Last updated: 2020-12-04

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File	Version	Author	Date	Message
html	7bae38d	wolfemd	2020-12-03	Build site.
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html	d72a9ed	wolfemd	2020-09-21	Build site.
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

# activate multithread OpenBLAS 
export OMP_NUM_THREADS=1

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

options(future.globals.maxSize = 3000 * 1024^2)
predModelADE <- runGenomicPredictions(blups, modelType = "ADE", grms = list(A = A, 
    D = D, AD = AD), gid = "GID", ncores = 13)
saveRDS(predModelADE, file = here::here("output", "genomicPredictions_ModelADE_threestage_IITA_2020Sep21.rds"))

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')))))

# 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()

Prediction using two-stages

Set-up

# activate multithread OpenBLAS 
export OMP_NUM_THREADS=1

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

options(future.globals.maxSize = 3000 * 1024^2)
predModelADE <- runGenomicPredictions(blups, modelType = "ADE", grms = list(A = A, 
    D = D, AD = AD), gid = "GID", ncores = 13)
saveRDS(predModelADE, file = here::here("output", "genomicPredictions_ModelADE_twostage_IITA_2020Sep21.rds"))

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
b9bb6f8	wolfemd	2020-12-03
9194239	wolfemd	2020-09-21

Next step

Results

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    viridisLite_0.3.0 htmltools_0.5.0  
 [9] yaml_2.2.1        rlang_0.4.9       later_1.1.0.1     pillar_1.4.7     
[13] withr_2.3.0       glue_1.4.2        DBI_1.1.0         dbplyr_2.0.0     
[17] modelr_0.1.8      readxl_1.3.1      lifecycle_0.2.0   munsell_0.5.0    
[21] gtable_0.3.0      cellranger_1.1.0  rvest_0.3.6       evaluate_0.14    
[25] labeling_0.4.2    knitr_1.30        ps_1.4.0          httpuv_1.5.4     
[29] fansi_0.4.1       broom_0.7.2       Rcpp_1.0.5        promises_1.1.1   
[33] backports_1.2.0   scales_1.1.1      formatR_1.7       jsonlite_1.7.1   
[37] farver_2.0.3      fs_1.5.0          hms_0.5.3         digest_0.6.27    
[41] stringi_1.5.3     rprojroot_2.0.2   grid_4.0.2        here_1.0.0       
[45] cli_2.2.0         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_1.7.9.2 rstudioapi_0.13   assertthat_0.2.1  rmarkdown_2.5    
[57] httr_1.4.2        R6_2.5.0          git2r_0.27.1      compiler_4.0.2

Predict GEBV

wolfemd

2020-Sep-16

Previous step

Objective

Prediction using three-stages

Set-up

Prediction

Write GEBVs

Prediction using two-stages

Set-up

Prediction

Write GEBVs

Next step