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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    Untracked:  output/chr8_DCas21_5841_WA_REFimputedAndFiltered.raw
    Untracked:  output/chr8_DCas21_5841_WA_REFimputedAndFiltered.sitesWithAlleles
    Untracked:  output/chr8_DCas21_5841_WA_REFimputedAndFiltered.vcf.gz
    Untracked:  output/chr9_DCas21_5841_WA_REFimputed.INFO
    Untracked:  output/chr9_DCas21_5841_WA_REFimputed.hwe
    Untracked:  output/chr9_DCas21_5841_WA_REFimputed.log
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    Untracked:  output/chr9_DCas21_5841_WA_REFimputedAndFiltered.alleleToCount
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    Untracked:  output/chr9_DCas21_5841_WA_REFimputedAndFiltered.raw
    Untracked:  output/chr9_DCas21_5841_WA_REFimputedAndFiltered.sitesWithAlleles
    Untracked:  output/chr9_DCas21_5841_WA_REFimputedAndFiltered.vcf.gz
    Untracked:  output/cvresults_ADE_1_2021May03.rds
    Untracked:  output/cvresults_ADE_2_2021May03.rds
    Untracked:  output/cvresults_A_2021May03.rds
    Untracked:  output/genomicPredictions_ModelADE_twostage_NRCRI_2021May03.rds
    Untracked:  output/genomicPredictions_ModelA_twostage_NRCRI_2021May03.rds
    Untracked:  output/genomicPredictions_NRCRI_2021May03.csv
    Untracked:  output/maxNOHAV_byStudy.csv

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/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 94ec811 wolfemd 2021-05-05 Completed prediction. Build site and push to GitHub / Cassavabase FTP server. Share to NRCRI.

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_2021May03.rds"))
rawdata %>% 
  summarise(Nplots=nrow(.),
            across(c(locationName,studyYear,studyName,TrialType,GID), ~length(unique(.)),.names = "N_{.col}")) %>% 
  rmarkdown::paged_table()

4575 unique clone names in the phenotype data, across >35K 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("CGM","CGMS1","CGMS2","MCMDS",
          "DM","DMsg","PLTHT","BRNHT1","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")

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 3212 genotyped-and-phenotyped clones!

Table of germplasmName-DNA-sample-name matches are here: output/OnlyChosen_germplasmName_to_FullSampleName_matches_NRCRI_2021May03.csv.

List of DNA-sample-names are here:

  1. RefPanel (containing NRCRI TP):
  2. New samples (DCAs21-5841):

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","NRCRI_ExptDesignsDetected_2021May03.rds"))
traits<-c("CGM","CGMS1","CGMS2","MCMDS",
          "DM","DMsg","PLTHT","BRNHT1","HI",
          "logDYLD","logFYLD","logTOPYLD","logRTNO")
blups<-readRDS(file=here::here("output","NRCRI_blupsForModelTraining_twostage_asreml_2021May03.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() + theme(axis.text.x = element_text(angle=90))

  labs(title = "Distribution of BLUP Reliabilities")
$title
[1] "Distribution of BLUP Reliabilities"

attr(,"class")
[1] "labels"

Marker density and distribution

library(tidyverse); library(magrittr);
snps<-readRDS(file=here::here("output","DosageMatrix_NRCRI_2021May03.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))

mrks %>% count(Chr) %>% rmarkdown::paged_table()

Prediction accuracy

  1. Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
rm(list=ls());gc()
          used (Mb) gc trigger  (Mb) limit (Mb)  max used  (Mb)
Ncells 1361872 72.8    3111690 166.2         NA   3111690 166.2
Vcells 2647567 20.2   96180895 733.9      65536 100121523 763.9
library(tidyverse); library(magrittr); 
cv<-readRDS(here::here("output","cvresults_A_2021May03.rds")) %>% 
  bind_rows(readRDS(here::here("output","cvresults_ADE_1_2021May03.rds"))) %>% 
  bind_rows(readRDS(here::here("output","cvresults_ADE_2_2021May03.rds"))) %>% 
  unnest(CVresults) %>% 
  select(-splits,-accuracy)
traits<-c("CGM","CGMS1","CGMS2","MCMDS",
          "DM","DMsg","PLTHT","BRNHT1","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, notch = T) + 
  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 = 90, 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_NRCRI_ModelA_2021May03.csv"), 
    stringsAsFactors = F) %>% 
  pivot_longer(cols = any_of(traits),names_to = "Trait",values_to = "GEBV")
gebvs %<>% 
  mutate(Trait=factor(Trait,levels=traits),
         Group=factor(Group,levels=c("nrTP","C1a","C1b","C2a","C2b","C3a","C3b")))

gebvs %>% 
  group_by(Trait, Group) %>% 
  summarize(meanGEBV = mean(GEBV), 
            stdErr = sd(GEBV)/sqrt(n()), 
            upperSE = meanGEBV + stdErr, 
            lowerSE = meanGEBV - stdErr) %>% 
  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") + 
  theme_bw() + 
  geom_hline(yintercept = 0, size = 1.15, 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 = "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 4.0.3 (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] Rcpp_1.0.6        lubridate_1.7.10  here_1.0.1        assertthat_0.2.1 
 [5] rprojroot_2.0.2   digest_0.6.27     utf8_1.2.1        R6_2.5.0         
 [9] cellranger_1.1.0  backports_1.2.1   reprex_2.0.0      evaluate_0.14    
[13] highr_0.9         httr_1.4.2        pillar_1.6.0      rlang_0.4.10     
[17] readxl_1.3.1      rstudioapi_0.13   whisker_0.4       jquerylib_0.1.3  
[21] rmarkdown_2.7     labeling_0.4.2    munsell_0.5.0     broom_0.7.6      
[25] compiler_4.0.3    httpuv_1.5.5      modelr_0.1.8      xfun_0.22        
[29] pkgconfig_2.0.3   htmltools_0.5.1.1 tidyselect_1.1.0  fansi_0.4.2      
[33] viridisLite_0.4.0 crayon_1.4.1      dbplyr_2.1.1      withr_2.4.2      
[37] later_1.1.0.1     grid_4.0.3        jsonlite_1.7.2    gtable_0.3.0     
[41] lifecycle_1.0.0   DBI_1.1.1         git2r_0.28.0      scales_1.1.1     
[45] cli_2.4.0         stringi_1.5.3     farver_2.1.0      fs_1.5.0         
[49] promises_1.2.0.1  xml2_1.3.2        bslib_0.2.4       ellipsis_0.3.1   
[53] generics_0.1.0    vctrs_0.3.7       tools_4.0.3       glue_1.4.2       
[57] hms_1.0.0         yaml_2.2.1        colorspace_2.0-0  rvest_1.0.0      
[61] knitr_1.32        haven_2.4.0       sass_0.3.1