Last updated: 2020-10-16
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Knit directory: NRCRI_2020GS/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 977f389 | wolfemd | 2020-10-16 | Publish NRCRI imputations for 2020 (DCas20_5510 and DCas20_5440) plus a |
Get BLUPs combining all trial data: Combine data from all trait-trials to get BLUPs for downstream genomic prediction.
Current Step:
Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
5-fold cross-validation. Replicate 5-times.
2 genomic models:
The data for the next step can be found on the cassavabase FTP server here.
Can be loaded directly to R from FTP.
NOTICE: You need enough RAM and a stable network connection. I do the next steps, including cross-validation on a server with plenty of RAM and a good, stable network connection, rather than on my personal computer (a laptop with 16 GB RAM).
The outputs (kinship matrices and filtered snp dosages) of the steps below, which are too large for GitHub, can be found on the cassavabase FTP server here.
# activate multithread OpenBLAS for fast compute of SigmaM (genotypic var-covar matrix)
export OMP_NUM_THREADS=56library(tidyverse); library(magrittr);
snps<-readRDS(file=url(paste0("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/",
"DosageMatrix_RefPanelAndGSprogeny_ReadyForGP_73019.rds")))
snps5510<-readRDS(here::here("output","DosageMatrix_DCas20_5510_WA_REFimputedAndFiltered.rds"))
snps5440<-readRDS(here::here("output","DosageMatrix_DCas20_5440_WA_REFimputedAndFiltered.rds"))
rownames(snps5510) # NR19C3aF1P0002....
rownames(snps5440) # NR18S1P0082...
snps2keep<-colnames(snps) %>%
.[. %in% colnames(snps5510)] %>%
.[. %in% colnames(snps5440)]
snps<-rbind(snps[,snps2keep],
snps5510[,snps2keep]) %>%
rbind(.,snps5440[,snps2keep])
gc()
#rm(list=(ls() %>% grep("snps",.,value = T, invert = T)))blups_nrcri<-readRDS(file=here::here("output","nrcri_blupsForModelTraining_twostage_asreml_2020Oct13.rds"))
# copy or download IITA BLUPs from GitHub to data/ directory
# https://wolfemd.github.io/IITA_2020GS/output/iita_blupsForModelTraining_twostage_asreml.rds
blups_iita<-readRDS(file=here::here("data","iita_blupsForModelTraining_twostage_asreml.rds"))
blups_nrcri %<>%
select(Trait,blups) %>%
unnest(blups) %>%
select(-`std error`) %>%
filter(GID %in% rownames(snps))
table(unique(blups_nrcri$GID) %in% rownames(snps)) # 2751
blups_iita %<>%
select(Trait,blups) %>%
unnest(blups) %>%
select(-`std error`) %>%
filter(GID %in% rownames(snps),
!grepl("TMS13F|TMS14F|TMS15F|2013_|TMS16F|TMS17F|TMS18F",GID))
table(unique(blups_iita$GID) %in% rownames(snps)) # 1234Going to use my own kinship function.
Make the kinships.
Below e.g. A*A makes a matrix that approximates additive-by-additive epistasis relationships.
A<-kinship(snps,type="add")
D<-kinship(snps,type="dom")
AD<-A*D
saveRDS(snps,file=here::here("output","DosageMatrix_NRCRI_2020Oct15.rds"))
saveRDS(A,file=here::here("output","Kinship_A_NRCRI_2020Oct15.rds"))
saveRDS(D,file=here::here("output","Kinship_D_NRCRI_2020Oct15.rds"))
saveRDS(AD,file=here::here("output","Kinship_AD_NRCRI_2020Oct15.rds"))
#rm(snps); gc()NOTICE: The outputs (kinship matrices and filtered snp dosages) of the steps below, which are too large for GitHub, can be found on the cassavabase FTP server here.
rm(list=ls())
library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))
blups_nrcri<-readRDS(file=here::here("output","nrcri_blupsForModelTraining_twostage_asreml_2020Oct13.rds"))
blups_iita<-readRDS(file=here::here("data","iita_blupsForModelTraining_twostage_asreml.rds"))
A<-readRDS(file=here::here("output","Kinship_A_NRCRI_2020Oct15.rds"))
blups_nrcri %<>%
select(Trait,blups) %>%
unnest(blups) %>%
select(-`std error`) %>%
filter(GID %in% rownames(A))
blups_iita %<>%
select(Trait,blups) %>%
unnest(blups) %>%
select(-`std error`) %>%
filter(GID %in% rownames(A),
!grepl("TMS13F|TMS14F|TMS15F|2013_|TMS16F|TMS17F|TMS18F",GID))
# Set-up a grouping variable for:
## nrTP, C1a, C1b and C2a.
## Nest by Trait.
c1a<-blups_nrcri$GID %>%
unique %>%
grep("c1a",.,value = T,ignore.case = T) %>%
union(.,blups_nrcri$GID %>% unique %>%
grep("^F",.,value = T,ignore.case = T) %>%
grep("c1b",.,value = T,ignore.case = T,invert = T))
c1b<-blups_nrcri$GID %>% unique %>% grep("c1b",.,value = T,ignore.case = T)
c2a<-blups_nrcri$GID %>% unique %>%
grep("C2a",.,value = T,ignore.case = T) %>%
grep("NR17",.,value = T,ignore.case = T)
c2b<-blups_nrcri$GID %>% unique %>%
grep("C2b",.,value = T,ignore.case = T) %>%
.[!. %in% c(c1a,c1b,c2a)]
nrTP<-setdiff(unique(blups_nrcri$GID),unique(c(c1a,c1b,c2a,c2b)))
cv2do<-blups_nrcri %>%
mutate(Group=case_when(GID %in% nrTP ~ "nrTP",
GID %in% c1a ~ "C1a",
GID %in% c1b ~ "C1b",
GID %in% c2a ~ "C2a",
GID %in% c2b ~ "C2b")) %>%
nest(TrainTestData=-Trait) %>%
left_join(blups_iita %>%
nest(augmentTP=-Trait))
cv2do %>% rmarkdown::paged_table()The function below runCrossVal() function implements nfold cross-validation. Specifically, for each of nrepeats it splits the data into nfolds sets according to gid. So if nfolds=5 then the the clones will be divided into 5 groups and 5 predictions will be made. In each prediction, 4/5 of the clones will be used to predict the remaining 1/5. Accuracy of the model is measured as the correlation between the BLUPs (adj. mean for each CLONE) in the test set and the GEBV (the prediction made of each clone when it was in the test set).
Below, 4 chunks of “5 reps x 5-fold” cross-validation are run on 1 large memory Cornell CBSU machine each (e.g. cbsulm16; 112 cores, 512 GB RAM).
starttime<-proc.time()[3]
cv_A_nrOnly<-cv2do %>%
mutate(CVresults=map(TrainTestData,~runCrossVal(TrainTestData=.,
modelType="A",
grms=list(A=A),
byGroup=TRUE,augmentTP=NULL,
nrepeats=5,nfolds=5,ncores=4,gid="GID")))
runtime<-proc.time()[3]-starttime; runtime
cv_A_nrOnly %<>% mutate(Dataset="NRalone",modelType="A") %>% dplyr::select(-TrainTestData,-augmentTP)
saveRDS(cv_A_nrOnly,file=here::here("output","cvresults_A_nrOnly_2020Oct15.rds"))For this one, try with ncores=1 instead of ncores=10.
starttime<-proc.time()[3]
cv_A_iitaAugmented<-cv2do %>%
mutate(isnullAugment=map_lgl(augmentTP,~is.null(.))) %>%
filter(!isnullAugment) %>%
select(-isnullAugment) %>%
mutate(CVresults=map2(TrainTestData,augmentTP,~runCrossVal(TrainTestData=.x,
modelType="A",
grms=list(A=A),
byGroup=TRUE,augmentTP=.y,
nrepeats=5,nfolds=5,ncores=1,gid="GID")))
runtime<-proc.time()[3]-starttime; runtime
cv_A_iitaAugmented %<>% mutate(Dataset="IITAaugmented",modelType="A") %>% dplyr::select(-TrainTestData,-augmentTP)
saveRDS(cv_A_iitaAugmented,file=here::here("output","cvresults_A_iitaAugmented_2020Oct15.rds"))options(future.globals.maxSize= 3000*1024^2)
D<-readRDS(file=here::here("output","Kinship_D_NRCRI_2020Oct15.rds"))
AD<-readRDS(file=here::here("output","Kinship_AD_NRCRI_2020Oct15.rds"))
starttime<-proc.time()[3]
cv_ADE_nrOnly<-cv2do %>%
mutate(CVresults=map(TrainTestData,~runCrossVal(TrainTestData=.,
modelType="ADE",
grms=list(A=A,D=D,AD=AD),
byGroup=TRUE,augmentTP=NULL,
nrepeats=5,nfolds=5,ncores=10,gid="GID")))
cv_ADE_nrOnly %<>% mutate(Dataset="NRalone",modelType="ADE") %>% dplyr::select(-TrainTestData,-augmentTP)
saveRDS(cv_ADE_nrOnly,file=here::here("output","cvresults_ADE_nrOnly.rds"))
runtime<-proc.time()[3]-starttime; runtimeoptions(future.globals.maxSize= 3000*1024^2)
D<-readRDS(file=here::here("output","Kinship_D_NRCRI_2020Oct15.rds"))
AD<-readRDS(file=here::here("output","Kinship_AD_NRCRI_2020Oct15.rds"))
starttime<-proc.time()[3]
cv_ADE_iitaAugmented<-cv2do %>%
mutate(isnullAugment=map_lgl(augmentTP,~is.null(.))) %>%
filter(!isnullAugment) %>%
dplyr::select(-isnullAugment) %>%
mutate(CVresults=map2(TrainTestData,augmentTP,~runCrossVal(TrainTestData=.x,
modelType="ADE",
grms=list(A=A,D=D,AD=AD),
byGroup=TRUE,augmentTP=.y,
nrepeats=5,nfolds=5,ncores=10,gid="GID")))
cv_ADE_iitaAugmented %<>% mutate(Dataset="IITAaugmented",modelType="ADE") %>% dplyr::select(-TrainTestData,-augmentTP)
saveRDS(cv_ADE_iitaAugmented,file=here::here("output","cvresults_ADE_iitaAugmented_2020Oct15.rds"))
runtime<-proc.time()[3]-starttime; runtimeoptions(future.globals.maxSize= 3000*1024^2)
D<-readRDS(file=here::here("output","Kinship_D_NRCRI_2020Oct15.rds"))
#AD<-readRDS(file=here::here("output","Kinship_AD_NRCRI_2020Oct15.rds"))
starttime<-proc.time()[3]
cv_AD_iitaAugmented<-cv2do %>%
mutate(isnullAugment=map_lgl(augmentTP,~is.null(.))) %>%
filter(!isnullAugment) %>%
dplyr::select(-isnullAugment) %>%
mutate(CVresults=map2(TrainTestData,augmentTP,~runCrossVal(TrainTestData=.x,
modelType="AD",
grms=list(A=A,D=D),
byGroup=TRUE,augmentTP=.y,
nrepeats=5,nfolds=5,ncores=4,gid="GID")))
cv_AD_iitaAugmented %<>% mutate(Dataset="IITAaugmented",modelType="AD") %>% dplyr::select(-TrainTestData,-augmentTP)
saveRDS(cv_AD_iitaAugmented,file=here::here("output","cvresults_AD_iitaAugmented_2020Oct15.rds"))
runtime<-proc.time()[3]-starttime; runtimeSee Results
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] magrittr_1.5 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.18 haven_2.3.1 colorspace_1.4-1
[5] vctrs_0.3.4 generics_0.0.2 htmltools_0.5.0 yaml_2.2.1
[9] blob_1.2.1 rlang_0.4.8 later_1.1.0.1 pillar_1.4.6
[13] withr_2.3.0 glue_1.4.2 DBI_1.1.0 dbplyr_1.4.4
[17] modelr_0.1.8 readxl_1.3.1 lifecycle_0.2.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.6 evaluate_0.14
[25] knitr_1.30 ps_1.4.0 httpuv_1.5.4 fansi_0.4.1
[29] broom_0.7.1 Rcpp_1.0.5 promises_1.1.1 backports_1.1.10
[33] scales_1.1.1 jsonlite_1.7.1 fs_1.5.0 hms_0.5.3
[37] digest_0.6.25 stringi_1.5.3 rprojroot_1.3-2 grid_4.0.2
[41] here_0.1 cli_2.1.0 tools_4.0.2 crayon_1.3.4
[45] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1 xml2_1.3.2
[49] reprex_0.3.0 lubridate_1.7.9 rstudioapi_0.11 assertthat_0.2.1
[53] rmarkdown_2.4 httr_1.4.2 R6_2.4.1 git2r_0.27.1
[57] compiler_4.0.2