Predict GEBV for NRCRI C2
wolfemd
2020-April-27
Last updated: 2020-10-16
Checks: 7 0
Knit directory: NRCRI_2020GS/
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| File | Version | Author | Date | Message |
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| html | 12bd17b | wolfemd | 2020-10-16 | Build site. |
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| Rmd | 8c45991 | wolfemd | 2020-04-28 | Publish the first set of analyses and files for NRCRI 2020 GS. |
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.
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$Trait)) %>%
nest(TrainingData=-Trait) %>%
mutate(Dataset="IITAaugmented"))
blupsPrediction
runGenomicPredictions function
#' @param blups nested data.frame with list-column "TrainingData" containing BLUPs
#' @param modelType string, A, AD or ADE representing model with Additive-only, Add. plus Dominance, and Add. plus Dom. plus. Epistasis (AA+AD+DD), respectively.
#' @param grms list of GRMs. Any genotypes in the GRMs get predicted with, or without phenotypes. Each element is named either A, D, AA, AD, DD. Matrices supplied must match required by A, AD and ADE models. For ADE grms=list(A=A,D=D,AA=AA,AD=AD,DD=DD).
runGenomicPredictions<-function(blups,modelType,grms,ncores=1,gid="GID",...){
require(sommer);
runOnePred<-possibly(function(trainingdata,modelType,grms){
trainingdata[[paste0(gid,"a")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["A"]]))
if(modelType %in% c("AD","ADE")){ trainingdata[[paste0(gid,"d")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["D"]]))
if(modelType=="ADE"){
#trainingdata[[paste0(gid,"aa")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["AA"]]))
trainingdata[[paste0(gid,"ad")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["AD"]]))
diag(grms[["AD"]])<-diag(grms[["AD"]])+1e-06
#trainingdata[[paste0(gid,"dd")]]<-factor(trainingdata[[gid]],levels=rownames(grms[["DD"]]))
}
}
# Set-up random model statements
randFormula<-paste0("~vs(",gid,"a,Gu=A)")
if(modelType %in% c("AD","ADE")){
randFormula<-paste0(randFormula,"+vs(",gid,"d,Gu=D)")
if(modelType=="ADE"){
randFormula<-paste0(randFormula,"+vs(",gid,"ad,Gu=AD)")
# "+vs(",gid,"aa,Gu=AA)",
# "+vs(",gid,"dd,Gu=DD)")
}
}
# Fit genomic prediction model
fit <- mmer(fixed = drgBLUP ~1,
random = as.formula(randFormula),
weights = WT,
data=trainingdata)
# Gather the BLUPs
gblups<-tibble(GID=as.character(names(fit$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
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
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