Genomic Prediction Analysis - Stage II of II: Cross-validation Round 1
wolfemd
2019-7-27
Last updated: 2019-11-21
Checks: 7 0
Knit directory: IITA_2019GS/
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Objective
Two training datasets versions:
- studyYear>2012
- allYears
Using newly imputed DArT+GBS sites to construct kinship.
Cross-validate the entire TP for Ismail’s desired traits. Isolate sets of folds within different chunks of the TP:
- GG, TMS13, TMS14, TMS15, TMS18 (Ikenne CET only)
See code below and a diagram in the results section below that for more detail.
Cross-validation scheme
Make a kinship matrix
Read and process dosages
library(tidyverse); library(magrittr); library(furrr); library(data.table)
options(mc.cores=18)
plan(multiprocess)
snps<-tibble(Chr=1:18) %>%
mutate(raw=future_pmap(.,function(Chr,...){
# NOTE: The filepath here directs to a directory on cbsurobbins.biohpc.cornell.edu
# It is too big to put on GitHub
# To do list is to figure out best-practice for sharing
filename<-paste0("/workdir/marnin/nextgenImputation2019/ImputationStageII_71219/chr",Chr,
"_ImputationReferencePanel_StageIIpartI_72219.raw")
snps<-fread(filename,
stringsAsFactors = F) %>%
as_tibble
return(snps) }))
# BLUPs pre-outlier removal
asModelsFit<-readRDS(file="data/iita_blupsForCrossVal_72619.rds")
clonesWithBLUPs<-asModelsFit %>% unnest(blups) %$% unique(GID)Subset to clones with BLUPs only.
snps %<>%
mutate(raw=map(raw,function(raw){
out<-raw %>%
as.data.frame %>%
column_to_rownames(var = "IID") %>%
dplyr::select(-FID,-PAT,-MAT,-SEX,-PHENOTYPE) %>%
as.matrix %>%
.[rownames(.) %in% clonesWithBLUPs,];
return(out) }))Concatenate per-chromosome dosage matrices.
Subset the BLUPs to genotyped-clones only.
MAF>1% filter
Remove SNPs with minor allele frequency (MAF) less than 0.01
maf_filter<-function(snps,thresh){
freq<-colMeans(snps, na.rm=T)/2; maf<-freq;
maf[which(maf > 0.5)]<-1-maf[which(maf > 0.5)]
snps1<-snps[,which(maf>thresh)];
return(snps1) }
snps %<>% maf_filter(.,0.01)
dim(snps) # [1] 6629 82992Save filtered dosage matrix for future purposes.
Note filepath is to a non-public location, for the time being.
Set-up training data
rm(list=ls()); gc()
library(tidyverse); library(magrittr);
blups<-readRDS(file="data/iita_blupsForCrossVal_72619.rds")
K<-readRDS(file=paste0("/workdir/IITA_2019GS/Kinship_IITA_TrainingPop_72619.rds"))
blups %<>%
rename(trainingData=blups) %>%
mutate(trainingData=map(trainingData,~filter(.,GID %in% rownames(K))))
tms13f<-rownames(K) %>% grep("TMS13F|2013_",.,value = T); length(tms13f) # 2395
tms14f<-rownames(K) %>% grep("TMS14F",.,value = T); length(tms14f) # 2171
tms15f<-rownames(K) %>% grep("TMS15F",.,value = T); length(tms15f) # 835
gg<-setdiff(rownames(K),c(tms13f,tms14f,tms15f)); length(gg) # 1228 (not strictly gg)
blups %<>%
mutate(seed_of_seeds=1:n(),
seeds=map(seed_of_seeds,function(seed_of_seeds,reps=5){
set.seed(seed_of_seeds);
outSeeds<-sample(1:1000,size = reps,replace = F);
return(outSeeds) }))
blups %<>%
select(-varcomp); gc()Create an directory for the output
Cross-validation function
The version write output to disk to save RAM
For each Trait-Dataset combination, run 5 reps of 5-fold cross-validation.
# trainingData<-blups$trainingData[[1]]; seeds<-blups$seeds[[1]]; nfolds<-5; reps<-5;
crossValidateFunc<-function(Trait,Dataset,trainingData,seeds,nfolds=5,reps=5,ncores=40,...){
trntstdata<-trainingData %>%
filter(GID %in% rownames(K))
K1<-K[rownames(K) %in% trntstdata$GID,
rownames(K) %in% trntstdata$GID]
rm(K,trainingData); gc()
# seed<-seeds[[1]]
# Nfolds=nfolds
makeFolds<-function(Nfolds=nfolds,seed){
genotypes<-rownames(K1)
set.seed(seed)
seed_per_group<-sample(1:10000,size = 4,replace = FALSE)
set.seed(seed_per_group[1])
FoldsThisRep_tms15<-tibble(CLONE=genotypes[genotypes %in% tms15f],
Group="TMS15F") %>%
mutate(Folds=sample(1:Nfolds,nrow(.),replace=T)) %>%
arrange(Folds) %>%
group_by(Group,Folds) %>%
nest(.key = Test)
set.seed(seed_per_group[2])
FoldsThisRep_tms14<-tibble(CLONE=genotypes[genotypes %in% tms14f],
Group="TMS14F") %>%
mutate(Folds=sample(1:Nfolds,nrow(.),replace=T)) %>%
arrange(Folds) %>%
group_by(Group,Folds) %>%
nest(.key = Test)
set.seed(seed_per_group[3])
FoldsThisRep_tms13<-tibble(CLONE=genotypes[genotypes %in% tms13f],
Group="TMS13F") %>%
mutate(Folds=sample(1:Nfolds,nrow(.),replace=T)) %>%
arrange(Folds) %>%
group_by(Group,Folds) %>%
nest(.key = Test)
set.seed(seed_per_group[4])
FoldsThisRep_gg<-tibble(CLONE=genotypes[genotypes %in% gg],
Group="GGetc") %>%
mutate(Folds=sample(1:Nfolds,nrow(.),replace=T)) %>%
arrange(Folds) %>%
group_by(Group,Folds) %>%
nest(.key = Test)
FoldsThisRep<-bind_rows(FoldsThisRep_tms15,FoldsThisRep_tms14) %>%
bind_rows(FoldsThisRep_tms13) %>%
bind_rows(FoldsThisRep_gg) %>%
mutate(Test=map(Test,~.$CLONE),
Train=map(Test,~genotypes[!genotypes %in% .]))
return(FoldsThisRep) }
crossval<-tibble(Rep=1:reps,seed=unlist(seeds)) %>%
mutate(Folds=map2(Rep,seed,~makeFolds(Nfolds=nfolds,seed=.y))) %>%
unnest()
#Test<-crossval$Test[[1]]; Train<-crossval$Train[[1]]
crossValidate<-function(Test,Train){
train<-Train
test<-Test
trainingdata<-trntstdata %>%
filter(GID %in% train) %>%
mutate(GID=factor(GID,levels=rownames(K1)))
require(sommer)
proctime<-proc.time()
fit <- mmer(fixed = drgBLUP ~1,
random = ~vs(GID,Gu=K1),
weights = WT,
data=trainingdata)
proc.time()-proctime
x<-fit$U$`u:GID`$drgBLUP
gebvs<-tibble(GID=names(x),
GEBV=as.numeric(x))
accuracy<-gebvs %>%
filter(GID %in% test) %>%
left_join(
trntstdata %>%
dplyr::select(GID,BLUP) %>%
filter(GID %in% test)) %$%
cor(GEBV,BLUP, use='complete.obs')
return(accuracy)
}
require(furrr)
options(mc.cores=ncores)
plan(multiprocess)
crossval<-crossval %>%
mutate(accuracy=future_map2(Test,Train,~crossValidate(Test=.x,Train=.y)))
saveRDS(crossval,file=paste0("output/CrossVal_72719/",
"CrossVal_",Trait,"_",Dataset,"_72719.rds"))
rm(list=ls()); gc()
}Run CV on two servers
Results
#rm(list=ls());gc()
library(tidyverse);
library(magrittr);
library(cowplot);
files<-list.files("output/CrossVal_72719/")
pathway<-"output/CrossVal_72719/"
cv<-tibble(Files=files) %>%
mutate(cvResults=map(Files,~readRDS(paste0(pathway,.))))
cv %<>%
mutate(Files=gsub(pathway,"",Files),
Files=gsub("_72719.rds","",Files),
Dataset=ifelse(grepl("2013toPresent",Files),"2013toPresent","HistoricalDataIncluded"),
Files=gsub("_2013toPresent","",Files),
Files=gsub("_HistoricalDataIncluded","",Files)) %>%
rename(Trait=Files) %>%
unnest(cols = cvResults) %>%
mutate(Ntrain=map_dbl(Train,~length(.)),
Ntest=map_dbl(Test,~length(.))) %>%
select(-Test,-Train) %>%
unnest(cols = accuracy)Figure 1
This plot shows only the analyses with all historical data included. It aims to compare the accuracy within each genetic group.
library(viridis)
cv %>% filter(Dataset=="HistoricalDataIncluded") %>%
ggplot(.,aes(x=Trait,y=accuracy,fill=Group)) +
geom_boxplot() +
#facet_wrap(~Group,nrow=1) +
geom_hline(yintercept = 0,color='darkred',size=1.25) +
theme_bw() +
theme(axis.text.x = element_text(angle=90,face='bold')) +
scale_fill_viridis_d()
| Version | Author | Date |
|---|---|---|
| a869b9e | wolfemd | 2019-11-21 |
Figure 2
This plot shows only the analyses excluding historical data
library(viridis)
cv %>% filter(Dataset!="HistoricalDataIncluded") %>%
ggplot(.,aes(x=Trait,y=accuracy,fill=Group)) +
geom_boxplot() +
#facet_wrap(~Group,nrow=1) +
geom_hline(yintercept = 0,color='darkred',size=1.25) +
theme_bw() +
theme(axis.text.x = element_text(angle=90,face='bold')) +
scale_fill_viridis_d()
| Version | Author | Date |
|---|---|---|
| a869b9e | wolfemd | 2019-11-21 |
Figure 3
This plot aims to compare training on all historical-included to using only 2013toPresent data. Though the advantage is not huge, I see a small benefit or no cost to including historical data. Thoughts? One thing to note: You can see that the advantage of historical data is, not suprisingly, most sig. in the Genetic Gain. Phenotyping your clones continuously for 20 years works.
library(viridis)
cv %>%
ggplot(.,aes(x=Trait,y=accuracy,fill=Dataset)) +
geom_boxplot() +
facet_wrap(~Group,nrow=1) +
geom_hline(yintercept = 0,color='darkred',size=1.25) +
theme_bw() +
theme(axis.text.x = element_text(angle=90,size = 10),
legend.position = "top") +
scale_fill_viridis_d()
| Version | Author | Date |
|---|---|---|
| a869b9e | wolfemd | 2019-11-21 |
Figure 4
Or again, if you don’t want to compare the genetic groups, this is just lumping everything per trait, historical data-included.
library(viridis)
cv %>% filter(Dataset=="HistoricalDataIncluded") %>%
ggplot(.,aes(x=Trait,y=accuracy,fill=Trait)) +
geom_boxplot() +
#facet_wrap(~Group,nrow=1) +
geom_hline(yintercept = 0,color='darkred',size=1.25) +
theme_bw() +
theme(axis.text.x = element_text(angle=90),
legend.position = 'none') +
scale_fill_viridis_d()
| Version | Author | Date |
|---|---|---|
| a869b9e | wolfemd | 2019-11-21 |
Figure 5
Adjust the y-limit from Figure 4
library(viridis)
cv %>% filter(Dataset=="HistoricalDataIncluded") %>%
ggplot(.,aes(x=Trait,y=accuracy,fill=Trait)) +
geom_boxplot() +
#facet_wrap(~Group,nrow=1) +
geom_hline(yintercept = 0,color='darkred',size=1.25) +
theme_bw() +
theme(axis.text.x = element_text(angle=90),
legend.position = 'none') + lims(y=c(0,1)) +
scale_fill_viridis_d()
| Version | Author | Date |
|---|---|---|
| a869b9e | wolfemd | 2019-11-21 |
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] viridis_0.5.1 viridisLite_0.3.0 cowplot_1.0.0 magrittr_1.5
[5] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
[9] readr_1.3.1 tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1
[13] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.11 haven_2.2.0
[4] lattice_0.20-38 colorspace_1.4-1 vctrs_0.2.0
[7] generics_0.0.2 htmltools_0.4.0 yaml_2.2.0
[10] rlang_0.4.1 later_1.0.0 pillar_1.4.2
[13] withr_2.1.2 glue_1.3.1 modelr_0.1.5
[16] readxl_1.3.1 lifecycle_0.1.0 munsell_0.5.0
[19] gtable_0.3.0 workflowr_1.5.0.9000 cellranger_1.1.0
[22] rvest_0.3.5 evaluate_0.14 labeling_0.3
[25] knitr_1.26 httpuv_1.5.2 broom_0.5.2
[28] Rcpp_1.0.3 promises_1.1.0 backports_1.1.5
[31] scales_1.1.0 jsonlite_1.6 farver_2.0.1
[34] fs_1.3.1 gridExtra_2.3 hms_0.5.2
[37] digest_0.6.22 stringi_1.4.3 grid_3.6.1
[40] rprojroot_1.3-2 cli_1.1.0 tools_3.6.1
[43] lazyeval_0.2.2 crayon_1.3.4 whisker_0.4
[46] pkgconfig_2.0.3 zeallot_0.1.0 xml2_1.2.2
[49] lubridate_1.7.4 assertthat_0.2.1 rmarkdown_1.17
[52] httr_1.4.1 rstudioapi_0.10 R6_2.4.1
[55] nlme_3.1-142 git2r_0.26.1 compiler_3.6.1