Last updated: 2021-01-21

Checks: 7 0

Knit directory: TARI_2020GS/

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Unstaged changes:
    Modified:   output/TARI_trials_NOT_identifiable.csv
    Modified:   output/maxNOHAV_byStudy.csv

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/04-GetGBLUPs.Rmd) and HTML (docs/04-GetGBLUPs.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd ac1cf61 wolfemd 2021-01-21 Kibaha samples added. Cross-validation and predictions redone.
html abaf52a wolfemd 2020-12-23 Build site.
Rmd fae176a wolfemd 2020-12-23 Publish the first set of analyses and files for TARI 2020 GS.

Previous step

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

Objective

Current Step

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

Set-up

cd /home/jj332_cas/marnin/TARI_2020GS/; 
export OMP_NUM_THREADS=56 # activate multithread OpenBLAS 
library(tidyverse); library(magrittr); 
source(here::here("code","gsFunctions.R"))
A<-readRDS(file=here::here("output","Kinship_A_TARI_2021Jan21.rds"))

# BLUPs from the 2 stage procedure
# (stage 1 of 2) 
blups<-readRDS(file=here::here("output","tari_blupsForModelTraining_twostage_asreml_2021Jan21.rds")) %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  select(-`std error`) %>% 
  filter(GID %in% rownames(A)) %>% 
  nest(TrainingData=-Trait)

Prediction

runGenomicPredictions()

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=14)
saveRDS(predModelA,file = here::here("output","genomicPredictions_ModelA_twostage_TARI_2021Jan21.rds"))

Model ADE

D<-readRDS(file=here::here("output","Kinship_D_TARI_2021Jan21.rds"))
AD<-readRDS(file=here::here("output","Kinship_AD_TARI_2021Jan21.rds"))
options(future.globals.maxSize= 3000*1024^2)
predModelADE<-runGenomicPredictions(blups,modelType="ADE",grms=list(A=A,D=D,AD=AD),gid="GID",ncores=14)
saveRDS(predModelADE,file = here::here("output","genomicPredictions_ModelADE_twostage_TARI_2021Jan21.rds"))

Write GEBV/GETV to disk

rm(list=ls()); gc()
          used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells  689841 36.9    1248728 66.7         NA  1248728 66.7
Vcells 1274174  9.8    8388608 64.0     102400  2576819 19.7
library(tidyverse); library(magrittr);
predModelA<-readRDS(file = here::here("output","genomicPredictions_ModelA_twostage_TARI_2021Jan21.rds"))
predModelADE<-readRDS(file = here::here("output","genomicPredictions_ModelADE_twostage_TARI_2021Jan21.rds"))
traits<-c("MCMDS","MCBSDS","CBSDRS","CGMS1","CGMS2","DM","PLTHT","HI",
          "logDYLD", "logFYLD","logTOPYLD","logRTNO")
# unique_gids<-predModelA %>% 
#   dplyr::select(genomicPredOut) %>% 
#   unnest(genomicPredOut) %>% 
#   select(-varcomps) %>% 
#   unnest(gblups) %$% 
#   GID %>% 
#   unique
# 
# c1a<-unique_gids %>% 
#   grep("c1a",.,value = T,ignore.case = T) %>% 
#   union(.,unique_gids %>% 
#           grep("^F",.,value = T,ignore.case = T) %>% 
#           grep("c1b",.,value = T,ignore.case = T,invert = T))
# c1b<-unique_gids%>% grep("c1b",.,value = T,ignore.case = T)
# c2a<-unique_gids %>% 
#   grep("C2a",.,value = T,ignore.case = T) %>% 
#   grep("NR17",.,value = T,ignore.case = T)
# c2b<-unique_gids %>% 
#   grep("C2b",.,value = T,ignore.case = T) %>% 
#   .[!. %in% c(c1a,c1b,c2a)]
# c3a<-unique_gids %>% 
#   grep("C3a",.,value = T,ignore.case = T) %>% 
#   .[!. %in% c(c1a,c1b,c2a,c2b)]
# nrTP<-setdiff(unique_gids,unique(c(c1a,c1b,c2a,c2b,c3a)))
## Format and write GEBV
predModelA %>% 
  select(Trait,genomicPredOut) %>% 
  unnest(genomicPredOut) %>% 
  select(-varcomps) %>% 
  unnest(gblups) %>% 
  select(-GETGV,-contains("PEV")) %>%
  spread(Trait,GEBV) %>% 
  mutate(Group=ifelse(grepl("[...]",GID),"DCas20_5629","TARI_TP")) %>% 
  select(Group,GID,any_of(traits)) %>% 
  arrange(desc(Group)) %>% 
  write.csv(., file = here::here("output","GEBV_TARI_ModelA_2021Jan21.csv"), row.names = F)

## Format and write GETGV
predModelADE %>% 
  select(Trait,genomicPredOut) %>% 
  unnest(genomicPredOut) %>% 
  select(-varcomps) %>% 
  unnest(gblups) %>% 
  select(GID,Trait,GETGV) %>% 
  spread(Trait,GETGV) %>% 
  mutate(Group=ifelse(grepl("[...]",GID),"DCas20_5629","TARI_TP")) %>% 
  select(Group,GID,any_of(traits)) %>% 
  arrange(desc(Group)) %>% 
  write.csv(., file = here::here("output","GETGV_TARI_ModelADE_2021Jan21.csv"), row.names = F)
# 
# ### Make a unified "tidy" long-form: 
# predModelA %>% 
#   select(Trait,Dataset,genomicPredOut) %>% 
#   unnest(genomicPredOut) %>% 
#   select(-varcomps) %>% 
#   unnest(gblups) %>% 
#   select(-GETGV) %>% 
#   full_join(predModelADE %>% 
#               select(Trait,Dataset,genomicPredOut) %>% 
#               unnest(genomicPredOut) %>% 
#               select(-varcomps) %>% 
#               unnest(gblups) %>% 
#               rename(GEBV_modelADE=GEBV,
#                      PEV_modelADE=PEVa) %>% 
#               select(-genomicPredOut)) %>% 
#   mutate(Group=case_when(GID %in% nrTP ~ "nrTP",
#                          GID %in% c1a ~ "C1a",
#                          GID %in% c1b ~ "C1b",
#                          GID %in% c2a ~ "C2a",
#                          GID %in% c2b ~ "C2b",
#                          GID %in% c3a ~ "C3a")) %>% 
#   relocate(Group,.before = GID) %>% 
#   write.csv(., file = here::here("output","genomicPredictions_TARI_2020Oct15.csv"), row.names = F)

Results

See Results: Home for plots and summary tables.


sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

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_2.0.1  forcats_0.5.0   stringr_1.4.0   dplyr_1.0.3    
 [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.2     tibble_3.0.5   
 [9] ggplot2_3.3.3   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0  xfun_0.20         haven_2.3.1       colorspace_2.0-0 
 [5] vctrs_0.3.6       generics_0.1.0    htmltools_0.5.1   yaml_2.2.1       
 [9] rlang_0.4.10      later_1.1.0.1     pillar_1.4.7      withr_2.4.0      
[13] glue_1.4.2        DBI_1.1.1         dbplyr_2.0.0      modelr_0.1.8     
[17] readxl_1.3.1      lifecycle_0.2.0   cellranger_1.1.0  munsell_0.5.0    
[21] gtable_0.3.0      rvest_0.3.6       evaluate_0.14     knitr_1.30       
[25] httpuv_1.5.5      fansi_0.4.2       broom_0.7.3       Rcpp_1.0.6       
[29] promises_1.1.1    backports_1.2.1   scales_1.1.1      jsonlite_1.7.2   
[33] fs_1.5.0          hms_1.0.0         digest_0.6.27     stringi_1.5.3    
[37] rprojroot_2.0.2   grid_4.0.2        here_1.0.1        cli_2.2.0        
[41] tools_4.0.2       crayon_1.3.4      whisker_0.4       pkgconfig_2.0.3  
[45] ellipsis_0.3.1    xml2_1.3.2        reprex_0.3.0      lubridate_1.7.9.2
[49] assertthat_0.2.1  rmarkdown_2.6     httr_1.4.2        rstudioapi_0.13  
[53] R6_2.5.0          git2r_0.28.0      compiler_4.0.2