Last updated: 2021-03-24
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Knit directory: PredictOutbredCrossVar/
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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
Modified: output/propHomozygous.rds
Modified: output/top100stdSI.rds
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Goal is to assess the accuracy of predicting untested crosses of outbred parents.
5-folds times 5-replications of parent-wise cross-validation, defined as follows:
library(tidyverse); library(magrittr); library(rsample)
ped<-readRDS(here::here("data","ped_awc.rds"))
set.seed(42)
parentfolds<-vfold_cv(tibble(Parents=union(ped$sireID,ped$damID)),v = 5,repeats = 5) %>%
dplyr::mutate(folds=map(splits,function(splits){
#splits<-parentfolds$splits[[1]]
testparents<-testing(splits)$Parents
trainparents<-training(splits)$Parents
offspring<-ped %>%
filter(sireID %in% testparents | damID %in% testparents) %$%
unique(FullSampleName)
grandkids<-ped %>%
filter(sireID %in% offspring | damID %in% offspring) %$%
unique(FullSampleName)
greatgrandkids<-ped %>%
filter(sireID %in% grandkids | damID %in% grandkids) %$%
unique(FullSampleName)
testset<-unique(c(offspring,grandkids,greatgrandkids)) %>% .[!. %in% c(testparents,trainparents)]
nontestdescendents<-ped %>%
filter(!FullSampleName %in% testset) %$%
unique(FullSampleName)
trainset<-union(testparents,trainparents) %>%
union(.,nontestdescendents)
out<-tibble(testparents=list(testparents),
trainset=list(trainset),
testset=list(testset))
return(out) })) %>%
unnest(folds)
# table(parentfolds$trainset[[1]] %in% parentfolds$testparents[[1]])
# table(parentfolds$testset[[1]] %in% parentfolds$testparents[[1]])
# table(parentfolds$testset[[1]] %in% parentfolds$trainset[[1]])
saveRDS(parentfolds,file = here::here("data","parentwise_crossVal_folds.rds"))# activate multithread OpenBLAS
export OMP_NUM_THREADS=88rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# BLUPs -----------
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) %>% # choosing de-regressed BLUPs as responses despite unweighted analysis
select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order
# Training datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>%
rename(Repeat=id,Fold=id2) %>%
select(Repeat,Fold,trainset,testset) %>%
pivot_longer(c(trainset,testset),
names_to = "Dataset",
values_to = "sampleIDs") %>%
crossing(Model=c("A","AD")) %>%
arrange(desc(Dataset),Repeat,Fold) %>%
mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))
# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>%
remove_invariant(.); dim(snps) # [1] 5591 38093
# fitMtBRR function -------------
## Wrapper function for BGLR::Multitrait()
## For a given set of training blups+snps for either model "A" or "AD"
source(here::here("code","fitMtBRR.R"))
# Parallelization specs ---------
require(furrr); options(mc.cores=10); plan(multiprocess)
options(future.globals.maxSize= 10000*1024^2)# cbsulm19 - Jun 17, 07:09am -
parentfolds %>%
slice(1:10) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))
# cbsulm21 - Jun 17, 07:10pm -
parentfolds %>%
slice(11:20) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))
# cbsulm22 - Jun 17, 07:10pm -
parentfolds %>%
slice(21:30) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))
# cbsulm26 - Jun 17, 07:10pm -
parentfolds %>%
slice(31:40) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))
# cbsulm27 - Jun 17, 07:10pm -
parentfolds %>%
slice(41:50) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))
# cbsulm19 - Jun 17, 11:51am -
parentfolds %>%
slice(51:60) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))
# cbsulm26 - Jun 17, 12:25pm -
parentfolds %>%
slice(61:70) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))
# cbsulm21 - Jun 17, 01:04pm -
parentfolds %>%
slice(71:80) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))
# cbsulm22 - Jun 17, 01:04pm -
parentfolds %>%
slice(81:90) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))
# cbsulm27 - Jun 17, 01:04pm -
parentfolds %>%
slice(91:100) %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))# activate multithread OpenBLAS
export OMP_NUM_THREADS=88rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# BLUPs -----------
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) %>% # choosing de-regressed BLUPs as responses despite unweighted analysis
select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order
# Training datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>%
rename(Repeat=id,Fold=id2) %>%
select(Repeat,Fold,trainset,testset) %>%
pivot_longer(c(trainset,testset),
names_to = "Dataset",
values_to = "sampleIDs") %>%
mutate(Model="DirectionalDom") %>%
arrange(desc(Dataset),Repeat,Fold) %>%
mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))
# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>%
remove_invariant(.); dim(snps) # [1] 5591 38093
# fitDirectionalDomMtBRR function -------------
## Wrapper function for BGLR::Multitrait()
## For a given set of training blups+snps, fit a directional dominance model as in Xiang et al. 2016.
## using "biologically" partitioned additive and dominance effects + a mean effect for overall proportion homozygous (inbreeding).
source(here::here("code","fitDirectionalDomMtBRR.R"))
# Parallelization specs ---------
require(furrr); options(mc.cores=10); plan(multiprocess)
options(future.globals.maxSize= 10000*1024^2)
# Divide parentfolds into chunks for each server ------------
nchunks<-5
parentfolds %<>%
mutate(Chunk=rep(1:nchunks, each=ceiling(nrow(.)/nchunks), length.out=nrow(.))) %>%
nest(data=c(-Chunk))# cbsulm19
chunk<-1;
# cbsulm21
chunk<-2;
# cbsulm22
chunk<-3;
# cbsulm26
chunk<-4;
# cbsulm27
chunk<-5;
# Start run on each server / chunk: Jun 29, 5:20pm
parentfolds$data[[chunk]] %>%
future_pmap(.,fitDirectionalDomMtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5)# activate multithread OpenBLAS
export OMP_NUM_THREADS=88rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
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) %>% # choosing de-regressed BLUPs as responses despite unweighted analysis
select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order
# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>%
remove_invariant(.); dim(snps) # [1] 5591 38093
# Training datasets -----------
geneticgroups<-blups %>%
filter(!grepl("TMS13|TMS14|TMS15",germplasmName)) %>%
mutate(Group="GG") %>%
bind_rows(blups %>%
filter(grepl("TMS13",germplasmName)) %>%
mutate(Group="TMS13")) %>%
bind_rows(blups %>%
filter(grepl("TMS14",germplasmName)) %>%
mutate(Group="TMS14")) %>%
bind_rows(blups %>%
filter(grepl("TMS15",germplasmName)) %>%
mutate(Group="TMS15")) %>%
bind_rows(blups %>%
mutate(Group="All")) %>%
nest(blups=-Group) %>%
crossing(Model=c("A","AD")) %>%
mutate(blups=map(blups,~filter(.,germplasmName %in% rownames(snps))),
outName=paste0("mt_",Group,"_",Model))
# fitMtBRR function -------------
## Wrapper function for BGLR::Multitrait()
## For a given set of training blups+snps for either model "A" or "AD"
source(here::here("code","fitMtBRR.R"))
# Parallelization specs ---------
require(furrr); options(mc.cores=10); plan(multiprocess)
options(future.globals.maxSize= 10000*1024^2)geneticgroups %>%
mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))# activate multithread OpenBLAS
export OMP_NUM_THREADS=88rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
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) %>% # choosing de-regressed BLUPs as responses despite unweighted analysis
select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order
# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>%
remove_invariant(.); dim(snps) # [1] 5591 38093
# Training datasets -----------
geneticgroups<-blups %>%
filter(!grepl("TMS13|TMS14|TMS15",germplasmName)) %>%
mutate(Group="GG") %>%
bind_rows(blups %>%
filter(grepl("TMS13",germplasmName)) %>%
mutate(Group="TMS13")) %>%
bind_rows(blups %>%
filter(grepl("TMS14",germplasmName)) %>%
mutate(Group="TMS14")) %>%
bind_rows(blups %>%
filter(grepl("TMS15",germplasmName)) %>%
mutate(Group="TMS15")) %>%
bind_rows(blups %>%
mutate(Group="All")) %>%
nest(blups=-Group) %>%
mutate(Model="DirectionalDom") %>%
mutate(blups=map(blups,~filter(.,germplasmName %in% rownames(snps))),
outName=paste0("mt_",Group,"_",Model))
# fitDirectionalDomMtBRR function -------------
## Wrapper function for BGLR::Multitrait()
## For a given set of training blups+snps, fit a directional dominance model as in Xiang et al. 2016.
## using "biologically" partitioned additive and dominance effects + a mean effect for overall proportion homozygous (inbreeding).
source(here::here("code","fitDirectionalDomMtBRR.R"))
# Parallelization specs ---------
require(furrr); options(mc.cores=5); plan(multiprocess)
options(future.globals.maxSize= 20000*1024^2)# Start run on cbsulm15: July 29, 11:15pm -
geneticgroups %>%
future_pmap(.,fitDirectionalDomMtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5)# activate multithread OpenBLAS
export OMP_NUM_THREADS=88rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# BLUPs -----------
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) %>% # choosing de-regressed BLUPs as responses despite unweighted analysis
select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order
# Training datasets -----------
## Parent-wise CV folds
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>%
rename(Repeat=id,Fold=id2) %>%
select(Repeat,Fold,trainset,testset) %>%
pivot_longer(c(trainset,testset),
names_to = "Dataset",
values_to = "sampleIDs") %>%
crossing(Model=c("A","AD")) %>%
arrange(desc(Dataset),Repeat,Fold) %>%
mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model)) %>%
select(Repeat,Fold,Dataset,Model,outName)
## Genetic groups
geneticgroups<-blups %>%
filter(!grepl("TMS13|TMS14|TMS15",germplasmName)) %>%
mutate(Group="GG") %>%
bind_rows(blups %>%
filter(grepl("TMS13",germplasmName)) %>%
mutate(Group="TMS13")) %>%
bind_rows(blups %>%
filter(grepl("TMS14",germplasmName)) %>%
mutate(Group="TMS14")) %>%
bind_rows(blups %>%
filter(grepl("TMS15",germplasmName)) %>%
mutate(Group="TMS15")) %>%
bind_rows(blups %>%
mutate(Group="All")) %>%
nest(blups=-Group) %>%
crossing(Model=c("A","AD")) %>%
mutate(blups=map(blups,~filter(.,germplasmName %in% rownames(snps))),
outName=paste0("mt_",Group,"_",Model))
# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>%
remove_invariant(.); dim(snps) # [1] 5591 38093
# Parallelization specs ---------
require(furrr); options(future.globals.maxSize=10000*1024^2)
plan(multiprocess); options(mc.cores=25);
# getGenomicBLUPs function -----------------
## Wrapper function for either Model=="A" or "AD"
## For a given set of multi-trait posterior mean marker effects
## and a given set of SNPs, load the effects and compute GEBV and GETGV
source(here::here("code","getGenomicBLUPs.R"))parentfolds %<>%
mutate(GBLUPs=future_pmap(.,getGenomicBLUPs,snps=snps))
saveRDS(parentfolds,here::here("output","gblups_parentwise_crossVal_folds.rds"))geneticgroups %<>%
mutate(GBLUPs=future_pmap(.,getGenomicBLUPs,snps=snps))
saveRDS(geneticgroups,here::here("output","gblups_geneticgroups.rds"))# activate multithread OpenBLAS
export OMP_NUM_THREADS=112rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# BLUPs -----------
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) %>% # choosing de-regressed BLUPs as responses despite unweighted analysis
select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order
# Training datasets -----------
## Parent-wise CV folds
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>%
rename(Repeat=id,Fold=id2) %>%
select(Repeat,Fold,trainset,testset) %>%
pivot_longer(c(trainset,testset),
names_to = "Dataset",
values_to = "sampleIDs") %>%
mutate(Model="DirectionalDom") %>%
arrange(desc(Dataset),Repeat,Fold) %>%
mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))
# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>%
remove_invariant(.); dim(snps) # [1] 5591 38093
# Parallelization specs ---------
require(furrr); options(future.globals.maxSize=10000*1024^2)
plan(multiprocess); options(mc.cores=25);
# getGenomicBLUPs function -----------------
## Similar to "getGenomicBLUPs.R"
## For a given set of multi-trait posterior mean marker effects
## and a given set of SNPs, load the effects and compute GEBV and GETGV
# 1. inbreeding effect for each trait is extracted from the BGLR output,
### divided by N snps and added to the vector of SNP effects
# 2. Allele substitution effects are computed as a+d(q-p) and used to predict GEBV
# 3. GETGV = sum(X_a*a + X_d*d)
source(here::here("code","getDirectionalDomGenomicBLUPs.R"))parentfolds %<>%
mutate(GBLUPs=future_pmap(.,getDirectionalDomGenomicBLUPs,snps=snps))
saveRDS(parentfolds,here::here("output","gblups_DirectionalDom_parentwise_crossVal_folds.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