Last updated: 2021-07-29
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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 | c843dbd | wolfemd | 2021-07-29 | Tidy, completed version. |
| Rmd | 6eb6a62 | wolfemd | 2021-07-29 | Debugging and full benchmark run work shown. |
| Rmd | 1d017cb | wolfemd | 2021-07-25 | Debugging completed for predictCrosses(). Work shown and tests passed also shown. Full run underway. |
| 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 | 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. |
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.
Genomic prediction of cross \(UC_{parent}\) and \(UC_{variety}\). Rank potential parents on SI. Predict all possible crosses of some portion of best parents.
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
RLoad 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"))# 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 shell ~/rocker2.sif;
# Project directory, so R will use as working dir.
cd /home/mw489/implementGMSinCassava/;
# 3) Start R
Rrequire(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 25 - 7:30pm
start<-proc.time()[3]
crossPreds_dirdom<-predictCrosses(modelType="DirDom",stdSelInt = 2.67,
selInd=TRUE, SIwts=SIwts,
CrossesToPredict=CrossesToPredict,
snpeffs=gpreds_dirdom$genomicPredOut[[1]],
dosages=dosages,
haploMat=haploMat,recombFreqMat=recombFreqMat,
ncores=20,nBLASthreads=5)
runtime<-proc.time()[3]-start; runtime/60
saveRDS(crossPreds_dirdom,file = here::here("output","genomicMatePredictions_top121parents_ModelDirDom.rds"))
# elapsed
# 2195.6
# # A tibble: 1 x 2
# tidyPreds rawPreds
# <list> <list>
# 1 <tibble [177,144 × 9]> <named list [2]>
# 36.6 hrs for 7381 crosses. Or about 5 hours per 1000 crossescbsulm17 - July 25 at 7:30pm
start<-proc.time()[3]
crossPreds_ad<-predictCrosses(modelType="AD",stdSelInt = 2.67,
selInd=TRUE, SIwts=SIwts,
CrossesToPredict=CrossesToPredict,
snpeffs=gpreds_ad$genomicPredOut[[1]],
dosages=dosages,
haploMat=haploMat,recombFreqMat=recombFreqMat,
ncores=20,nBLASthreads=5)
runtime<-proc.time()[3]-start; runtime/60
saveRDS(crossPreds_ad,file = here::here("output","genomicMatePredictions_top121parents_ModelAD.rds"))
# elapsed
# 1511.56
# # A tibble: 1 x 2
# tidyPreds rawPreds
# <list> <list>
# 1 <tibble [177,144 × 9]> <named list [2]>
# 1511.56 or 25.33 hrs for 7381 crosses
# About 3.4 hrs per 1000 crosses?Add genetic groups and tidy format
library(tidyverse);
gpreds_ad<-readRDS(file = here::here("output","genomicPredictions_ModelAD.rds"))
gpreds_ad$gblups[[1]] %>%
mutate(GeneticGroup=case_when(grepl("2013_|TMS13",GID)~"C1",
grepl("TMS14",GID)~"C2",
grepl("TMS15",GID)~"C3",
grepl("TMS18",GID)~"C4",
!grepl("2013_|TMS13|TMS14|TMS15|TMS18",GID)~"PreGS"),
GeneticGroup=factor(GeneticGroup,levels = c("PreGS","C1","C2","C3","C4"))) %>%
relocate(GeneticGroup,.after = "predOf") %>%
arrange(predOf,desc(SELIND)) %>%
write.csv(.,file = here::here("output","genomicPredictions_ModelAD.csv"),
row.names = F)
gpreds_dirdom<-readRDS(file = here::here("output","genomicPredictions_ModelDirDom.rds"))
gpreds_dirdom$gblups[[1]] %>%
mutate(GeneticGroup=case_when(grepl("2013_|TMS13",GID)~"C1",
grepl("TMS14",GID)~"C2",
grepl("TMS15",GID)~"C3",
grepl("TMS18",GID)~"C4",
!grepl("2013_|TMS13|TMS14|TMS15|TMS18",GID)~"PreGS"),
GeneticGroup=factor(GeneticGroup,levels = c("PreGS","C1","C2","C3","C4"))) %>%
relocate(GeneticGroup,.after = "predOf") %>%
arrange(predOf,desc(SELIND)) %>%
write.csv(.,file = here::here("output","genomicPredictions_ModelDirDom.csv"),
row.names = F)crossPreds_dirdom<-readRDS(file = here::here("output","genomicMatePredictions_top121parents_ModelDirDom.rds"))
crossPreds_ad<-readRDS(file = here::here("output","genomicMatePredictions_top121parents_ModelAD.rds"))
crossPreds_ad$tidyPreds[[1]] %>%
mutate(sireGroup=case_when(grepl("2013_|TMS13",sireID)~"C1",
grepl("TMS14",sireID)~"C2",
grepl("TMS15",sireID)~"C3",
grepl("TMS18",sireID)~"C4",
!grepl("2013_|TMS13|TMS14|TMS15|TMS18",sireID)~"PreGS"),
damGroup=case_when(grepl("2013_|TMS13",damID)~"C1",
grepl("TMS14",damID)~"C2",
grepl("TMS15",damID)~"C3",
grepl("TMS18",damID)~"C4",
!grepl("2013_|TMS13|TMS14|TMS15|TMS18",damID)~"PreGS"),
CrossGroup=paste0(sireGroup,"x",damGroup)) %>%
relocate(contains("Group"),.before = "Nsegsnps") %>%
write.csv(.,file = here::here("output","genomicMatePredictions_top121parents_ModelAD.csv"),
row.names = F)
crossPreds_dirdom$tidyPreds[[1]] %>%
mutate(sireGroup=case_when(grepl("2013_|TMS13",sireID)~"C1",
grepl("TMS14",sireID)~"C2",
grepl("TMS15",sireID)~"C3",
grepl("TMS18",sireID)~"C4",
!grepl("2013_|TMS13|TMS14|TMS15|TMS18",sireID)~"PreGS"),
damGroup=case_when(grepl("2013_|TMS13",damID)~"C1",
grepl("TMS14",damID)~"C2",
grepl("TMS15",damID)~"C3",
grepl("TMS18",damID)~"C4",
!grepl("2013_|TMS13|TMS14|TMS15|TMS18",damID)~"PreGS"),
CrossGroup=paste0(sireGroup,"x",damGroup)) %>%
relocate(contains("Group"),.before = "Nsegsnps") %>%
write.csv(.,file = here::here("output","genomicMatePredictions_top121parents_ModelDirDom.csv"),
row.names = F)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)