Last updated: 2021-03-24
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Knit directory: PredictOutbredCrossVar/
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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
Modified: output/propHomozygous.rds
Modified: output/top100stdSI.rds
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For each of the genetic groups (GG, C1, C2, C3 , ALL):
Compute the posterior mean variances and covariances from the on-disk-stored, post-burnIn, thinned posterior samples of marker effects.
Models: A, AD, DirDom
For the directional dominance (DirDom) marker effects set. Add inbreeding/propHom effect to vector d.
# activate multithread OpenBLAS
export OMP_NUM_THREADS=112rm(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))
# Parallelization specs ---------
require(furrr); options(future.globals.maxSize=50000*1024^2)
plan(multiprocess); options(mc.cores=10);
# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5
# getVarComps function -----------
## Wrapper function for getMultiTraitPMVs_A and getMultiTraitPMVs_AD
## For a given Model / data chunk, load stored posterior marker effects
## Compute vars/covars
source(here::here("code","getVarComps.R"))# cbsulm12 - Done!
geneticgroups %<>%
mutate(PMV=future_pmap(.,getVarComps,snps=snps,nIter=30000, burnIn=5000,thin=5))
saveRDS(geneticgroups,file=here::here("output","pmv_varcomps_geneticgroups.rds"))# activate multithread OpenBLAS
export OMP_NUM_THREADS=112rm(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("DirDomA","DirDomAD")) %>%
mutate(blups=map(blups,~filter(.,germplasmName %in% rownames(snps))),
outName=paste0("mt_",Group,"_DirectionalDom"))
# Parallelization specs ---------
require(furrr); options(future.globals.maxSize=50000*1024^2)
plan(multiprocess); options(mc.cores=10);
# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5
# getDirectionalDomVarComps function -----------
## Wrapper function for getMultiTraitPMVs_A and getMultiTraitPMVs_AD
## For a given Model / data chunk, load stored posterior marker effects
## Compute vars/covars
source(here::here("code","getDirectionalDomVarComps.R"))# cbsulm12 - Done!
geneticgroups %<>%
mutate(PMV=future_pmap(.,getDirectionalDomVarComps,snps=snps,nIter=30000, burnIn=5000,thin=5))
saveRDS(geneticgroups,file=here::here("output","pmv_DirectionalDom_varcomps_geneticgroups.rds"))library(tidyverse); library(magrittr);
geneticgroups<-readRDS(here::here("output","pmv_varcomps_geneticgroups.rds")) %>%
bind_rows(readRDS(here::here("output","pmv_DirectionalDom_varcomps_geneticgroups.rds")))
geneticgroups %<>%
select(-blups) %>%
unnest_wider(PMV) %>%
select(-runtime) %>%
unnest(pmv) %>%
mutate_if(is.numeric,~round(.,6)) %>%
pivot_longer(cols=c(VPM,PMV),names_to = "VarMethod",values_to = "Var")# Selection weights -----------
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))
## Predicted Index Variances
geneticgroups_SI<-geneticgroups %>%
nest(varcovars=c(Trait1,Trait2,Var)) %>%
mutate(varcovars=map(varcovars,
function(varcovars){
# pairwise to square symmetric matrix
gmat<-varcovars %>%
spread(Trait2,Var) %>%
column_to_rownames(var = "Trait1") %>%
as.matrix %>%
.[indices$Trait,indices$Trait]
gmat[lower.tri(gmat)]<-t(gmat)[lower.tri(gmat)]
return(gmat) }),
# compute index variances
stdSI=map_dbl(varcovars,~t(indices$stdSI)%*%.%*%indices$stdSI),
biofortSI=map_dbl(varcovars,~t(indices$biofortSI)%*%.%*%indices$biofortSI)) %>%
# discard var-covar matrix
select(-varcovars) %>%
pivot_longer(cols = c(stdSI,biofortSI),
names_to = "Trait1",
values_to = "Var") %>%
mutate(Trait2=Trait1)
geneticgroups %<>% bind_rows(geneticgroups_SI)
rm(geneticgroups_SI)saveRDS(geneticgroups,file=here::here("output","pmv_varcomps_geneticgroups_tidy_includingSIvars.rds"))library(tidyverse); library(magrittr); library(BGLR)
geneticgroups_dd<-readRDS(here::here("output","pmv_DirectionalDom_varcomps_geneticgroups.rds")) %>%
distinct(Group,outName) %>%
mutate(mtbrrFit=map(outName,~readRDS(here::here("output/mtMarkerEffects",paste0(.,".rds"))))) %>%
unnest_wider(mtbrrFit) %>%
select(-runtime,-snpIDs,-outName) %>%
mutate(Dataset="GeneticGroups")
parentfolds_dd<-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(outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model)) %>%
mutate(mtbrrFit=map(outName,~readRDS(here::here("output/mtMarkerEffects",paste0(.,".rds"))))) %>%
unnest_wider(mtbrrFit) %>%
select(-runtime,-snpIDs,-sampleIDs,-outName,-Model)
ddEffects<-bind_rows(geneticgroups_dd,parentfolds_dd) %>%
mutate(inbreff=map(mtbrrFit,function(mtbrrFit){
traits<-colnames(mtbrrFit$yHat)
beta<-mtbrrFit$ETA$GmeanD$beta
SD.beta<-mtbrrFit$ETA$GmeanD$SD.beta
colnames(beta)<-colnames(SD.beta)<-traits
inbeffs<-bind_rows(as_tibble(beta),as_tibble(SD.beta)) %>%
t(.) %>%
as.data.frame %>%
rownames_to_column(var="Trait") %>%
rename(InbreedingEffect=V1,
InbreedingEffectSD=V2)
return(inbeffs) })) %>%
select(-mtbrrFit) %>%
unnest(inbreff)
saveRDS(ddEffects,file=here::here("output","ddEffects.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