Last updated: 2021-08-19
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Knit directory: IITA_2021GS/
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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 | 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.
# 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
RLoad input data
# 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) Run the “DirDom” modelTypes built into runGenomicPredictions(). Output will contain both GBLUPs for selection of clones and SNP effects to use as input for prediction of cross usefulness and subsequent mate selection.
Get effects for: full_set (~31K SNPs), medium_set (~13K, LD-pruned) and reduced_set (~9K SNPs, LD-pruned).
# cbsulm30 - 112 cores, 512 GB RAM - 2021 Aug 10 - 8:40am
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: 24.066 mins"
# cbsulm30 - 112 cores, 512 GB RAM - 2021 Aug 10 - 8:40am
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: 22.431 mins"
# cbsulm20 - 88 cores, 512 GB RAM - 2021 Aug 10 - 2:50pm
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: 13.299 mins"snps2keep<-snpsets %>%
filter(Set==snpSet) %>%
select(snps2keep) %>%
unnest(snps2keep)
dosages<-dosages[,snps2keep$FULL_SNP_ID]
rm(snpsets); gc()
starttime<-proc.time()[3]
gpreds<-runGenomicPredictions(modelType="DirDom",selInd=TRUE, SIwts=SIwts,
getMarkEffs=TRUE,
returnPEV=FALSE,
blups=blups,grms=grms,dosages=dosages,
ncores=11,nBLASthreads=5)
saveRDS(gpreds,
file = here::here("output",
paste0("genomicPredictions_",snpSet,"_2021Aug09.rds")))
endtime<-proc.time()[3]; print(paste0("Time elapsed: ",
round((endtime-starttime)/60,3)," mins"))# 1) start a screen shell
screen; # or screen -r if re-attaching...
# 2) start the singularity Linux shell inside that
singularity shell ~/rocker2.sif;
# Project directory, so R will use as working dir.
cd /home/mw489/IITA_2021GS/;
# 3) Start R
R# NEEDED LIBRARIES
require(tidyverse); require(magrittr); library(qs)
library(genomicMateSelectR)
# BLUPs
blups<-readRDS(file=here::here("data","blups_forGP.rds")) %>%
dplyr::select(-varcomp)
# DOSAGE MATRIX (UNFILTERED)
dosages<-readRDS(file=here::here("data","dosages_IITA_2021Aug09.rds"))
# RECOMBINATION FREQUENCY MATRIX (UNFILTERED)
recombFreqMat<-qread(file=here::here("data",
"recombFreqMat_1minus2c_2021Aug02.qs"))
# HAPLOTYPE MATRIX (UNFILTERED)
## keep only haplos for parents-in-the-pedigree
## those which will be used in prediction, saves memory
haploMat<-readRDS(file=here::here("data","haps_IITA_2021Aug09.rds"))
parents<-union(ped$sireID,ped$damID)
parenthaps<-sort(c(paste0(parents,"_HapA"),
paste0(parents,"_HapB")))
haploMat<-haploMat[parenthaps,]
# SNP SETS TO ANALYZE
snpsets<-readRDS(file = here::here("data","snpsets.rds"))
# SELECTION INDEX WEIGHTS
## from IYR+IK
## note that not ALL predicted traits are on index
SIwts<-c(logFYLD=20,
HI=10,
DM=15,
MCMDS=-10,
logRTNO=12,
logDYLD=20,
logTOPYLD=15,
PLTHT=10)
# LIST OF ACCESSIONS LIKELY IN THE FIELD
accessions_infield<-readRDS(here::here("data",
"accessions_possibly_infield_2021Aug10.rds"))# gpreds_full<-readRDS(file = here::here("output","genomicPredictions_full_set_2021Aug09.rds"))
# gpreds_medium<-readRDS(file = here::here("output","genomicPredictions_medium_set_2021Aug09.rds"))
# gpreds_reduced<-readRDS(file = here::here("output","genomicPredictions_reduced_set_2021Aug09.rds"))
### Quick check that GBLUPs from full and medium_set are strongly correlated
### This will be an additional assurance that similar cross variances
### will be predicted by the reduced SNP model
### Cor between SELIND GEBV and GETGV between full_set and reduced_set of SNPs?
# left_join(gpreds_full$gblups[[1]] %>% select(GID,predOf,SELIND) %>% rename(SELIND_full=SELIND),
# gpreds_reduced$gblups[[1]] %>% select(GID,predOf,SELIND) %>% rename(SELIND_reduced=SELIND)) %>%
# group_by(predOf) %>%
# summarize(SELIND_corModels=cor(SELIND_full,SELIND_reduced))
# predOf SELIND_corModels
# GEBV 0.0363334
# GETGV 0.7809016
## TERRIBLE!! :(
### Cor between SELIND GEBV and GETGV between full_set and medium_set of SNPs?
# left_join(gpreds_full$gblups[[1]] %>% select(GID,predOf,SELIND) %>% rename(SELIND_full=SELIND),
# gpreds_medium$gblups[[1]] %>% select(GID,predOf,SELIND) %>% rename(SELIND_medium=SELIND)) %>%
# group_by(predOf) %>%
# summarize(SELIND_corModels=cor(SELIND_full,SELIND_medium))
# predOf SELIND_corModels
# GEBV 0.9901325
# GETGV 0.9887326
## EXCELLENT!! :)Choose the best parents for which to predict crosses. Use the GBLUPs from the full_set of SNPs.
Take the union of the top 300 clones on the SELIND in terms of GEBV and of GETGV. Probably they will be a very similar list.
# SELECT THE BEST PARENTS AS CROSSES-TO-BE-PREDICTED
nParentsToSelect<-300
gpreds_full<-readRDS(file = here::here("output","genomicPredictions_full_set_2021Aug09.rds"))
union_bestGEBVandGETGV<-union(gpreds_full$gblups[[1]] %>%
filter(predOf=="GEBV") %>%
arrange(desc(SELIND)) %>%
slice(1:nParentsToSelect) %$% GID,
gpreds_full$gblups[[1]] %>%
filter(predOf=="GETGV") %>%
arrange(desc(SELIND)) %>%
slice(1:nParentsToSelect) %$% GID)
rm(gpreds_full);
length(union_bestGEBVandGETGV)
# [1] 365 parents in top nParentsToSelect on SELIND for GEBV/GETGV
# KEEP ONLY CANDIDATE PARENTS EXPECTED TO BE IN THE FIELD
table(union_bestGEBVandGETGV %in% accessions_infield$FullSampleName)
# FALSE TRUE
# 165 200
parentsToPredictCrosses<-union_bestGEBVandGETGV %>%
.[. %in% accessions_infield$FullSampleName]
CrossesToPredict<-crosses2predict(parentsToPredictCrosses)
nrow(CrossesToPredict)
# [1] 20100 possible crosses of 200 parents
saveRDS(parentsToPredictCrosses,
file = here::here("output",
"parentsToPredictCrosses_2021Aug10.rds"))
saveRDS(CrossesToPredict,
file = here::here("output",
"CrossesToPredict_2021Aug10.rds"))Predict all pairwise crosses of those 200 parents.
# cbsulm17 - 112 cores, 512 GB RAM - 2021 Aug 11 - 8:10am
snpSet<-"full_set"
grms<-list(A=readRDS(here::here("output","kinship_A_IITA_2021Aug09.rds")),
D=readRDS(here::here("output","kinship_Dgeno_IITA_2021Aug09.rds")))
predTheMeans<-TRUE; predTheVars<-FALSE
gpreds<-readRDS(file = here::here("output","genomicPredictions_full_set_2021Aug09.rds")); gc()
## takes ~2 minutes, predicting means only
# cbsulm17 - 112 cores, 512 GB RAM - 2021 Aug 10 - 8:25am
snpSet<-"medium_set"
grms<-list(A=readRDS(here::here("output","kinship_A_MediumSNPset_IITA_2021Aug09.rds")),
D=readRDS(here::here("output","kinship_Dgeno_MediumSNPset_IITA_2021Aug09.rds")))
predTheMeans<-TRUE; predTheVars<-TRUE
gpreds<-readRDS(file = here::here("output","genomicPredictions_medium_set_2021Aug09.rds")); gc()
# 1263.904snps2keep<-snpsets %>%
filter(Set==snpSet) %>%
select(snps2keep) %>%
unnest(snps2keep)
dosages<-dosages[,snps2keep$FULL_SNP_ID]
haploMat<-haploMat[,snps2keep$FULL_SNP_ID]
recombFreqMat<-recombFreqMat[snps2keep$FULL_SNP_ID,snps2keep$FULL_SNP_ID]
rm(snpsets); gc()
snpSet; dim(dosages); predTheMeans; predTheVars
start<-proc.time()[3]
crossPreds<-predictCrosses(modelType="DirDom",stdSelInt = 2.0627128,
selInd=TRUE, SIwts=SIwts,
CrossesToPredict=CrossesToPredict,
snpeffs=gpreds$genomicPredOut[[1]],
dosages=dosages,
haploMat=haploMat,recombFreqMat=recombFreqMat,
ncores=20,nBLASthreads=5,
predTheMeans = predTheMeans,
predTheVars = predTheVars)
runtime<-proc.time()[3]-start; runtime/60
saveRDS(crossPreds,file = here::here("output",
paste0("genomicMatePredictions_",
snpSet,"_2021Aug10.rds")))Add genetic groups and cohort identifiers and tidy format
library(tidyverse); library(magrittr)
gpreds<-readRDS(file = here::here("output","genomicPredictions_full_set_2021Aug09.rds"))
gpreds$gblups[[1]] %>%
mutate(GeneticGroup=case_when(grepl("2013_|TMS13",GID)~"C1",
grepl("TMS14",GID)~"C2",
grepl("TMS15",GID)~"C3",
grepl("TMS18",GID)~"C4",
grepl("TMS20",GID)~"C5",
grepl("TMS16",GID)~"TMS16",
grepl("TMS17",GID)~"TMS17",
grepl("TMS19",GID)~"TMS19",
!grepl("2013_|TMS13|TMS14|TMS15|TMS16|TMS17|TMS18|TMS19|TMS20",GID)~"PreGS"),
#GeneticGroup=factor(GeneticGroup,levels = c("PreGS","C1","C2","C3","C4","C5","TMS16","TMS17","TMS19")),
Cohort=case_when(grepl("2013_|TMS13",GID)~"TMS13",
grepl("TMS14",GID)~"TMS14",
grepl("TMS15",GID)~"TMS15",
grepl("TMS16",GID)~"TMS16",
grepl("TMS17",GID)~"TMS17",
grepl("TMS18",GID)~"TMS18",
grepl("TMS19",GID)~"TMS19",
grepl("TMS20",GID)~"TMS20",
!grepl("2013_|TMS13|TMS14|TMS15|TMS16|TMS17|TMS18|TMS19|TMS20",GID)~"PreGS")) %>%
relocate(GeneticGroup,.after = "predOf") %>%
relocate(Cohort,.after = "GeneticGroup") %>%
arrange(predOf,desc(SELIND)) %>%
write.csv(.,file = here::here("output","genomicPredictions_full_set_2021Aug09.csv"),
row.names = F)NOTE: For cross predictions, check that the predMean from full and medium set are highly correlated. As long as that is true, combine the predMean from full set with pred var from medium set.
crossPreds_full<-readRDS(file = here::here("output","genomicMatePredictions_full_set_2021Aug10.rds"))
crossPreds_medium<-readRDS(file = here::here("output","genomicMatePredictions_medium_set_2021Aug10.rds"))crossPreds_full$tidyPreds[[1]] %>%
rename(predMean_full=predMean) %>%
left_join(crossPreds_medium$tidyPreds[[1]] %>%
rename(predMean_medium=predMean)) %>%
group_by(predOf,Trait) %>%
summarize(corPredMeans=cor(predMean_full,predMean_medium),.groups = 'drop') %>%
arrange(desc(corPredMeans)) %$% summary(corPredMeans)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9493 0.9798 0.9928 0.9855 0.9960 0.9981 The lowest corPredMeans was 0.95 for the SELIND, perhaps unsurprisingly.
Mean corPredMeans=0.9855. I think we are good to go.
Make a plot (below) to examine the scaling of predMean_medium vs. predMean_full to be sure that combining predMean_full with predSD_medium is safe. Seems like it. Everything is on the same scale as expected.
# crossPreds_full$tidyPreds[[1]] %>%
# rename(predMean_full=predMean) %>%
# left_join(crossPreds_medium$tidyPreds[[1]] %>%
# rename(predMean_medium=predMean)) %>%
# ggplot(aes(x=predMean_full,y=predMean_medium)) +
# geom_point() +
# geom_abline(slope=1,color='darkred') +
# facet_wrap(~Trait, scales='free')Recompute predUsefulness using predMean_full before saving to disk.
crossPreds_full$tidyPreds[[1]] %>%
left_join(crossPreds_medium$tidyPreds[[1]] %>%
rename(predMean_medium=predMean)) %>%
mutate(predUsefulness=predMean+(2.0627128*predSD),
sireGroup=case_when(grepl("2013_|TMS13",sireID)~"TMS13",
grepl("TMS14",sireID)~"TMS14",
grepl("TMS15",sireID)~"TMS15",
grepl("TMS16",sireID)~"TMS16",
grepl("TMS17",sireID)~"TMS17",
grepl("TMS18",sireID)~"TMS18",
grepl("TMS19",sireID)~"TMS19",
grepl("TMS20",sireID)~"TMS20",
!grepl("2013_|TMS13|TMS14|TMS15|TMS16|TMS17|TMS18|TMS19|TMS20",sireID)~"PreGS"),
damGroup=case_when(grepl("2013_|TMS13",damID)~"TMS13",
grepl("TMS14",damID)~"TMS14",
grepl("TMS15",damID)~"TMS15",
grepl("TMS16",damID)~"TMS16",
grepl("TMS17",damID)~"TMS17",
grepl("TMS18",damID)~"TMS18",
grepl("TMS19",damID)~"TMS19",
grepl("TMS20",damID)~"TMS20",
!grepl("2013_|TMS13|TMS14|TMS15|TMS16|TMS17|TMS18|TMS19|TMS20",damID)~"PreGS"),
CrossGroup=paste0(sireGroup,"x",damGroup)) %>%
relocate(contains("Group"),.before = "Nsegsnps") %>%
relocate(predMean,.before = "predMean_medium") %>%
arrange(predOf,Trait,desc(predUsefulness)) %>%
write.csv(.,file = here::here("output","genomicMatePredictions_2021Aug10.csv"),
row.names = F)