Last updated: 2020-12-23

Checks: 7 0

Knit directory: TARI_2020GS/

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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 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_2020Dec21.rds"))
D <- readRDS(file = here::here("output", "Kinship_D_TARI_2020Dec21.rds"))
AD <- readRDS(file = here::here("output", "Kinship_AD_TARI_2020Dec21.rds"))

# BLUPs from the 2 stage procedure (stage 1 of 2)
blups <- readRDS(file = here::here("output", "tari_blupsForModelTraining_twostage_asreml_2020Dec20.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_2020Dec21.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 = 14)
saveRDS(predModelADE, file = here::here("output", "genomicPredictions_ModelADE_twostage_TARI_2020Dec21.rds"))

Write GEBVs to disk

rm(list = ls())
gc()
          used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells  696057 37.2    1239058 66.2         NA  1239058 66.2
Vcells 1291799  9.9    8388608 64.0     102400  2631872 20.1
library(tidyverse)
library(magrittr)
predModelA <- readRDS(file = here::here("output", "genomicPredictions_ModelA_twostage_TARI_2020Dec21.rds"))
predModelADE <- readRDS(file = here::here("output", "genomicPredictions_ModelADE_twostage_TARI_2020Dec21.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_2020Dec21.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_2020Dec21.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_1.0.2    
 [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_0.19         haven_2.3.1       colorspace_2.0-0 
 [5] vctrs_0.3.5       generics_0.1.0    htmltools_0.5.0   yaml_2.2.1       
 [9] rlang_0.4.9       later_1.1.0.1     pillar_1.4.7      withr_2.3.0      
[13] glue_1.4.2        DBI_1.1.0         dbplyr_2.0.0      modelr_0.1.8     
[17] readxl_1.3.1      lifecycle_0.2.0   munsell_0.5.0     gtable_0.3.0     
[21] cellranger_1.1.0  rvest_0.3.6       evaluate_0.14     knitr_1.30       
[25] ps_1.5.0          httpuv_1.5.4      fansi_0.4.1       broom_0.7.2      
[29] Rcpp_1.0.5        promises_1.1.1    backports_1.2.1   scales_1.1.1     
[33] formatR_1.7       jsonlite_1.7.2    fs_1.5.0          hms_0.5.3        
[37] digest_0.6.27     stringi_1.5.3     rprojroot_2.0.2   grid_4.0.2       
[41] here_1.0.1        cli_2.2.0         tools_4.0.2       crayon_1.3.4     
[45] whisker_0.4       pkgconfig_2.0.3   ellipsis_0.3.1    xml2_1.3.2       
[49] reprex_0.3.0      lubridate_1.7.9.2 rstudioapi_0.13   assertthat_0.2.1 
[53] rmarkdown_2.6     httr_1.4.2        R6_2.5.0          git2r_0.27.1     
[57] compiler_4.0.2