBVs %>% 
#   filter(!grepl("_ReDoSelfs_",File)) %>% # dim() # [1] 941660      9 # verify redo's removed
#   filter(sireID==damID) %>% # dim() # [1] 6120    9 original self's predictions remain at this point
predUntestedCrossVarBVs %<>% 
  # remove all selfs from the ClassicAD model
  filter(sireID!=damID | (sireID==damID & Model=="DirDomBV")) %>% # dim() # [1] 935540      9
  # add back the corrected predictions for selfs
  bind_rows(.,redoBVs)

redoTGVs<-predUntestedCrossVarTGVs %>% 
  filter(grepl("_ReDoSelfs_",File))
dim(redoTGVs) # [1] 12240   11
predUntestedCrossVarTGVs %<>% 
  # remove all selfs
  filter(sireID!=damID | (sireID==damID & Model=="DirDomAD")) %>% # dim() # [1] 1871080     11
  # add back the corrected predictions for selfs
  bind_rows(.,redoTGVs)
# dim(predUntestedCrossVarTGVs) # [1] 1883320       9

predUntestedCrossVars<-bind_rows(predUntestedCrossVarBVs,
                                 predUntestedCrossVarTGVs)
  # left_join(predUntestedCrossVarBVs %>% 
  #                                  rename(predVarBV=predVarA) %>% 
  #                                  select(-totcomputetime,-File),
  #                                predUntestedCrossVarTGVs %>% 
  #                                  rename(predVarTGV=predVarTot) %>% 
  #                                  select(-predVarA,-predVarD,-totcomputetime,-File)) %>% 
  # pivot_longer(cols=c(predVarBV,predVarTGV),
  #              names_to = "predOf",values_to = "predVar")
# predUntestedCrossVars<-bind_rows(predUntestedCrossVarBVs,
#                                  predUntestedCrossVarTGVs)
#predUntestedCrossVars %<>% select(Model,Trait1,Trait2,sireID,damID,predOf,predVar,Nsegsnps,totcomputetime)

saveRDS(predUntestedCrossVars,here::here("output/crossPredictions","predictedUntestedCrossVars_tidy_traits.rds"))

Tidy predicted means

library(tidyverse); library(magrittr); library(predCrossVar)
predictedUntestedCrossMeansBV<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossMeansBV.rds")) %>%
  pluck("predictedCrossMeans")
predictedUntestedCrossMeansTGV<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossMeansTGV.rds")) %>%
  pluck("predictedCrossMeans")
predictedUntestedCrossMeansDirDom<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossMeansDirDom.rds")) %>%
  pluck("predictedCrossMeans")
predictedUntestedCrossMeans<-predictedUntestedCrossMeansBV %>% 
  left_join(predictedUntestedCrossMeansTGV) %>% 
  mutate(Model="ClassicAD") %>% 
  bind_rows(predictedUntestedCrossMeansDirDom %>% 
              mutate(Model="DirDom"))
saveRDS(predictedUntestedCrossMeans,here::here("output/crossPredictions","predictedUntestedCrossMeans_tidy_traits.rds"))

Compute predictions on SI

Our focus in evaluating predictions of untested crosses will be on predictions on the two SI.

predVarBV - Model A (ClassicAD)
predVarTGV - Model AD (ClassicAD)
predVarBV - DirDomA (DirDom)
predVarTGV - DirDomAD (DirDom)

times

stdSI
biofortSI

for predicted means and variances.

Variances

library(tidyverse); library(magrittr);
# Selection weights -----------
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))

predictedUntestedCrossVars<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossVars_tidy_traits.rds"))  
#  select(-predVarD,-totcomputetime)

## Predicted Index Variances
predictedUntestedCrossVars_SI<-predictedUntestedCrossVars %>% 
  # filter(Model=="A") %>% 
  # mutate(Model="ClassicAD") %>% 
  # rename(predVarBV=predVarA) %>% 
  # select(-predVarTot) %>% 
  # left_join(predictedUntestedCrossVars %>% 
  #             filter(Model=="ClassicAD") %>% 
  #             rename(predVarTGV=predVarTot) %>% 
  #             select(-predVarA)) %>% 
  # bind_rows(predictedUntestedCrossVars %>% 
  #             filter(Model=="DirDomAD") %>% 
  #             rename(predVarBV=predVarA,
  #                    predVarTGV=predVarTot)) %>% 
  # pivot_longer(cols=c(predVarBV,predVarTGV),names_to = "predOf",values_to = "predVar") %>% 
  nest(varcovars=c(Trait1,Trait2,predVar)) 
require(furrr); options(mc.cores=50); plan(multiprocess)
predictedUntestedCrossVars_SI %<>% 
  mutate(varcovars=future_map(varcovars,
                       function(varcovars){
                         # pairwise to square symmetric matrix
                         gmat<-varcovars %>% 
                           spread(Trait2,predVar) %>% 
                           column_to_rownames(var = "Trait1") %>% 
                           as.matrix %>% 
                           .[indices$Trait,indices$Trait]
                         gmat[lower.tri(gmat)]<-t(gmat)[lower.tri(gmat)]
                         return(gmat) }))

predictedUntestedCrossVars_SI %<>% 
  mutate(stdSI=future_map_dbl(varcovars,~t(indices$stdSI)%*%.%*%indices$stdSI),
         biofortSI=future_map_dbl(varcovars,~t(indices$biofortSI)%*%.%*%indices$biofortSI)) %>% 
  select(-varcovars)
predictedUntestedCrossVars_SI %<>% 
  pivot_longer(cols = c(stdSI,biofortSI),
               names_to = "Trait1", 
               values_to = "predVar") %>% 
  mutate(Trait2=Trait1)
#   pivot_wider(names_from = "predOf", values_from = "predVar")
# predictedUntestedCrossVars_SI %<>% 
#   pivot_longer(cols = c(predVarBV,predVarTGV), names_to = "predOf", values_to = "predVar")

predictedUntestedCrossVars_SI %<>% 
  select(Trait1,Trait2,sireID,damID,Nsegsnps,Model,predOf,predVar)
saveRDS(predictedUntestedCrossVars_SI,here::here("output/crossPredictions","predictedUntestedCrossVars_SelIndices.rds"))

Means

# Selection weights -----------
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))
predictedUntestedCrossMeans<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossMeans_tidy_traits.rds"))
predictedUntestedCrossMeans %<>% 
  pivot_longer(cols = c(sireGEBV,damGEBV,predMeanBV,predMeanGV), names_to = "predOf", values_to = "predMean") 

## Predicted Index Means
predictedUntestedCrossMeans %<>% 
  spread(Trait,predMean) %>% 
  nest(predMeans=all_of(indices$Trait)) %>% 
  mutate(stdSI=map_dbl(predMeans,~as.matrix(.)%*%indices$stdSI),
         biofortSI=map_dbl(predMeans,~as.matrix(.)%*%indices$biofortSI)) %>% 
  select(-predMeans) %>% 
  pivot_longer(cols = c(stdSI,biofortSI), names_to = "Trait", values_to = "predMean")

predictedUntestedCrossMeans %<>% 
  pivot_wider(names_from = "predOf", values_from = "predMean") %>% 
  select(sireID,damID,Model,Trait,sireGEBV,damGEBV,predMeanBV,predMeanGV)

saveRDS(predictedUntestedCrossMeans,here::here("output/crossPredictions","predictedUntestedCrossMeans_SelIndices.rds"))

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6        whisker_0.4       knitr_1.31        magrittr_2.0.1   
 [5] R6_2.5.0          rlang_0.4.10      fansi_0.4.2       stringr_1.4.0    
 [9] tools_4.0.3       xfun_0.22         utf8_1.2.1        git2r_0.28.0     
[13] jquerylib_0.1.3   htmltools_0.5.1.1 ellipsis_0.3.1    rprojroot_2.0.2  
[17] yaml_2.2.1        digest_0.6.27     tibble_3.1.0      lifecycle_1.0.0  
[21] crayon_1.4.1      later_1.1.0.1     sass_0.3.1        vctrs_0.3.6      
[25] promises_1.2.0.1  fs_1.5.0          glue_1.4.2        evaluate_0.14    
[29] rmarkdown_2.7     stringi_1.5.3     bslib_0.2.4       compiler_4.0.3   
[33] pillar_1.5.1      jsonlite_1.7.2    httpuv_1.5.5      pkgconfig_2.0.3

Predict Untested Cross Means and Variances

2020-July-30

Crosses to predict

Preducted untested cross variances

Model A and AD

Set-up

Run Var(TGV) predictions (Model AD)

Run Var(BV) predictions (Model A)

Model DirDom

Set-up

Run Var(TGV) predictions

Run Var(BV) predictions

Re-predict self-crosses

Model A and AD

Set-up

Run Var(TGV) predictions (Model AD)

Run Var(BV) predictions (Model A)

Predict untested cross means

Set-up

Run mean predictions (Model A and AD)

Run mean predictions (Model DirDom)

Process prediction results

Tidy predicted variances

Tidy predicted means

Compute predictions on SI

Variances

Means