Example of AlphaSimHlpR simulation with a burn-in period
Marnin Wolfe
2021-Sep-17
Last updated: 2021-09-18
Checks: 7 0
Knit directory: BreedingSchemeOpt/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 107fc24 | wolfemd | 2021-09-18 | Example simulations using new product pipeline and popimprovement functions, flexible to changes in VDP structure post burn-in. |
| html | eb13eb3 | wolfemd | 2021-08-29 | Build site. |
| Rmd | 7bccac8 | wolfemd | 2021-08-29 | Run some full-scale burn-in sims - IITA specs - tuning Ne and nQTL |
| html | e0d20bd | wolfemd | 2021-08-27 | Build site. |
| Rmd | 9d369ee | wolfemd | 2021-08-27 | Publish burnInSims with the toy example completed and the full analysis almost ready to run. |
Below, I show an example of how to run a simulation that includes a burn-in and post burn-in phase.
- Use
runBurnInScheme()function (not yet inAlphaSimHlpR; sourced fromcode/runBurnInScheme.R) to initiate a simulation with phenotypic selection. - Use
runSchemesPostBurnIn()with a newbsp(sourced fromcode/runSchemesPostBurnIn.R) to continue simulating restarting the previously started (burnt-in) simulations created in Step 1 but potentially with newbspsettings.
Install the version of AlphaSimHlpR that I have been working on.
# To install the latest version
devtools::install_github("wolfemd/AlphaSimHlpR", ref = 'master')See full function reference manual here in my forked-repo of AlphaSimHlpR
A small example
Test the code with a small example. Source functions not yet included in AlphaSimHlpR from code/ directory.
suppressMessages(library(AlphaSimHlpR))
suppressMessages(library(tidyverse))
suppressMessages(library(genomicMateSelectR))
select <- dplyr::select
schemeDF<-read.csv(here::here("data","baselineScheme - Test.csv"),
header = T, stringsAsFactors = F)
bsp<-specifyBSP(schemeDF = schemeDF,
nChr = 3,effPopSize = 100,quickHaplo = F,
segSites = 400, nQTL = 40, nSNP = 100, genVar = 40,
gxeVar = NULL, gxyVar = 15, gxlVar = 10,gxyxlVar = 5,
meanDD = 0.5,varDD = 0.01,relAA = 0.5,
stageToGenotype = "PYT",
nParents = 10, nCrosses = 4, nProgeny = 50,nClonesToNCRP = 3,
phenoF1toStage1 = T,errVarPreStage1 = 500,
useCurrentPhenoTrain = F,
nCyclesToKeepRecords = 30,
nTrainPopCycles=6,
nYrsAsCandidates=1,
maxTrainingPopSize=500)
source(here::here("code","runBurnInSchemes.R"))
source(here::here("code","runSchemesPostBurnIn.R"))I created a CSV to specify a data.frame schemeDF defining stage-specific breeding scheme inputs.
schemeDF %>% rmarkdown::paged_table()Run 10 cycles of burn-in simulation.
The default is to set-up phenotypic selection only during burn-in.
burnInSims<-runBurnInSchemes(bsp = bsp,
nBurnInCycles=10,
selCritPop="parentSelCritBLUP",
selCritPipe="selCritIID",
iniFunc="initializeScheme",
productFunc="productPipeline",
popImprovFunc="popImprovByParentSel",
nReplications=4,nSimCores=4,
nBLASthreads=1,nThreadsMacs2=1)
saveRDS(burnInSims,file = here::here("output","test_burnInSims_2021Sep17.rds"))Two sets of post burn-in simulations, both with same bsp overall.
continue with phenotypic selection, no change.
Switch to
parentSelCritGEBV.
NOTE: Below, switch (by default) to productPipelinePostBurnIn for the product advancement pipeline and it’s corresponding selection criteria productSelCritBLUP
burnInSims<-readRDS(file = here::here("output","test_burnInSims_2021Sep17.rds"))
postBurnIn_PS<-runSchemesPostBurnIn(simulations = burnInSims,
nPostBurnInCycles=10,
selCritPop="parentSelCritBLUP",
selCritPipe="productSelCritBLUP",
productFunc="productPipelinePostBurnIn",
popImprovFunc="popImprovByParentSel",
nSimCores=4,
nBLASthreads=1)
saveRDS(postBurnIn_PS,file = here::here("output","test_burnInSims_PS_2021Sep17.rds"))
#postBurnIn_PS$SimOutput[[1]]$records$stageOutputs
postBurnIn_GS<-runSchemesPostBurnIn(simulations = burnInSims,
nPostBurnInCycles=10,
selCritPop="parentSelCritGEBV",
selCritPipe="productSelCritBLUP",
productFunc="productPipelinePostBurnIn",
popImprovFunc="popImprovByParentSel",
nSimCores=4,
nBLASthreads=1)
saveRDS(postBurnIn_GS,file = here::here("output","test_burnInSims_GS_2021Sep17.rds"))
#postBurnIn_GS$SimOutput[[1]]$records$stageOutputsforSimPlot<-readRDS(file = here::here("output","test_burnInSims_GS_2021Sep17.rds")) %>%
mutate(PostBurnIn="GS") %>%
bind_rows(readRDS(file = here::here("output","test_burnInSims_PS_2021Sep17.rds")) %>%
mutate(PostBurnIn="PS")) %>%
unnest_wider(SimOutput) %>%
select(SimRep,PostBurnIn,records) %>%
unnest_wider(records) %>%
select(SimRep,PostBurnIn,stageOutputs) %>%
unnest() %>%
filter(stage=="F1") %>%
mutate(YearPostBurnIn=year-10)
library(patchwork)
meanGplot<-forSimPlot %>%
mutate(SimRep=paste0(PostBurnIn,SimRep)) %>%
group_by(PostBurnIn,YearPostBurnIn,year,stage) %>%
summarize(meanGenMean=mean(genValMean),
seGenMean=sd(genValMean)/sqrt(n())) %>%
ggplot(.,aes(x=YearPostBurnIn)) +
geom_ribbon(aes(ymin = meanGenMean - seGenMean,
ymax = meanGenMean + seGenMean,
fill = PostBurnIn),
alpha=0.75) +
geom_line(aes(y = meanGenMean, color=PostBurnIn))
sdGplot<-forSimPlot %>%
mutate(SimRep=paste0(PostBurnIn,SimRep)) %>%
group_by(PostBurnIn,YearPostBurnIn,year,stage) %>%
summarize(meanGenSD=mean(genValSD),
seGenSD=sd(genValSD)/sqrt(n())) %>%
ggplot(.,aes(x=YearPostBurnIn)) +
geom_ribbon(aes(ymin = meanGenSD - seGenSD,
ymax = meanGenSD + seGenSD,
fill = PostBurnIn),
alpha=0.75) +
geom_line(aes(y = meanGenSD, color=PostBurnIn))
(meanGplot | sdGplot) + patchwork::plot_layout(guides = 'collect') &
theme_bw() & geom_vline(xintercept = 0, color='darkred')
Change the VDP
Try post burn-in sims with an altered VDP (specified in the bsp).
Do not change pop. genetic / genomic parameters.
Try removing middle “AYT” stage of the example schemeDF.
suppressMessages(library(AlphaSimHlpR))
suppressMessages(library(tidyverse))
suppressMessages(library(genomicMateSelectR))
select <- dplyr::select
schemeDF<-read.csv(here::here("data","baselineScheme - Test.csv"),
header = T, stringsAsFactors = F)
source(here::here("code","runSchemesPostBurnIn.R"))
burnInSims<-readRDS(file = here::here("output","test_burnInSims_2021Sep17.rds"))
newBSP<-specifyBSP(schemeDF = schemeDF %>% filter(stageNames!="AYT"),
nChr = 3,effPopSize = 100,quickHaplo = F,
segSites = 400, nQTL = 40, nSNP = 100, genVar = 40,
gxeVar = NULL, gxyVar = 15, gxlVar = 10,gxyxlVar = 5,
meanDD = 0.5,varDD = 0.01,relAA = 0.5,
stageToGenotype = "PYT",
nParents = 10, nCrosses = 4, nProgeny = 50,nClonesToNCRP = 3,
phenoF1toStage1 = T,errVarPreStage1 = 500,
useCurrentPhenoTrain = F,
nCyclesToKeepRecords = 30,
nTrainPopCycles=6,
nYrsAsCandidates=1,
maxTrainingPopSize=500)
postBurnIn_PS_newBSP<-runSchemesPostBurnIn(simulations = burnInSims,
newBSP = newBSP,
nPostBurnInCycles=10,
selCritPop="parentSelCritBLUP",
selCritPipe="productSelCritBLUP",
productFunc="productPipelinePostBurnIn",
popImprovFunc="popImprovByParentSel",
nSimCores=4,
nBLASthreads=1)
saveRDS(postBurnIn_PS_newBSP,file = here::here("output","test_burnInSims_PS_noAYT_2021Sep17.rds"))
postBurnIn_GS_newBSP<-runSchemesPostBurnIn(simulations = burnInSims,
newBSP = newBSP,
nPostBurnInCycles=10,
selCritPop="parentSelCritGEBV",
selCritPipe="productSelCritBLUP",
productFunc="productPipelinePostBurnIn",
popImprovFunc="popImprovByParentSel",
nSimCores=4,
nBLASthreads=1)
saveRDS(postBurnIn_GS_newBSP,file = here::here("output","test_burnInSims_GS_noAYT_2021Sep17.rds"))forSimPlot<-readRDS(file = here::here("output","test_burnInSims_GS_2021Sep17.rds")) %>%
mutate(PostBurnIn="GS") %>%
bind_rows(readRDS(file = here::here("output","test_burnInSims_PS_2021Sep17.rds")) %>%
mutate(PostBurnIn="PS")) %>%
bind_rows(readRDS(file = here::here("output","test_burnInSims_PS_noAYT_2021Sep17.rds")) %>%
mutate(PostBurnIn="PS_noAYT")) %>%
bind_rows(readRDS(file = here::here("output","test_burnInSims_GS_noAYT_2021Sep17.rds")) %>%
mutate(PostBurnIn="GS_noAYT")) %>%
unnest_wider(SimOutput) %>%
select(SimRep,PostBurnIn,records) %>%
unnest_wider(records) %>%
select(SimRep,PostBurnIn,stageOutputs) %>%
unnest() %>%
filter(stage=="F1") %>%
mutate(YearPostBurnIn=year-10)
library(patchwork)
meanGplot<-forSimPlot %>%
mutate(SimRep=paste0(PostBurnIn,SimRep)) %>%
group_by(PostBurnIn,YearPostBurnIn,year,stage) %>%
summarize(meanGenMean=mean(genValMean),
seGenMean=sd(genValMean)/sqrt(n())) %>%
ggplot(.,aes(x=YearPostBurnIn)) +
geom_ribbon(aes(ymin = meanGenMean - seGenMean,
ymax = meanGenMean + seGenMean,
fill = PostBurnIn),
alpha=0.75) +
geom_line(aes(y = meanGenMean, color=PostBurnIn))
sdGplot<-forSimPlot %>%
mutate(SimRep=paste0(PostBurnIn,SimRep)) %>%
group_by(PostBurnIn,YearPostBurnIn,year,stage) %>%
summarize(meanGenSD=mean(genValSD),
seGenSD=sd(genValSD)/sqrt(n())) %>%
ggplot(.,aes(x=YearPostBurnIn)) +
geom_ribbon(aes(ymin = meanGenSD - seGenSD,
ymax = meanGenSD + seGenSD,
fill = PostBurnIn),
alpha=0.75) +
geom_line(aes(y = meanGenSD, color=PostBurnIn))
(meanGplot | sdGplot) + patchwork::plot_layout(guides = 'collect') &
theme_bw() & geom_vline(xintercept = 0, color='darkred')
Run full-scale burn-in sims
Previously, I used an empirical approach to estimate TrialType-specific error variances in terms of the IITA selection index (SELIND). See that analysis here.
TO DO: Need to run full-scale burn-in simulations for each breeding program.
20 burn-in cycles to match examples by EiB.
Genome / Pop specs
18 chrom,
Ne = 1000,
nSNP = 300 SNP/chrom (matches EiB examples)
nQTLperChr = 1000
nSegSites = 2000
Genetic architecture and Error variance
genVar = 750and stage-specificerrVarinput from hereThe max estimated
errVarwas for CET at ~3500,so a
genVarof 750 is to set up a entry level h2 around 0.2
meanDD = 0.3andvarDD = 0.05- or
MeanDD=0.23andVarDD=0.06, based loosely on this estimate and note.
- or
Var(GxYr) == Var(G), again matching EiB example
- What about GxL and GxLxYr?
read.csv(here::here("data","baselineScheme - IITA.csv"),
header = T, stringsAsFactors = F) %>%
select(-errVars,-PlantsPerPlot) %>%
left_join(readRDS(here::here("data","siErrorVarEst_byTrialType_directApproach_2021Aug25.rds")) %>%
select(-VarEsts) %>%
rename(errVars=siErrorVarEst)) %>%
select(-TrialType) %>%
mutate(trainingPopCycles=20) %>%
rmarkdown::paged_table()Breeding Scheme (
schemeDFprinted above)Skips SDN stage. Is there an UYT2 (second year of UYT) to sim?
phenoF1toStage1 = FALSEPopulation Improvement
nParents = 50, nCrosses = 100, nProgeny = 25,nClonesToNCRP = 3- Or
nParents = 100, nCrosses = 250, nProgeny = 10,nClonesToNCRP = 3(EiB example)
Additional Settings
nCyclesToKeepRecords = 30(all)… what effect does this actually have? Just on storage of output?trainingPopCycles = 15…means 15 years of each stage used in each prediction…
What about an alternative: set a fixed TP size e.g. 5000 clones.
- This might be faster since the number of clones / dimension of kinship matrix is primary slow point.
Run multiple versions of an burn-in simulation for 20 cycles.
Next steps
Complete full-scale burn-in simulations
Burn-in and baseline simulations for National programs (NaCRRI, TARI, NRCRI, EMBRAPA). Still need input re: selection index weights and current program structure.
Begin the actually interesting simulations
Optimize budgets
Compare alternative VDPs
Test mate selection, optimal contributions and ultimately optimizing mating plans.
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] patchwork_1.1.1 genomicMateSelectR_0.2.0 forcats_0.5.1
[4] stringr_1.4.0 purrr_0.3.4 readr_2.0.1
[7] tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5
[10] tidyverse_1.3.1 AlphaSimHlpR_0.2.1 dplyr_1.0.7
[13] AlphaSimR_1.0.4 R6_2.5.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 here_1.0.1 lubridate_1.7.10 assertthat_0.2.1
[5] rprojroot_2.0.2 digest_0.6.27 utf8_1.2.2 cellranger_1.1.0
[9] backports_1.2.1 reprex_2.0.1 evaluate_0.14 highr_0.9
[13] httr_1.4.2 pillar_1.6.2 rlang_0.4.11 readxl_1.3.1
[17] rstudioapi_0.13 whisker_0.4 jquerylib_0.1.4 rmarkdown_2.11
[21] labeling_0.4.2 munsell_0.5.0 broom_0.7.9 compiler_4.1.1
[25] httpuv_1.6.3 modelr_0.1.8 xfun_0.26 pkgconfig_2.0.3
[29] htmltools_0.5.2 tidyselect_1.1.1 fansi_0.5.0 crayon_1.4.1
[33] tzdb_0.1.2 dbplyr_2.1.1 withr_2.4.2 later_1.3.0
[37] grid_4.1.1 jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.0
[41] DBI_1.1.1 git2r_0.28.0 magrittr_2.0.1 scales_1.1.1
[45] cli_3.0.1 stringi_1.7.4 farver_2.1.0 fs_1.5.0
[49] promises_1.2.0.1 xml2_1.3.2 bslib_0.3.0 ellipsis_0.3.2
[53] generics_0.1.0 vctrs_0.3.8 tools_4.1.1 glue_1.4.2
[57] hms_1.1.0 fastmap_1.1.0 yaml_2.2.1 colorspace_2.0-2
[61] rvest_1.0.1 knitr_1.34 haven_2.4.3 sass_0.4.0