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Two-stage genomic prediction refers to the following procedure:
Stage 1: Fit a linear mixed model to the data without genomic data. Individuals (e.g. clones / accessions) are modeled as independent and identically distributed (i.i.d.) random effects. The BLUPs for this random effect represent the measurable total genetic values of each individual. All the experimental design variation, e.g. replication and blocking effects have been controlled for in the creation of our new response variable, the BLUPs from the gneotype random effect.
Stage 2: Using a modified version of the BLUPs from step 1 as the response variable, fit a genomic prediction model, which now has reduced size because the number of observations is now the same as the number of individuals.
NOTE: In the animal breeding literature single-step often refers to predictions that combine pedigree and marker information simultaneously. That is not our meaning here.
The code below represents Stage I.
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);
dbdata<-readRDS(file=here::here("output","NRCRI_CuratedTrials_2020April27.rds"))
traits<-c("CGM","CGMS1","CGMS2","MCMDS","DM","PLTHT","BRNHT1","HI","logFYLD","logTOPYLD","logRTNO")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.
nestForMultiTrialAnalysis<-function(curatedTrialData){
nested_trialdata<-curatedTrialData %>%
# remove trait-trial models that failed
filter(!is.na(H2)) %>%
# remove some per-trial summaries we don't want at this stage
select(-H2,-VarComps,-Model,-Noutliers,-propMiss) %>%
unnest(BLUPs) %>%
nest(MultiTrialTraitData=c(-Trait))
return(nested_trialdata)
}# A tibble: 10 x 3
Trait MultiTrialTraitData N_blups
<chr> <list> <dbl>
1 CGMS1 <tibble [10,152 × 12]> 10152
2 CGMS2 <tibble [5,461 × 12]> 5461
3 MCMDS <tibble [6,952 × 12]> 6952
4 PLTHT <tibble [5,163 × 12]> 5163
5 BRNHT1 <tibble [3,523 × 12]> 3523
6 HI <tibble [6,352 × 12]> 6352
7 logFYLD <tibble [6,355 × 12]> 6355
8 logTOPYLD <tibble [6,352 × 12]> 6352
9 logRTNO <tibble [6,118 × 12]> 6118
10 DM <tibble [3,699 × 12]> 3699# A tibble: 3,638 x 2
GID n
<chr> <int>
1 101330 2
2 101403 2
3 101431 2
4 101447 1
5 101499 1
6 101634 1
7 10499 1
8 106224 5
9 10649 2
10 1070576 1
# … with 3,628 more rowsWhere is the data from Ugo Ikeogu’s (and subsequent) work on NIRs? I’m thinking someone needs to be trying to use esp. the DM NIRs data for genomic prediction!?
The function below 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.
# curatedTrialData<-dbdata$MultiTrialTraitData[[1]]
# rm(curatedTrialData)
fitMultiTrialModel<-function(curatedTrialData,GID="GID"){
require(lme4)
modelFormula<-paste0("drgBLUP ~ (1|",GID,")")
fit_model<-possibly(function(modelFormula,curatedTrialData){
model_out<-lmer(as.formula(modelFormula),
data=curatedTrialData,
weights = WT)
return(model_out) },
otherwise = NA)
model_out<-fit_model(modelFormula,curatedTrialData)
summary(model_out)
if(is.na(model_out)){
out <-tibble(H2=NA,VarComps=list(NULL),BLUPs=list(NULL),Model=modelFormula)
} else {
varcomps<-as.data.frame(VarCorr(model_out))[,c("grp","vcov")] %>%
spread(grp,vcov)
Vg<-varcomps$GID
H2<-Vg/(Vg+varcomps$Residual)
BLUP<-ranef(model_out, condVar=TRUE)[[GID]]
PEV <- c(attr(BLUP, "postVar"))
blups<-tibble(GID=rownames(BLUP),BLUP=BLUP$`(Intercept)`,PEV=PEV) %>%
mutate(REL=1-(PEV/Vg),
drgBLUP=BLUP/REL,
WT=(1-H2)/((0.1 + (1-REL)/REL)*H2))
out <- tibble(H2=H2,
VarComps=list(varcomps),
BLUPs=list(blups),
Model=modelFormula) }
return(out)
}Run the function with the simplest possible loop using the purrr function map().
# A tibble: 10 x 5
Trait H2 VarComps BLUPs Model
<chr> <dbl> <list> <list> <chr>
1 CGMS1 0.113 <df[,2] [1 × 2]> <tibble [3,226 × 6]> drgBLUP ~ (1|GID)
2 CGMS2 0.00168 <df[,2] [1 × 2]> <tibble [2,333 × 6]> drgBLUP ~ (1|GID)
3 MCMDS 0.0796 <df[,2] [1 × 2]> <tibble [3,597 × 6]> drgBLUP ~ (1|GID)
4 PLTHT 0.0556 <df[,2] [1 × 2]> <tibble [2,703 × 6]> drgBLUP ~ (1|GID)
5 BRNHT1 0.0337 <df[,2] [1 × 2]> <tibble [1,981 × 6]> drgBLUP ~ (1|GID)
6 HI 0.127 <df[,2] [1 × 2]> <tibble [3,292 × 6]> drgBLUP ~ (1|GID)
7 logFYLD 0.0341 <df[,2] [1 × 2]> <tibble [3,302 × 6]> drgBLUP ~ (1|GID)
8 logTOPYLD 0.0678 <df[,2] [1 × 2]> <tibble [3,200 × 6]> drgBLUP ~ (1|GID)
9 logRTNO 0.0305 <df[,2] [1 × 2]> <tibble [3,218 × 6]> drgBLUP ~ (1|GID)
10 DM 0.105 <df[,2] [1 × 2]> <tibble [2,425 × 6]> drgBLUP ~ (1|GID)# dbdata<-readRDS(here::here("output","nrcri_blupsForModelTraining_2020April21.rds"))
# dbdata %>%
# #filter(studyName=="18C2acrossingblockCETubiaja") %>%
# select(Trait,modelOutput) %>%
# unnest(modelOutput) %>%
# select(Trait,BLUPs) %>%
# unnest(BLUPs) %>%
# filter(grepl("c2",GID,ignore.case = T)) %>% count(GID)
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] lme4_1.1-23 Matrix_1.2-18 magrittr_1.5 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 here_0.1 lattice_0.20-41
[5] ps_1.4.0 assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25
[9] utf8_1.1.4 R6_2.4.1 cellranger_1.1.0 backports_1.1.10
[13] reprex_0.3.0 evaluate_0.14 httr_1.4.2 pillar_1.4.6
[17] rlang_0.4.8 readxl_1.3.1 minqa_1.2.4 rstudioapi_0.11
[21] nloptr_1.2.2.2 whisker_0.4 blob_1.2.1 rmarkdown_2.4
[25] splines_4.0.2 statmod_1.4.34 munsell_0.5.0 broom_0.7.1
[29] compiler_4.0.2 httpuv_1.5.4 modelr_0.1.8 xfun_0.18
[33] pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0 fansi_0.4.1
[37] crayon_1.3.4 dbplyr_1.4.4 withr_2.3.0 later_1.1.0.1
[41] MASS_7.3-53 grid_4.0.2 nlme_3.1-149 jsonlite_1.7.1
[45] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1
[49] scales_1.1.1 cli_2.1.0 stringi_1.5.3 fs_1.5.0
[53] promises_1.1.1 xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2
[57] vctrs_0.3.4 boot_1.3-25 tools_4.0.2 glue_1.4.2
[61] hms_0.5.3 yaml_2.2.1 colorspace_1.4-1 rvest_0.3.6
[65] knitr_1.30 haven_2.3.1