Previous step

Objective

Set-up training-testing data

Prediction

runGenomicPredictions function
Plot Predictions
Write GEBVs

Next step

Last updated: 2020-10-16

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Knit directory: NRCRI_2020GS/

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Previous step

Check prediction accuracy: Evaluate prediction accuracy with cross-validation.

Objective

Current Step

Genomic prediction of GS C2: Predict genomic BLUPs (GEBV and GETGV) for all selection candidates using all available data.

# activate multithread OpenBLAS 
export OMP_NUM_THREADS=48
#export OMP_NUM_THREADS=88
#export OMP_NUM_THREADS=88

Set-up training-testing data

library(tidyverse); library(magrittr); 
A<-readRDS(file=here::here("output","Kinship_A_NRCRI_2020April27.rds"))
D<-readRDS(file=here::here("output","Kinship_A_NRCRI_2020April27.rds"))
#AA<-readRDS(file=here::here("output","Kinship_AA_NRCRI_2020April27.rds"))
AD<-readRDS(file=here::here("output","Kinship_AD_NRCRI_2020April27.rds"))
#DD<-readRDS(file=here::here("output","Kinship_DD_NRCRI_2020April27.rds"))

blups_iita<-readRDS(file=here::here("data","iita_blupsForCrossVal_outliersRemoved_73019.rds"))
blups_iita<-blups_iita %>% 
  dplyr::select(Trait,blups) %>% 
  unnest(blups) %>% 
  dplyr::select(-`std error`) %>% 
  filter(GID %in% rownames(A),
         !grepl("TMS13F|TMS14F|TMS15F|2013_",GID)) # don't want IITA GS progenies
blups_nrcri<-readRDS(file=here::here("output","nrcri_blupsForModelTraining_2020April27.rds"))
blups_nrcri<-blups_nrcri %>% 
  dplyr::select(Trait,modelOutput) %>% 
  unnest(modelOutput) %>% 
  dplyr::select(Trait,BLUPs) %>% 
  unnest(BLUPs) %>%  
  filter(GID %in% rownames(A))

blups<-blups_nrcri %>%
  nest(TrainingData=-Trait) %>% 
  mutate(Dataset="NRCRIalone") %>% 
  bind_rows(blups_nrcri %>% 
              bind_rows(blups_iita %>% filter(Trait %in% blups_nrcri$Trait)) %>% 
              nest(TrainingData=-Trait) %>% 
              mutate(Dataset="IITAaugmented"))
blups

Prediction

runGenomicPredictions function

#' @param blups nested data.frame with list-column "TrainingData" containing BLUPs
#' @param modelType string, A, AD or ADE representing model with Additive-only, Add. plus Dominance, and Add. plus Dom. plus. Epistasis (AA+AD+DD), respectively.
#' @param grms list of GRMs. Any genotypes in the GRMs get predicted with, or without phenotypes. Each element is named either A, D, AA, AD, DD. Matrices supplied must match required by A, AD and ADE models. For ADE grms=list(A=A,D=D,AA=AA,AD=AD,DD=DD). 
runGenomicPredictions<-function(blups,modelType,grms,ncores=1,gid="GID",...){
  require(sommer); 
  runOnePred<-possibly(function(trainingdata,modelType,grms){
    trainingdata[[paste0(gid,"a")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["A"]]))
    if(modelType %in% c("AD","ADE")){ trainingdata[[paste0(gid,"d")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["D"]])) 
    if(modelType=="ADE"){
      #trainingdata[[paste0(gid,"aa")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["AA"]]))
      trainingdata[[paste0(gid,"ad")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["AD"]]))
      diag(grms[["AD"]])<-diag(grms[["AD"]])+1e-06
      #trainingdata[[paste0(gid,"dd")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["DD"]]))
    }
    }
    # Set-up random model statements
    randFormula<-paste0("~vs(",gid,"a,Gu=A)")
    if(modelType %in% c("AD","ADE")){
      randFormula<-paste0(randFormula,"+vs(",gid,"d,Gu=D)")
      if(modelType=="ADE"){
        randFormula<-paste0(randFormula,"+vs(",gid,"ad,Gu=AD)")
                            # "+vs(",gid,"aa,Gu=AA)",
                            # "+vs(",gid,"dd,Gu=DD)")
      }
    }
    # Fit genomic prediction model  
    fit <- mmer(fixed = drgBLUP ~1,
                random = as.formula(randFormula),
                weights = WT,
                data=trainingdata)
    # Gather the BLUPs
    gblups<-tibble(GID=as.character(names(fit$U[[paste0("u:",gid,"a")]]$drgBLUP)),
                   GEBV=as.numeric(fit$U[[paste0("u:",gid,"a")]]$drgBLUP))
    if(modelType %in% c("AD","ADE")){
      gblups %<>% mutate(GEDD=as.numeric(fit$U[[paste0("u:",gid,"d")]]$drgBLUP))
      if(modelType=="ADE"){
        gblups %<>% mutate(#GEEDaa=as.numeric(fit$U[[paste0("u:",gid,"aa")]]$drgBLUP),
                           GEEDad=as.numeric(fit$U[[paste0("u:",gid,"ad")]]$drgBLUP))
                           #GEEDdd=as.numeric(fit$U[[paste0("u:",gid,"dd")]]$drgBLUP)) 
      }
    }
    # Calc GETGVs 
    ## Note that for modelType=="A", GEBV==GETGV
    gblups %<>% 
      mutate(GETGV=rowSums(.[,grepl("GE",colnames(.))])) 
    varcomps<-summary(fit)$varcomp
    out<-tibble(gblups=list(gblups),varcomps=list(varcomps))
    return(out) 
  },otherwise = NA)
  ## Run models across all train-test splits
  ## Parallelize 
  require(furrr); plan(multiprocess); options(mc.cores=ncores);
  predictions<-blups %>% 
      mutate(genomicPredOut=future_map(TrainingData,~runOnePred(trainingdata=.,
                                                                modelType=modelType,grms=grms)))
  return(predictions) 
}

cbsulm18 (88 cores; 512GB)

Model A

options(future.globals.maxSize= 1500*1024^2)
predModelA<-runGenomicPredictions(blups,modelType="A",grms=list(A=A),gid="GID",ncores=1)
saveRDS(predModelA,file = here::here("output","genomicPredictions_ModelA_NRCRI_2020April27.rds"))

Model ADE

options(future.globals.maxSize= 3000*1024^2)
predModelADE<-runGenomicPredictions(blups,modelType="ADE",grms=list(A=A,D=D,AD=AD),gid="GID",ncores=20)
saveRDS(predModelADE,file = here::here("output","genomicPredictions_ModelADE_NRCRI_2020April27.rds"))

Plot Predictions

library(tidyverse); library(magrittr);
predModelA<-readRDS(file = here::here("output","genomicPredictions_ModelA_NRCRI_2020April27.rds"))
predModelADE<-readRDS(file = here::here("output","genomicPredictions_ModelADE_NRCRI_2020April27.rds"))

predModelA %>% 
  dplyr::select(Trait,Dataset,genomicPredOut) %>% 
  unnest(genomicPredOut) %>% 
  select(-varcomps) %>% 
  unnest(gblups) %>% 
  select(-GETGV) %>% rename(GEBV_A=GEBV) %>% 
  left_join(predModelADE %>% 
  mutate(islgl=map_lgl(genomicPredOut,is.logical)) %>% 
  filter(islgl==FALSE) %>% 
  dplyr::select(Trait,Dataset,genomicPredOut) %>% 
  unnest(genomicPredOut) %>% 
  select(-varcomps) %>% 
  unnest(gblups) %>% 
  select(Trait,Dataset,GID,GEBV) %>% rename(GEBV_ADE=GEBV)) %>% 
  mutate(GeneticGroup=NA,
         GeneticGroup=ifelse(grepl("NR17F",GID,ignore.case = T),"C2a",
                             ifelse(grepl("C2bF",GID,ignore.case = T),"C2b",
                                    ifelse(grepl("^F",GID) & !grepl("C1b",GID),"C1a",
                                           ifelse(grepl("C1b",GID,
                                                        ignore.case = T),"C1b","TrainingPop"))))) %>% 
  ggplot(.,aes(x=GEBV_A,y=GEBV_ADE,color=GeneticGroup)) + 
  geom_point() + theme_bw() + geom_abline(slope=1, color='darkred') + 
  facet_wrap(~Trait, scales = 'free') + 
  labs(title="Compare GEBV from A-only model to GEBV from A+D+E model")

Version	Author	Date
f3f6163	wolfemd	2020-04-28

predModelADE %>% 
  mutate(islgl=map_lgl(genomicPredOut,is.logical)) %>% 
  filter(islgl==FALSE) %>% 
  dplyr::select(Trait,Dataset,genomicPredOut) %>% 
  unnest(genomicPredOut) %>% 
  select(-varcomps) %>% 
  unnest(gblups) %>% 
  select(Trait,Dataset,GID,GEBV,GETGV) %>% 
  mutate(GeneticGroup=NA,
         GeneticGroup=ifelse(grepl("NR17F",GID,ignore.case = T),"C2a",
                             ifelse(grepl("C2bF",GID,ignore.case = T),"C2b",
                                    ifelse(grepl("^F",GID) & !grepl("C1b",GID),"C1a",
                                           ifelse(grepl("C1b",GID,
                                                        ignore.case = T),"C1b","TrainingPop"))))) %>% 
  ggplot(.,aes(x=GEBV,y=GETGV,color=GeneticGroup)) + 
  geom_point() + theme_bw() + geom_abline(slope=1, color='darkred') + 
  facet_wrap(~Trait, scales = 'free') +
  labs(title="Model: ADE - Compare GEBV to GETGV")

Version	Author	Date
f3f6163	wolfemd	2020-04-28

Write GEBVs

predModelA %>% 
  dplyr::select(Trait,Dataset,genomicPredOut) %>% 
  unnest(genomicPredOut) %>% 
  select(-varcomps) %>% 
  unnest(gblups) %>% 
  select(-GETGV) %>% 
  spread(Trait,GEBV) %>% 
  mutate(GeneticGroup=NA,
         GeneticGroup=ifelse(grepl("NR17F",GID,ignore.case = T),"C2a",
                             ifelse(grepl("C2bF",GID,ignore.case = T),"C2b",
                                    ifelse(grepl("^F",GID) & !grepl("C1b",GID),"C1a",
                                           ifelse(grepl("C1b",GID,
                                                        ignore.case = T),"C1b","TrainingPop"))))) %>% 
  nest(GEBVs=-Dataset) %>% 
  mutate(write=map2(Dataset,GEBVs,~write.csv(x = .y, file = here::here("output",paste0("GEBV_NRCRI_",.x,"_ModelA_2020April27.csv")))))

# A tibble: 2 x 3
  Dataset       GEBVs                 write 
  <chr>         <list>                <list>
1 IITAaugmented <tibble [7,062 × 12]> <NULL>
2 NRCRIalone    <tibble [7,062 × 12]> <NULL>

## Format and write GEBV
predModelADE %>% 
  mutate(islgl=map_lgl(genomicPredOut,is.logical)) %>% 
  filter(islgl==FALSE) %>% 
  dplyr::select(Trait,Dataset,genomicPredOut) %>% 
  unnest(genomicPredOut) %>% 
  select(-varcomps) %>% 
  unnest(gblups) %>% 
  select(Trait,Dataset,GID,GEBV) %>% 
  spread(Trait,GEBV) %>% 
  mutate(GeneticGroup=NA,
         GeneticGroup=ifelse(grepl("NR17F",GID,ignore.case = T),"C2a",
                             ifelse(grepl("C2bF",GID,ignore.case = T),"C2b",
                                    ifelse(grepl("^F",GID) & !grepl("C1b",GID),"C1a",
                                           ifelse(grepl("C1b",GID,
                                                        ignore.case = T),"C1b","TrainingPop"))))) %>% 
  nest(GEBVs=-Dataset) %>% 
  mutate(write=map2(Dataset,GEBVs,~write.csv(x = .y, file = here::here("output",paste0("GEBV_NRCRI_",.x,"_ModelADE_2020April27.csv")))))

# A tibble: 2 x 3
  Dataset       GEBVs                 write 
  <chr>         <list>                <list>
1 IITAaugmented <tibble [7,062 × 10]> <NULL>
2 NRCRIalone    <tibble [7,062 × 10]> <NULL>

## Format and write GETGV
predModelADE %>% 
  mutate(islgl=map_lgl(genomicPredOut,is.logical)) %>% 
  filter(islgl==FALSE) %>% 
  dplyr::select(Trait,Dataset,genomicPredOut) %>% 
  unnest(genomicPredOut) %>% 
  select(-varcomps) %>% 
  unnest(gblups) %>% 
  select(Trait,Dataset,GID,GETGV) %>% 
  spread(Trait,GETGV) %>% 
  mutate(GeneticGroup=NA,
         GeneticGroup=ifelse(grepl("NR17F",GID,ignore.case = T),"C2a",
                             ifelse(grepl("C2bF",GID,ignore.case = T),"C2b",
                                    ifelse(grepl("^F",GID) & !grepl("C1b",GID),"C1a",
                                           ifelse(grepl("C1b",GID,
                                                        ignore.case = T),"C1b","TrainingPop"))))) %>% 
  nest(GETGVs=-Dataset) %>% 
  mutate(write=map2(Dataset,GETGVs,
                    ~write.csv(x = .y, 
                               file = here::here("output",paste0("GETGV_NRCRI_",.x,"_ModelADE_2020April27.csv")))))

# A tibble: 2 x 3
  Dataset       GETGVs                write 
  <chr>         <list>                <list>
1 IITAaugmented <tibble [7,062 × 10]> <NULL>
2 NRCRIalone    <tibble [7,062 × 10]> <NULL>

Next step

Estimate genetic gain

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6

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] magrittr_1.5    forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2    
 [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.2     tibble_3.0.4   
 [9] ggplot2_3.3.2   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0 xfun_0.18        haven_2.3.1      colorspace_1.4-1
 [5] vctrs_0.3.4      generics_0.0.2   htmltools_0.5.0  yaml_2.2.1      
 [9] utf8_1.1.4       blob_1.2.1       rlang_0.4.8      later_1.1.0.1   
[13] pillar_1.4.6     withr_2.3.0      glue_1.4.2       DBI_1.1.0       
[17] dbplyr_1.4.4     modelr_0.1.8     readxl_1.3.1     lifecycle_0.2.0 
[21] munsell_0.5.0    gtable_0.3.0     cellranger_1.1.0 rvest_0.3.6     
[25] evaluate_0.14    labeling_0.3     knitr_1.30       ps_1.4.0        
[29] httpuv_1.5.4     fansi_0.4.1      broom_0.7.1      Rcpp_1.0.5      
[33] promises_1.1.1   backports_1.1.10 scales_1.1.1     jsonlite_1.7.1  
[37] farver_2.0.3     fs_1.5.0         hms_0.5.3        digest_0.6.25   
[41] stringi_1.5.3    rprojroot_1.3-2  grid_4.0.2       here_0.1        
[45] cli_2.1.0        tools_4.0.2      crayon_1.3.4     whisker_0.4     
[49] pkgconfig_2.0.3  ellipsis_0.3.1   xml2_1.3.2       reprex_0.3.0    
[53] lubridate_1.7.9  rstudioapi_0.11  assertthat_0.2.1 rmarkdown_2.4   
[57] httr_1.4.2       R6_2.4.1         git2r_0.27.1     compiler_4.0.2

Predict GEBV for NRCRI C2

wolfemd

2020-April-27