Predict GEBV
wolfemd
2020-Sep-16
Last updated: 2020-09-21
Checks: 7 0
Knit directory: IITA_2020GS/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 9194239 | wolfemd | 2020-09-21 | Build site. |
| Rmd | 97778e7 | wolfemd | 2020-09-21 | Big update. Two types of pipeline to get BLUPs, GEBVs and GETGVs: |
| html | d6d72f8 | wolfemd | 2020-09-17 | Build site. |
| html | 7e156dd | wolfemd | 2020-09-17 | Build site. |
| Rmd | 7ea8b80 | wolfemd | 2020-09-17 | All steps including genomic predicting (excluding cross-validation), |
Previous step
- Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
Objective
Current Step
- Genomic prediction: Predict genomic BLUPs (GEBV and GETGV) for all selection candidates using all available data.
Prediction using three-stages
Set-up
library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))
A<-readRDS(file=here::here("output","Kinship_A_IITA_2020Sep16.rds"))
D<-readRDS(file=here::here("output","Kinship_D_IITA_2020Sep16.rds"))
AD<-readRDS(file=here::here("output","Kinship_AD_IITA_2020Sep16.rds"))
blups<-readRDS(file=here::here("output","iita_blupsForModelTraining.rds")) %>%
select(Trait,modelOutput) %>%
unnest(modelOutput) %>%
select(Trait,BLUPs) %>%
unnest(BLUPs) %>%
filter(GID %in% rownames(A)) %>%
nest(TrainingData=-Trait)Prediction
cbsurobbins (112 cores; 512GB)
Model A
options(future.globals.maxSize= 1500*1024^2)
predModelA<-runGenomicPredictions(blups,modelType="A",grms=list(A=A),gid="GID",ncores=13)
saveRDS(predModelA,file = here::here("output","genomicPredictions_ModelA_threestage_IITA_2020Sep21.rds"))Model ADE
Write GEBVs
library(tidyverse); library(magrittr);
predModelA<-readRDS(file = here::here("output","genomicPredictions_ModelA_threestage_IITA_2020Sep21.rds"))
predModelADE<-readRDS(file = here::here("output","genomicPredictions_ModelADE_threestage_IITA_2020Sep21.rds"))
traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI","logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")predModelA %>%
dplyr::select(Trait,genomicPredOut) %>%
unnest(genomicPredOut) %>%
select(-varcomps) %>%
unnest(gblups) %>%
select(-GETGV) %>%
spread(Trait,GEBV) %>%
mutate(GeneticGroup=NA,
GeneticGroup=ifelse(grepl("TMS18",GID,ignore.case = T),"TMS18",
ifelse(grepl("TMS15",GID,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",GID,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",GID,ignore.case = T),"TMS13","GGetc"))))) %>%
select(GeneticGroup,GID,all_of(traits)) %>% arrange(desc(GeneticGroup)) %>%
write.csv(., file = here::here("output","GEBV_IITA_ModelA_threestage_IITA_2020Sep21.csv"), row.names = F)## Format and write GETGV
predModelADE %>%
dplyr::select(Trait,genomicPredOut) %>%
unnest(genomicPredOut) %>%
select(-varcomps) %>%
unnest(gblups) %>%
select(Trait,GID,GETGV) %>%
spread(Trait,GETGV) %>%
mutate(GeneticGroup=NA,
GeneticGroup=ifelse(grepl("TMS18",GID,ignore.case = T),"TMS18",
ifelse(grepl("TMS15",GID,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",GID,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",GID,ignore.case = T),"TMS13","GGetc"))))) %>%
select(GeneticGroup,GID,all_of(traits)) %>% arrange(desc(GeneticGroup)) %>%
write.csv(., file = here::here("output","GETGV_IITA_ModelADE_threestage_IITA_2020Sep21.csv"), row.names = F)# gebv_vs_getgv<-predModelA %>%
# dplyr::select(Trait,genomicPredOut) %>%
# unnest(genomicPredOut) %>%
# select(-varcomps) %>%
# unnest(gblups) %>%
# select(-GETGV) %>%
# left_join(predModelADE %>%
# dplyr::select(Trait,genomicPredOut) %>%
# unnest(genomicPredOut) %>%
# select(-varcomps) %>%
# unnest(gblups) %>%
# select(Trait,GID,GETGV)) %>%
# mutate(GeneticGroup=NA,
# GeneticGroup=ifelse(grepl("TMS18",GID,ignore.case = T),"TMS18",
# ifelse(grepl("TMS15",GID,ignore.case = T),"TMS15",
# ifelse(grepl("TMS14",GID,ignore.case = T),"TMS14",
# ifelse(grepl("TMS13|2013_",GID,ignore.case = T),"TMS13","GGetc")))))Prediction using two-stages
Set-up
library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))
A<-readRDS(file=here::here("output","Kinship_A_IITA_2020Sep16.rds"))
D<-readRDS(file=here::here("output","Kinship_D_IITA_2020Sep16.rds"))
AD<-readRDS(file=here::here("output","Kinship_AD_IITA_2020Sep16.rds"))
# BLUPs from the 2 stage procedure
# (stage 1 of 2) using the 2019 procedure
blups<-readRDS(file=here::here("output","iita_blupsForModelTraining_twostage_asreml.rds")) %>%
dplyr::select(Trait,blups) %>%
unnest(blups) %>%
filter(GID %in% rownames(A)) %>%
nest(TrainingData=-Trait)Prediction
cbsurobbins (112 cores; 512GB)
Model A
options(future.globals.maxSize= 1500*1024^2)
predModelA<-runGenomicPredictions(blups,modelType="A",grms=list(A=A),gid="GID",ncores=13)
saveRDS(predModelA,file = here::here("output","genomicPredictions_ModelA_twostage_IITA_2020Sep21.rds"))Model ADE
Write GEBVs
library(tidyverse); library(magrittr);
predModelA<-readRDS(file = here::here("output","genomicPredictions_ModelA_twostage_IITA_2020Sep21.rds"))
predModelADE<-readRDS(file = here::here("output","genomicPredictions_ModelADE_twostage_IITA_2020Sep21.rds"))
traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI","logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")predModelA %>%
dplyr::select(Trait,genomicPredOut) %>%
unnest(genomicPredOut) %>%
select(-varcomps) %>%
unnest(gblups) %>%
select(-GETGV,-contains("PEV")) %>%
spread(Trait,GEBV) %>%
mutate(GeneticGroup=NA,
GeneticGroup=ifelse(grepl("TMS18",GID,ignore.case = T),"TMS18",
ifelse(grepl("TMS15",GID,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",GID,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",GID,ignore.case = T),"TMS13","GGetc"))))) %>%
select(GeneticGroup,GID,any_of(traits)) %>% arrange(desc(GeneticGroup)) %>%
write.csv(., file = here::here("output","GEBV_IITA_ModelA_twostage_IITA_2020Sep21.csv"), row.names = F)## Format and write GETGV
predModelADE %>%
dplyr::select(Trait,genomicPredOut) %>%
unnest(genomicPredOut) %>%
select(-varcomps) %>%
unnest(gblups) %>%
select(Trait,GID,GETGV) %>%
spread(Trait,GETGV) %>%
mutate(GeneticGroup=NA,
GeneticGroup=ifelse(grepl("TMS18",GID,ignore.case = T),"TMS18",
ifelse(grepl("TMS15",GID,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",GID,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",GID,ignore.case = T),"TMS13","GGetc"))))) %>%
select(GeneticGroup,GID,any_of(traits)) %>% arrange(desc(GeneticGroup)) %>%
write.csv(., file = here::here("output","GETGV_IITA_ModelADE_twostage_IITA_2020Sep21.csv"), row.names = F)gebv_vs_getgv<-predModelA %>%
dplyr::select(Trait,genomicPredOut) %>%
unnest(genomicPredOut) %>%
select(-varcomps) %>%
unnest(gblups) %>%
select(-GETGV) %>%
left_join(predModelADE %>%
dplyr::select(Trait,genomicPredOut) %>%
unnest(genomicPredOut) %>%
select(-varcomps) %>%
unnest(gblups) %>%
select(Trait,GID,GETGV)) %>%
mutate(GeneticGroup=NA,
GeneticGroup=ifelse(grepl("TMS18",GID,ignore.case = T),"TMS18",
ifelse(grepl("TMS15",GID,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",GID,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",GID,ignore.case = T),"TMS13","GGetc")))))gebv_vs_getgv %>%
ggplot(.,aes(x=GEBV,y=GETGV,color=GeneticGroup)) +
geom_point(alpha=0.7) +
geom_abline(slope=1, color='darkred', linetype='dashed') +
theme_bw() +
facet_wrap(~Trait, ncol=3, scales='free') +
scale_color_viridis_d()
| Version | Author | Date |
|---|---|---|
| 9194239 | wolfemd | 2020-09-21 |
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_1.3.1 tidyr_1.1.2 tibble_3.0.3
[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_0.17 haven_2.3.1 colorspace_1.4-1
[5] vctrs_0.3.4 generics_0.0.2 viridisLite_0.3.0 htmltools_0.5.0
[9] yaml_2.2.1 blob_1.2.1 rlang_0.4.7 later_1.1.0.1
[13] pillar_1.4.6 withr_2.2.0 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_1.29 httpuv_1.5.4
[29] fansi_0.4.1 broom_0.7.0 Rcpp_1.0.5 promises_1.1.1
[33] backports_1.1.9 scales_1.1.1 jsonlite_1.7.1 farver_2.0.3
[37] fs_1.5.0 hms_0.5.3 digest_0.6.25 stringi_1.5.3
[41] rprojroot_1.3-2 grid_4.0.2 here_0.1 cli_2.0.2
[45] tools_4.0.2 crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3
[49] ellipsis_0.3.1 xml2_1.3.2 reprex_0.3.0 lubridate_1.7.9
[53] assertthat_0.2.1 rmarkdown_2.3 httr_1.4.2 rstudioapi_0.11
[57] R6_2.4.1 git2r_0.27.1 compiler_4.0.2