Last updated: 2020-12-03
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Knit directory: IITA_2020GS/
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| html | b9bb6f8 | wolfemd | 2020-12-03 | Build site. |
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| Rmd | 97778e7 | wolfemd | 2020-09-21 | Big update. Two types of pipeline to get BLUPs, GEBVs and GETGVs: |
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| Rmd | 7ea8b80 | wolfemd | 2020-09-17 | All steps including genomic predicting (excluding cross-validation), |
Three-stage procedure:
This next step fits models to each trait, combining curated data (BLUPs) from each trial, which we computed in the previous step.
rm(list = ls())
library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
dbdata <- readRDS(here::here("output", "IITA_CuratedTrials.rds"))
traits <- c("MCMDS", "DM", "PLTHT", "BRNHT1", "BRLVLS", "HI", "logFYLD", "logTOPYLD",
"logRTNO", "TCHART", "LCHROMO", "ACHROMO", "BCHROMO")Starting with the curated trial data (which correspond to per-trait, per-trial BLUPs) from the previous step.
Nest by trait. Need to restructure the data from per-trial BLUPs by regrouping by trait.
dbdata <- nestForMultiTrialAnalysis(dbdata)dbdata %>% mutate(N_blups = map_dbl(MultiTrialTraitData, nrow)) %>% rmarkdown::paged_table()Function fitMultiTrialModel() takes de-regressed BLUPs as response and corresponding weights on error variances are applied. Output includes BLUPs for each clone that combine data across trials and are suitable for downstream genomic prediction work.
Apply the fitMultiTrialModel() to each chunk of trials (per trait) using the purrr function map().
dbdata %<>% mutate(modelOutput = map(MultiTrialTraitData, fitMultiTrialModel))dbdata %>% select(-MultiTrialTraitData) %>% unnest(modelOutput) %>% unnest(VarComps) %>%
rmarkdown::paged_table()saveRDS(dbdata, file = here::here("output", "iita_blupsForModelTraining.rds"))Between the July 2019 and current genomic evaluation for IITA, I made “upgrades” (?) to the prediction procedure. Instead of the two-stage procedure implemented in 2019, I used a three-stage procedure in 2020 (described at the top). This was previously implemented for the NRCRI prediction done in April 2020.
I think it is essential to compare the “three-stage” approach with the “two-stage”!
Two-stage procedure:
Work below represents Stage 1 of the Two-stage procedure.
# activate multithread OpenBLAS for fast matrix algebra
export OMP_NUM_THREADS=56rm(list = ls())
library(tidyverse)
library(magrittr)
# source(here::here('code','gsFunctions.R'))
dbdata <- readRDS(here::here("output", "IITA_ExptDesignsDetected.rds"))
traits <- c("MCMDS", "DM", "PLTHT", "BRNHT1", "BRLVLS", "HI", "logFYLD", "logTOPYLD",
"logRTNO", "TCHART", "LCHROMO", "ACHROMO", "BCHROMO")Nest by trait. Need to restructure the data from per-trial by regrouping by trait.
dbdata %<>% dplyr::select(-MaxNOHAV) %>% unnest(TrialData) %>% dplyr::select(programName,
locationName, studyYear, TrialType, studyName, CompleteBlocks, IncompleteBlocks,
yearInLoc, trialInLocYr, repInTrial, blockInRep, observationUnitDbId, germplasmName,
FullSampleName, GID, all_of(traits), PropNOHAV) %>% mutate(IncompleteBlocks = ifelse(IncompleteBlocks ==
TRUE, "Yes", "No"), CompleteBlocks = ifelse(CompleteBlocks == TRUE, "Yes", "No")) %>%
pivot_longer(cols = all_of(traits), names_to = "Trait", values_to = "Value") %>%
filter(!is.na(Value), !is.na(GID)) %>% nest(MultiTrialTraitData = c(-Trait))To fit the mixed-model I used last year, I am again resorting to asreml. I fit random effects for rep and block only where complete and incomplete blocks, respectively are indicated in the trial design variables. sommer should be able to fit the same model via the at() function, but I am having trouble with it and sommer is much slower even without a dense covariance (i.e. a kinship), compared to lme4::lmer() or asreml().
dbdata %<>% mutate(fixedFormula = ifelse(Trait %in% c("logFYLD", "logRTNO", "logTOPYLD"),
"Value ~ yearInLoc", "Value ~ yearInLoc + PropNOHAV"), randFormula = paste0("~idv(GID) + idv(trialInLocYr) + at(CompleteBlocks,'Yes'):repInTrial ",
"+ at(IncompleteBlocks,'Yes'):blockInRep"))
dbdata %>% mutate(Nobs = map_dbl(MultiTrialTraitData, nrow)) %>% select(Trait, Nobs,
fixedFormula, randFormula) %>% rmarkdown::paged_table()# randFormula<-paste0('~vs(GID) + vs(trialInLocYr) +
# vs(at(CompleteBlocks,'Yes'),repInTrial) +
# vs(at(IncompleteBlocks,'Yes'),blockInRep)') library(sommer) fit <- mmer(fixed =
# Value ~ 1 + yearInLoc, random = as.formula(randFormula), data=trainingdata,
# getPEV=TRUE)Includes rounds of outlier removal and re-fitting.
fitASfunc<-function(fixedFormula,randFormula,MultiTrialTraitData,...){
# test arguments for function
# ----------------------
# MultiTrialTraitData<-dbdata$MultiTrialTraitData[[7]]
# #Trait<-dbdata$Trait[[3]]
# fixedFormula<-dbdata$fixedFormula[[7]]
# randFormula<-dbdata$randFormula[[7]]
# test<-fitASfunc(fixedFormula,randFormula,MultiTrialTraitData)
# ----------------------
require(asreml);
fixedFormula<-as.formula(fixedFormula)
randFormula<-as.formula(randFormula)
# fit asreml
out<-asreml(fixed = fixedFormula,
random = randFormula,
data = MultiTrialTraitData,
maxiter = 40, workspace=800e6, na.method.X = "omit")
#### extract residuals - Round 1
outliers1<-which(abs(scale(out$residuals))>3.3)
if(length(outliers1)>0){
x<-MultiTrialTraitData[-outliers1,]
# re-fit
out<-asreml(fixed = fixedFormula,
random = randFormula,
data = x,
maxiter = 40, workspace=800e6, na.method.X = "omit")
#### extract residuals - Round 2
outliers2<-which(abs(scale(out$residuals))>3.3)
if(length(outliers2)>0){
#### remove outliers
x<-x[-outliers2,]
# final re-fit
out<-asreml(fixed = fixedFormula,
random = randFormula,
data = x, maxiter = 40,workspace=800e6, na.method.X = "omit")
}
}
if(length(outliers1)==0){ outliers1<-NULL }
if(length(outliers2)==0){ outliers2<-NULL }
ll<-summary(out,all=T)$loglik
varcomp<-summary(out,all=T)$varcomp
Vg<-varcomp["GID!GID.var","component"]
Ve<-varcomp["R!variance","component"]
H2=Vg/(Vg+Ve)
blups<-summary(out,all=T)$coef.random %>%
as.data.frame %>%
rownames_to_column(var = "GID") %>%
dplyr::select(GID,solution,`std error`) %>%
filter(grepl("GID",GID)) %>%
rename(BLUP=solution) %>%
mutate(GID=gsub("GID_","",GID),
PEV=`std error`^2, # asreml specific
REL=1-(PEV/Vg), # Reliability
drgBLUP=BLUP/REL, # deregressed BLUP
WT=(1-H2)/((0.1 + (1-REL)/REL)*H2)) # weight for use in Stage 2
out<-tibble(loglik=ll,Vg,Ve,H2,
blups=list(blups),
varcomp=list(varcomp),
outliers1=list(outliers1),
outliers2=list(outliers2))
return(out) }library(furrr)
options(mc.cores = 13)
plan(multiprocess)
library(asreml)
dbdata %<>% mutate(fitAS = future_pmap(., fitASfunc))
dbdata %<>% select(-fixedFormula, -randFormula, -MultiTrialTraitData) %>% unnest(fitAS)saveRDS(dbdata, file = here::here("output", "iita_blupsForModelTraining_twostage_asreml.rds"))
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
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] lme4_1.1-25 Matrix_1.2-18 magrittr_2.0.1 forcats_0.5.0
[5] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[9] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 lubridate_1.7.9.2 here_1.0.0 lattice_0.20-41
[5] ps_1.4.0 assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.27
[9] R6_2.5.0 cellranger_1.1.0 backports_1.2.0 reprex_0.3.0
[13] evaluate_0.14 httr_1.4.2 pillar_1.4.7 rlang_0.4.9
[17] readxl_1.3.1 minqa_1.2.4 rstudioapi_0.13 nloptr_1.2.2.2
[21] whisker_0.4 rmarkdown_2.5 splines_4.0.2 statmod_1.4.35
[25] munsell_0.5.0 broom_0.7.2 compiler_4.0.2 httpuv_1.5.4
[29] modelr_0.1.8 xfun_0.19 pkgconfig_2.0.3 htmltools_0.5.0
[33] tidyselect_1.1.0 fansi_0.4.1 crayon_1.3.4 dbplyr_2.0.0
[37] withr_2.3.0 later_1.1.0.1 MASS_7.3-53 grid_4.0.2
[41] nlme_3.1-150 jsonlite_1.7.1 gtable_0.3.0 lifecycle_0.2.0
[45] DBI_1.1.0 git2r_0.27.1 formatR_1.7 scales_1.1.1
[49] cli_2.2.0 stringi_1.5.3 fs_1.5.0 promises_1.1.1
[53] xml2_1.3.2 ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.5
[57] boot_1.3-25 tools_4.0.2 glue_1.4.2 hms_0.5.3
[61] yaml_2.2.1 colorspace_2.0-0 rvest_0.3.6 knitr_1.30
[65] haven_2.3.1