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Last updated: 2020-10-16

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Knit directory: NRCRI_2020GS/

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File	Version	Author	Date	Message
Rmd	977f389	wolfemd	2020-10-16	Publish NRCRI imputations for 2020 (DCas20_5510 and DCas20_5440) plus a

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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","NRCRI_ExptDesignsDetected_2020Oct13.rds"))
rawdata %>% 
  summarise(Nplots=nrow(.),
            across(c(locationName,studyYear,studyName,TrialType,GID), ~length(unique(.)),.names = "N_{.col}")) %>% 
  rmarkdown::paged_table()

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()

BLUPs

library(tidyverse); library(magrittr);
dbdata<-readRDS(file=here::here("output","nrcri_blupsForModelTraining_twostage_asreml_2020Oct13.rds"))
dbdata %>% 
  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) %>% 
  mutate(across(is.numeric,~round(.,4))) %>% 
  rmarkdown::paged_table()

Prediction accuracy.

3. 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 1126298 60.2    2085370 111.4         NA  2085370 111.4
Vcells 1992436 15.3    8388608  64.0     102400  5405596  41.3

library(tidyverse); library(magrittr); 
cv<-readRDS(here::here("output","cvresults_A_nrOnly_2020Oct15.rds")) %>% 
  bind_rows(readRDS(here::here("output","cvresults_A_iitaAugmented_2020Oct15.rds"))) %>% 
  bind_rows(readRDS(here::here("output","cvresults_ADE_nrOnly_2020Oct15.rds"))) %>% 
  bind_rows(readRDS(here::here("output","cvresults_ADE_iitaAugmented_2020Oct15.rds"))) %>% 
  # the ADE model failed for most CV folds for MCMDS-IITAaugmented
  # but not for any other case
  # I am not sure why.
  # So I also ran model AD for IITAaugmented again
  # no problem there
  bind_rows(readRDS(here::here("output","cvresults_AD_iitaAugmented_2020Oct15.rds"))) %>% 
  unnest(CVresults) %>% 
  select(-splits,-accuracy)
traits<-c("MCMDS","DM","logFYLD","logTOPYLD","logRTNO","HI","PLTHT","BRNHT1","CGM","CGMS1","CGMS2")
cv %<>% 
  mutate(Trait=factor(Trait,levels=traits),
         GroupName=factor(GroupName,levels=c("nrTP","C1a","C1b","C2a","C2b")),
         Dataset=factor(Dataset,levels=c("NRalone","IITAaugmented")),
         modelType=factor(modelType,levels=c("A","AD","ADE")))

Table of mean accuracies

cv %>% 
  group_by(Trait,GroupName,Dataset) %>% 
  # 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))) %>% 
  rmarkdown::paged_table()

Boxplot of accuracies

Version 1: Compare NRalone vs. IITAaugmented

Facet by Groups. X-axis Traits. Fill color by Dataset (NRalone vs. IITAaugmented).

2 plots: (1) model A –> GEBV, (2) model ADE –> GETGV

cv %>% 
  filter(modelType=="A") %>% 
  ggplot(.,aes(x=Trait,y=accGETGV,fill=Dataset)) + 
  geom_boxplot(position = "dodge",color='gray50',size=0.5) + 
  facet_wrap(~GroupName,nrow=1,scales='free_x') + 
  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=10, angle = 0),
        axis.title.y = element_text(face='bold', size=12),
        plot.title = element_text(face='bold'),
        legend.position = 'bottom') + 
  scale_fill_viridis_d() + coord_flip() + 
  labs(title="Prediction Accuracies - Additive-only model", y="GEBV Accuracy",x=NULL) +
  geom_hline(yintercept = 0, color='darkred')

cv %>% 
  filter(modelType=="ADE") %>% 
  ggplot(.,aes(x=Trait,y=accGETGV,fill=Dataset)) + 
  geom_boxplot(position = "dodge",color='gray50',size=0.5) + 
  facet_wrap(~GroupName,nrow=1,scales='free_x') + 
  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=10, angle = 0),
        axis.title.y = element_text(face='bold', size=12),
        plot.title = element_text(face='bold'),
        legend.position = 'bottom') + 
  scale_fill_viridis_d() + coord_flip() + 
  labs(title="Prediction Accuracies - Additive plus Dominance plus AxD epistasis model", y="GETGV Accuracy",x=NULL) +
  geom_hline(yintercept = 0, color='darkred')

### Version 2: Compare models A vs. ADE

Facet by Groups. X-axis Traits. Fill color by Model (A vs. ADE).

2 plots: NRonly, IITAaugmented

cv %>%
  filter(Dataset=="NRalone") %>% 
  ggplot(.,aes(x=Trait,y=accGETGV,fill=modelType)) + 
  geom_boxplot(position = "dodge",color='gray50',size=0.5) + 
  facet_wrap(~GroupName,nrow=1,scales='free_x') + 
  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=10, angle = 0),
        axis.title.y = element_text(face='bold', size=12),
        plot.title = element_text(face='bold'),
        legend.position = 'bottom') + 
  scale_fill_viridis_d() + coord_flip() + 
  labs(title="Prediction Accuracies - NRCRI TP alone", y="Accuracy",x=NULL) +
  geom_hline(yintercept = 0, color='darkred')

cv %>%
  filter(Dataset=="IITAaugmented") %>% 
  ggplot(.,aes(x=Trait,y=accGETGV,fill=modelType)) + 
  geom_boxplot(position = "dodge",color='gray50',size=0.5) + 
  facet_wrap(~GroupName,nrow=1,scales='free_x') + 
  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=10, angle = 0),
        axis.title.y = element_text(face='bold', size=12),
        plot.title = element_text(face='bold'),
        legend.position = 'bottom') + 
  scale_fill_viridis_d() + coord_flip() + 
  labs(title="Prediction Accuracies - NRCRI + IITA TP", y="Accuracy",x=NULL) +
  geom_hline(yintercept = 0, color='darkred')

# Genetic Gain

library(tidyverse); library(magrittr)
traits<-c("MCMDS","DM","logFYLD","logTOPYLD","logRTNO","HI","PLTHT","BRNHT1","CGM","CGMS1","CGMS2")
preds<-read.csv(here::here("output","genomicPredictions_NRCRI_2020Oct15.csv"), stringsAsFactors = F)
preds %<>% 
  mutate(Trait=factor(Trait,levels=traits),
         Group=factor(Group,levels=c("nrTP","C1a","C1b","C2a","C2b","C3a")),
         Dataset=factor(Dataset,levels=c("NRCRIalone","IITAaugmented")))

pred_summary<-preds %>% 
  select(Trait,Dataset,Group,GID,GEBV,GETGV) %>% 
  pivot_longer(c(GEBV,GETGV),values_to = "gBLUP", names_to = "predictionOf") %>% 
  group_by(Trait,Dataset,Group,predictionOf) %>% 
  summarize(gBLUPmean=mean(gBLUP),
            stdErr=sd(gBLUP)/sqrt(n()),
            upperSE=gBLUPmean+stdErr,
            lowerSE=gBLUPmean-stdErr) %>% ungroup()
pred_summary %>% rmarkdown::paged_table()

pred_summary %>% 
  filter(predictionOf=="GEBV") %>%  
  ggplot(.,aes(x=Group,y=gBLUPmean,fill=Dataset)) + 
  geom_bar(stat = 'identity', color='gray50', size=0.5, position = position_dodge(1.1)) + 
  geom_linerange(aes(ymax=upperSE,
                     ymin=lowerSE), 
                 color='gray60', size=0.5,position = position_dodge(1.1)) + 
  facet_wrap(~Trait,scales='free_y') + 
  theme_bw() +
  geom_hline(yintercept = 0, size=1.1, color='black') + 
  theme(axis.text.x = element_text(face = 'bold',angle = 90, size=12),
        axis.title.y = element_text(face = 'bold',size=14),
        legend.position = 'bottom',
        strip.background.x = element_blank(),
        strip.text = element_text(face='bold',size=14),
        plot.title = element_text(face='bold')) + 
  scale_fill_viridis_d() + 
  labs(x=NULL,y="Mean gBLUPs",title="Genetic Gain", subtitle = "Comparing GEBVs using NRCRI TP vs. IITA augmented data")

pred_summary %>% 
  filter(Dataset=="NRCRIalone") %>%  
  ggplot(.,aes(x=Group,y=gBLUPmean,fill=predictionOf)) + 
  geom_bar(stat = 'identity', color='gray50', size=0.5, position = position_dodge(1.1)) + 
  geom_linerange(aes(ymax=upperSE,
                     ymin=lowerSE), 
                 color='gray60', size=0.5,position = position_dodge(1.1)) + 
  facet_wrap(~Trait,scales='free_y') + 
  theme_bw() +
  geom_hline(yintercept = 0, size=1.1, color='black') + 
  theme(axis.text.x = element_text(face = 'bold',angle = 90, size=12),
        axis.title.y = element_text(face = 'bold',size=14),
        legend.position = 'bottom',
        strip.background.x = element_blank(),
        strip.text = element_text(face='bold',size=14),
        plot.title = element_text(face='bold')) + 
  scale_fill_viridis_d() + 
  labs(x=NULL,y="Mean gBLUPs",title="Genetic Gain", subtitle = "Comparing GEBV and GETGV  predicted with the NRCRI TP alone")

Session information

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6

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_1.5    forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2    
 [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.2     tibble_3.0.4   
 [9] ggplot2_3.3.2   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0  xfun_0.18         haven_2.3.1       colorspace_1.4-1 
 [5] vctrs_0.3.4       generics_0.0.2    viridisLite_0.3.0 htmltools_0.5.0  
 [9] yaml_2.2.1        blob_1.2.1        rlang_0.4.8       later_1.1.0.1    
[13] pillar_1.4.6      withr_2.3.0       glue_1.4.2        DBI_1.1.0        
[17] dbplyr_1.4.4      modelr_0.1.8      readxl_1.3.1      lifecycle_0.2.0  
[21] munsell_0.5.0     gtable_0.3.0      cellranger_1.1.0  rvest_0.3.6      
[25] evaluate_0.14     labeling_0.3      knitr_1.30        ps_1.4.0         
[29] httpuv_1.5.4      fansi_0.4.1       broom_0.7.1       Rcpp_1.0.5       
[33] promises_1.1.1    backports_1.1.10  scales_1.1.1      jsonlite_1.7.1   
[37] farver_2.0.3      fs_1.5.0          hms_0.5.3         digest_0.6.25    
[41] stringi_1.5.3     rprojroot_1.3-2   grid_4.0.2        here_0.1         
[45] cli_2.1.0         tools_4.0.2       crayon_1.3.4      whisker_0.4      
[49] pkgconfig_2.0.3   ellipsis_0.3.1    xml2_1.3.2        reprex_0.3.0     
[53] lubridate_1.7.9   rstudioapi_0.11   assertthat_0.2.1  rmarkdown_2.4    
[57] httr_1.4.2        R6_2.4.1          git2r_0.27.1      compiler_4.0.2