Last updated: 2021-07-14

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Knit directory: implementGMSinCassava/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/06-GenomicPredictions.Rmd) and HTML (docs/06-GenomicPredictions.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 772750a wolfemd 2021-07-14 DirDom model and selection index calc fully integrated functions.
Rmd b43ee90 wolfemd 2021-07-12 Completed predictCrosses function, which predicts cross usefullness on SELINDs and component traits. Work is shown.
Rmd 0ac841c wolfemd 2021-07-11 Full genomic prediction including (1) genomic prediction of clone GBLUPs and (2) prediction of cross usefulness. (1) is completed. Work shown. (2) is TO DO still.

Previous step

  1. Parent-wise cross-validation: Compute parent-wise cross-validation folds using the validated pedigree. Fit models to get marker effects and make subsequent predictions of cross means and (co)variances.

Current steps

  1. Genomic prediction of clone GEBV/GETGV. Fit GBLUP model, using genotypic add-dom partition. NEW: modelType=“DirDom”, include genome-wide inbreeding effect in GEBV/GETGV predictions after backsolving SNP effects. For all models, extract GBLUPs and backsolve SNP effects for use in cross usefulness predictions (mean+variance predictions). ALSO NEW: selection index predictions.

  2. Genomic prediction of cross \(UC_{parent}\) and \(UC_{variety}\). Rank potential parents on SI. Predict all possible crosses of some portion of best parents.

Genomic prediction of clone GEBV/GETGV

Operate R within a singularity Linux shell within a screen shell.

# 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; 
# Project directory, so R will use as working dir.
cd /home/mw489/implementGMSinCassava/;
# 3) Start R
R

Load input data

require(tidyverse); require(magrittr); 
# 5 threads per Rsession for matrix math (openblas)
RhpcBLASctl::blas_set_num_threads(5)
# GENOMIC MATE SELECTION FUNCTIONS
source(here::here("code","gmsFunctions.R")) 
source(here::here("code","predCrossVar.R")) 

# GENOMIC RELATIONSHIP MATRICES (GRMS)
grms<-list(A=readRDS(file=here::here("output","kinship_A_IITA_2021May13.rds")),
           D=readRDS(file=here::here("output","kinship_domGenotypic_IITA_2021July5.rds")))

# DOSAGE MATRIX
dosages<-readRDS(file=here::here("data",
                                 "dosages_IITA_filtered_2021May13.rds"))

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

# 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) 

Run the “AD” and “DirDom” modelTypes built into runGenomicPredictions().

Output contains both GBLUPs for selection of indivuals and SNP effects to use as input for prediction of cross usefulness and subsequent mate selection.

start<-proc.time()[3]
gpreds_ad<-runGenomicPredictions(modelType="AD",selInd=TRUE, SIwts=SIwts,
                                 getMarkEffs=TRUE,
                                 returnPEV=FALSE,
                                 blups=blups,grms=grms,dosages=dosages,
                                 ncores=20,nBLASthreads=5)
runtime<-proc.time()[3]-start; runtime/60
saveRDS(gpreds_ad,file = here::here("output","genomicPredictions_ModelAD.rds"))

gpreds_dirdom<-runGenomicPredictions(modelType="DirDom",selInd=TRUE, SIwts=SIwts,
                                     getMarkEffs=TRUE,
                                     returnPEV=FALSE,
                                     blups=blups,grms=grms,dosages=dosages,
                                     ncores=20,nBLASthreads=5)
runtime<-proc.time()[3]-start; runtime/60
saveRDS(gpreds_dirdom,file = here::here("output","genomicPredictions_ModelDirDom.rds"))

Genomic mate predictions: cross usefulness

  1. Use GBLUPs on SELIND to choose some to fraction of the clones.
  2. For those selected parents, predict the SELIND usefulness for all pairwise matings.
# 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; 
# Project directory, so R will use as working dir.
cd /home/mw489/implementGMSinCassava/;
# 3) Start R
R

Load input data

require(tidyverse); require(magrittr); 
# 5 threads per Rsession for matrix math (openblas)
#RhpcBLASctl::blas_set_num_threads(5)
# GENOMIC MATE SELECTION FUNCTIONS
source(here::here("code","gmsFunctions.R")) 
source(here::here("code","predCrossVar.R")) 

# DOSAGE MATRIX
dosages<-readRDS(file=here::here("data",
                                 "dosages_IITA_filtered_2021May13.rds"))

# RECOMBINATION FREQUENCY MATRIX
recombFreqMat<-readRDS(file=here::here("data",
                                       "recombFreqMat_1minus2c_2021May13.rds"))
# HAPLOTYPE MATRIX
## 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_filtered_2021May13.rds"))
parents<-union(ped$sireID,ped$damID) 
parenthaps<-sort(c(paste0(parents,"_HapA"),
                   paste0(parents,"_HapB")))
haploMat<-haploMat[parenthaps,colnames(recombFreqMat)]

# 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) 

# SNP EFFECTS - FROM PREVIOUS STEP 
### Two models AD and DirDom
gpreds_ad<-readRDS(file = here::here("output","genomicPredictions_ModelAD.rds"))
gpreds_dirdom<-readRDS(file = here::here("output","genomicPredictions_ModelDirDom.rds"))
### Cor between SELIND GEBV and GETGV between models?
# left_join(gpreds_ad$gblups[[1]] %>% select(GID,predOf,SELIND) %>% rename(SELINDad=SELIND),
#           gpreds_dirdom$gblups[[1]] %>% select(GID,predOf,SELIND) %>% rename(SELINDdirdom=SELIND)) %>% 
#   group_by(predOf) %>% summarize(SELIND_corModels=cor(SELINDad,SELINDdirdom))
# predOf SELIND_corModels
# GEBV  0.9890091           
# GETGV 0.9373170   

# SELECTION OF CROSSES-TO-BE-PREDICTED
nParentsToSelect<-100
union_bestGEBVandGETGVdirdom<-union(gpreds_dirdom$gblups[[1]] %>% 
                                         filter(predOf=="GEBV") %>% 
                                         arrange(desc(SELIND)) %>% 
                                         slice(1:nParentsToSelect) %$% GID,
                                       gpreds_dirdom$gblups[[1]] %>% 
                                         filter(predOf=="GETGV") %>% 
                                         arrange(desc(SELIND)) %>% 
                                         slice(1:nParentsToSelect) %$% GID)
union_bestGEBVandGETGVad<-union(gpreds_ad$gblups[[1]] %>% 
                                         filter(predOf=="GEBV") %>% 
                                         arrange(desc(SELIND)) %>% 
                                         slice(1:nParentsToSelect) %$% GID,
                                       gpreds_ad$gblups[[1]] %>% 
                                         filter(predOf=="GETGV") %>% 
                                         arrange(desc(SELIND)) %>% 
                                         slice(1:nParentsToSelect) %$% GID)
union_bestGEBVandGETGV<-union(union_bestGEBVandGETGVdirdom,
                              union_bestGEBVandGETGVad)
length(union_bestGEBVandGETGV) 
# [1] 121 parents in top nParentsToSelect on SELIND for GEBV/GETGV - DirDom/AD model.

CrossesToPredict<-crosses2predict(union_bestGEBVandGETGV)
nrow(CrossesToPredict)
# [1] 7381 

Run the “AD” and “DirDom” modelTypes.

cbsulm15 - July 13 - 3:30pm

start<-proc.time()[3]
crossPreds_dirdom<-predictCrosses(modelType="DirDom",stdSelInt = 2.67, 
                                  selInd=TRUE, SIwts=SIwts,
                                  CrossesToPredict=CrossesToPredict,
                                  snpeffs=gpreds_dirdom$genomicPredOut[[1]],
                                  dosages=dosages,
                                  haploMat=haploMat,recombFreqMat=recombFreqMat,
                                  ncores=15,nBLASthreads=5)
runtime<-proc.time()[3]-start; runtime/60
saveRDS(crossPreds_dirdom,file = here::here("output","genomicMatePredictions_top121parents_ModelDirDom.rds"))

# Error: Problem with `mutate()` column `predVars`.
# ℹ `predVars = future_pmap(...)`.
# ✖ Failed to retrieve the value of MultisessionFuture (<none>) from cluster RichSOCKnode #20 (PID 28094 on ‘localhost’). The reason reported was ‘error reading from connection’
# Run `rlang::last_error()` to see where the error occurred.
# In addition: Warning message:
# In list(...) : closing unused connection 3 (localhost)
# > runtime<-proc.time()[3]-start; runtime/60
#  elapsed
# 45.65038

cbsulm15

start<-proc.time()[3]
crossPreds_ad<-predictCrosses(modelType="AD",stdSelInt = 2.67, 
                              selInd=TRUE, SIwts=SIwts,
                              CrossesToPredict=CrossesToPredict,
                              snpeffs=gpreds_ad$genomicPredOut[[1]],
                              dosages=dosages,
                              haploMat=haploMat,recombFreqMat=recombFreqMat,
                              ncores=15,nBLASthreads=5)
runtime<-proc.time()[3]-start; runtime/60
saveRDS(crossPreds_ad,file = here::here("output","genomicMatePredictions_top121parents_ModelAD.rds"))

[TO DO] Write predictions to disk

Add genetic groups and tidy format

# unique_gids<-predModelA %>%
#   dplyr::select(genomicPredOut) %>%
#   unnest(genomicPredOut) %>%
#   select(-varcomps) %>%
#   unnest(gblups) %$%
#   GID %>%
#   unique
# 
# c1a<-unique_gids %>%
#   grep("c1a",.,value = T,ignore.case = T) %>%
#   union(.,unique_gids %>%
#           grep("^F",.,value = T,ignore.case = T) %>%
#           grep("c1b",.,value = T,ignore.case = T,invert = T))
# c1b<-unique_gids%>% grep("c1b",.,value = T,ignore.case = T)
# c2a<-unique_gids %>%
#   grep("C2a",.,value = T,ignore.case = T) %>%
#   grep("NR17",.,value = T,ignore.case = T)
# c2b<-unique_gids %>%
#   grep("C2b",.,value = T,ignore.case = T) %>%
#   .[!. %in% c(c1a,c1b,c2a)]
# c3a<-unique_gids %>%
#   grep("C3a",.,value = T,ignore.case = T) %>%
#   .[!. %in% c(c1a,c1b,c2a,c2b)]
# c3b<-unique_gids %>%
#   grep("NR20C3",.,value = T,ignore.case = T) %>%
#   .[!. %in% c(c1a,c1b,c2a,c2b,c3a)]
# nrTP<-setdiff(unique_gids,unique(c(c1a,c1b,c2a,c2b,c3a,c3b)))

Write CSV’s

# ## Format and write GEBV
# predModelA %>% 
#   select(Trait,genomicPredOut) %>% 
#   unnest(genomicPredOut) %>% 
#   select(-varcomps) %>% 
#   unnest(gblups) %>% 
#   select(-GETGV,-contains("PEV")) %>%
#   spread(Trait,GEBV) %>% 
#   mutate(Group=case_when(GID %in% nrTP ~ "nrTP",
#                          GID %in% c1a ~ "C1a",
#                          GID %in% c1b ~ "C1b",
#                          GID %in% c2a ~ "C2a",
#                          GID %in% c2b ~ "C2b",
#                          GID %in% c3a ~ "C3a",
#                          GID %in% c3b ~ "C3b")) %>% 
#   select(Group,GID,any_of(traits)) %>% 
#   arrange(desc(Group)) %>% 
#   write.csv(., file = here::here("output","GEBV_NRCRI_ModelA_2021May03.csv"), row.names = F)
# 
# ## Format and write GETGV
# predModelADE %>% 
#   select(Trait,genomicPredOut) %>% 
#   unnest(genomicPredOut) %>% 
#   select(-varcomps) %>% 
#   unnest(gblups) %>% 
#   select(GID,Trait,GETGV) %>% 
#   spread(Trait,GETGV) %>% 
#   mutate(Group=case_when(GID %in% nrTP ~ "nrTP",
#                          GID %in% c1a ~ "C1a",
#                          GID %in% c1b ~ "C1b",
#                          GID %in% c2a ~ "C2a",
#                          GID %in% c2b ~ "C2b",
#                          GID %in% c3a ~ "C3a",
#                          GID %in% c3b ~ "C3b")) %>% 
#   select(Group,GID,any_of(traits)) %>% 
#   arrange(desc(Group)) %>% 
#   write.csv(., file = here::here("output","GETGV_NRCRI_ModelADE_2021May03.csv"), row.names = F)
# 
# ### Make a unified "tidy" long-form: 
# predModelA %>% 
#   select(Trait,genomicPredOut) %>% 
#   unnest(genomicPredOut) %>% 
#   select(-varcomps) %>% 
#   unnest(gblups) %>% 
#   select(-GETGV) %>% 
#   full_join(predModelADE %>% 
#               select(Trait,genomicPredOut) %>% 
#               unnest(genomicPredOut) %>% 
#               select(-varcomps) %>% 
#               unnest(gblups) %>% 
#               rename(GEBV_modelADE=GEBV,
#                      PEV_modelADE=PEVa) %>% 
#               select(-genomicPredOut)) %>% 
#   mutate(Group=case_when(GID %in% nrTP ~ "nrTP",
#                          GID %in% c1a ~ "C1a",
#                          GID %in% c1b ~ "C1b",
#                          GID %in% c2a ~ "C2a",
#                          GID %in% c2b ~ "C2b",
#                          GID %in% c3a ~ "C3a",
#                          GID %in% c3b ~ "C3b")) %>% 
#   relocate(Group,.before = GID) %>% 
#   write.csv(., file = here::here("output","genomicPredictions_NRCRI_2021May03.csv"), row.names = F)

Results

See Results: Home for plots and summary tables.


sessionInfo()