Last updated: 2021-08-11

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Knit directory: IITA_2021GS/

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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 e4df79f wolfemd 2021-08-11 Completed IITA_2021GS pipeline including imputation and genomic prediction. Last bit of cross-validation and cross-prediction finishes in 24 hrs.
html 934141c wolfemd 2021-07-14 Build site.
Rmd 2953ee6 wolfemd 2021-07-14 Correct the link to the Results.html page throughout.
html e66bdad wolfemd 2021-06-10 Build site.
Rmd a8452ba wolfemd 2021-06-10 Initial build of the entire page upon completion of all

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.

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’s memory intnsity and sommer is much slower even without a dense covariance (i.e. a kinship), compared to lme4::lmer() or asreml().

To use asreml I require to access the license available only on cbsurobbins.biohpc.cornell.edu.

This is the only step like this in the pipeline.

cbsurobbins is using a SLURM job scheduler now. According to instructions, start an interactive bash shell with requested amount of recources, as follows:

screen;
cd ~/IITA_2021GS/;
salloc -n 8 --mem=60G --time=06:00:00;
# salloc: Granted job allocation 833
# salloc: Waiting for resource configuration
# salloc: Nodes cbsurobbins are ready for job
R;

Set-up training datasets

library(tidyverse); library(magrittr);
library(genomicMateSelectR)
dbdata<-readRDS(here::here("output","IITA_ExptDesignsDetected_2021Aug08.rds"))
traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI",
          "logDYLD", # <-- logDYLD now included. 
          "logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")

# **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()
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()

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[[7]]
  # fixedFormula<-dbdata$fixedFormula[[7]]
  # randFormula<-dbdata$randFormula[[7]]
  #test<-fitASfunc(fixedFormula,randFormula,MultiTrialTraitData)
  # ----------------------
  MultiTrialTraitData %<>%
    mutate(across(c(GID,yearInLoc,
                    CompleteBlocks,
                    IncompleteBlocks,
                    trialInLocYr,
                    repInTrial,
                    blockInRep),as.factor)) %>% 
    droplevels
  
  require(asreml); 
  fixedFormula<-as.formula(fixedFormula)
  randFormula<-as.formula(randFormula)
  # fit asreml 
  out<-asreml(fixed = fixedFormula,
              random = randFormula,
              data = MultiTrialTraitData, 
              maxiter = 40, workspace=1000e6, 
              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=1000e6, 
                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=1000e6, 
                  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))
  gc()
  return(out) }

Run asreml

Ran in small chunks. Still learning SLURM scheduler used on server.

library(asreml)

require(furrr); plan(multisession, workers = 4)
options(future.globals.maxSize=+Inf); options(future.rng.onMisuse="ignore")
test<-dbdata %>%
  slice(1:4) %>% 
  mutate(fitAS=future_pmap(.,fitASfunc))
saveRDS(test,file=here::here("output","test_2021Aug09.rds"))
plan(sequential)
rm(test); gc()

require(furrr); plan(multisession, workers = 5)
options(future.globals.maxSize=+Inf); options(future.rng.onMisuse="ignore")
test1<-dbdata %>%
  slice(5:9) %>% 
  mutate(fitAS=future_pmap(.,fitASfunc))
plan(sequential)
saveRDS(test1,file=here::here("output","test1_2021Aug09.rds"))
rm(test1); gc();

require(furrr); plan(multisession, workers = 5)
options(future.globals.maxSize=+Inf); options(future.rng.onMisuse="ignore")
test2<-dbdata %>%
  slice(10:14) %>% 
  mutate(fitAS=future_pmap(.,fitASfunc))
plan(sequential)
saveRDS(test2,file=here::here("output","test2_2021Aug09.rds"))

dbdata<-readRDS(here::here("output","test_2021Aug09.rds")) %>% 
  bind_rows(readRDS(here::here("output","test1_2021Aug09.rds"))) %>% 
  bind_rows(readRDS(here::here("output","test2_2021Aug09.rds"))) %>% 
  select(-fixedFormula,-randFormula,-MultiTrialTraitData)
dbdata %<>%
  unnest(fitAS)

Output file

saveRDS(dbdata,file=here::here("output","IITA_blupsForModelTraining_twostage_asreml_2021Aug09.rds"))

Results

See Results: Home for plots and summary tables.

Next step

  1. Validate the pedigree obtained from cassavabase: Before setting up a cross-validation scheme for predictions that depend on a correct pedigree, add a basic verification step to the pipeline. Not trying to fill unknown or otherwise correct the pedigree. Assess evidence that relationship is correct, remove if incorrect.

sommer version attempt

Set-up the singularity shell and R environment

# 1) start a screen shell 
screen; # or screen -r if re-attaching...
# 2) start the singularity Linux shell inside that
#singularity shell /workdir/$USER/rocker.sif; 
singularity pull rocker.sif docker://rocker/tidyverse:latest;
singularity shell ~/rocker.sif; 
#singularity shell ~/rocker2.sif; 
# Project directory, so R will use as working dir.
cd /home/mw489/IITA_2021GS/;
# 3) Start R
R
# libPath<-"/home/mw489/R/x86_64-pc-linux-gnu-library/4.1"
# withr::with_libpaths(new=libPath, devtools::install_github("wolfemd/genomicMateSelectR", ref = 'master'))
# library(tidyverse); library(magrittr);
# library(genomicMateSelectR)
# dbdata<-readRDS(here::here("output","IITA_ExptDesignsDetected_2021Aug08.rds"))
# traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI",
#           "logDYLD", # <-- logDYLD now included. 
#           "logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")
# 
# # **Nest by trait.** Need to restructure the data from per-trial by regrouping by trait. 
# dbdata<-nestDesignsDetectedByTraits(dbdata,traits)
# RhpcBLASctl::blas_set_num_threads(56)
# 
# MultiTrialTraitData<-dbdata$MultiTrialTraitData[[2]]
# fixedFormula<-"Value ~ yearInLoc"
# randFormula<-paste0("~vs(GID) + vs(trialInLocYr) + vs(at(CompleteBlocks,'Yes'),repInTrial) + vs(at(IncompleteBlocks,'Yes'),blockInRep)")
# library(sommer)
# fit <- sommer::mmer(fixed = as.formula(fixedFormula),
#                     random = as.formula(randFormula),
#                     weights = WT,
#                     data=MultiTrialTraitData,
#                     date.warning = F,
#                     getPEV = F)
# MultiTrialTraitData %>% distinct(GID)
# Error: cannot allocate vector of size 537.8 Gb

sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] genomicMateSelectR_0.2.0 magrittr_2.0.1           forcats_0.5.1           
 [4] stringr_1.4.0            dplyr_1.0.7              purrr_0.3.4             
 [7] readr_2.0.0              tidyr_1.1.3              tibble_3.1.3            
[10] ggplot2_3.3.5            tidyverse_1.3.1          workflowr_1.6.2         

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7        lubridate_1.7.10  here_1.0.1        assertthat_0.2.1 
 [5] rprojroot_2.0.2   digest_0.6.27     utf8_1.2.2        R6_2.5.0         
 [9] cellranger_1.1.0  backports_1.2.1   reprex_2.0.1      evaluate_0.14    
[13] httr_1.4.2        pillar_1.6.2      rlang_0.4.11      readxl_1.3.1     
[17] rstudioapi_0.13   whisker_0.4       jquerylib_0.1.4   rmarkdown_2.10   
[21] munsell_0.5.0     broom_0.7.9       compiler_4.1.0    httpuv_1.6.1     
[25] modelr_0.1.8      xfun_0.25         pkgconfig_2.0.3   htmltools_0.5.1.1
[29] tidyselect_1.1.1  fansi_0.5.0       crayon_1.4.1      tzdb_0.1.2       
[33] dbplyr_2.1.1      withr_2.4.2       later_1.2.0       grid_4.1.0       
[37] jsonlite_1.7.2    gtable_0.3.0      lifecycle_1.0.0   DBI_1.1.1        
[41] git2r_0.28.0      scales_1.1.1      cli_3.0.1         stringi_1.7.3    
[45] fs_1.5.0          promises_1.2.0.1  xml2_1.3.2        bslib_0.2.5.1    
[49] ellipsis_0.3.2    generics_0.1.0    vctrs_0.3.8       tools_4.1.0      
[53] glue_1.4.2        hms_1.1.0         yaml_2.2.1        colorspace_2.0-2 
[57] rvest_1.0.1       knitr_1.33        haven_2.4.3       sass_0.4.0