Burn-in simulations for cassava GS
Marnin Wolfe
2021-Aug-26
Last updated: 2021-08-29
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Knit directory: BreedingSchemeOpt/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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. |
Previous step
I developed an empirical approach to estimate TrialType-specific error variances in terms of the IITA selection index (SELIND). See that analysis here.
Generate burn-in simulations
- Build a
runBurnInScheme()forAlphaSimHlpR. - Output of
runBurnInScheme()is input for downstream optimization and comparison of scenarios.
Previously, used control files to set-up bsp. Implemented specifyBSP(), which creates a bsp using a data.frame of stage-specific breeding scheme plus all other AlphaSimHlpR arguments as inputs.
- See full function reference 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.
Use my newly created specifyBSP() function to create the bsp input for sims.
- 3 chrom, Ne = 100, 300 SNP (100/chrom)
- Select 10 parents, make 4 random crosses with 50 progeny each
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,
selCritPipeAdv = selCritIID,
selCritPopImprov = selCritIID)
source(here::here("code","runBurnInSchemes.R"))I created a CSV to specify a data.frame schemeDF defining stage-specific breeding scheme inputs.
schemeDF %>% rmarkdown::paged_table()runBurnInSchemes(): basically runBreedingScheme() but without the final call records <- lastCycStgOut(records, bsp, SP) so that sims can be continued based on the records and bsp.
Also, set up function include the potentially parallel execution of multiple replications of each sim scheme.
Run 16 replicate simulations of 10 cycles phenotypic selection with a small breeding scheme on a laptop.
burnIn_test<-runBurnInSchemes(bsp = bsp,
nBurnInCycles=10,
selCritPop="selCritIID",
selCritPipe="selCritIID",
iniFunc="initializeScheme",
productFunc="productPipeline",
popImprovFunc="popImprov1Cyc",
nReplications=16,ncores=8,
nBLASthreads=1,nThreadsMacs2=1)
saveRDS(burnIn_test,file = here::here("output","burnIn_test.rds"))burnIn_test<-readRDS(here::here("output","burnIn_test.rds"))
forSimPlot<-burnIn_test %>%
unnest_wider(burnInSim) %>%
select(SimRep,records) %>%
unnest_wider(records) %>%
select(SimRep,stageOutputs) %>%
unnest() %>%
filter(stage=="F1") %>%
mutate(Year=year-max(year))
library(patchwork)
meanGplot<-forSimPlot %>%
group_by(Year,year,stage) %>%
summarize(meanGenMean=mean(genValMean),
seGenMean=sd(genValMean)/n()) %>%
ggplot(.,aes(x=Year)) +
geom_ribbon(aes(ymin = meanGenMean - seGenMean,
ymax = meanGenMean + seGenMean),
fill = "grey70", alpha=0.75) +
geom_line(aes(y = meanGenMean))
sdGplot<-forSimPlot %>%
group_by(Year,year,stage) %>%
summarize(meanGenSD=mean(genValSD),
seGenSD=sd(genValSD)/n()) %>%
ggplot(.,aes(x=Year)) +
geom_ribbon(aes(ymin = meanGenSD - seGenSD,
ymax = meanGenSD + seGenSD),
fill = "grey70", alpha=0.75) +
geom_line(aes(y = meanGenSD))
(meanGplot | sdGplot) & theme_bw()
| Version | Author | Date |
|---|---|---|
| e0d20bd | wolfemd | 2021-08-27 |
Ribbon plot mean and std. error computed across replicated simulations.
Next, execute the runSchemesPostBurnIn() function to continue running the 16 replicate initiated sims for an additional 10 post-burn in cycles of phenotypic selection.
source(here::here("code","runSchemesPostBurnIn.R"))
simulations<-readRDS(here::here("output","burnIn_test.rds"))
postBurnIn_test<-runSchemesPostBurnIn(simulations = simulations,
newBSP=NULL, # so you can change the scheme after burn-in
nPostBurnInCycles=10,
selCritPop="selCritIID",
selCritPipe="selCritIID",
productFunc="productPipeline",
popImprovFunc="popImprov1Cyc",
ncores=8,
nBLASthreads=1,nThreadsMacs2=1)
saveRDS(postBurnIn_test,file = here::here("output","postBurnIn_test.rds"))postBurnIn_test<-readRDS(here::here("output","postBurnIn_test.rds"))
forSimPlot<-postBurnIn_test %>%
unnest_wider(SimOutput) %>%
select(SimRep,records) %>%
unnest_wider(records) %>%
select(SimRep,stageOutputs) %>%
unnest() %>%
filter(stage=="F1") %>%
mutate(YearPostBurnIn=year-10)
library(patchwork)
meanGplot<-forSimPlot %>%
group_by(YearPostBurnIn,year,stage) %>%
summarize(meanGenMean=mean(genValMean),
seGenMean=sd(genValMean)/n()) %>%
ggplot(.,aes(x=YearPostBurnIn)) +
geom_ribbon(aes(ymin = meanGenMean - seGenMean,
ymax = meanGenMean + seGenMean),
fill = "grey70", alpha=0.75) +
geom_line(aes(y = meanGenMean))
sdGplot<-forSimPlot %>%
group_by(YearPostBurnIn,year,stage) %>%
summarize(meanGenSD=mean(genValSD),
seGenSD=sd(genValSD)/n()) %>%
ggplot(.,aes(x=YearPostBurnIn)) +
geom_ribbon(aes(ymin = meanGenSD - seGenSD,
ymax = meanGenSD + seGenSD),
fill = "grey70", alpha=0.75) +
geom_line(aes(y = meanGenSD))
(meanGplot | sdGplot) & theme_bw() & geom_vline(xintercept = 0, color='darkred')
| Version | Author | Date |
|---|---|---|
| e0d20bd | wolfemd | 2021-08-27 |
Now run the same 16 burnt-in sims for 10 cycles of GS.
# source(here::here("code","runSchemesPostBurnIn.R"))
# simulations<-readRDS(here::here("output","burnIn_test.rds"))
# postBurnInGS_test<-runSchemesPostBurnIn(simulations = simulations,
# newBSP=NULL,
# nPostBurnInCycles=10,
# selCritPop="parentSelCritGEBV",
# selCritPipe="selCritIID",
# productFunc="productPipeline",
# popImprovFunc="popImprov1Cyc",
# ncores=8,
# nBLASthreads=1,nThreadsMacs2=1)
# saveRDS(postBurnInGS_test,file = here::here("output","postBurnInGS_test.rds"))This actually ran all night on my laptop without finishing…. even the toy example with GS is non-trivial to run.
Run full-scale burn-in sims
Set-up multiple iterations of a simulation with selCritIID as burn-in.
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.
I run multiple versions of an burn-in simulation for 20 cycles. See plots below! My first try, with Ne=1000 seemed to have “too much” genetic variation so I did extra “tuning” simulations.
# 1) start a screen shell
screen;
# 2) reserve interactive slurm
salloc -n 20 --mem 60G;
# 3) start the singularity Linux shell inside that
singularity shell ~/rocker2.sif;
# Project directory, so R will use as working dir.
cd /home/mw489/BreedingSchemeOpt/;
# 3) Start R
export OMP_NUM_THREADS=1;
RsuppressMessages(library(AlphaSimHlpR))
suppressMessages(library(tidyverse))
suppressMessages(library(genomicMateSelectR))
select <- dplyr::select
source(here::here("code","runBurnInSchemes.R"))
RhpcBLASctl::blas_set_num_threads(1)
schemeDF<-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)
bsp<-specifyBSP(schemeDF = schemeDF,
nChr = 18,effPopSize = 1000,quickHaplo = F,
segSites = 2000, nQTL = 1000, nSNP = 300, genVar = 750,
gxeVar = NULL, gxyVar = 750, gxlVar = 375,gxyxlVar = 150,
meanDD = 0.23,varDD = 0.05,#relAA = 0.05,
stageToGenotype = "CET",
nParents = 100, nCrosses = 250, nProgeny = 10,nClonesToNCRP = 3,
phenoF1toStage1 = F,errVarPreStage1 = 500,
useCurrentPhenoTrain = F,
nCyclesToKeepRecords = 30,
# selCrits are overwritten by runBreedingScheme_wBurnIn
selCritPipeAdv = selCritIID, # thus have no actual effect
selCritPopImprov = selCritIID)
schemeDF %<>%
mutate(trainingPopCycles=10)
bsp1<-specifyBSP(schemeDF = schemeDF,
nChr = 18,effPopSize = 200,quickHaplo = F,
segSites = 2000, nQTL = 500, nSNP = 300, genVar = 750,
gxeVar = NULL, gxyVar = 750, gxlVar = 375,gxyxlVar = 150,
meanDD = 0.23,varDD = 0.05,#relAA = 0.05,
stageToGenotype = "CET",
nParents = 100, nCrosses = 250, nProgeny = 10,nClonesToNCRP = 3,
phenoF1toStage1 = F,errVarPreStage1 = 500,
useCurrentPhenoTrain = F,
nCyclesToKeepRecords = 30,
# selCrits are overwritten by runBreedingScheme_wBurnIn
selCritPipeAdv = selCritIID, # thus have no actual effect
selCritPopImprov = selCritIID)
bsp2<-specifyBSP(schemeDF = schemeDF,
nChr = 18,effPopSize = 200,quickHaplo = F,
segSites = 2000, nQTL = 150, nSNP = 300, genVar = 750,
gxeVar = NULL, gxyVar = 750, gxlVar = 375,gxyxlVar = 150,
meanDD = 0.23,varDD = 0.05,#relAA = 0.05,
stageToGenotype = "CET",
nParents = 100, nCrosses = 250, nProgeny = 10,nClonesToNCRP = 3,
phenoF1toStage1 = F,errVarPreStage1 = 500,
useCurrentPhenoTrain = F,
nCyclesToKeepRecords = 30,
# selCrits are overwritten by runBreedingScheme_wBurnIn
selCritPipeAdv = selCritIID, # thus have no actual effect
selCritPopImprov = selCritIID)
bsp3<-specifyBSP(schemeDF = schemeDF,
nChr = 18,effPopSize = 100,quickHaplo = F,
segSites = 2000, nQTL = 150, nSNP = 300, genVar = 750,
gxeVar = NULL, gxyVar = 750, gxlVar = 375,gxyxlVar = 150,
meanDD = 0.23,varDD = 0.05,#relAA = 0.05,
stageToGenotype = "CET",
nParents = 100, nCrosses = 250, nProgeny = 10,nClonesToNCRP = 3,
phenoF1toStage1 = F,errVarPreStage1 = 500,
useCurrentPhenoTrain = F,
nCyclesToKeepRecords = 30,
# selCrits are overwritten by runBreedingScheme_wBurnIn
selCritPipeAdv = selCritIID, # thus have no actual effect
selCritPopImprov = selCritIID)
# bsp4<-specifyBSP(schemeDF = schemeDF,
# nChr = 18,effPopSize = 100,quickHaplo = F,
# segSites = 2000, nQTL = 150, nSNP = 300, genVar = 350,
# gxeVar = NULL, gxyVar = 350, gxlVar = 125,gxyxlVar = 50,
# meanDD = 0.23,varDD = 0.05,#relAA = 0.05,
# stageToGenotype = "CET",
# nParents = 100, nCrosses = 250, nProgeny = 10,nClonesToNCRP = 3,
# phenoF1toStage1 = F,errVarPreStage1 = 500,
# useCurrentPhenoTrain = F,
# nCyclesToKeepRecords = 30,
# # selCrits are overwritten by runBreedingScheme_wBurnIn
# selCritPipeAdv = selCritIID, # thus have no actual effect
# selCritPopImprov = selCritIID)start<-proc.time()[3]
burnInSims<-runBurnInSchemes(bsp = bsp,
nBurnInCycles=20,
selCritPop="selCritIID",
selCritPipe="selCritIID",
iniFunc="initializeScheme",
productFunc="productPipeline",
popImprovFunc="popImprov1Cyc",
nReplications=20,ncores=20,
nBLASthreads=1,nThreadsMacs2=1)
end<-proc.time()[3]; print(paste0((end-start)/60," mins elapsed"))
saveRDS(burnInSims,file = here::here("output","burnInSims_iita_2021Aug27.rds"))
# [1] "163.0525 mins elapsed"
start<-proc.time()[3]
burnInSims<-runBurnInSchemes(bsp = bsp1,
nBurnInCycles=20,
selCritPop="selCritIID",
selCritPipe="selCritIID",
iniFunc="initializeScheme",
productFunc="productPipeline",
popImprovFunc="popImprov1Cyc",
nReplications=20,ncores=20,
nBLASthreads=1,nThreadsMacs2=1)
saveRDS(burnInSims,file = here::here("output","burnInSims_bsp1_iita_2021Aug27.rds"))
burnInSims<-runBurnInSchemes(bsp = bsp2,
nBurnInCycles=20,
selCritPop="selCritIID",
selCritPipe="selCritIID",
iniFunc="initializeScheme",
productFunc="productPipeline",
popImprovFunc="popImprov1Cyc",
nReplications=20,ncores=20,
nBLASthreads=1,nThreadsMacs2=1)
saveRDS(burnInSims,file = here::here("output","burnInSims_bsp2_iita_2021Aug27.rds"))
burnInSims<-runBurnInSchemes(bsp = bsp3,
nBurnInCycles=20,
selCritPop="selCritIID",
selCritPipe="selCritIID",
iniFunc="initializeScheme",
productFunc="productPipeline",
popImprovFunc="popImprov1Cyc",
nReplications=20,ncores=20,
nBLASthreads=1,nThreadsMacs2=1)
saveRDS(burnInSims,file = here::here("output","burnInSims_bsp3_iita_2021Aug27.rds"))
# burnInSims<-runBurnInSchemes(bsp = bsp4,
# nBurnInCycles=20,
# selCritPop="selCritIID",
# selCritPipe="selCritIID",
# iniFunc="initializeScheme",
# productFunc="productPipeline",
# popImprovFunc="popImprov1Cyc",
# nReplications=20,ncores=20,
# nBLASthreads=1,nThreadsMacs2=1)
# saveRDS(burnInSims,file = here::here("output","burnInSims_bsp4_iita_2021Aug27.rds"))
end<-proc.time()[3]; print(paste0((end-start)/60," mins elapsed"))suppressMessages(library(AlphaSimHlpR))
suppressMessages(library(tidyverse))
suppressMessages(library(genomicMateSelectR))
select <- dplyr::select
forSimPlot<-tibble(bsp="0",Ne=1000,nQTL=1000, genVar=750) %>%
bind_rows(tibble(bsp="1",Ne=200,nQTL=500, genVar=750)) %>%
bind_rows(tibble(bsp="2",Ne=200,nQTL=150, genVar=750)) %>%
bind_rows(tibble(bsp="3",Ne=100,nQTL=150, genVar=750)) %>%
#bind_rows(tibble(bsp="4",Ne=100,nQTL=150, genVar=350)) %>%
mutate(sims=paste0("burnInSims_bsp",bsp,"_iita_2021Aug27.rds"),
sims=ifelse(bsp=="0","burnInSims_iita_2021Aug27.rds",sims),
sims=map(sims,~readRDS(here::here("output",.))))
forSimPlot %<>%
mutate(sims=map(sims,function(sims){
sims %>%
mutate(burnInSim=map(burnInSim,~.$records$stageOutputs)) })) %>%
unnest(sims) %>%
unnest(burnInSim) %>%
filter(stage=="F1") %>%
mutate(Year=year-max(year))
gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 2234841 119.4 5652228 301.9 NA 3892255 207.9
Vcells 14807306 113.0 5903741208 45042.0 65536 6677701760 50946.9forSimPlot %<>%
mutate(Pop=paste0("Ne",Ne,"_nQTL",nQTL))
library(patchwork)
meanGplot<-forSimPlot %>%
group_by(Pop,Ne,nQTL,genVar,Year,year,stage) %>%
summarize(meanGenMean=mean(genValMean),
seGenMean=sd(genValMean)/n()) %>% ungroup() %>%
ggplot(.,aes(x=Year,group=Pop)) +
geom_ribbon(aes(ymin = meanGenMean - seGenMean,
ymax = meanGenMean + seGenMean,
fill=Pop),
alpha=0.75) +
geom_line(aes(y = meanGenMean, color=Pop))
sdGplot<-forSimPlot %>%
group_by(Pop,Ne,nQTL,genVar,Year,year,stage) %>%
summarize(meanGenSD=mean(genValSD),
seGenSD=sd(genValSD)/n()) %>% ungroup() %>%
ggplot(.,aes(x=Year,group=Pop)) +
geom_ribbon(aes(ymin = meanGenSD - seGenSD,
ymax = meanGenSD + seGenSD,
fill=Pop),
alpha=0.75) +
geom_line(aes(y = meanGenSD))
(meanGplot | sdGplot) & theme_bw()
Should we just run those longer till they show any sign of slowing genetic gain?
Next steps
Complete 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 4.1.0 (2021-05-18)
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.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_3.1.3 ggplot2_3.3.5
[10] tidyverse_1.3.1 AlphaSimHlpR_0.2.1 dplyr_1.0.7
[13] AlphaSimR_1.0.3 R6_2.5.0 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_2.10
[21] labeling_0.4.2 munsell_0.5.0 broom_0.7.9 compiler_4.1.0
[25] httpuv_1.6.1 modelr_0.1.8 xfun_0.25 pkgconfig_2.0.3
[29] htmltools_0.5.1.1 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_1.2.0
[37] grid_4.1.0 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_1.7.3 farver_2.1.0 fs_1.5.0
[49] promises_1.2.0.1 xml2_1.3.2 bslib_0.2.5.1 ellipsis_0.3.2
[53] generics_0.1.0 vctrs_0.3.8 tools_4.1.0 glue_1.4.2
[57] hms_1.1.0 yaml_2.2.1 colorspace_2.0-2 rvest_1.0.1
[61] knitr_1.33 haven_2.4.3 sass_0.4.0