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/02-GetBLUPs.Rmd) and HTML (docs/02-GetBLUPs.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. Prepare training dataset: Download data from DB, “Clean” and format DB data.

Get multi-trial BLUPs from raw data (two-stage)

Two-stage procedure:

  1. Fit mixed-model to multi-trial dataset and extract BLUPs, de-regressed BLUPs and weights. Include two rounds of outlier removal.
  2. Genomic prediction with drg-BLUPs from multi-trial analysis as input.

Work below represents Stage 1 of the Two-stage procedure.

Set-up training datasets

# activate multithread OpenBLAS for fast matrix algebra
export OMP_NUM_THREADS=56
rm(list = ls())
library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
dbdata <- readRDS(here::here("output", "TARI_ExptDesignsDetected_2020Dec18.rds"))
traits <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD", 
    "logFYLD", "logTOPYLD", "logRTNO")

# **Nest by trait.** Need to restructure the data from per-trial by regrouping by
# trait.
dbdata <- nestDesignsDetectedByTraits(dbdata, traits)
dbdata %>% mutate(N_blups = map_dbl(MultiTrialTraitData, nrow)) %>% rmarkdown::paged_table()

To fit the mixed-model I used last year, I am again resorting to asreml. I fit random effects for rep and block only where complete and incomplete blocks, respectively are indicated in the trial design variables. sommer should be able to fit the same model via the at() function, but I am having trouble with it and sommer is much slower even without a dense covariance (i.e. a kinship), compared to lme4::lmer() or asreml().

dbdata %<>% mutate(fixedFormula = ifelse(Trait %in% c("logDYLD", "logFYLD", "logRTNO", 
    "logTOPYLD"), "Value ~ yearInLoc + PropNOHAV", "Value ~ yearInLoc"), randFormula = paste0("~idv(GID) + idv(trialInLocYr) + at(CompleteBlocks,'Yes'):repInTrial ", 
    "+ at(IncompleteBlocks,'Yes'):blockInRep"))
dbdata %>% mutate(Nobs = map_dbl(MultiTrialTraitData, nrow)) %>% select(Trait, Nobs, 
    fixedFormula, randFormula) %>% rmarkdown::paged_table()
# randFormula<-paste0('~vs(GID) + vs(trialInLocYr) +
# vs(at(CompleteBlocks,'Yes'),repInTrial) +
# vs(at(IncompleteBlocks,'Yes'),blockInRep)') library(sommer) fit <- mmer(fixed =
# Value ~ 1 + yearInLoc, random = as.formula(randFormula), data=trainingdata,
# getPEV=TRUE)

Function to run asreml

Includes rounds of outlier removal and re-fitting.

fitASfunc<-function(fixedFormula,randFormula,MultiTrialTraitData,...){
  # test arguments for function
  # ----------------------
  # MultiTrialTraitData<-dbdata$MultiTrialTraitData[[7]]
  # #Trait<-dbdata$Trait[[3]]
  # fixedFormula<-dbdata$fixedFormula[[7]]
  # randFormula<-dbdata$randFormula[[7]]
  # test<-fitASfunc(fixedFormula,randFormula,MultiTrialTraitData)
  # ----------------------
  require(asreml); 
  fixedFormula<-as.formula(fixedFormula)
  randFormula<-as.formula(randFormula)
  # fit asreml 
  out<-asreml(fixed = fixedFormula,
              random = randFormula,
              data = MultiTrialTraitData, 
              maxiter = 40, workspace=800e6, na.method.X = "omit")
  #### extract residuals - Round 1
  
  outliers1<-which(abs(scale(out$residuals))>3.3)
  
  if(length(outliers1)>0){
    
    x<-MultiTrialTraitData[-outliers1,]
    # re-fit
    out<-asreml(fixed = fixedFormula,
                random = randFormula,
                data = x, 
                maxiter = 40, workspace=800e6, na.method.X = "omit")
    #### extract residuals - Round 2
    outliers2<-which(abs(scale(out$residuals))>3.3)
    if(length(outliers2)>0){
      #### remove outliers
      x<-x[-outliers2,]
      # final re-fit
      out<-asreml(fixed = fixedFormula,
                  random = randFormula,
                  data = x, maxiter = 40,workspace=800e6, na.method.X = "omit")
    }
  }
  if(length(outliers1)==0){ outliers1<-NULL }
  if(length(outliers2)==0){ outliers2<-NULL }
  
  ll<-summary(out,all=T)$loglik
  varcomp<-summary(out,all=T)$varcomp
  Vg<-varcomp["GID!GID.var","component"]
  Ve<-varcomp["R!variance","component"]
  H2=Vg/(Vg+Ve)
  blups<-summary(out,all=T)$coef.random %>%
    as.data.frame %>%
    rownames_to_column(var = "GID") %>%
    dplyr::select(GID,solution,`std error`) %>%
    filter(grepl("GID",GID)) %>%
    rename(BLUP=solution) %>%
    mutate(GID=gsub("GID_","",GID),
           PEV=`std error`^2, # asreml specific
           REL=1-(PEV/Vg), # Reliability
           drgBLUP=BLUP/REL, # deregressed BLUP
           WT=(1-H2)/((0.1 + (1-REL)/REL)*H2)) # weight for use in Stage 2
  out<-tibble(loglik=ll,Vg,Ve,H2,
              blups=list(blups),
              varcomp=list(varcomp),
              outliers1=list(outliers1),
              outliers2=list(outliers2))
  return(out) }

Run asreml

library(furrr)
options(mc.cores = 14)
plan(multiprocess)
library(asreml)
dbdata %<>% mutate(fitAS = future_pmap(., fitASfunc))
dbdata %<>% select(-fixedFormula, -randFormula, -MultiTrialTraitData) %>% unnest(fitAS)

Output file

saveRDS(dbdata, file = here::here("output", "tari_blupsForModelTraining_twostage_asreml_2020Dec20.rds"))

Results

See Results: Home for plots and summary tables.

Next step

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

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