Last updated: 2020-12-04

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Knit directory: IITA_2020GS/

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Rmd 79b6430 wolfemd 2020-12-04 Update the analysis of rate-of-gain. Include regressions and output
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Rmd 9718666 wolfemd 2020-12-03 Refresh BLUPs and GBLUPs with trials harvested so far. Include

Previous step

  1. Get BLUPs combining all trial data: Detect experimental designs, Combine data from all trait-trials to get BLUPs for downstream genomic predictions.

Current Step

  1. Genomic prediction: Predict GETGV specifically, for all selection candidates using all available data.

Prediction using two-stages

Set-up

# activate multithread OpenBLAS 
export OMP_NUM_THREADS=44
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_2020Dec03.rds")) %>% 
    dplyr::select(Trait, blups) %>% unnest(blups) %>% filter(GID %in% rownames(A)) %>% 
    nest(TrainingData = -Trait)

Prediction

cbsurobbins (112 cores; 512GB)

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_IITA_2020Dec03.rds"))

Write GEBVs

library(tidyverse); library(magrittr);
predModelADE<-readRDS(file = here::here("output","genomicPredictions_ModelADE_twostage_IITA_2020Dec03.rds"))
traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI",
          "logDYLD", # <-- logDYLD now included. 
          "logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")
## 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_2020Dec03.csv"), row.names = F)

Next step

  1. 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] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5      whisker_0.4     knitr_1.30      magrittr_2.0.1 
 [5] R6_2.5.0        rlang_0.4.9     stringr_1.4.0   tools_4.0.2    
 [9] xfun_0.19       git2r_0.27.1    htmltools_0.5.0 ellipsis_0.3.1 
[13] yaml_2.2.1      digest_0.6.27   rprojroot_2.0.2 tibble_3.0.4   
[17] lifecycle_0.2.0 crayon_1.3.4    formatR_1.7     later_1.1.0.1  
[21] vctrs_0.3.5     promises_1.1.1  fs_1.5.0        glue_1.4.2     
[25] evaluate_0.14   rmarkdown_2.5   stringi_1.5.3   compiler_4.0.2 
[29] pillar_1.4.7    httpuv_1.5.4    pkgconfig_2.0.3