Last updated: 2021-08-19

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File Version Author Date Message
html e029efc wolfemd 2021-08-12 Build site.
Rmd efebeab wolfemd 2021-08-12 Cross-validation and genomic mate predictions complete. All results updated.
html 1c03315 wolfemd 2021-08-11 Build site.
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.
Rmd f7af63a wolfemd 2021-07-26 Link to and add placeholders for extraction of a VCF from PHG DB and subsequent prepaation of inputs for subsequent analyses.
html 934141c wolfemd 2021-07-14 Build site.
html cc1eb4b wolfemd 2021-07-14 Build site.
Rmd 772750a wolfemd 2021-07-14 DirDom model and selection index calc fully integrated functions.
Rmd 97db806 wolfemd 2021-07-11 Work shown finding and fixing a bug where at least one getMarkEffs model failed. Problem was with use of plan(multicore) + OpenBLAS both using forking. Instead use plan(multisession).
Rmd 2bc9644 wolfemd 2021-07-09 Re-run cross-val with meanPredAccuracy SELIND handling fixed, but debug work not shown anymore.
Rmd 889d98a wolfemd 2021-07-09 test and fix bug in meanPredAccuracy() output when SIwts contain only subset of traits predicted.
Rmd 4308b87 wolfemd 2021-07-08 Full run 5-reps x 5-fold parent-wise cross-val both models DirDom and AD.
Rmd 7888dee wolfemd 2021-07-08 Work fully shown, testing and integrating DirDom model into crossval funcs. Now using R inside a singularity via rocker. Controlling OpenBLAS inside R session with RhpcBLASctl::blas_set_num_threads() and much more.
html 5e45aac wolfemd 2021-06-18 Build site.
Rmd fa20501 wolfemd 2021-06-18 Initial results are ready to publish and share with colleagues for
Rmd 12cc368 wolfemd 2021-06-18 runParentWiseCrossVal for 1 full rep, 5 folds. Found issue with CBSU R compilation but NOT with my code!
html e66bdad wolfemd 2021-06-10 Build site.
Rmd a8452ba wolfemd 2021-06-10 Initial build of the entire page upon completion of all
Rmd 6a5ef32 wolfemd 2021-06-09 meanPredAccuracy() now also included with function moved to “parentWiseCrossVal.R”. NOTE on previous commit: cross-validation functions are NOT in “predCrossVar.R”.
Rmd 63067f7 wolfemd 2021-06-07 Function varPredAccuracy() debugged / tested and moved to predCrossVar.R
Rmd 66c0bde wolfemd 2021-06-07 Remove old and unused code. STILL IN PROGRESS at the computeVarPredAccuracy step.
Rmd 3c085ee wolfemd 2021-06-07 Cross-validation code IN PROGRESS. Currently working on computeVarPredAccuracy.

Previous step

  1. Preprocess data files: Prepare haplotype and dosage matrices, pedigree and BLUPs, genetic map and recombination frequency matrix, for use in predictions.

Parent-wise cross-validation

Assess the accuracy of predicted previously unobserved crosses.

# 1) start a screen shell 
screen; # or screen -r if re-attaching...
# 2) start the singularity Linux shell inside that
singularity shell ~/rocker2.sif; 
# Project directory, so R will use as working dir.
cd /home/mw489/IITA_2021GS/;
# 3) Start R
R
# NEEDED LIBRARIES
require(tidyverse); require(magrittr); library(qs)
library(genomicMateSelectR)

# PEDIGREE
ped<-read.table(here::here("output","verified_ped.txt"),
                header = T, stringsAsFactors = F) %>% 
  rename(GID=FullSampleName,
         damID=DamID,
         sireID=SireID) %>% 
  dplyr::select(GID,sireID,damID)

# Keep only families with _at least_ 2 offspring
ped %<>%
  semi_join(ped %>% count(sireID,damID) %>% filter(n>1) %>% ungroup())

# BLUPs
blups<-readRDS(file=here::here("data","blups_forGP.rds")) %>% 
  dplyr::select(-varcomp)

# DOSAGE MATRIX (UNFILTERED)
dosages<-readRDS(file=here::here("data","dosages_IITA_2021Aug09.rds"))

# RECOMBINATION FREQUENCY MATRIX (UNFILTERED)
recombFreqMat<-qread(file=here::here("data",
                                     "recombFreqMat_1minus2c_2021Aug02.qs"))

# HAPLOTYPE MATRIX (UNFILTERED)
## keep only haplos for parents-in-the-pedigree
## those which will be used in prediction, saves memory
haploMat<-readRDS(file=here::here("data","haps_IITA_2021Aug09.rds"))
parents<-union(ped$sireID,ped$damID) 
parenthaps<-sort(c(paste0(parents,"_HapA"),
                   paste0(parents,"_HapB")))
haploMat<-haploMat[parenthaps,]

# SNP SETS TO ANALYZE
snpsets<-readRDS(file = here::here("data","snpsets.rds"))

# SELECTION INDEX WEIGHTS
## from IYR+IK
## note that not ALL predicted traits are on index
SIwts<-c(logFYLD=20,
         HI=10,
         DM=15,
         MCMDS=-10,
         logRTNO=12,
         logDYLD=20,
         logTOPYLD=15,
         PLTHT=10) 
# cbsulm14 - 112 cores, 512 GB RAM - 2021 Aug 10 - 1:10pm
snpSet<-"full_set"
grms<-list(A=readRDS(here::here("output","kinship_A_IITA_2021Aug09.rds")), 
           D=readRDS(here::here("output","kinship_Dgeno_IITA_2021Aug09.rds")))
# [1] "Marker-effects Computed. Took  3.08366 hrs"
# [1] "Done predicting fam vars. Took 51.41 mins for 209 crosses"
# [1] "Done predicting fam vars. Took 20.63 mins for 209 crosses"
# [1] "Done predicting fam vars. Took 39.99 mins for 155 crosses"
# [1] "Done predicting fam vars. Took 15.56 mins for 155 crosses"
# [1] "Done predicting fam vars. Took 57.26 mins for 226 crosses"
# [1] "Done predicting fam vars. Took 21.83 mins for 226 crosses"
# [1] "Done predicting fam vars. Took 53.94 mins for 223 crosses"
# [1] "Done predicting fam vars. Took 20.51 mins for 223 crosses"
# [1] "Done predicting fam vars. Took 53.21 mins for 204 crosses"
# [1] "Done predicting fam vars. Took 20.25 mins for 204 crosses"
# [1] "Done predicting fam vars. Took 48.73 mins for 188 crosses"
# [1] "Done predicting fam vars. Took 18.62 mins for 188 crosses"
# [1] "Done predicting fam vars. Took 55.69 mins for 228 crosses"
# [1] "Done predicting fam vars. Took 21.25 mins for 228 crosses"
# [1] "Done predicting fam vars. Took 56.76 mins for 235 crosses"
# [1] "Done predicting fam vars. Took 22.43 mins for 235 crosses"
# [1] "Done predicting fam vars. Took 47.4 mins for 194 crosses"
# [1] "Done predicting fam vars. Took 18.68 mins for 194 crosses"
# [1] "Done predicting fam vars. Took 42.87 mins for 175 crosses"
# [1] "Done predicting fam vars. Took 16.79 mins for 175 crosses"
# [1] "Done predicting fam vars. Took 54.11 mins for 216 crosses"
# [1] "Done predicting fam vars. Took 21.92 mins for 216 crosses"
# [1] "Done predicting fam vars. Took 56.88 mins for 235 crosses"
# [1] "Done predicting fam vars. Took 22.41 mins for 235 crosses"
# ....
# [1] "Accuracies predicted. Took  32.68635 hrs total.Goodbye!"
# [1] "Time elapsed: 1961.205 mins"

# cbsulm30 - 112 cores, 512 GB RAM - 2021 Aug 10 - 2:40pm
snpSet<-"medium_set"
grms<-list(A=readRDS(here::here("output","kinship_A_MediumSNPset_IITA_2021Aug09.rds")), 
           D=readRDS(here::here("output","kinship_Dgeno_MediumSNPset_IITA_2021Aug09.rds")))
# [1] "Marker-effects Computed. Took  1.38038 hrs"
# [1] "Predicting cross variances and covariances"
# Joining, by = c("Repeat", "Fold")
# [1] "Done predicting fam vars. Took 10 mins for 209 crosses"
# [1] "Done predicting fam vars. Took 4.03 mins for 209 crosses"
# [1] "Done predicting fam vars. Took 7.57 mins for 155 crosses"
# [1] "Done predicting fam vars. Took 3.19 mins for 155 crosses"
# [1] "Done predicting fam vars. Took 11.54 mins for 226 crosses"
# [1] "Done predicting fam vars. Took 4.36 mins for 226 crosses"
# [1] "Done predicting fam vars. Took 10.82 mins for 223 crosses"
# [1] "Done predicting fam vars. Took 4.11 mins for 223 crosses"
# [1] "Done predicting fam vars. Took 10.27 mins for 204 crosses"
# [1] "Done predicting fam vars. Took 3.93 mins for 204 crosses"
# [1] "Done predicting fam vars. Took 9.74 mins for 188 crosses"
# [1] "Done predicting fam vars. Took 3.7 mins for 188 crosses"
# [1] "Done predicting fam vars. Took 11 mins for 228 crosses"
# [1] "Done predicting fam vars. Took 4.19 mins for 228 crosses"
# [1] "Done predicting fam vars. Took 11.14 mins for 235 crosses"
# [1] "Done predicting fam vars. Took 4.25 mins for 235 crosses"
# [1] "Done predicting fam vars. Took 9.32 mins for 194 crosses"
# [1] "Done predicting fam vars. Took 3.77 mins for 194 crosses"
# [1] "Done predicting fam vars. Took 8.57 mins for 175 crosses"
# [1] "Done predicting fam vars. Took 3.43 mins for 175 crosses"
# [1] "Accuracies predicted. Took  7.3515 hrs total.Goodbye!"
# > saveRDS(parentWiseCV,
# +         file = here::here("output",
# +                           paste0("parentWiseCV_",snpSet,"_CrossPredAccuracy.rds")))
# > endtime<-proc.time()[3]; print(paste0("Time elapsed: ",
# +                                       round((endtime-starttime)/60,3)," mins"))
# [1] "Time elapsed: 441.107 mins"



# cbsulm17 - 112 cores, 512 GB RAM - 2021 Aug 10 - 1:10pm
snpSet<-"reduced_set"
grms<-list(A=readRDS(here::here("output","kinship_A_ReducedSNPset_IITA_2021Aug09.rds")), 
           D=readRDS(here::here("output","kinship_Dgeno_ReducedSNPset_IITA_2021Aug09.rds")))
# FAILED WITH ERRORS BELOW

“reduced_set” failed with errors in the code chunk below:

snpSet<-"reduced_set"
grms<-list(A=readRDS(here::here("output","kinship_A_ReducedSNPset_IITA_2021Aug09.rds")), 
           D=readRDS(here::here("output","kinship_Dgeno_ReducedSNPset_IITA_2021Aug09.rds")))
# [1] "Time elapsed: 318.057 mins"
# Run `rlang::last_error()` to see where the error occurred.
# > saveRDS(parentWiseCV,
# +         file = here::here("output",
# +                           paste0("parentWiseCV_",snpSet,"_CrossPredAccuracy.rds")))
# Error in saveRDS(parentWiseCV, file = here::here("output", paste0("parentWiseCV_",  :
#   object 'parentWiseCV' not found
# > endtime<-proc.time()[3]; print(paste0("Time elapsed: ",
# +                                       round((endtime-starttime)/60,3)," mins"))
# [1] "Time elapsed: 318.057 mins"
# >
# > rlang::last_error()
# <error/dplyr:::mutate_error>
# Problem with `mutate()` column `predVars`.
# ℹ `predVars = future_map(...)`.
# ✖ error writing to connection
# Backtrace:
#   1. genomicMateSelectR::runParentWiseCrossVal(...)
#  25. base::.handleSimpleError(...)
#  26. dplyr:::h(simpleError(msg, call))
# Run `rlang::last_trace()` to see the full context.
# Backtrace:
#      █
#   1. ├─genomicMateSelectR::runParentWiseCrossVal(...)
#   2. │ └─genomicMateSelectR:::varPredAccuracy(...)
#   3. │   └─`%<>%`(...)
#   4. ├─dplyr::mutate(...)
#   5. ├─dplyr:::mutate.data.frame(...)
#   6. │ └─dplyr:::mutate_cols(.data, ..., caller_env = caller_env())
#   7. │   ├─base::withCallingHandlers(...)
#   8. │   └─mask$eval_all_mutate(quo)
#   9. ├─furrr::future_map(...)
#  10. │ └─furrr:::furrr_map_template(...)
#  11. │   └─furrr:::furrr_template(...)
#  12. │     └─future::future(...)
#  13. │       └─future:::makeFuture(...)
#  14. │         └─future:::strategy(..., envir = envir, workers = workers)
#  15. │           ├─future::run(future)
#  16. │           └─future:::run.ClusterFuture(future)
#  17. │             ├─base::suppressWarnings(...)
#  18. │             │ └─base::withCallingHandlers(...)
#  19. │             └─parallel::clusterCall(cl, fun = gassign, name, value)
#  20. │               └─parallel:::sendCall(cl[[i]], fun, list(...))
#  21. │                 └─parallel:::postNode(...)
#  22. │                   ├─parallel:::sendData(con, list(type = type, data = value, tag = tag))
#  23. │                   └─parallel:::sendData.SOCK0node(...)
#  24. │                     └─base::serialize(data, node$con, xdr = FALSE)
#  25. └─base::.handleSimpleError(...)
#  26.   └─dplyr:::h(simpleError(msg, call))
# <error/simpleError>
# error writing to connection
# >

In the following code chunk, I manually debug / run the component functions of runParentWiseCrossVal() one-by-one only to find no error. Perhaps a server memory issue? Either way, “reduced_set” cross-val. is also completed…

# TRY TO FIX FIND AND FIX THE ERROR THAT KILLED THE REDUCED_SET ANALYSIS
## EVEN THOUGH IT ISN'T A VIP RESULT
## THE ERROR THAT KILLED IT NEEDS FOUND!
snpSet<-"reduced_set"
grms<-list(A=readRDS(here::here("output","kinship_A_ReducedSNPset_IITA_2021Aug09.rds")), 
           D=readRDS(here::here("output","kinship_Dgeno_ReducedSNPset_IITA_2021Aug09.rds")))

snps2keep<-snpsets %>% 
  filter(Set==snpSet) %>% 
  select(snps2keep) %>% 
  unnest(snps2keep)

dosages<-dosages[,snps2keep$FULL_SNP_ID]
haploMat<-haploMat[,snps2keep$FULL_SNP_ID]
recombFreqMat<-recombFreqMat[snps2keep$FULL_SNP_ID,snps2keep$FULL_SNP_ID]
rm(snpsets); gc()

parentfolds<-makeParentFolds(ped=ped,gid="GID",
                             nrepeats=5,
                             nfolds=5,
                             seed=121212)
markEffs<-genomicMateSelectR:::getMarkEffs(parentfolds,blups=blups,gid="GID",modelType="DirDom",
                                           grms=grms,dosages=dosages,
                                           ncores=20,nBLASthreads=5)
saveRDS(parentfolds,file=here::here("output","TMP_parentWiseCV_reduced_set_parentfolds.rds"))
saveRDS(markEffs,file=here::here("output","TMP_parentWiseCV_reduced_set_markerEffects.rds"))

starttime<-proc.time()[3]
cvPredVars<-genomicMateSelectR:::predictCrossVars(modelType="DirDom",snpeffs=markEffs,
                                                  parentfolds=parentfolds,
                                                  haploMat=haploMat,recombFreqMat=recombFreqMat,
                                                  ncores=20,nBLASthreads=5)
# [1] "Done predicting fam vars. Took 4.56 mins for 209 crosses"
# [1] "Done predicting fam vars. Took 2.35 mins for 209 crosses"
# [1] "Done predicting fam vars. Took 3.57 mins for 155 crosses"
# [1] "Done predicting fam vars. Took 1.92 mins for 155 crosses"
# [1] "Done predicting fam vars. Took 4.94 mins for 226 crosses"
# [1] "Done predicting fam vars. Took 2.66 mins for 226 crosses"
# [1] "Done predicting fam vars. Took 4.89 mins for 223 crosses"
# [1] "Done predicting fam vars. Took 2.45 mins for 223 crosses"
# [1] "Done predicting fam vars. Took 4.75 mins for 204 crosses"
# [1] "Done predicting fam vars. Took 2.21 mins for 204 crosses"
# [1] "Done predicting fam vars. Took 3.95 mins for 188 crosses"
# [1] "Done predicting fam vars. Took 2.2 mins for 188 crosses"
# [1] "Done predicting fam vars. Took 4.88 mins for 228 crosses"
# [1] "Done predicting fam vars. Took 2.58 mins for 228 crosses"
# [1] "Done predicting fam vars. Took 5.01 mins for 235 crosses"
# [1] "Done predicting fam vars. Took 2.74 mins for 235 crosses"
# [1] "Done predicting fam vars. Took 4.31 mins for 194 crosses"
# [1] "Done predicting fam vars. Took 2.27 mins for 194 crosses"
# [1] "Done predicting fam vars. Took 3.98 mins for 175 crosses"
# [1] "Done predicting fam vars. Took 2.09 mins for 175 crosses"
# [1] "Done predicting fam vars. Took 4.57 mins for 216 crosses"
# [1] "Done predicting fam vars. Took 2.63 mins for 216 crosses"
# [1] "Done predicting fam vars. Took 4.83 mins for 235 crosses"
# [1] "Done predicting fam vars. Took 2.62 mins for 235 crosses"
# [1] "Done predicting fam vars. Took 4.57 mins for 217 crosses"
# [1] "Done predicting fam vars. Took 2.49 mins for 217 crosses"
# [1] "Done predicting fam vars. Took 3.46 mins for 161 crosses"
# [1] "Done predicting fam vars. Took 1.94 mins for 161 crosses"
# [1] "Done predicting fam vars. Took 4.23 mins for 187 crosses"
# [1] "Done predicting fam vars. Took 2.32 mins for 187 crosses"
# [1] "Done predicting fam vars. Took 4.15 mins for 178 crosses"
# [1] "Done predicting fam vars. Took 2.07 mins for 178 crosses"
# [1] "Done predicting fam vars. Took 4.99 mins for 221 crosses"
# [1] "Done predicting fam vars. Took 2.54 mins for 221 crosses"
# [1] "Done predicting fam vars. Took 4.06 mins for 188 crosses"
# [1] "Done predicting fam vars. Took 2.28 mins for 188 crosses"
# [1] "Done predicting fam vars. Took 5.52 mins for 259 crosses"
# [1] "Done predicting fam vars. Took 2.86 mins for 259 crosses"
# [1] "Done predicting fam vars. Took 3.55 mins for 161 crosses"
# [1] "Done predicting fam vars. Took 1.91 mins for 161 crosses"
# [1] "Done predicting fam vars. Took 4.68 mins for 211 crosses"
# [1] "Done predicting fam vars. Took 2.48 mins for 211 crosses"
# [1] "Done predicting fam vars. Took 4.02 mins for 187 crosses"
# [1] "Done predicting fam vars. Took 2.21 mins for 187 crosses"
# [1] "Done predicting fam vars. Took 4.04 mins for 186 crosses"
# [1] "Done predicting fam vars. Took 2.11 mins for 186 crosses"
# [1] "Done predicting fam vars. Took 4.5 mins for 216 crosses"
# [1] "Done predicting fam vars. Took 2.45 mins for 216 crosses"

cvPredMeans<-genomicMateSelectR:::predictCrossMeans(modelType="DirDom",ncores=20,
                                                    snpeffs=markEffs,
                                                    parentfolds=parentfolds,
                                                    doseMat=dosages)

varPredAcc<-genomicMateSelectR:::varPredAccuracy(modelType = "DirDom",ncores=20,
                                                 crossValOut = cvPredVars,
                                                 snpeffs = markEffs,
                                                 ped = ped,selInd = TRUE,SIwts = SIwts)

## Mean prediction accuracies
meanPredAcc<-genomicMateSelectR:::meanPredAccuracy(modelType = "DirDom",
                                                   crossValOut = cvPredMeans,
                                                   snpeffs = markEffs,
                                                   ped = ped,selInd = TRUE,SIwts = SIwts)

accuracy_out<-list(meanPredAccuracy=meanPredAcc,
                   varPredAccuracy=varPredAcc)
saveRDS(accuracy_out,
        file = here::here("output",
                          paste0("parentWiseCV_",snpSet,"_CrossPredAccuracy.rds")))
# In the end, the code completed successfully.
# There doesn't seem to be a problem with the "reduced_set" or the package...
# Assuming the "full_set" (still running) completes without problems
# It may have been a server error or memory issue.... 
snps2keep<-snpsets %>% 
  filter(Set==snpSet) %>% 
  select(snps2keep) %>% 
  unnest(snps2keep)

dosages<-dosages[,snps2keep$FULL_SNP_ID]
haploMat<-haploMat[,snps2keep$FULL_SNP_ID]
recombFreqMat<-recombFreqMat[snps2keep$FULL_SNP_ID,snps2keep$FULL_SNP_ID]
rm(snpsets); gc()

starttime<-proc.time()[3]
parentWiseCV<-runParentWiseCrossVal(nrepeats=5,nfolds=5,seed=121212,
                                    modelType="DirDom",
                                    ncores=20,nBLASthreads=5,
                                    outName=NULL,
                                    ped=ped,
                                    blups=blups,
                                    dosages=dosages,
                                    haploMat=haploMat,
                                    grms=grms,
                                    recombFreqMat = recombFreqMat,
                                    selInd = TRUE, SIwts = SIwts)
saveRDS(parentWiseCV,
        file = here::here("output",
                          paste0("parentWiseCV_",snpSet,"_CrossPredAccuracy.rds")))
endtime<-proc.time()[3]; print(paste0("Time elapsed: ",
                                      round((endtime-starttime)/60,3)," mins"))

Standard clone-wise cross-validation

Run k-fold cross-validation and assess the accuracy of predicted previously unobserved genotypes (individuals) based on the available training data.

# 1) start a screen shell 
screen; # or screen -r if re-attaching...
# 2) start the singularity Linux shell inside that
singularity shell ~/rocker2.sif; 
# Project directory, so R will use as working dir.
cd /home/mw489/IITA_2021GS/;
# 3) Start R
R
# NEEDED LIBRARIES
require(tidyverse); require(magrittr); library(qs)
library(genomicMateSelectR)

# BLUPs
blups<-readRDS(file=here::here("data","blups_forGP.rds")) %>% 
  dplyr::select(-varcomp) %>% 
  rename(TrainingData=blups) # for compatibility with runCrossVal() function 

# DOSAGE MATRIX (UNFILTERED)
dosages<-readRDS(file=here::here("data","dosages_IITA_2021Aug09.rds"))

# SNP SETS TO ANALYZE
snpsets<-readRDS(file = here::here("data","snpsets.rds"))

# SELECTION INDEX WEIGHTS
## from IYR+IK
## note that not ALL predicted traits are on index
SIwts<-c(logFYLD=20,
         HI=10,
         DM=15,
         MCMDS=-10,
         logRTNO=12,
         logDYLD=20,
         logTOPYLD=15,
         PLTHT=10) 
# cbsulm19 - 88 cores, 512 GB RAM - 2021 Aug 10 - 6:45am
snpSet<-"full_set"
grms<-list(A=readRDS(here::here("output","kinship_A_IITA_2021Aug09.rds")), 
           D=readRDS(here::here("output","kinship_Dgeno_IITA_2021Aug09.rds")))
# [1] "Time elapsed: 209.1 mins"

# cbsulm20 - 88 cores, 512 GB RAM - 2021 Aug 10 - 6:45am 
snpSet<-"reduced_set"
grms<-list(A=readRDS(here::here("output","kinship_A_ReducedSNPset_IITA_2021Aug09.rds")), 
           D=readRDS(here::here("output","kinship_Dgeno_ReducedSNPset_IITA_2021Aug09.rds")))
# [1] "Time elapsed: 157.748 mins"

# cbsulm19 - 88 cores, 512 GB RAM - 2021 Aug 10 - 2:45pm
snpSet<-"medium_set"
grms<-list(A=readRDS(here::here("output","kinship_A_MediumSNPset_IITA_2021Aug09.rds")), 
           D=readRDS(here::here("output","kinship_Dgeno_MediumSNPset_IITA_2021Aug09.rds")))
# [1] "Time elapsed: 169.183 mins"
snps2keep<-snpsets %>% 
  filter(Set==snpSet) %>% 
  select(snps2keep) %>% 
  unnest(snps2keep)

dosages<-dosages[,snps2keep$FULL_SNP_ID]
rm(snpsets); gc()

starttime<-proc.time()[3]
standardCV<-runCrossVal(blups=blups,
                        modelType="DirDom",
                        selInd=TRUE,SIwts=SIwts,
                        grms=grms,dosages=dosages,
                        nrepeats=5,nfolds=5,
                        ncores=17,nBLASthreads=5,
                        gid="GID",seed=424242)
saveRDS(standardCV,
        file = here::here("output",
                          paste0("standardCV_",snpSet,"_ClonePredAccuracy.rds")))
endtime<-proc.time()[3]; print(paste0("Time elapsed: ",
                                      round((endtime-starttime)/60,3)," mins"))

Next step / Results

  1. Genomic predictions:
  • First, predict of individual GEBV/GETGV for all selection candidates using all available data and return marker effects for use downstream.
  • Next, Select a top set of candidate parents, for whom we would like to predict cross performances.
  • Finally, predict all pairwise crosses of candidate parents and evaluate them for genomic mate selection.
  • Select the top crosses and plant a crossing nursery with the parents indicated.

Cross-validation results here


sessionInfo()