Last updated: 2019-11-21
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Knit directory: IITA_2019GS/
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Two-stage genomic prediction refers to the following procedure:
Stage 1: Fit a linear mixed model to the data without genomic data. Individuals (e.g. clones / accessions) are modeled as independent and identically distributed (i.i.d.) random effects. The BLUPs for this random effect represent the measurable total genetic values of each individual. All the experimental design variation, e.g. replication and blocking effects have been controlled for in the creation of our new response variable, the BLUPs from the gneotype random effect.
Stage 2: Using a modified version of the BLUPs from step 1 as the response variable, fit a genomic prediction model, which now has reduced size because the number of observations is now the same as the number of individuals.
NOTE: In the animal breeding literature single-step often refers to predictions that combine pedigree and marker information simultaneously. That is not our meaning here.
The code below represents Stage I.
Read in the trial data and group it by trait
rm(list=ls()); gc()
library(tidyverse);library(magrittr)
trials<-readRDS("data/IITA_ExptDesignsDetected_72619.rds")
phenos<-trials %>%
unnest(TrialData) %>%
select(programName,locationName,studyYear,TrialType,studyName,
CompleteBlocks,IncompleteBlocks,
yearInLoc,trialInLocYr,repInTrial,blockInRep,observationUnitDbId,
germplasmName,FullSampleName,
Trait,Value,MaxNOHAV,NOHAV,PropHAV,
TCHARTcovar,CMDcovar) %>%
mutate(GID=ifelse(!is.na(FullSampleName),FullSampleName,germplasmName),
IncompleteBlocks=ifelse(IncompleteBlocks==TRUE,"Yes","No"),
CompleteBlocks=ifelse(CompleteBlocks==TRUE,"Yes","No")) %>%
group_by(Trait) %>%
nest(.key = "TrainingData")
rm(trials); gc()For certain traits, made alternative versions after discussion with IYR.
Curates yield traits based on PropHAV, and CMD severity.
Splits DM according to TCHART, with yellow when >2, else white.
# Set yield traits to missing if <5% or 75% plants harvested (PropHAV)
phenos %<>%
bind_rows(
phenos %>%
filter(Trait %in% c("logRTNO","logFYLD","logTOPYLD")) %>%
mutate(TrainingData=map(TrainingData,~filter(.,!is.na(PropHAV) & PropHAV>=0.5)),
Trait=paste0(Trait,"_propHAVpt5"))
) %>%
bind_rows(
phenos %>%
filter(Trait %in% c("logRTNO","logFYLD","logTOPYLD")) %>%
mutate(TrainingData=map(TrainingData,~filter(.,!is.na(PropHAV) & PropHAV>=0.75)),
Trait=paste0(Trait,"_propHAVpt75"))
) %>%
bind_rows(
phenos %>% # Or set missing when NOHAV<5
filter(Trait %in% c("logRTNO","logFYLD","logTOPYLD")) %>%
mutate(TrainingData=map(TrainingData,~filter(.,!is.na(NOHAV) & NOHAV>5)),
Trait=paste0(Trait,"_nohav5"))
)
phenos %<>%
bind_rows(
phenos %>% # Or set missing if CMD severity was>2
filter(Trait %in% c("logRTNO","logFYLD","logTOPYLD")) %>%
mutate(TrainingData=map(TrainingData,~filter(.,!is.na(CMDcovar) & CMDcovar<=2)),
Trait=paste0(Trait,"_lowCMD"))
) %>%
bind_rows(
phenos %>% # white roots only
filter(Trait=="DM") %>%
mutate(TrainingData=map(TrainingData,~filter(.,!is.na(TCHARTcovar) & TCHARTcovar<=2)),
Trait=paste0(Trait,"_white"))
) %>%
bind_rows(
phenos %>% # yellow roots only
filter(Trait=="DM") %>%
mutate(TrainingData=map(TrainingData,~filter(.,!is.na(TCHARTcovar) & TCHARTcovar>2)),
Trait=paste0(Trait,"_yellow"))
)In addition, we wanted to do at list a basic check on the cost/benefit of continuing to use data from earlier than 2012. So for every trait possible, I
phenos %<>%
mutate(TrainingData=map(TrainingData,~filter(.,as.numeric(studyYear)>2012)),
Dataset="2013toPresent") %>%
bind_rows(phenos %>%
mutate(Dataset="HistoricalDataIncluded") %>%
filter(!Trait %in% c("BRNHT1","PLTHT"))) # BRNHT1 and PLTHT lacked "historical" observations
# phenos %>%
# mutate(Nobs=map_dbl(TrainingData,~nrow(.))) %>%
# select(Trait,Nobs,Dataset) %>%
# spread(Dataset,Nobs) %>%
# mutate(HowManyHistoricalObs=HistoricalDataIncluded-`2013toPresent`) %>% # %$% summary(HowManyHistoricalObs)
# arrange(HowManyHistoricalObs)
# Basically, only BRNHT1 and PLTHT lacked "historical" observationsIID models, get BLUPs from asreml
Set-up the models to be fit for each data chunk
library(furrr) # for parallel processing using purrr functions
options(mc.cores=12)
plan(multiprocess)
library(asreml) # cbsurobbins license as of July 2019
phenos %<>%
mutate(asFixedFormula="Value ~ yearInLoc",
asFixedFormula=ifelse(grepl("logRTNO",Trait) | grepl("logFYLD",Trait) | grepl("logTOPYLD",Trait),
paste0(asFixedFormula," + PropHAV"),asFixedFormula),
asRandFormula=paste0("~idv(GID) + idv(trialInLocYr) + at(CompleteBlocks,'Yes'):repInTrial ",
"+ at(IncompleteBlocks,'Yes'):blockInRep"))Fit the models in parallel, keeping only key information to save space
Save for future analysis.
asModelsFit<-phenos %>%
mutate(fitAS=future_pmap(.,function(asFixedFormula,asRandFormula,TrainingData,...){
# debugging
# -------------
# asFixedFormula<-phenos$asFixedFormula[[1]]
# asRandFormula<-phenos$asRandFormula[[1]]
# TrainingData<-phenos$TrainingData[[1]]
# -------------
out<-asreml(as.formula(asFixedFormula),
random = as.formula(asRandFormula),
data = TrainingData, maxiter = 40,workspace=400e6)
ll<-summary(out,all=T)$loglik
varcomp<-summary(out,all=T)$varcomp
Vg<-varcomp["GID!GID.var","component"]
Ve<-varcomp["R!variance","component"]
H2=Vg/(Vg+Ve)
blups<-summary(out,all=T)$coef.random %>%
as.data.frame %>%
rownames_to_column(var = "GID") %>%
select(GID,solution,`std error`) %>%
filter(grepl("GID",GID)) %>%
rename(BLUP=solution) %>%
mutate(GID=gsub("GID_","",GID),
PEV=`std error`^2, # asreml specific
REL=1-(PEV/Vg), # Reliability
drgBLUP=BLUP/REL, # deregressed BLUP
WT=(1-H2)/((0.1 + (1-REL)/REL)*H2)) # weight for use in Stage 2
out<-tibble(loglik=ll,Vg,Ve,H2,
blups=list(blups),varcomp=list(varcomp))
return(out) }))
asModelsFit %<>%
select(-TrainingData,-asFixedFormula,-asRandFormula) %>%
unnest(fitAS)
saveRDS(asModelsFit,file="data/iita_blupsForCrossVal_72619.rds")Redo set-up of training data chunks. Based on preliminary results, discontinued anlaysis for the yield trait variants.
Curation in this case is just outlier removal based on residuals, followed by refitting of the Stage I mixed-model to get new BLUPs.
rm(list=ls()); gc()
library(tidyverse);library(magrittr)
trials<-readRDS("data/IITA_ExptDesignsDetected_72619.rds")
phenos<-trials %>%
unnest(TrialData) %>%
dplyr::select(programName,locationName,studyYear,TrialType,studyName,
CompleteBlocks,IncompleteBlocks,
yearInLoc,trialInLocYr,repInTrial,blockInRep,observationUnitDbId,
germplasmName,FullSampleName,
Trait,Value,MaxNOHAV,NOHAV,PropHAV,
TCHARTcovar,CMDcovar) %>%
mutate(GID=ifelse(!is.na(FullSampleName),FullSampleName,germplasmName),
IncompleteBlocks=ifelse(IncompleteBlocks==TRUE,"Yes","No"),
CompleteBlocks=ifelse(CompleteBlocks==TRUE,"Yes","No")) %>%
group_by(Trait) %>%
nest(.key = "TrainingData")
rm(trials); gc()
phenos %<>%
bind_rows(
phenos %>%
filter(Trait=="DM") %>%
mutate(TrainingData=map(TrainingData,~filter(.,!is.na(TCHARTcovar) & TCHARTcovar<=2)),
Trait=paste0(Trait,"_white"))
) %>%
bind_rows(
phenos %>%
filter(Trait=="DM") %>%
mutate(TrainingData=map(TrainingData,~filter(.,!is.na(TCHARTcovar) & TCHARTcovar>2)),
Trait=paste0(Trait,"_yellow"))
)
phenos %<>%
mutate(asFixedFormula="Value ~ yearInLoc",
asFixedFormula=ifelse(grepl("logRTNO",Trait) | grepl("logFYLD",Trait) | grepl("logTOPYLD",Trait),
paste0(asFixedFormula," + PropHAV"),asFixedFormula),
asRandFormula=paste0("~idv(GID) + idv(trialInLocYr) + at(CompleteBlocks,'Yes'):repInTrial ",
"+ at(IncompleteBlocks,'Yes'):blockInRep"))Refit mixed-models and this time recover studentized residuals and flag outliers as observations with |studentized-residuals|>3.3
fitASmodelsWithOutlierDetect<-function(asFixedFormula,asRandFormula,TrainingData,...){
# debug function
# ---------------------------
# asFixedFormula<-phenos$asFixedFormula[[1]]
# asRandFormula<-phenos$asRandFormula[[1]]
# TrainingData<-phenos$TrainingData[[1]]
#rm(asFixedFormula,asRandFormula,TrainingData); gc()
# ---------------------------
out<-asreml(as.formula(asFixedFormula),
random = as.formula(asRandFormula),
data = TrainingData,
maxiter = 40,workspace=400e6,aom=T)
stdRes <- out$aom$R[,"stdCondRes"]
nedf <- out$nedf
studRes <- stdRes / sqrt( (nedf - stdRes^2)/(nedf - 1) )
outliers<-which(abs(studRes)>3.3)
ll<-summary(out,all=T)$loglik
varcomp<-summary(out,all=T)$varcomp
vg<-varcomp["GID!GID.var","component"]
ve<-varcomp["R!variance","component"]
H2tmp<-vg/(vg+ve)
blups<-summary(out,all=T)$coef.random %>%
as.data.frame %>%
rownames_to_column(var = "GID") %>%
dplyr::select(GID,solution,`std error`) %>%
filter(grepl("GID",GID)) %>%
rename(BLUP=solution) %>%
mutate(GID=gsub("GID_","",GID),
PEV=`std error`^2,
REL=1-(PEV/vg),
drgBLUP=BLUP/REL,
WT=(1-H2tmp)/((0.1 + (1-REL)/REL)*H2tmp))
out<-list(loglik=ll,Vg=vg,Ve=ve,H2=H2tmp,
blups=list(blups),
varcomp=list(varcomp),
Outliers=list(outliers))
return(out) }Fit models with asreml
library(furrr)
options(mc.cores=12); plan(multiprocess)
library(asreml)
asModelsFit<-phenos %>%
mutate(fitAS=future_pmap(.,fitASmodelsWithOutlierDetect))Count and remove outliers
asModelsFit %<>%
mutate(NoutR1=map_dbl(fitAS,~length(.$Outliers[[1]])))
asModelsFit %<>%
mutate(OutliersR1=map(fitAS,~.$Outliers[[1]]))
asModelsFit %<>%
mutate(TrainingData=map2(TrainingData,fitAS,function(TrainingData,fitAS){
outliers2remove<-fitAS$Outliers[[1]]
out<-TrainingData[-outliers2remove,]
return(out) }))Refit models with asreml after removing outliers
Repeat for a second cycle of outlier removal
asModelsFit %<>%
mutate(NoutR2=map_dbl(fitAS,~length(.$Outliers[[1]])),
OutliersR2=map(fitAS,~.$Outliers[[1]]),
TrainingData=map2(TrainingData,fitAS,function(TrainingData,fitAS){
outliers2remove<-fitAS$Outliers[[1]]
out<-TrainingData[-outliers2remove,]
return(out) }),
fitAS=future_pmap(.,fitASmodelsWithOutlierDetect))Format and save blups for cross-validation, fit after 2 rounds of outlier removal
Stage II: Cross-validation Run 1
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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
loaded via a namespace (and not attached):
[1] workflowr_1.5.0.9000 Rcpp_1.0.3 rprojroot_1.3-2
[4] digest_0.6.22 later_1.0.0 R6_2.4.1
[7] backports_1.1.5 git2r_0.26.1 magrittr_1.5
[10] evaluate_0.14 stringi_1.4.3 rlang_0.4.1
[13] fs_1.3.1 promises_1.1.0 whisker_0.4
[16] rmarkdown_1.17 tools_3.6.1 stringr_1.4.0
[19] glue_1.3.1 httpuv_1.5.2 xfun_0.11
[22] yaml_2.2.0 compiler_3.6.1 htmltools_0.4.0
[25] knitr_1.26