Fit multi-trait Bayesian Ridge Regressions

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Last updated: 2021-01-03

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Knit directory: PredictOutbredCrossVar/

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    Modified:   output/pmv_DirectionalDom_varcomps_geneticgroups.rds
    Modified:   output/pmv_varcomps_geneticgroups.rds
    Modified:   output/pmv_varcomps_geneticgroups_tidy_includingSIvars.rds
    Modified:   workflowr_log.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/fitMtBRRs.Rmd) and HTML (docs/fitMtBRRs.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	22e6c87	wolfemd	2021-01-03	Build site.
Rmd	ef3f7b3	wolfemd	2021-01-02	Compile submission version of all Rmds with outstanding, uncommitted
Rmd	2e13628	wolfemd	2020-11-25	Misc minor changes
html	3dbb1e8	wolfemd	2020-10-08	Site built for first COMPLETE draft, shared with co-authors.
html	b06eee7	wolfemd	2020-08-31	Build site.
html	7a4e168	wolfemd	2020-07-31	Build site.
html	a5ac99f	wolfemd	2020-07-31	Build site.
html	ff4fc8b	wolfemd	2020-07-31	Build site.
html	e6f8fe8	wolfemd	2020-07-31	Build site.
Rmd	07509a8	wolfemd	2020-07-31	Organized and ready to be published as a workflowr HTML page.
Rmd	a1950f7	wolfemd	2020-07-29	Directional dominance fully implemented, except in prediction of untested crosses. Presentation to NGC Leader’s call included.
Rmd	c0b292a	wolfemd	2020-06-23	Most analyses coded and complete. Moved all wrapper functions to code/*.R scripts.
Rmd	a6ca472	wolfemd	2020-06-16	Remove analysis to compute PMVs AND subsequent plotting from fitMtBRRs.Rmd to getPMVarComps.Rmd.
Rmd	4846c0e	wolfemd	2020-06-14	All analyses completed for one-rep of parent-wise cross-val. as of June 9 Edinburgh CBDG talk.

---

Parent-wise cross-validation

Define parent-wise cross-validation folds

Goal is to assess the accuracy of predicting untested crosses of outbred parents.

5-folds times 5-replications of parent-wise cross-validation, defined as follows:

• 209 parents in IITA pedigree
• Divide set of parents into k-folds
• For each k
  • Training data: Test parents + all non-test parent descendants.
    • That is, remove all offspring, grandchildren, greatgrandchildren, etc. of test parents
    • Predict each cross test parents were involved in
  • Validation data: The inverse of the training set, including all offspring and descendants of the test parents (but not the test parents themselves or the other training samples).
  • Use genomic-model on validation set to get at observed mean and variance in BV / TGV in families

library(tidyverse); library(magrittr); library(rsample)
ped<-readRDS(here::here("data","ped_awc.rds"))
set.seed(42)
parentfolds<-vfold_cv(tibble(Parents=union(ped$sireID,ped$damID)),v = 5,repeats = 5) %>% 
  dplyr::mutate(folds=map(splits,function(splits){
    #splits<-parentfolds$splits[[1]]
    testparents<-testing(splits)$Parents
    trainparents<-training(splits)$Parents
    offspring<-ped %>% 
      filter(sireID %in% testparents | damID %in% testparents) %$% 
      unique(FullSampleName)
    grandkids<-ped %>% 
      filter(sireID %in% offspring | damID %in% offspring) %$% 
      unique(FullSampleName)
    greatgrandkids<-ped %>% 
      filter(sireID %in% grandkids | damID %in% grandkids) %$% 
      unique(FullSampleName)
    testset<-unique(c(offspring,grandkids,greatgrandkids)) %>% .[!. %in% c(testparents,trainparents)]
    nontestdescendents<-ped %>% 
      filter(!FullSampleName %in% testset) %$% 
      unique(FullSampleName)
    trainset<-union(testparents,trainparents) %>% 
      union(.,nontestdescendents)
    out<-tibble(testparents=list(testparents),
                trainset=list(trainset),
                testset=list(testset))
    return(out) })) %>% 
  unnest(folds)
# table(parentfolds$trainset[[1]] %in% parentfolds$testparents[[1]])
# table(parentfolds$testset[[1]] %in% parentfolds$testparents[[1]])
# table(parentfolds$testset[[1]] %in% parentfolds$trainset[[1]])
saveRDS(parentfolds,file = here::here("data","parentwise_crossVal_folds.rds"))

Fit A and AD models

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  # choosing de-regressed BLUPs as responses despite unweighted analysis
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# Training datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>% 
  rename(Repeat=id,Fold=id2) %>% 
  select(Repeat,Fold,trainset,testset) %>% 
  pivot_longer(c(trainset,testset),
               names_to = "Dataset",
               values_to = "sampleIDs") %>% 
  crossing(Model=c("A","AD")) %>% 
  arrange(desc(Dataset),Repeat,Fold) %>% 
  mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
         outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); dim(snps) # [1] 5591 38093

# fitMtBRR function -------------
## Wrapper function for BGLR::Multitrait()
## For a given set of training blups+snps for either model "A" or "AD"
source(here::here("code","fitMtBRR.R"))

# Parallelization specs ---------
require(furrr); options(mc.cores=10); plan(multiprocess)
options(future.globals.maxSize= 10000*1024^2)

Run MtBRRs

# cbsulm19 - Jun 17, 07:09am - 
parentfolds %>% 
  slice(1:10) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

# cbsulm21 - Jun 17, 07:10pm - 
parentfolds %>% 
  slice(11:20) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

# cbsulm22 - Jun 17, 07:10pm - 
parentfolds %>% 
  slice(21:30) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

# cbsulm26 - Jun 17, 07:10pm - 
parentfolds %>% 
  slice(31:40) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

# cbsulm27 - Jun 17, 07:10pm - 
parentfolds %>% 
  slice(41:50) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

# cbsulm19 - Jun 17, 11:51am - 
parentfolds %>% 
  slice(51:60) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

# cbsulm26 - Jun 17, 12:25pm - 
parentfolds %>% 
  slice(61:70) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

# cbsulm21 - Jun 17, 01:04pm - 
parentfolds %>% 
  slice(71:80) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

# cbsulm22 - Jun 17, 01:04pm - 
parentfolds %>% 
  slice(81:90) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

# cbsulm27 - Jun 17, 01:04pm - 
parentfolds %>% 
  slice(91:100) %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

Fit DirDom model

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  # choosing de-regressed BLUPs as responses despite unweighted analysis
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# Training datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>% 
  rename(Repeat=id,Fold=id2) %>% 
  select(Repeat,Fold,trainset,testset) %>% 
  pivot_longer(c(trainset,testset),
               names_to = "Dataset",
               values_to = "sampleIDs") %>% 
  mutate(Model="DirectionalDom") %>% 
  arrange(desc(Dataset),Repeat,Fold) %>% 
  mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
         outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); dim(snps) # [1] 5591 38093

# fitDirectionalDomMtBRR function -------------
## Wrapper function for BGLR::Multitrait()
## For a given set of training blups+snps, fit a directional dominance model as in Xiang et al. 2016.
## using "biologically" partitioned additive and dominance effects + a mean effect for overall proportion homozygous (inbreeding).
source(here::here("code","fitDirectionalDomMtBRR.R"))

# Parallelization specs ---------
require(furrr); options(mc.cores=10); plan(multiprocess)
options(future.globals.maxSize= 10000*1024^2)

# Divide parentfolds into chunks for each server ------------
nchunks<-5
parentfolds %<>% 
  mutate(Chunk=rep(1:nchunks, each=ceiling(nrow(.)/nchunks), length.out=nrow(.))) %>% 
  nest(data=c(-Chunk))

Run Mt BRRs

# cbsulm19
chunk<-1;
# cbsulm21
chunk<-2;
# cbsulm22
chunk<-3;
# cbsulm26
chunk<-4;
# cbsulm27
chunk<-5;
# Start run on each server / chunk: Jun 29, 5:20pm
parentfolds$data[[chunk]] %>% 
  future_pmap(.,fitDirectionalDomMtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5)

Genetic groups

• GG, GG+C1, GG+C1+C2, GG+C1+C2+C3

Fit A and AD models

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  # choosing de-regressed BLUPs as responses despite unweighted analysis
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); dim(snps) # [1] 5591 38093

# Training datasets -----------
geneticgroups<-blups %>% 
  filter(!grepl("TMS13|TMS14|TMS15",germplasmName)) %>% 
  mutate(Group="GG") %>% 
  bind_rows(blups %>% 
              filter(grepl("TMS13",germplasmName)) %>% 
              mutate(Group="TMS13")) %>% 
  bind_rows(blups %>% 
              filter(grepl("TMS14",germplasmName)) %>% 
              mutate(Group="TMS14")) %>% 
  bind_rows(blups %>% 
              filter(grepl("TMS15",germplasmName)) %>% 
              mutate(Group="TMS15")) %>% 
  bind_rows(blups %>% 
              mutate(Group="All")) %>% 
  nest(blups=-Group) %>% 
  crossing(Model=c("A","AD")) %>% 
  mutate(blups=map(blups,~filter(.,germplasmName %in% rownames(snps))),
         outName=paste0("mt_",Group,"_",Model))

# fitMtBRR function -------------
## Wrapper function for BGLR::Multitrait()
## For a given set of training blups+snps for either model "A" or "AD"
source(here::here("code","fitMtBRR.R"))

# Parallelization specs ---------
require(furrr); options(mc.cores=10); plan(multiprocess)
options(future.globals.maxSize= 10000*1024^2)

Run Mt BRRs

geneticgroups %>% 
  mutate(mtbrrFit=future_pmap(.,fitmtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5))

Fit DirDom model

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  # choosing de-regressed BLUPs as responses despite unweighted analysis
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); dim(snps) # [1] 5591 38093

# Training datasets -----------
geneticgroups<-blups %>% 
  filter(!grepl("TMS13|TMS14|TMS15",germplasmName)) %>% 
  mutate(Group="GG") %>% 
  bind_rows(blups %>% 
              filter(grepl("TMS13",germplasmName)) %>% 
              mutate(Group="TMS13")) %>% 
  bind_rows(blups %>% 
              filter(grepl("TMS14",germplasmName)) %>% 
              mutate(Group="TMS14")) %>% 
  bind_rows(blups %>% 
              filter(grepl("TMS15",germplasmName)) %>% 
              mutate(Group="TMS15")) %>% 
  bind_rows(blups %>% 
              mutate(Group="All")) %>% 
  nest(blups=-Group) %>% 
  mutate(Model="DirectionalDom") %>% 
  mutate(blups=map(blups,~filter(.,germplasmName %in% rownames(snps))),
         outName=paste0("mt_",Group,"_",Model))

# fitDirectionalDomMtBRR function -------------
## Wrapper function for BGLR::Multitrait()
## For a given set of training blups+snps, fit a directional dominance model as in Xiang et al. 2016.
## using "biologically" partitioned additive and dominance effects + a mean effect for overall proportion homozygous (inbreeding).
source(here::here("code","fitDirectionalDomMtBRR.R"))

# Parallelization specs ---------
require(furrr); options(mc.cores=5); plan(multiprocess)
options(future.globals.maxSize= 20000*1024^2)

Run Mt BRRs

# Start run on cbsulm15: July 29, 11:15pm - 
geneticgroups %>% 
  future_pmap(.,fitDirectionalDomMtBRR,snps=snps,outPath="output/mtMarkerEffects", nIter=30000, burnIn=5000,thin=5)

Get Genomic BLUPs

Fit A and AD models

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  # choosing de-regressed BLUPs as responses despite unweighted analysis
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# Training datasets -----------
## Parent-wise CV folds
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>% 
  rename(Repeat=id,Fold=id2) %>% 
  select(Repeat,Fold,trainset,testset) %>% 
  pivot_longer(c(trainset,testset),
               names_to = "Dataset",
               values_to = "sampleIDs") %>% 
  crossing(Model=c("A","AD")) %>% 
  arrange(desc(Dataset),Repeat,Fold) %>% 
  mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
         outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model)) %>% 
  select(Repeat,Fold,Dataset,Model,outName)
## Genetic groups
geneticgroups<-blups %>% 
  filter(!grepl("TMS13|TMS14|TMS15",germplasmName)) %>% 
  mutate(Group="GG") %>% 
  bind_rows(blups %>% 
              filter(grepl("TMS13",germplasmName)) %>% 
              mutate(Group="TMS13")) %>% 
  bind_rows(blups %>% 
              filter(grepl("TMS14",germplasmName)) %>% 
              mutate(Group="TMS14")) %>% 
  bind_rows(blups %>% 
              filter(grepl("TMS15",germplasmName)) %>% 
              mutate(Group="TMS15")) %>% 
  bind_rows(blups %>% 
              mutate(Group="All")) %>% 
  nest(blups=-Group) %>% 
  crossing(Model=c("A","AD")) %>% 
  mutate(blups=map(blups,~filter(.,germplasmName %in% rownames(snps))),
         outName=paste0("mt_",Group,"_",Model))

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); dim(snps) # [1] 5591 38093

# Parallelization specs ---------
require(furrr); options(future.globals.maxSize=10000*1024^2)
plan(multiprocess); options(mc.cores=25); 

# getGenomicBLUPs function -----------------
## Wrapper function for either Model=="A" or "AD"
## For a given set of multi-trait posterior mean marker effects
## and a given set of SNPs, load the effects and compute GEBV and GETGV
source(here::here("code","getGenomicBLUPs.R"))

Compute GEBV and GETGV

Parent-wise CV folds

parentfolds %<>% 
  mutate(GBLUPs=future_pmap(.,getGenomicBLUPs,snps=snps))
saveRDS(parentfolds,here::here("output","gblups_parentwise_crossVal_folds.rds"))

Genetic groups

geneticgroups %<>% 
 mutate(GBLUPs=future_pmap(.,getGenomicBLUPs,snps=snps))
saveRDS(geneticgroups,here::here("output","gblups_geneticgroups.rds"))

Fit DirDom model

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=112

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  # choosing de-regressed BLUPs as responses despite unweighted analysis
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# Training datasets -----------
## Parent-wise CV folds
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>% 
  rename(Repeat=id,Fold=id2) %>% 
  select(Repeat,Fold,trainset,testset) %>% 
  pivot_longer(c(trainset,testset),
               names_to = "Dataset",
               values_to = "sampleIDs") %>% 
  mutate(Model="DirectionalDom") %>% 
  arrange(desc(Dataset),Repeat,Fold) %>% 
  mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
         outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); dim(snps) # [1] 5591 38093

# Parallelization specs ---------
require(furrr); options(future.globals.maxSize=10000*1024^2)
plan(multiprocess); options(mc.cores=25); 

# getGenomicBLUPs function -----------------
## Similar to "getGenomicBLUPs.R"
## For a given set of multi-trait posterior mean marker effects
## and a given set of SNPs, load the effects and compute GEBV and GETGV
# 1. inbreeding effect for each trait is extracted from the BGLR output,
### divided by N snps and added to the vector of SNP effects
# 2. Allele substitution effects are computed as a+d(q-p) and used to predict GEBV
# 3. GETGV = sum(X_a*a + X_d*d)
source(here::here("code","getDirectionalDomGenomicBLUPs.R"))

Compute GEBV and GETGV

parentfolds %<>% 
  mutate(GBLUPs=future_pmap(.,getDirectionalDomGenomicBLUPs,snps=snps))
saveRDS(parentfolds,here::here("output","gblups_DirectionalDom_parentwise_crossVal_folds.rds"))

Session information

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] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5      rstudioapi_0.13 whisker_0.4     knitr_1.30     
 [5] magrittr_2.0.1  R6_2.5.0        rlang_0.4.9     stringr_1.4.0  
 [9] tools_4.0.2     xfun_0.19       git2r_0.27.1    htmltools_0.5.0
[13] ellipsis_0.3.1  rprojroot_2.0.2 yaml_2.2.1      digest_0.6.27  
[17] tibble_3.0.4    lifecycle_0.2.0 crayon_1.3.4    later_1.1.0.1  
[21] vctrs_0.3.5     promises_1.1.1  fs_1.5.0        glue_1.4.2     
[25] evaluate_0.14   rmarkdown_2.6   stringi_1.5.3   compiler_4.0.2 
[29] pillar_1.4.7    httpuv_1.5.4    pkgconfig_2.0.3