Last updated: 2021-03-24
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Knit directory: PredictOutbredCrossVar/
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Modified: output/crossRealizations/realizedCrossVars.rds
Modified: output/crossRealizations/realizedCrossVars_BLUPs.rds
Modified: output/crossRealizations/realized_cross_means_and_covs_traits.rds
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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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| Rmd | 00e004e | Marnin Wolfe | 2021-03-13 | Compute the predicted, observed and accuracy of usefulness prediction for component traits, in addition to the selection indicies. |
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This step is to create validation data to which predictions of cross means and variances will be correlated.
Compute the empirical means and variances for each validation-family:
VarBV_a
VarBV_dirdom
VarTGV_ad
VarTGV_dirdom
Compute the empirical means and variances for each validation-family, based on the GEBV/GETGV of actual offspring, and using the marker-effects from models fit to the test-set for each fold/rep of parent-wise cross-validation.
rm(list=ls());
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# Testing datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>%
rename(Repeat=id,Fold=id2) %>%
select(Repeat,Fold,testparents)
# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
nest(FamilyMembers=FullSampleName)
# Crosses To Predict -------------
parentfolds %<>%
mutate(CrossesToPredict=map(testparents,~filter(ped,sireID %in% . | damID %in% .))) %>%
select(-testparents)
# Genomic BLUPs from testsets --------------
gblups<-readRDS(here::here("output","gblups_parentwise_crossVal_folds.rds")) %>%
filter(Dataset=="testset") %>% select(-Dataset,-outName)
gblups_dd<-readRDS(here::here("output","gblups_DirectionalDom_parentwise_crossVal_folds.rds")) %>%
filter(Dataset=="testset") %>% select(-Dataset,-sampleIDs) %>%
mutate(Model="DirDom")
gblups %<>%
unnest(GBLUPs) %>%
nest(GBLUPs=-c(Repeat,Fold,Model,predOf)) %>%
left_join(parentfolds)
gblups_dd %<>%
select(Repeat,Fold,Model,GBLUPs) %>%
unnest(GBLUPs) %>%
nest(GBLUPs=-c(Repeat,Fold,Model,predOf)) %>%
left_join(parentfolds)
gblups<-bind_rows(gblups,gblups_dd);
rm(gblups_dd,parentfolds,ped)
# Selection weights -----------
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))
gblups %>% count(Model,predOf)getCrossMeansAndCovs<-function(CrossesToPredict,GBLUPs,...){
CrossesToPredict %<>%
mutate(famgblups=map(FamilyMembers,~rename(.,germplasmName=FullSampleName) %>%
left_join(GBLUPs) %>%
as.data.frame %>%
column_to_rownames(var = "germplasmName")),
means=map(famgblups,~summarise_all(.,mean,na.rm=T)),
covMat=map(famgblups,~cov(.,use='pairwise.complete.obs'))) %>%
select(-FamilyMembers)
return(CrossesToPredict)
}realized_means_and_covs<-gblups %>%
mutate(observedCrossMeansAndCovs=pmap(.,getCrossMeansAndCovs)) %>%
select(-CrossesToPredict,-GBLUPs)
realized_means_and_covs %<>% unnest(observedCrossMeansAndCovs)
saveRDS(realized_means_and_covs,file = here::here("output/crossRealizations","realized_cross_means_and_covs_traits.rds"))
realized_means_and_covs %>% slice(1:3)realized_means_and_covs_si<-realized_means_and_covs %>%
mutate(meanStdSI=map_dbl(means,~as.matrix(.)%*%indices$stdSI),
meanBiofortSI=map_dbl(means,~as.matrix(.)%*%indices$biofortSI),
varStdSI=map_dbl(covMat,~indices$stdSI%*%as.matrix(.)%*%indices$stdSI),
varBiofortSI=map_dbl(covMat,~indices$biofortSI%*%as.matrix(.)%*%indices$biofortSI)) %>%
select(-famgblups,-means,-covMat)
saveRDS(realized_means_and_covs_si,file = here::here("output/crossRealizations","realized_cross_means_and_vars_selindices.rds"))
realized_means_and_covs_si %>% slice(1:3)Tidy / format the realized cross means and variances.
Combine traits and sel. indices. Split into two separate tibbles, one for means, one for variances.
Output will be ready to combine with predictions to measure accuracy.
library(tidyverse); library(magrittr);
realized_means_and_covs_traits<-readRDS(here::here("output/crossRealizations","realized_cross_means_and_covs_traits.rds"))
realized_means_and_covs_si<-readRDS(here::here("output/crossRealizations","realized_cross_means_and_vars_selindices.rds"))# Means ---------------
## Traits
realized_means_traits<-realized_means_and_covs_traits %>%
select(Repeat,Fold,Model,predOf,sireID,damID,means) %>%
unnest(means) %>%
pivot_longer(cols = c(-Repeat,-Fold,-Model,-predOf,-sireID,-damID),names_to = "Trait", values_to = "obsMean") %>%
mutate(obsOf=ifelse(predOf=="BV","MeanBV","MeanTGV")) %>%
select(-predOf)
## SIs
realized_means_si<-realized_means_and_covs_si %>%
select(Repeat,Fold,Model,predOf,sireID,damID,contains("mean")) %>%
rename(stdSI=meanStdSI,
biofortSI=meanBiofortSI) %>%
pivot_longer(cols = c(-Repeat,-Fold,-Model,-predOf,-sireID,-damID),names_to = "Trait", values_to = "obsMean") %>%
mutate(obsOf=ifelse(predOf=="BV","MeanBV","MeanTGV")) %>%
select(-predOf)
## Combine
realized_means<-bind_rows(realized_means_si,realized_means_traits)
rm(realized_means_traits,realized_means_si)
realized_means %>% count(Model,obsOf) %>% spread(obsOf,n)# (Co)variances -------------
## Traits
realized_vars_traits<-realized_means_and_covs_traits %>%
select(Repeat,Fold,Model,predOf,sireID,damID,covMat) %>%
mutate(covMat=map(covMat,function(covMat){
covMat[lower.tri(covMat, diag = F)]<-NA
covMat %<>%
as.data.frame %>%
rownames_to_column(var = "Trait1") %>%
pivot_longer(cols = -Trait1, names_to = "Trait2", values_to = "obsVar",values_drop_na = TRUE)
return(covMat)})) %>%
unnest(covMat) %>%
mutate(obsOf=ifelse(predOf=="BV","VarBV","VarTGV")) %>%
select(-predOf)
## SIs
realized_vars_si<-realized_means_and_covs_si %>%
select(Repeat,Fold,Model,predOf,sireID,damID,contains("var")) %>%
rename(stdSI=varStdSI,
biofortSI=varBiofortSI) %>%
pivot_longer(cols = c(-Repeat,-Fold,-Model,-predOf,-sireID,-damID),names_to = "Trait1", values_to = "obsVar") %>%
mutate(obsOf=ifelse(predOf=="BV","VarBV","VarTGV"),
Trait2=Trait1) %>%
select(-predOf)
## Combine
realized_vars<-bind_rows(realized_vars_si,realized_vars_traits)
# rename(obsVar=VarSCA))
rm(realized_vars_si,realized_vars_traits)
realized_vars %>% count(Model,obsOf) %>% spread(obsOf,n)saveRDS(realized_means,here::here("output/crossRealizations","realizedCrossMeans.rds"))
saveRDS(realized_vars,here::here("output/crossRealizations","realizedCrossVars.rds"))rm(list=ls());
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# Testing datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>%
rename(Repeat=id,Fold=id2) %>%
select(Repeat,Fold,testparents)
# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
nest(FamilyMembers=FullSampleName)
# Crosses To Predict -------------
parentfolds %<>%
mutate(CrossesToPredict=map(testparents,~filter(ped,sireID %in% . | damID %in% .))) %>%
select(-testparents)
# Selection weights -----------
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))
# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>%
select(Trait,blups) %>%
unnest(blups) %>%
select(Trait,germplasmName,BLUP) %>%
spread(Trait,BLUP)
crossblups<-parentfolds %>%
unnest(CrossesToPredict) %>%
distinct(sireID,damID,FamilyMembers) %>%
unnest(FamilyMembers) %>%
rename(germplasmName=FullSampleName) %>%
left_join(blups) %>%
select(sireID,damID,germplasmName,all_of(indices$Trait)) %>%
nest(famblups=c(germplasmName,all_of(indices$Trait))) %>%
mutate(stdSI=map(famblups,~as.data.frame(.) %>%
column_to_rownames(var = "germplasmName") %>%
as.matrix(.)%*%indices$stdSI),
biofortSI=map(famblups,~as.data.frame(.) %>%
column_to_rownames(var = "germplasmName") %>%
as.matrix(.)%*%indices$biofortSI))
# Cross Means
crossmeans<-crossblups %>%
mutate(means=map(famblups,~summarize_if(.,is.numeric,mean,na.rm=T))) %>%
unnest(means) %>%
mutate(stdSI=map_dbl(stdSI,mean,na.rm=T),
biofortSI=map_dbl(biofortSI,mean,na.rm=T)) %>%
select(-famblups) %>%
pivot_longer(cols = c(-sireID,-damID), names_to = "Trait", values_to = "obsMean", values_drop_na = TRUE)
# Cross (Co)Vars
crossvars<-crossblups %>%
mutate(varcovars=map(famblups,~cov(as.data.frame(.) %>%
column_to_rownames(var = "germplasmName") %>%
as.matrix,
use = 'pairwise.complete.obs')),
varcovars=map(varcovars,function(varcovars){
varcovars[lower.tri(varcovars, diag = F)]<-NA
varcovars %<>%
as.data.frame %>%
rownames_to_column(var = "Trait1") %>%
pivot_longer(cols = -Trait1, names_to = "Trait2", values_to = "obsVar",values_drop_na = TRUE)
return(varcovars)})) %>%
select(-famblups,-stdSI,-biofortSI) %>%
unnest(varcovars)
crossvars<-bind_rows(crossvars,crossblups %>%
mutate(stdSI=map_dbl(stdSI,var,na.rm=T),
biofortSI=map_dbl(biofortSI,var,na.rm=T)) %>%
select(-famblups) %>%
pivot_longer(cols = c(-sireID,-damID), names_to = "Trait1", values_to = "obsVar", values_drop_na = TRUE) %>%
mutate(Trait2=Trait1))saveRDS(crossmeans,here::here("output/crossRealizations","realizedCrossMeans_BLUPs.rds"))
saveRDS(crossvars,here::here("output/crossRealizations","realizedCrossVars_BLUPs.rds"))For each family, compute the proportion of offspring subsequently used as parents.
Compute the realized “Usefulness” of the cross in terms of population improvement / parent-development as the mean GEBV of family members that are themselves parents.
[NEW]: compute realized UCs for component traits in addition to the selection indicies.
library(tidyverse); library(magrittr); library(predCrossVar)
indices<-readRDS(here::here("data","selection_index_weights_4traits.rds"))
realized_means_and_covs<-readRDS(here::here("output/crossRealizations",
"realized_cross_means_and_covs_traits.rds"))
ped<-readRDS(here::here("data","ped_awc.rds"))
parents<-union(ped$sireID,ped$damID)
realized_cross_gebvs<-realized_means_and_covs %>%
filter(predOf=="BV") %>%
mutate(stdSI=map(famgblups,~as.matrix(.)%*%indices$stdSI),
biofortSI=map(famgblups,~as.matrix(.)%*%indices$biofortSI),
GEBV=map2(stdSI,biofortSI,~tibble(germplasmName=rownames(.x),stdSI=.x,biofortSI=.y)),
famgblups=map(famgblups,~rownames_to_column(.,var = "germplasmName")),
GEBV=map2(GEBV,famgblups,~left_join(.x,.y))) %>%
select(Repeat,Fold,Model,predOf,sireID,damID,GEBV) %>%
unnest(GEBV) %>%
pivot_longer(cols = c(stdSI,biofortSI,DM,logFYLD,MCMDS,TCHART),
values_to = "GEBV", names_to = "Trait") %>%
mutate(IsParent=ifelse(germplasmName %in% parents,TRUE,FALSE)) %>%
nest(Family=c(germplasmName,IsParent,GEBV))
realizedparentmetrics<-realized_cross_gebvs %>%
mutate(FamSize=map_dbl(Family,nrow),
NmembersUsedAsParent=map_dbl(Family,~length(which(.$IsParent==TRUE))),
propUsedAsParent=map_dbl(Family,~length(which(.$IsParent==TRUE))/nrow(.)),
realizedUCparent=map_dbl(Family,~mean(.$GEBV[.$IsParent==TRUE],na.rm = T)),
# also compute the mean of top 1% of the family
# correspond ~std. intensity = 2.67
# provides alternative validation data for usefulness criteria
# invariant to actual selection intensity
meanTop1pctGEBV=map_dbl(Family,~slice_max(.data = .,
order_by = GEBV,
n=ceiling(0.01*nrow(.))) %$% mean(GEBV))) %>%
select(-Family) %>%
ungroup()
rm(realized_cross_gebvs)For each family, compute the proportion of offspring that have been advanced on the variety development pipeline (VDP) to different stages.
Compute the stage-specific “Usefulness” of the cross in terms of variety development as the mean GETGV of family members advanced to each stage.
# Read for web the raw plot-data used as of summer 2019 for IITA GS
trials<-readRDS(url("https://raw.github.com/wolfemd/IITA_2019GS/master/data/IITA_ExptDesignsDetected_72619.rds"))
realizedVDPmetrics<-ped %>%
rename(GID=FullSampleName) %>%
left_join(trials %>%
filter(Trait %in% indices$Trait) %>%
unnest_legacy(TrialData) %>%
select(germplasmName,observationUnitDbId,TrialType) %>%
distinct %>%
count(germplasmName,TrialType,name = "Nplots") %>%
filter(TrialType %in% c("GeneticGain","CET","ExpCET","PYT","AYT","UYT")) %>%
rename(GID=germplasmName) %>%
spread(TrialType,Nplots) %>%
select(c("GID","GeneticGain","CET","ExpCET","PYT","AYT","UYT"))) %>%
mutate(IsPhenotyped=rowSums(.[,c("GeneticGain","CET","ExpCET","PYT","AYT","UYT")],na.rm = T)>0,
advancedPastCET=rowSums(.[,c("GeneticGain","PYT","AYT","UYT")],na.rm = T)>0,
advancedPastPYT=rowSums(.[,c("GeneticGain","AYT","UYT")],na.rm = T)>0,
advancedPastAYT=rowSums(.[,c("GeneticGain","UYT")],na.rm = T)>0) %>%
select(-c("GeneticGain","CET","ExpCET","PYT","AYT","UYT")) %>%
rename(germplasmName=GID)realized_cross_getgvs<-realized_means_and_covs %>%
filter(predOf=="TGV") %>%
mutate(stdSI=map(famgblups,~as.matrix(.)%*%indices$stdSI),
biofortSI=map(famgblups,~as.matrix(.)%*%indices$biofortSI),
GETGV=map2(stdSI,biofortSI,~tibble(germplasmName=rownames(.x),stdSI=.x,biofortSI=.y)),
famgblups=map(famgblups,~rownames_to_column(.,var = "germplasmName")),
GETGV=map2(GETGV,famgblups,~left_join(.x,.y))) %>%
select(Repeat,Fold,Model,predOf,sireID,damID,GETGV) %>%
unnest(GETGV) %>%
pivot_longer(cols = c(stdSI,biofortSI,DM,logFYLD,MCMDS,TCHART),
values_to = "GETGV", names_to = "Trait") %>%
left_join(realizedVDPmetrics)
realizedVDPmetrics<-realized_cross_getgvs %>%
nest(data=c(germplasmName,GETGV,IsPhenotyped,contains("advancedPast"))) %>%
mutate(FamSize=map_dbl(data,nrow),
NmembersPhenotyped=map_dbl(data,~length(which(.$IsPhenotyped==TRUE))),
NmembersPastCET=map_dbl(data,~length(which(.$advancedPastCET==TRUE))),
NmembersPastPYT=map_dbl(data,~length(which(.$advancedPastPYT==TRUE))),
NmembersPastAYT=map_dbl(data,~length(which(.$advancedPastAYT==TRUE))),
propPhenotyped=map_dbl(data,~length(which(.$IsPhenotyped==TRUE))/nrow(.)),
propPastCET=map_dbl(data,~length(which(.$advancedPastCET==TRUE))/nrow(.)),
propPastPYT=map_dbl(data,~length(which(.$advancedPastPYT==TRUE))/nrow(.)),
propPastAYT=map_dbl(data,~length(which(.$advancedPastAYT==TRUE))/nrow(.)),
realizedUCatCET=map_dbl(data,~mean(.$GETGV[.$IsPhenotyped==TRUE],na.rm = T)),
realizedUCatPYT=map_dbl(data,~mean(.$GETGV[.$advancedPastCET==TRUE],na.rm = T)),
realizedUCatAYT=map_dbl(data,~mean(.$GETGV[.$advancedPastPYT==TRUE],na.rm = T)),
realizedUCatUYT=map_dbl(data,~mean(.$GETGV[.$advancedPastAYT==TRUE],na.rm = T)),
# also compute the mean of top 1% of the family
# correspond ~std. intensity = 2.67
# provides alternative validation data for usefulness criteria
# invariant to actual selection intensity
meanTop1pctGETGV=map_dbl(data, ~slice_max(.data = .,
order_by = GETGV,
n=ceiling(0.01*nrow(.))) %$% mean(GETGV))) %>%
select(-data) %>%
ungroup()
rm(realized_cross_getgvs,trials)Combine parent + variety metrics
realizedcrossmetrics<-left_join(realizedparentmetrics %>%
select(-predOf) %>%
mutate(Model=ifelse(Model=="A","ClassicAD",Model)),
realizedVDPmetrics %>%
select(-predOf) %>%
mutate(Model=ifelse(Model=="AD","ClassicAD",Model))) %>%
ungroup()To compare realized and predicted “usefulness criteria”, we need to compute the realized, standardized selection intensity.
Compute the standrardized selection intensity from the proportion selected. This will give NaN for propSel==0. Progeny as crosses with NaN UC were never themselves used as parents. Their “usefulness” has not be “observed” / “evaluated”.
realizedcrossmetrics %<>%
mutate(realIntensityParent=map_dbl(propUsedAsParent,intensity),
realIntensityCET=map_dbl(propPhenotyped,intensity),
realIntensityPYT=map_dbl(propPastCET,intensity),
realIntensityAYT=map_dbl(propPastPYT,intensity),
realIntensityUYT=map_dbl(propPastAYT,intensity))saveRDS(realizedcrossmetrics,file=here::here("output/crossRealizations","realizedCrossMetrics.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