Last updated: 2021-01-21

Checks: 7 0

Knit directory: TARI_2020GS/

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

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20201215)

The command set.seed(20201215) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

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Cache: none

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File paths: relative

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Repository version: ac1cf61

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The results in this page were generated with repository version ac1cf61. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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Unstaged changes:
    Modified:   output/TARI_trials_NOT_identifiable.csv
    Modified:   output/maxNOHAV_byStudy.csv

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/05-Results.Rmd) and HTML (docs/05-Results.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 ac1cf61 wolfemd 2021-01-21 Kibaha samples added. Cross-validation and predictions redone.
html 0748284 wolfemd 2020-12-23 Build site.
Rmd 6a50ab2 wolfemd 2020-12-23 Tweak one heading…
html abaf52a wolfemd 2020-12-23 Build site.
Rmd fae176a wolfemd 2020-12-23 Publish the first set of analyses and files for TARI 2020 GS.

Raw data

Summary of the number of unique plots, locations, years, etc. in the cleaned plot-basis data. See here for details..

library(tidyverse); library(magrittr);
rawdata<-readRDS(file=here::here("output","TARI_ExptDesignsDetected_2021Jan21.rds"))
rawdata %>% 
  summarise(Nplots=nrow(.),
            across(c(locationName,studyYear,studyName,TrialType,GID), ~length(unique(.)),.names = "N_{.col}")) %>% 
  rmarkdown::paged_table()

So ~3700 unique clone names (plus ~5000 “C1” seedlings) in the phenotype data, across >18K plots. This is not the same number of clones as are expected to be genotyped-and-phenotyped.

Break down the plots based on the trial design and TrialType (really a grouping of the population that is breeding program specific), captured by two logical variables, CompleteBlocks and IncompleteBlocks.

rawdata %>% 
  count(TrialType,CompleteBlocks,IncompleteBlocks) %>% 
  spread(TrialType,n) %>% 
  rmarkdown::paged_table()

Next, look at breakdown of plots by TrialType (rows) and locations (columns):

rawdata %>% 
  count(locationName,TrialType) %>% 
  spread(locationName,n) %>% 
  rmarkdown::paged_table()
traits<-c("MCMDS","MCBSDS","CBSDRS","CGMS1","CGMS2","DM","PLTHT","HI",
          "logDYLD", "logFYLD","logTOPYLD","logRTNO")
rawdata %>% 
  select(locationName,studyYear,studyName,TrialType,any_of(traits)) %>% 
  pivot_longer(cols = any_of(traits), values_to = "Value", names_to = "Trait") %>% 
  ggplot(.,aes(x=Value,fill=Trait)) + geom_histogram() + facet_wrap(~Trait, scales='free') + 
  theme_bw() + scale_fill_viridis_d() + 
  labs(title = "Distribution of Raw Phenotypic Values")

Version Author Date
abaf52a wolfemd 2020-12-23

How many genotyped-and-phenotyped clones?

rawdata %>% 
  select(locationName,studyYear,studyName,TrialType,germplasmName,FullSampleName,GID,any_of(traits)) %>% 
  pivot_longer(cols = any_of(traits), values_to = "Value", names_to = "Trait") %>%
  filter(!is.na(Value),!is.na(FullSampleName)) %>%
  distinct(germplasmName,FullSampleName,GID) %>% 
  rmarkdown::paged_table()

There are 872!

Table of germplasmName-DNA-sample-name matches are here: output/OnlyChosen_germplasmName_to_FullSampleName_matches_TARI_2021Jan21.csv.

List of DNA-sample-names are here:

  1. RefPanel (containing TARI TP): output/rownames_DosageMatrix_ImputationReferencePanel_StageVI_91119.csv
  2. New samples (DCAs20-5629): output/rownames_DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.csv

BLUPs

These are the BLUPs combining data for each clone across trials/locations without genomic information, used as input for genomic prediction downstream.

library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))
dbdata<-readRDS(here::here("output","TARI_ExptDesignsDetected_2021Jan21.rds"))
traits<-c("MCMDS","MCBSDS","CBSDRS","CGMS1","CGMS2","DM","PLTHT","HI",
          "logDYLD", "logFYLD","logTOPYLD","logRTNO")
blups<-readRDS(file=here::here("output","tari_blupsForModelTraining_twostage_asreml_2021Jan21.rds")) 

blups %>% 
  left_join(nestDesignsDetectedByTraits(dbdata,traits) %>% 
  mutate(Nplots=map_dbl(MultiTrialTraitData,nrow)) %>% 
    select(Trait,Nplots)) %>% 
  mutate(Nclones=map_dbl(blups,~nrow(.)),
         NoutliersRemoved=map2_dbl(outliers1,outliers2,~length(.x)+length(.y))) %>% 
  #relocate(c(Nclones,NoutliersRemoved),.after = Trait) %>% 
  #select(-blups,-varcomp,-outliers1,-outliers2) %>% 
  select(Trait,Nplots,Nclones,NoutliersRemoved,Vg,Ve,H2) %>% 
  mutate(across(is.numeric,~round(.,4))) %>% arrange(desc(H2)) %>% 
  rmarkdown::paged_table()
blups %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  ggplot(.,aes(x=drgBLUP,fill=Trait)) + geom_histogram() + facet_wrap(~Trait, scales='free') + 
  theme_bw() + scale_fill_viridis_d() + 
  labs(title = "Distribution of de-regressed BLUP Values")

blups %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  ggplot(.,aes(x=Trait,y=REL,fill=Trait)) + geom_boxplot(notch=T) + #facet_wrap(~Trait, scales='free') + 
  theme_bw() + scale_fill_viridis_d() + 
  labs(title = "Distribution of BLUP Reliabilities")

Version Author Date
abaf52a wolfemd 2020-12-23

Marker density and distribution

library(tidyverse); library(magrittr);
snps<-readRDS(file=here::here("output","DosageMatrix_TARI_2020Dec21.rds"))
mrks<-colnames(snps) %>% 
  tibble(SNP_ID=.) %>% 
  separate(SNP_ID,c("Chr","Pos","Allele"),"_") %>% 
  mutate(Chr=as.integer(gsub("S","",Chr)),
         Pos=as.numeric(Pos))
mrks %>% 
  ggplot(.,aes(x=Pos,fill=as.character(Chr))) + geom_histogram() + 
  facet_wrap(~Chr,scales = 'free') + theme_bw() + 
  scale_fill_viridis_d() + theme(legend.position = 'none',axis.text.x = element_text(angle=90))

Version Author Date
abaf52a wolfemd 2020-12-23 
mrks %>% count(Chr) %>% rmarkdown::paged_table()

Prediction accuracy

  1. Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
    • Compare prediction accuracy with vs. without IITA’s training data to augment.
rm(list=ls());gc()
          used (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
Ncells 1331714 71.2    2974134 158.9         NA  2974134 158.9
Vcells 2619596 20.0   83402764 636.4     102400 86811098 662.4
library(tidyverse); library(magrittr); 
cv<-readRDS(here::here("output","cvresults_A_2021Jan21.rds")) %>% 
  bind_rows(readRDS(here::here("output","cvresults_ADE_2021Jan21.rds"))) %>% 
  unnest(CVresults) %>% 
  select(-splits,-accuracy)
traits<-c("MCMDS","MCBSDS","CBSDRS","CGMS1","CGMS2","DM","PLTHT","HI",
          "logDYLD", "logFYLD","logTOPYLD","logRTNO")
cv %<>% 
  mutate(Trait=factor(Trait,levels=traits),
         modelType=factor(modelType,levels=c("A","ADE")))

Table of mean accuracies

5-fold cross-validation, replicated 20 times.

Mean accuracy and upper/lower 95% interval.

Two prediction models: A (additive-only) and ADE (additive + dominance + additive-by-dominance epistasis).

cv %>% 
  group_by(Trait,modelType) %>% 
  # use accGETGV. For modelA we GETGV==GEBV. For modelADE we don't want GEBV, just GETGV.
  summarize(meanAccuracy=mean(accGETGV,na.rm=T),
            lower5pct=quantile(accGETGV,probs = c(0.05),na.rm=T),
            upper5pct=quantile(accGETGV,probs = c(0.95),na.rm=T)) %>% 
  mutate(across(is.numeric,~round(.,2))) %>% arrange(modelType,desc(meanAccuracy)) %>% 
  rmarkdown::paged_table()

Boxplot of accuracies

5-fold cross-validation, replicated 20 times.

Two prediction models: A (additive-only) and ADE (additive + dominance + additive-by-dominance epistasis).

cv %>% 
  ggplot(.,aes(x=Trait,y=accGETGV,fill=modelType)) + 
  geom_boxplot(position = "dodge2",color='gray50',size=0.5) + 
  geom_hline(yintercept = 0, color='darkred') + 
  theme_bw() + 
  theme(strip.text.x = element_text(face='bold', size=12),
        axis.text.y = element_text(face='bold', size=14, angle = 0),
        axis.text.x = element_text(face='bold', size=18, angle = 45, hjust = 1),
        axis.title.y = element_text(face='bold', size=12),
        plot.title = element_text(face='bold'),
        legend.text = element_text(face='bold',size=16),
        legend.title = element_text(face='bold',size=16),
        legend.position = 'bottom') + 
  scale_fill_viridis_d() + 
  labs(title="Prediction Accuracies", y="GEBV or GETGV accuracy",x=NULL) +
  geom_hline(yintercept = 0, color='darkred')

  1. Accuracy estimates are most improved relative to previously. I didn’t run the precise cross-validation folds so the judgement is based on visual comparison to the Dec. 2020 plot.
  2. DYLD and FYLD are not well predicted and I would not recommend using them based on selection.

Genetic Gain

library(tidyverse)
library(magrittr)
gebvs <- read.csv(here::here("output", "GEBV_TARI_ModelA_2021Jan21.csv"), 
    stringsAsFactors = F)
gebvs %>% 
  select(GID, Group, any_of(traits)) %>% 
  pivot_longer(cols = any_of(traits), 
               names_to = "Trait", values_to = "GEBV") %>% 
  group_by(Trait, Group) %>% 
  summarize(meanGEBV = mean(GEBV), 
            stdErr = sd(GEBV)/sqrt(n()), 
            upperSE = meanGEBV + stdErr, 
            lowerSE = meanGEBV - stdErr) %>% 
  ungroup() %>% 
  mutate(Group=factor(Group,levels=c("TARI_TP","DCas20_5629"))) %>% 
  ggplot(., aes(x = Group, 
                y = meanGEBV, 
                fill = Trait)) + 
  geom_bar(stat = "identity", color = "gray60", 
           size = 1.25) + 
  geom_linerange(aes(ymax = upperSE, ymin = lowerSE), color = "gray60", size = 1.25) + 
  facet_wrap(~Trait, scales = "free_y", ncol = 2) + 
  theme_bw() + 
  geom_hline(yintercept = 0, size = 1.15, color = "black") + 
  theme(axis.text.x = element_text(face = "bold", angle = 0, size = 12), 
        axis.title.y = element_text(face = "bold", size = 14), 
        legend.position = "none", 
        strip.background.x = element_blank(), 
        strip.text = element_text(face = "bold", size = 14)) + 
  scale_fill_viridis_d() + 
  labs(x = NULL, y = "Mean GEBVs")


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] magrittr_2.0.1  forcats_0.5.0   stringr_1.4.0   dplyr_1.0.3    
 [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.2     tibble_3.0.5   
 [9] ggplot2_3.3.3   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0  xfun_0.20         haven_2.3.1       colorspace_2.0-0 
 [5] vctrs_0.3.6       generics_0.1.0    viridisLite_0.3.0 htmltools_0.5.1  
 [9] yaml_2.2.1        rlang_0.4.10      later_1.1.0.1     pillar_1.4.7     
[13] withr_2.4.0       glue_1.4.2        DBI_1.1.1         dbplyr_2.0.0     
[17] modelr_0.1.8      readxl_1.3.1      lifecycle_0.2.0   cellranger_1.1.0 
[21] munsell_0.5.0     gtable_0.3.0      rvest_0.3.6       evaluate_0.14    
[25] labeling_0.4.2    knitr_1.30        httpuv_1.5.5      fansi_0.4.2      
[29] broom_0.7.3       Rcpp_1.0.6        promises_1.1.1    backports_1.2.1  
[33] scales_1.1.1      jsonlite_1.7.2    farver_2.0.3      fs_1.5.0         
[37] hms_1.0.0         digest_0.6.27     stringi_1.5.3     rprojroot_2.0.2  
[41] grid_4.0.2        here_1.0.1        cli_2.2.0         tools_4.0.2      
[45] crayon_1.3.4      whisker_0.4       pkgconfig_2.0.3   ellipsis_0.3.1   
[49] xml2_1.3.2        reprex_0.3.0      lubridate_1.7.9.2 assertthat_0.2.1 
[53] rmarkdown_2.6     httr_1.4.2        rstudioapi_0.13   R6_2.5.0         
[57] git2r_0.28.0      compiler_4.0.2