Last updated: 2021-05-05

Checks: 7 0

Knit directory: NRCRI_2021GS/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

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Environment: empty

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Seed: set.seed(20210427)

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Session information: recorded

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Cache: none

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File paths: relative

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Repository version: 94ec811

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Unstaged changes:
    Modified:   README.md

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/03-CrossValidation.Rmd) and HTML (docs/03-CrossValidation.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
Rmd	94ec811	wolfemd	2021-05-05	Completed prediction. Build site and push to GitHub / Cassavabase FTP server. Share to NRCRI.

Previous step

Get BLUPs combining all trial data: Combine data from all trait-trials to get BLUPs for downstream genomic prediction.
- Fit mixed-model to multi-trial dataset and extract BLUPs, de-regressed BLUPs and weights. Include two rounds of outlier removal.

Cross-validation

5-fold cross-validation. Replicate 5-times.

2 genomic models:

Additive-only (A)
Addititive plus dominance plus additive-by-dominance epistasis (ADE)

Prep. genomic data

Get SNP data from FTP

The data for the next step can be found on the cassavabase FTP server here.

Can be loaded directly to R from FTP.

NOTICE: You need enough RAM and a stable network connection. I do the next steps, including cross-validation on a server with plenty of RAM and a good, stable network connection, rather than on my personal computer (a laptop with 16 GB RAM).

The outputs (kinship matrices and filtered snp dosages) of the steps below, which are too large for GitHub, can be found on the cassavabase FTP server here.

# activate multithread OpenBLAS for fast compute of SigmaM (genotypic var-covar matrix)
cd /home/jj332_cas/marnin/NRCRI_2021GS/; 
export OMP_NUM_THREADS=56

library(tidyverse); library(magrittr); 
# RefPanel (including primary TP and original GS progeny)
# Dosage matrix
snps_refpanel<-readRDS(file=url(paste0("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/",
                              "DosageMatrix_RefPanelAndGSprogeny_ReadyForGP_73019.rds")))
# 2020 GS progeny dosages (C3a)
## these don't have phenotypes yet. including for convenience 
## and so we don't get suprised by their absence in the next iteration
snps5510<-readRDS(file=url(paste0("ftp://ftp.cassavabase.org/marnin_datasets/NRCRI_2020GS/output/",
                                  "DosageMatrix_DCas20_5510_WA_REFimputedAndFiltered.rds")))
snps5440<-readRDS(file=url(paste0("ftp://ftp.cassavabase.org/marnin_datasets/NRCRI_2020GS/output/",
                                  "DosageMatrix_DCas20_5440_WA_REFimputedAndFiltered.rds")))
## DCas21-5841 Dosage matrix (C3b)
snps5841<-readRDS(here::here("output","DosageMatrix_DCas21_5841_WA_REFimputedAndFiltered.rds"))

snps2keep<-colnames(snps_refpanel) %>% 
  .[. %in% colnames(snps5841)] %>% 
  .[. %in% colnames(snps5510)] %>% 
  .[. %in% colnames(snps5440)]

snps<-rbind(snps_refpanel[,snps2keep],
            snps5510[,snps2keep]) %>% 
  rbind(.,snps5440[,snps2keep]) %>% 
  rbind(.,snps5841[,snps2keep])
gc()
dim(snps) # [1] 22936 20559

#rm(list=(ls() %>% grep("snps",.,value = T, invert = T)))

blups<-readRDS(file=here::here("output","NRCRI_blupsForModelTraining_twostage_asreml_2021May03.rds"))
blups %<>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  select(-`std error`) %>% 
  filter(GID %in% rownames(snps))
table(unique(blups$GID) %in% rownames(snps)) 
# TRUE
# 3114

samples2Keep<-unique(blups$GID) %>% 
  union(.,grep("c2|c3",rownames(snps),value = T, ignore.case = T)) %>% 
  union(.,rownames(snps5510)) %>% 
  union(.,rownames(snps5440)) %>% 
  union(.,rownames(snps5841))

length(samples2Keep) # [1] 7286 
snps<-snps[samples2Keep,]; 
gc()

MAF>1% filter

source(here::here("code","gsFunctions.R"))
snps %<>% maf_filter(.,0.01)
dim(snps) # [1]  7286 20550

Make Add, Dom and Epi kinships

Going to use my own kinship function.

Make the kinships.

Below e.g. A*A makes a matrix that approximates additive-by-additive epistasis relationships.

A<-kinship(snps,type="add")
D<-kinship(snps,type="dom")
AD<-A*D

saveRDS(snps,file=here::here("output","DosageMatrix_NRCRI_2021May03.rds"))
saveRDS(A,file=here::here("output","Kinship_A_NRCRI_2021May03.rds"))
saveRDS(D,file=here::here("output","Kinship_D_NRCRI_2021May03.rds"))
saveRDS(AD,file=here::here("output","Kinship_AD_NRCRI_2021May03.rds"))
#rm(snps); gc()

NOTICE: The outputs (kinship matrices and filtered snp dosages) of the steps below, which are too large for GitHub, can be found on the cassavabase FTP server here.

Cross-validation

cd /home/jj332_cas/marnin/NRCRI_2021GS/; 
export OMP_NUM_THREADS=56 # activate multithread OpenBLAS 
##### [considered] 
######/programs/R-4.0.0/bin/R # switched to R V4, having trouble with sommer/Matrix in v3.5
## may have to reinstall packages

Set-up training-testing data

rm(list=ls())
library(tidyverse); library(magrittr); 
source(here::here("code","gsFunctions.R"))
blups<-readRDS(file=here::here("output","NRCRI_blupsForModelTraining_twostage_asreml_2021May03.rds"))

A<-readRDS(file=here::here("output","Kinship_A_NRCRI_2021May03.rds"))
blups %<>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  select(-`std error`) %>% 
  filter(GID %in% rownames(A))

cv2do<-blups %>%
  nest(TrainTestData=-Trait)
cv2do %>% rmarkdown::paged_table()

# # A tibble: 12 x 2
#    Trait     TrainTestData     
#    <chr>     <list>            
#  1 CGMS1     <tibble [2,986 × 6]>
#  2 CGMS2     <tibble [2,144 × 6]>
#  3 MCMDS     <tibble [2,987 × 6]>
#  4 DMsg      <tibble [791 × 6]>  
#  5 PLTHT     <tibble [2,505 × 6]>
#  6 BRNHT1    <tibble [1,860 × 6]>
#  7 HI        <tibble [2,711 × 6]>
#  8 logFYLD   <tibble [2,688 × 6]>
#  9 logTOPYLD <tibble [2,702 × 6]>
# 10 logRTNO   <tibble [2,693 × 6]>
# 11 DM        <tibble [2,130 × 6]>
# 12 logDYLD   <tibble [2,064 × 6]>

cv2do$TrainTestData[[6]] %>% head %>% rmarkdown::paged_table()

#                         GID       BLUP       PEV       REL    drgBLUP       WT
# 1  AR124:250107818  8.647876 46.46010 0.4054099  21.331190 7.276556
# 2 AR1410:250399710  4.063514 44.17898 0.4346033   9.349938 8.137156
# 3  AR144:250107805 -5.134758 44.26454 0.4335084 -11.844655 8.103540
# 4  AR155:250134515 -6.878125 54.83950 0.2981715 -23.067685 4.645796
# 5  AR182:250134520 -2.311664 62.32273 0.2024021 -11.421147 2.821257
# 6  AR311:250134529  4.939141 58.27090 0.2542569  19.425787 3.758532

The function below runCrossVal() function implements nfold cross-validation. Specifically, for each of nrepeats it splits the data into nfolds sets according to gid. So if nfolds=5 then the the clones will be divided into 5 groups and 5 predictions will be made. In each prediction, 4/5 of the clones will be used to predict the remaining 1/5. Accuracy of the model is measured as the correlation between the BLUPs (adj. mean for each CLONE) in the test set and the GEBV (the prediction made of each clone when it was in the test set).

Below, 20 reps x 5-fold cross-validation are run on 1 large memory Cornell CBSU machine each (e.g. cbsulm29; 104 cores, 512 GB RAM).

CV - modelType=“A”

starttime<-proc.time()[3]
cv_A<-cv2do %>% 
  mutate(CVresults=map(TrainTestData,~runCrossVal(TrainTestData=.,
                                                  modelType="A",
                                                  grms=list(A=A),
                                                  byGroup=FALSE,augmentTP=NULL,
                                                  nrepeats=20,nfolds=5,ncores=25,gid="GID")))
runtime<-proc.time()[3]-starttime; runtime

cv_A %<>% mutate(modelType="A") %>% dplyr::select(-TrainTestData)
saveRDS(cv_A,file=here::here("output","cvresults_A_2021May03.rds"))

CV - modelType=“ADE”

# split it to two tranches of 10 reps
 
options(future.globals.maxSize= 3000*1024^2)
D<-readRDS(file=here::here("output","Kinship_D_NRCRI_2021May03.rds"))
AD<-readRDS(file=here::here("output","Kinship_AD_NRCRI_2021May03.rds"))
starttime<-proc.time()[3]
cv_ADE_1<-cv2do %>% 
  mutate(CVresults=map(TrainTestData,~runCrossVal(TrainTestData=.,
                                                  modelType="ADE",
                                                  grms=list(A=A,D=D,AD=AD),
                                                  byGroup=FALSE,augmentTP=NULL,
                                                  nrepeats=10,nfolds=5,ncores=5,gid="GID")))
cv_ADE_1 %<>% mutate(modelType="ADE") %>% dplyr::select(-TrainTestData)
saveRDS(cv_ADE_1,file=here::here("output","cvresults_ADE_1_2021May03.rds"))
runtime<-proc.time()[3]-starttime; runtime

starttime<-proc.time()[3]
cv_ADE_2<-cv2do %>% 
  mutate(CVresults=map(TrainTestData,~runCrossVal(TrainTestData=.,
                                                  modelType="ADE",
                                                  grms=list(A=A,D=D,AD=AD),
                                                  byGroup=FALSE,augmentTP=NULL,
                                                  nrepeats=10,nfolds=5,ncores=5,gid="GID")))
cv_ADE_2 %<>% mutate(modelType="ADE") %>% dplyr::select(-TrainTestData)
saveRDS(cv_ADE_2,file=here::here("output","cvresults_ADE_2_2021May03.rds"))
runtime<-proc.time()[3]-starttime; runtime

Results

See Results: Home for plots and summary tables.

Next step

Genomic prediction: Predict genomic BLUPs (GEBV and GETGV) for all selection candidates using all available data.

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/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] magrittr_2.0.1  forcats_0.5.1   stringr_1.4.0   dplyr_1.0.5    
 [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.3     tibble_3.1.1   
 [9] ggplot2_3.3.3   tidyverse_1.3.1 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0  xfun_0.22         bslib_0.2.4       haven_2.4.0      
 [5] colorspace_2.0-0  vctrs_0.3.7       generics_0.1.0    htmltools_0.5.1.1
 [9] yaml_2.2.1        utf8_1.2.1        rlang_0.4.10      jquerylib_0.1.3  
[13] later_1.1.0.1     pillar_1.6.0      withr_2.4.2       glue_1.4.2       
[17] DBI_1.1.1         dbplyr_2.1.1      readxl_1.3.1      modelr_0.1.8     
[21] lifecycle_1.0.0   cellranger_1.1.0  munsell_0.5.0     gtable_0.3.0     
[25] rvest_1.0.0       evaluate_0.14     knitr_1.32        httpuv_1.5.5     
[29] fansi_0.4.2       broom_0.7.6       Rcpp_1.0.6        promises_1.2.0.1 
[33] backports_1.2.1   scales_1.1.1      jsonlite_1.7.2    fs_1.5.0         
[37] hms_1.0.0         digest_0.6.27     stringi_1.5.3     rprojroot_2.0.2  
[41] grid_4.0.3        here_1.0.1        cli_2.4.0         tools_4.0.3      
[45] sass_0.3.1        crayon_1.4.1      whisker_0.4       pkgconfig_2.0.3  
[49] ellipsis_0.3.1    xml2_1.3.2        reprex_2.0.0      lubridate_1.7.10 
[53] rstudioapi_0.13   assertthat_0.2.1  rmarkdown_2.7     httr_1.4.2       
[57] R6_2.5.0          git2r_0.28.0      compiler_4.0.3

Check prediction accuracy

Marnin Wolfe

2021-May-03

Previous step

Cross-validation

Prep. genomic data

Get SNP data from FTP

MAF>1% filter

Make Add, Dom and Epi kinships

Cross-validation

Set-up training-testing data

CV - modelType=“A”

CV - modelType=“ADE”

Results

Next step