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Last updated: 2021-07-29

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Knit directory: implementGMSinCassava/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/07-Results.Rmd) and HTML (docs/07-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	5a53779	wolfemd	2021-07-29	Publish the nearly complete and polished version of the project.
html	934141c	wolfemd	2021-07-14	Build site.
html	cc1eb4b	wolfemd	2021-07-14	Build site.
Rmd	772750a	wolfemd	2021-07-14	DirDom model and selection index calc fully integrated functions.

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The summaries below correspond to the results of analyses outlined here and linked above.

Raw data

Summary of the number of unique plots, locations, years, etc. in the cleaned plot-basis data (output/IITA_ExptDesignsDetected_2021May10.rds, download from FTP). See the data cleaning step here for details.

library(tidyverse); library(magrittr); library(ragg)
rawdata<-readRDS(file=here::here("output","IITA_ExptDesignsDetected_2021May10.rds"))
rawdata %>% 
  summarise(Nplots=nrow(.),
            across(c(locationName,studyYear,studyName,TrialType,GID), ~length(unique(.)),.names = "N_{.col}")) %>% 
  rmarkdown::paged_table()

This is not the same number of clones as are expected to be genotyped-and-phenotyped.

Next, a break down of 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","DM","PLTHT","BRNHT1","BRLVLS","HI",
          "logDYLD", # <-- logDYLD now included. 
          "logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")
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")

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
cc1eb4b	wolfemd	2021-07-14

How many genotyped-and-phenotyped clones?

genotypedAndPhenotypedClones<-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)

There are 8149 genotyped-and-phenotyped clones!

genotypedAndPhenotypedClones %>% 
  rmarkdown::paged_table()

BLUPs

Summarize the BLUPs from the training data, leveraging for each clone data across trials/locations without genomic information and to be used as input for genomic prediction downstream (data/blups_forCrossVal.rds, download from FTP). See the mixed-model analysis step here and a subsequent processing step here for details.

blups<-readRDS(file=here::here("data","blups_forCrossVal.rds")) 
gidWithBLUPs<-blups %>% select(Trait,blups) %>% unnest(blups) %$% unique(GID)
rawdata %>% 
  select(observationUnitDbId,GID,any_of(blups$Trait)) %>% 
  pivot_longer(cols = any_of(blups$Trait), 
               names_to = "Trait", 
               values_to = "Value",values_drop_na = T) %>% 
  filter(GID %in% gidWithBLUPs) %>% 
  group_by(Trait) %>% 
  summarize(Nplots=n()) %>% 
  ungroup() %>% 
  left_join(blups %>% 
              mutate(Nclones=map_dbl(blups,~nrow(.)),
                     avgREL=map_dbl(blups,~mean(.$REL)),
                     Vg=map_dbl(varcomp,~.["GID!GID.var","component"]),
                     Ve=map_dbl(varcomp,~.["R!variance","component"]),
                     H2=Vg/(Vg+Ve)) %>% 
              select(-blups,-varcomp)) %>% 
  mutate(across(is.numeric,~round(.,3))) %>% arrange(desc(H2)) %>% 
  rmarkdown::paged_table()

• Nplots, Nclones: the number of unique plots and clones per trait
• avgREL: the mean reliability of BLUPs, where for the jth clone, \(REL_j = 1 - \frac{PEV_j}{V_g}\)
• Vg, Ve, H2: the genetic and residual variance components and the broad sense heritability (\(H^2=\frac{V_g}{V_g+V_e}\)).

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() + theme(legend.position = 'none') + 
  labs(title = "Distribution of de-regressed BLUP Values")

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
cc1eb4b	wolfemd	2021-07-14

• de-regressed BLUPs or \(drgBLUP_j = \frac{BLUP_j}{REL_j}\)

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),
        legend.position = 'none') + 
  labs(title = "Distribution of BLUP Reliabilities")

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
cc1eb4b	wolfemd	2021-07-14

Marker density and distribution

Summarize the marker data (data/dosages_IITA_filtered_2021May13.rds, download from FTP). See the pre-processing steps here.

library(tidyverse); library(magrittr);
getwd()

[1] "/Users/mw489/Google Drive/NextGenGS/implementGMSinCassava"

snps<-readRDS(file=here::here("data","dosages_IITA_filtered_2021May13.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))
rm(snps);
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))

In total, 34981 SNPs genome-wide. Broken down by chromosome:

mrks %>% count(Chr,name = "Nsnps") %>% rmarkdown::paged_table()

Pedigree validation

Introduced new downstream procedures (the parent-wise cross-validation, which rely on a trusted pedigree. To support this, introduced a new pedigree-validation step. The pedigree and validation results are summarized below.

The verified pedigree (output/verified_ped.txt), can be downloaded from the FTP here).

library(tidyverse); library(magrittr);
ped2check_genome<-readRDS(file=here::here("output","ped2check_genome.rds"))
ped2check_genome %<>% 
  select(IID1,IID2,Z0,Z1,Z2,PI_HAT)
ped2check<-read.table(file=here::here("output","ped2genos.txt"),
                      header = F, stringsAsFactors = F) %>% 
  rename(FullSampleName=V1,DamID=V2,SireID=V3)
ped2check %<>% 
  select(FullSampleName,DamID,SireID) %>% 
  inner_join(ped2check_genome %>% 
               rename(FullSampleName=IID1,DamID=IID2) %>% 
               bind_rows(ped2check_genome %>% 
                           rename(FullSampleName=IID2,DamID=IID1))) %>% 
  distinct %>% 
  mutate(FemaleParent=case_when(Z0<0.32 & Z1>0.67~"Confirm",
                                       SireID==DamID & PI_HAT>0.6 & Z0<0.3 & Z2>0.32~"Confirm",
                                       TRUE~"Reject")) %>% 
  select(-Z0,-Z1,-Z2,-PI_HAT) %>% 
  inner_join(ped2check_genome %>% 
               rename(FullSampleName=IID1,SireID=IID2) %>% 
               bind_rows(ped2check_genome %>% 
                           rename(FullSampleName=IID2,SireID=IID1))) %>% 
  distinct %>% 
  mutate(MaleParent=case_when(Z0<0.32 & Z1>0.67~"Confirm",
                                       SireID==DamID & PI_HAT>0.6 & Z0<0.3 & Z2>0.32~"Confirm",
                                       TRUE~"Reject")) %>% 
  select(-Z0,-Z1,-Z2,-PI_HAT)
rm(ped2check_genome)

ped2check %<>% 
  mutate(Cohort=NA,
         Cohort=ifelse(grepl("TMS18",FullSampleName,ignore.case = T),"TMS18",
                       ifelse(grepl("TMS15",FullSampleName,ignore.case = T),"TMS15",
                              ifelse(grepl("TMS14",FullSampleName,ignore.case = T),"TMS14",
                                     ifelse(grepl("TMS13|2013_",FullSampleName,ignore.case = T),"TMS13","GGetc")))))

Proportion of accessions with male, female or both parents in pedigree confirm-vs-rejected?

ped2check %>% 
  count(FemaleParent,MaleParent) %>% 
  mutate(Prop=round(n/sum(n),2))

  FemaleParent MaleParent    n Prop
1      Confirm    Confirm 4259 0.77
2      Confirm     Reject  563 0.10
3       Reject    Confirm  382 0.07
4       Reject     Reject  313 0.06

Proportion of accessions within each Cohort with pedigree records confirmed-vs-rejected?

ped2check %>% 
  count(Cohort,FemaleParent,MaleParent) %>% 
  spread(Cohort,n) %>% 
  mutate(across(is.numeric,~round(./sum(.),2))) %>% 
  rmarkdown::paged_table()

Use only fully-confirmed families / trios. Remove any without both parents confirmed.

ped<-read.table(here::here("output","verified_ped.txt"),
                header = T, stringsAsFactors = F) %>% 
  mutate(Cohort=NA,
         Cohort=ifelse(grepl("TMS18",FullSampleName,ignore.case = T),"TMS18",
                       ifelse(grepl("TMS15",FullSampleName,ignore.case = T),"TMS15",
                              ifelse(grepl("TMS14",FullSampleName,ignore.case = T),"TMS14",
                                     ifelse(grepl("TMS13|2013_",FullSampleName,ignore.case = T),"TMS13","GGetc")))))

Summary of family sizes

ped %>% 
  count(SireID,DamID) %$% summary(n)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    1.00    3.00    5.85    8.00   77.00 

ped %>% nrow(.) # 4259 pedigree entries

[1] 4259

ped %>% 
  count(Cohort,name = "Number of Verified Pedigree Entries")

  Cohort Number of Verified Pedigree Entries
1  GGetc                                  18
2  TMS13                                1786
3  TMS14                                1302
4  TMS15                                 589
5  TMS18                                 564

ped %>% 
  distinct(Cohort,SireID,DamID) %>% 
  count(Cohort,name = "Number of Families per Cohort")

  Cohort Number of Families per Cohort
1  GGetc                            16
2  TMS13                           120
3  TMS14                           233
4  TMS15                           197
5  TMS18                           164

730 families. Mean size 5.85, range 1-77.

Parent-wise Cross-validation

I have introduced a new procedure to assess the accuracy of genomic predictions of cross means and variances on a selection index, which is called (parent-wise cross-validation. The actual parent-wise cross-validation folds (output/parentfolds.rds) used are summarized below and can be downloaded here).

parentfolds<-readRDS(file=here::here("output","parentfolds.rds"))
summarized_parentfolds<-parentfolds %>% 
  mutate(Ntestparents=map_dbl(testparents,length),
         Ntrainset=map_dbl(trainset,length),
         Ntestset=map_dbl(testset,length),
         NcrossesToPredict=map_dbl(CrossesToPredict,nrow)) %>% 
  select(Repeat,Fold,starts_with("N"))
summarized_parentfolds %>% 
  rmarkdown::paged_table()

summarized_parentfolds %>% summarize(across(is.numeric,median,.names = "median{.col}"))

# A tibble: 1 × 4
  medianNtestparents medianNtrainset medianNtestset medianNcrossesToPredict
               <dbl>           <dbl>          <dbl>                   <dbl>
1                 55            2053           2125                     195

Selection index weights (according to IITA)

# SELECTION INDEX WEIGHTS
## from IYR+IK
## note that not ALL predicted traits are on index
c(logFYLD=20,
  HI=10,
  DM=15,
  MCMDS=-10,
  logRTNO=12,
  logDYLD=20,
  logTOPYLD=15,
  PLTHT=10)

  logFYLD        HI        DM     MCMDS   logRTNO   logDYLD logTOPYLD     PLTHT 
       20        10        15       -10        12        20        15        10 

Selection index accuracy

library(ggdist)
accuracy_ad<-readRDS(here::here("output","cvAD_5rep5fold_predAccuracy.rds"))
accuracy_dirdom<-readRDS(here::here("output","cvDirDom_5rep5fold_predAccuracy.rds"))
accuracy<-accuracy_ad$meanPredAccuracy %>% 
  bind_rows(accuracy_dirdom$meanPredAccuracy) %>% 
  filter(Trait=="SELIND") %>% 
  mutate(VarComp=gsub("Mean","",predOf),
         predOf="Mean") %>% 
  bind_rows(accuracy_ad$varPredAccuracy %>% 
              bind_rows(accuracy_dirdom$varPredAccuracy) %>% 
              filter(Trait1=="SELIND") %>% 
              rename(Trait=Trait1) %>% 
              select(-Trait2) %>% 
              mutate(VarComp=gsub("Var","",predOf),
                     predOf="Var")) %>% 
  select(-predVSobs)

colors<-viridis::viridis(4)[1:2]

The figure below shows the ultimate summary of the cross-validation, the estimated accuracy predicting cross-means and cross-variances on the selection index. See further below for a break down by trait. Two models were tested and are compared: modelType=AD and modelType=DirDom. The y-axis “AccuracyEst” is the family-size weighted correlation between the predicted and observed cross means and variances. Predictions of breeding value (BV) and total genetic value (TGV) are distinguished in all plots and relate to the value of a cross for producing future parents and/or elite varieties, respectively among their offspring. Predictions of BV use only allele substitution effects (\(\alpha\)). Predictions of TGV incorporate dominance effects/variance.

accuracy %>% 
  mutate(predOf=factor(predOf,levels=c("Mean","Var")),
         VarComp=factor(VarComp,levels=c("BV","TGV"))) %>% 
  ggplot(.,aes(x=VarComp,y=AccuracyEst,fill=VarComp)) +
  ggdist::stat_halfeye(adjust=0.5,.width = 0,fill='gray',width=0.75) +  
  geom_boxplot(width=0.12,notch = TRUE) +
  ggdist::stat_dots(side = "left",justification = 1.1,
                    binwidth = 0.03,dotsize=0.6) +
  theme_bw() + 
  scale_fill_manual(values = colors) + 
  geom_hline(yintercept = 0, color='black', size=0.9) +
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        strip.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.title = element_text(face='bold',color = 'black'),
        strip.text.x = element_text(face='bold',color='black',size=14),
        axis.text.y = element_text(face = 'bold',color='black'),
        legend.text = element_text(face='bold'),
        legend.position = 'bottom') +
  facet_grid(predOf~modelType,scales = 'free') + 
  #facet_wrap(~predOf+modelType,scales = 'free_y',nrow=1) + 
  labs(title="Selection Index Prediction Accuracy") + 
  coord_cartesian(xlim = c(1.2, NA))

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cc1eb4b	wolfemd	2021-07-14

The DirDom model is at least as good, if not better than AD. Suggest proceeding to consider only DirDom model genomic predictions.

Prediction accuracy estimates are in output/ (here) with filenames ending _predAccuracy.rds.

For details on the cross-validation scheme, see the article (and for even more, the corresponding supplemental documentation here).

Cross-mean prediction accuracy

accuracy_ad$meanPredAccuracy %>% 
  bind_rows(accuracy_dirdom$meanPredAccuracy) %>% 
  select(-predVSobs) %>% 
  mutate(Trait=factor(Trait,levels=c("SELIND",blups$Trait)),
         predOf=factor(paste0(predOf,"_",modelType),levels=c("MeanBV_AD","MeanTGV_AD","MeanBV_DirDom","MeanTGV_DirDom"))) %>% 
  ggplot(.,aes(x=predOf,y=AccuracyEst,fill=predOf,color=modelType)) + 
  geom_boxplot(notch = TRUE, color='gray40') +
  # ggdist::stat_dots(side = "left", justification = 1.3,
  #                   binwidth = 0.03,dotsize=0.5,layout="swarm") +
  scale_fill_manual(values = viridis::viridis(4)[1:4]) + 
  scale_color_manual(values = viridis::viridis(4)[1:4]) + 
  geom_hline(yintercept = 0, color='black', size=0.8) +
  facet_grid(.~Trait) + 
  labs(title="Accuracy predicting cross-means") + 
  theme_bw() + 
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        strip.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position='bottom',
        axis.text.y = element_text(face='bold'),
        axis.title.y = element_text(face='bold'),
        strip.text.x = element_text(face='bold'),
        plot.title = element_text(face='bold'),
        legend.title = element_text(face='bold'),
        legend.text = element_text(face='bold'),
        panel.spacing = unit(0.2, "lines"))

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cc1eb4b	wolfemd	2021-07-14

        # axis.text.y = element_text(face='bold',size=18),
        # axis.title.y = element_text(face='bold',size=18),
        # strip.text.x = element_text(face='bold', size=20),
        # plot.title = element_text(face='bold', size=24),
        # legend.title = element_text(face='bold', size=20),
        # legend.text = element_text(face='bold', size=20),

Cross variance param prediction accruacy

Cross variance pred. accuracy

accuracy_ad$varPredAccuracy %>% 
  bind_rows(accuracy_dirdom$varPredAccuracy) %>% 
  select(-predVSobs) %>% 
  filter(Trait1==Trait2) %>% 
  mutate(Trait1=factor(Trait1,levels=c("SELIND",blups$Trait)),
         predOf=factor(paste0(predOf,"_",modelType),
                       levels=c("VarBV_AD","VarTGV_AD",
                                "VarBV_DirDom","VarTGV_DirDom"))) %>% 
  ggplot(.,aes(x=predOf,y=AccuracyEst,fill=predOf,color=modelType)) + 
  geom_boxplot(notch = TRUE,color='gray40') +
  # ggdist::stat_dots(side = "left", justification = 1.3,layout='swarm',
  #                   binwidth = 0.03,dotsize=0.4) +
  scale_fill_manual(values = viridis::viridis(4)[1:4]) + 
  scale_color_manual(values = viridis::viridis(4)[1:4]) + 
  geom_hline(yintercept = 0, color='black', size=0.8) +
  facet_grid(.~Trait1) + 
  labs(title="Accuracy predicting cross-variances") + 
  theme_bw() + 
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        strip.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position='bottom',
        axis.text.y = element_text(face='bold'),
        axis.title.y = element_text(face='bold'),
        strip.text.x = element_text(face='bold'),
        plot.title = element_text(face='bold'),
        legend.title = element_text(face='bold'),
        legend.text = element_text(face='bold'),
        panel.spacing = unit(0.2, "lines"))

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Version	Author	Date
cc1eb4b	wolfemd	2021-07-14

        # axis.text.y = element_text(face='bold',size=18),
        # axis.title.y = element_text(face='bold',size=18),
        # strip.text.x = element_text(face='bold', size=20),
        # plot.title = element_text(face='bold', size=24),
        # legend.title = element_text(face='bold', size=20),
        # legend.text = element_text(face='bold', size=20),

Cross co-variance pred. accuracy

accuracy_ad$varPredAccuracy %>% 
  bind_rows(accuracy_dirdom$varPredAccuracy) %>% 
  select(-predVSobs) %>% 
  filter(Trait1!="SELIND",Trait2!="SELIND",
         Trait1!=Trait2) %>% 
  mutate(#Trait1=factor(Trait1,levels=c("SELIND",blups$Trait)),
         #Trait2=factor(Trait2,levels=c("SELIND",blups$Trait)),
         Trait1=factor(Trait1,levels=c(blups$Trait)),
         Trait2=factor(Trait2,levels=c(blups$Trait)),
         predOf=factor(paste0(predOf,"_",modelType),
                       levels=c("VarBV_AD","VarTGV_AD",
                                "VarBV_DirDom","VarTGV_DirDom"))) %>% 
  ggplot(.,aes(x=predOf,y=AccuracyEst,fill=predOf,color=modelType)) + 
  geom_boxplot(notch = TRUE) +
  scale_fill_manual(values = viridis::viridis(4)[1:4]) + 
  scale_color_manual(values = viridis::viridis(4)[1:4]) + 
  geom_hline(yintercept = 0, color='gray40', size=0.6) +
  facet_wrap(~Trait1+Trait2,nrow=5) + 
  labs(title="Accuracy predicting cross-covariances") + 
  theme_bw() + 
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        strip.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position='bottom',
        axis.text.y = element_text(face='bold'),
        axis.title.y = element_text(face='bold'),
        strip.text.x = element_text(face='bold'),
        plot.title = element_text(face='bold'),
        legend.title = element_text(face='bold'),
        legend.text = element_text(face='bold'),
        panel.spacing = unit(0.2, "lines"))

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Version	Author	Date
cc1eb4b	wolfemd	2021-07-14

rm(list=ls())

Genomic Predictions

After evaluating prediction accuracy, the genomic prediction step implements full-training dataset predictions and outputs tidy tables of selection criteria, including rankings on the selection index. For the sake of example, I selected 121 parents that were the union of parents ranking in the top 100 highest SELIND GEBV and/or GETGV as predicted by the DirDom and/or AD models. I predicted all 7381 crosses between these 121 pre-chosen parents and summarize those predictions below.

I find the accuracy results above compelling enough to focus on DirDom only below. In addition, below I focus on the selection index predictions (SELIND). Predictions of all component traits are also available. Feedback on presentation of results welcome!

• Download CSV of genomic predictions here: Standard genomic predictions of the individual GEBV and GETGV for all selection candidates. Use for parent and/or clone selection.
• Download CSV of genomic mate predictions here: Genomic predictions of cross means, variance and usefulness on the selection index and for component traits.

Genetic trends (and gain estimates)

Below, I start by looking at the genetic trends using selection index GEBV and GETGV of the individuals in the population, based on the DirDom model.

I highlight the top potential parents, for which all possible pairwise crosses are subsequently predicted and plotted.

# GBLUPs
### Two models AD and DirDom
gpreds_dirdom<-readRDS(file = here::here("output","genomicPredictions_ModelDirDom.rds"))
si_getgvs<-gpreds_dirdom$gblups[[1]] %>% 
  filter(predOf=="GETGV") %>% 
  select(GID,SELIND)

## IITA Germplasm Ages
ggcycletime<-readxl::read_xls(here::here("data","PedigreeGeneticGainCycleTime_aafolabi_01122020.xls")) %>% 
  mutate(Year_Accession=as.numeric(Year_Accession))

# Need germplasmName field from raw trial data to match GEBV and cycle time
rawdata<-readRDS(here::here("output","IITA_ExptDesignsDetected_2021May10.rds"))
for_trend_plot<-si_getgvs %>% 
  left_join(rawdata %>% 
              distinct(germplasmName,GID)) %>% 
  group_by(GID) %>% 
  slice(1) %>% 
  ungroup()
# table(ggcycletime$Accession %in% si_getgvs$germplasmName)
# FALSE  TRUE 
#   193   614 

for_trend_plot %<>% 
  left_join(.,ggcycletime %>% 
              rename(germplasmName=Accession) %>% 
              mutate(Year_Accession=as.numeric(Year_Accession))) %>% 
  mutate(Year_Accession=case_when(grepl("2013_|TMS13",germplasmName)~2013,
                                  grepl("TMS14",germplasmName)~2014,
                                  grepl("TMS15",germplasmName)~2015,
                                  grepl("TMS18",germplasmName)~2018,
                                  !grepl("2013_|TMS13|TMS14|TMS15|TMS18",germplasmName)~Year_Accession))

# Declare the "eras" as PreGS\<2012 and GS\>=2013.
for_trend_plot %<>% 
  filter(Year_Accession>2012 | Year_Accession<2005)
for_trend_plot %<>% 
  mutate(GeneticGroup=ifelse(Year_Accession>=2013,"GS","PreGS"))

plottheme<-theme_bw() + 
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        strip.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position='bottom',
        axis.text.y = element_text(face='bold'),
        axis.title.y = element_text(face='bold'),
        strip.text.x = element_text(face='bold'),
        plot.title = element_text(face='bold'),
        legend.title = element_text(face='bold'),
        legend.text = element_text(face='bold'),
        panel.spacing = unit(0.2, "lines")) 

First, for the IITA population, I use historical data on age of clones to perform a regression of GETGV on year-cloned compared the post 2012 (GS) to pre-GS era. The plot below shows the GETGV (y-axis) versus the year each accession was cloned.

for_trend_plot %>% 
  select(GeneticGroup,GID,Year_Accession,SELIND) %>% 
  ggplot(.,aes(x=Year_Accession,y=SELIND,color=GeneticGroup)) + 
  geom_point(size=1.25) + 
  geom_smooth(method=lm, se=TRUE, size=1.5) + 
  plottheme + theme(panel.grid.major = element_line()) + 
  scale_color_viridis_d() + 
  labs(title = "Selection Index GETGV vs. Accession Age by 'era' [GS vs. PreGS]",
       subtitle = "SI GETGV from modelType='DirDom'") 

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Version	Author	Date
cc1eb4b	wolfemd	2021-07-14

Next, some fancy boxplot / half-violin plots to compare the distribution of GETGV across the cycles.

si_getgvs %>% 
  mutate(GeneticGroup=case_when(grepl("2013_|TMS13",GID)~"C1",
                                grepl("TMS14",GID)~"C2",
                                grepl("TMS15",GID)~"C3",
                                grepl("TMS18",GID)~"C4",
                                !grepl("2013_|TMS13|TMS14|TMS15|TMS18",GID)~"PreGS"),
         GeneticGroup=factor(GeneticGroup,levels = c("PreGS","C1","C2","C3","C4"))) %>% 
  ggplot(.,aes(x=GeneticGroup,y=SELIND,fill=GeneticGroup)) + 
  ggdist::stat_halfeye(adjust=0.5,.width = 0,fill='gray',width=0.75) +  
  geom_boxplot(width=0.12,notch = TRUE) +
  ggdist::stat_dots(side = "left",justification = 1.1,
                    binwidth = 0.03,dotsize=0.6) + 
  plottheme + 
  scale_fill_viridis_d()

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Version	Author	Date
cc1eb4b	wolfemd	2021-07-14

Lastly, for continuity sake, barplots of the mean +/- std. error of GEBV across the cycles.

si_gebvs<-gpreds_dirdom$gblups[[1]] %>% 
  filter(predOf=="GEBV") %>% 
  select(GID,SELIND) %>% 
  mutate(GeneticGroup=case_when(grepl("2013_|TMS13",GID)~"C1",
                                  grepl("TMS14",GID)~"C2",
                                  grepl("TMS15",GID)~"C3",
                                  grepl("TMS18",GID)~"C4",
                                  !grepl("2013_|TMS13|TMS14|TMS15|TMS18",GID)~"PreGS"),
         GeneticGroup=factor(GeneticGroup,levels = c("PreGS","C1","C2","C3","C4")))
si_gebvs %>% 
  group_by(GeneticGroup) %>% 
  summarize(meanGEBV=mean(SELIND),
            stdErr=sd(SELIND)/sqrt(n()),
            upperSE=meanGEBV+stdErr,
            lowerSE=meanGEBV-stdErr) %>% 
  ggplot(.,aes(x=GeneticGroup,y=meanGEBV,fill=GeneticGroup)) + 
  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=1) + 
  theme_bw() +
  geom_hline(yintercept = 0, size=1.15, color='black') + 
  plottheme + 
  scale_fill_viridis_d() + 
  labs(x=NULL,y="Mean GEBVs",
       title="Mean +/- Std. Error Selection Index GEBV by Cycle")

Parent selection

library(tidyverse); library(magrittr); library(ggdist)
# crossPreds<-readRDS(file = here::here("output","genomicMatePredictions_top121parents_ModelDirDom.rds"))
# crossPreds<-crossPreds$tidyPreds[[1]]
crossPreds<-read.csv(here::here("output","genomicMatePredictions_top121parents_ModelAD.csv"), stringsAsFactors = F, header = T)

I predicted 7381 crosses of 121 parents originally selected as the union of elite parents predicted by both DirDom and AD models. So not all these parents are the absolute top in terms of SI GETGV and the DirDom model, which I will plot below. Since I made the predictions for those extra parents, they are plotted here.

for_selected_plot<-si_getgvs %>% 
  mutate(GeneticGroup=case_when(grepl("2013_|TMS13",GID)~"C1",
                                grepl("TMS14",GID)~"C2",
                                grepl("TMS15",GID)~"C3",
                                grepl("TMS18",GID)~"C4",
                                !grepl("2013_|TMS13|TMS14|TMS15|TMS18",GID)~"PreGS"))
for_selected_plot %>% 
  mutate(Cycle="AllGermplasm") %>% 
  bind_rows(for_selected_plot %>% 
              filter(GID %in% union(crossPreds$sireID,crossPreds$damID)) %>% 
              mutate(Cycle="SelectedParents")) %>% 
  mutate(Cycle=factor(Cycle,levels = c("AllGermplasm","SelectedParents")),
         GeneticGroup=factor(GeneticGroup,levels = c("PreGS","C1","C2","C3","C4"))) %>% 
  ggplot(.,aes(x=Cycle,y=SELIND,fill=Cycle)) + 
  ggdist::stat_halfeye(adjust=0.5,.width = 0,fill='gray',width=0.75) +  
  geom_boxplot(width=0.09,notch = TRUE) +
  ggdist::stat_dots(aes(color=GeneticGroup),side = "left",justification = 1.1,dotsize=.8) + 
  scale_fill_viridis_d() + scale_color_viridis_d() + 
  plottheme + 
  labs(title="Distribution of selection index GETGV in parents selected for mate predictions",
       subtitle="compared to the overall population")

Genomic mate selection

library(tidyverse); library(magrittr); library(ggdist)
# crossPreds<-readRDS(file = here::here("output","genomicMatePredictions_top121parents_ModelDirDom.rds"))
# crossPreds<-crossPreds$tidyPreds[[1]]
crossPreds<-read.csv(here::here("output","genomicMatePredictions_top121parents_ModelAD.csv"), stringsAsFactors = F, header = T)
forplot<-crossPreds %>% 
  filter(Trait=="SELIND") %>% 
  select(sireID,damID,CrossGroup,predOf,predMean,predSD)

cross_group_order<-crossPreds %>% 
  filter(Trait=="SELIND") %>% 
  distinct(sireGroup,damGroup) %>% 
  mutate(sireGroup=factor(sireGroup,levels=c("PreGS","C1","C2","C3","C4")),
         damGroup=factor(damGroup,levels=c("PreGS","C1","C2","C3","C4"))) %>% 
  arrange(desc(sireGroup),desc(damGroup)) %>% 
  mutate(CrossGroup=paste0(sireGroup,"x",damGroup)) %$% 
  CrossGroup

intensity<-function(propSel){ dnorm(qnorm(1-propSel))/propSel } # standardized selection intensity
stdSelInt=intensity(0.05); # stdSelInt [1] 2.062713
# qnorm(0.95); # [1] 1.644854
forplot %<>% 
  mutate(predUsefulness=predMean+(predSD*stdSelInt),
         CrossGroup=factor(CrossGroup,levels=c(cross_group_order)))

The standard budget for genotyping has been 2500 new clones per year.

Suppose we choose to create 50 families of 50 siblings each, from the the 7381 predicted crosses.

Quick digression: The input file has a pre-computed predUsefulness variable. I used stdSelInt=2.67 when making the predictions with the predictCrosses() function, but that corresponds to selecting the top 1% of each family. For a family of 50, the top 1% means only a single clone per family. In retrospect, I have decided to re-compute the predUsefulness targeting selection of the top 5% (or top 5 offspring) from each family. This corresponds to using stdSelInt = 2.0627128 and a selection threshold std. deviation of 1.6448536.

Crosses may be of interest for their predicted \(UC_{parent}\) (predOf=="BV") and/or \(UC_{variety}\) (predOf=="TGV").

Each crossing nursery needs to produce both new exceptional parents and elite candidate cultivars. These will not necessarily be the same individuals or come from the same crosses.

# forplot %>% 
#   select(-predMean,-predSD) %>% 
#   spread(predOf,predUsefulness) %$% 
#   cor(BV,TGV) 
# [1] 0.999988
# The correlation between predUC BV and TGV is extremely high


# forplot %>% 
#   group_by(predOf) %>% 
#   slice_max(order_by = predUsefulness, n = 50) %>% ungroup() %>% 
#   distinct(sireID,damID) %>% nrow() # 50
# Also, the exact same 50 are ranked top predUC

In this case, the same 50 crosses are best for both \(UC_{parent}\) (predOf=="BV") and \(UC_{variety}\) (predOf=="TGV").

First, display the entire set of predicted crosses, ranked by their selection index \(UC_{variety}\). This will be more clear in the next plots with fewer families: the x-axis is simply the descending rank of predicted \(UC_{variety}\) for each cross. The y-axis shows the mean (dot) and distribution quantiles (linerange) based on the predicted mean and standard deviation of each cross. Crosses are color coded according to the “genetic group” of the parents.

forplot %>% 
  filter(predOf=="TGV") %>% 
  #slice_max(order_by = predUsefulness, n = 100) %>% 
  arrange(desc(predUsefulness)) %>% 
  mutate(Rank=1:nrow(.)) %>% 
  ggplot(aes(x = Rank, dist = "norm", 
             arg1 = predMean, arg2 = predSD, 
             fill=CrossGroup, color=CrossGroup),
         alpha=0.5) +
  stat_dist_pointinterval() +
  #stat_dist_gradientinterval(n=50) +
  scale_fill_viridis_d() + scale_color_viridis_d() + 
  plottheme + theme()

  labs(x = paste0("Cross Rank ",expression(bold("UC"["variety"]~" (TGV)"))),
       y = "Selection Index GETGV",
       title = "Predicted distribution (mean and variability) of all crosses")

$x
[1] "Cross Rank bold(\"UC\"[\"variety\"] ~ \" (TGV)\")"

$y
[1] "Selection Index GETGV"

$title
[1] "Predicted distribution (mean and variability) of all crosses"

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

Next, sub

forplot %>% 
  filter(predOf=="TGV") %>% 
  slice_max(order_by = predUsefulness, n = 50) %>% 
  arrange(desc(predUsefulness)) %>% 
  mutate(Rank=1:nrow(.)) %>% 
    ggplot(aes(x = Rank, dist = "norm", 
               arg1 = predMean, arg2 = predSD, 
               fill=CrossGroup, color=CrossGroup),
           alpha=0.5) +
    stat_dist_gradientinterval(n=100) +
    scale_fill_viridis_d() + scale_color_viridis_d() + 
  plottheme + theme(axis.text.x = element_text(face='bold'),
                    axis.title.x = element_text(face = 'bold')) + 
  labs(x = expression(bold("Rank on SELIND - UC"["variety"])),
       y = "Selection Index GETGV",
       title = "Predicted distribution of the top 50 crosses")

The best 5 crosses to make:

forplot %>% 
  filter(predOf=="TGV") %>% 
  slice_max(order_by = predUsefulness, n = 5) %>% rmarkdown::paged_table()

Interestingly (?) no cross of C4 (TMS18) clones are yet recommended (in the top 50) on this ranking. The highest rank C4 cross is the 214th from top (see below) along with two other TMS18 x TMS15 crosses.

forplot %>% 
  filter(predOf=="TGV") %>% 
  arrange(desc(predUsefulness)) %>% 
  mutate(Rank=1:nrow(.)) %>% 
  filter(grepl("C4",CrossGroup)) %>% slice(1:10)

                      sireID                  damID CrossGroup predOf predMean
1  TMS15F1471P0080:250482464 TMS18F1457P0005_A19406      C3xC4    TGV 215.8932
2  TMS15F1471P0080:250482464 TMS18F1457P0009_A19236      C3xC4    TGV 217.7389
3  TMS15F1471P0080:250482464 TMS18F1399P0036_A18283      C3xC4    TGV 209.1322
4    2013_10139_12:250164378 TMS18F1457P0005_A19406      C1xC4    TGV 201.2473
5    2013_10139_12:250164378 TMS18F1457P0009_A19236      C1xC4    TGV 203.0930
6    2013_10139_22:250164388 TMS18F1457P0005_A19406      C1xC4    TGV 201.5410
7    2013_10139_22:250164388 TMS18F1457P0009_A19236      C1xC4    TGV 203.3867
8    2013_10139_12:250164378 TMS18F1399P0036_A18283      C1xC4    TGV 194.4863
9    2013_10139_22:250164388 TMS18F1399P0036_A18283      C1xC4    TGV 194.7800
10 TMS15F1471P0080:250482464 TMS18F1485P0024_A19068      C3xC4    TGV 203.2205
     predSD predUsefulness Rank
1  20.69547       258.5820  214
2  19.57696       258.1205  215
3  22.20299       254.9306  238
4  23.50480       249.7309  375
5  22.53923       249.5849  386
6  23.15062       249.2941  410
7  22.16216       249.1009  424
8  24.85371       245.7523  791
9  24.50146       245.3195  845
10 20.04130       244.5600 1004

In this last plot, the area under the top 5% of each crosses predicted distribution is highlighted. The mean of individuals from under the highlighted area is the \(UC_{variety}\). There are also 50 dots for each cross illustrating the hypothetical outcome of creating 50 progeny.

forplot %>% 
  filter(predOf=="TGV") %>% 
  slice_max(order_by = predUsefulness, n = 50) %>% 
  arrange(desc(predUsefulness)) %>% 
  mutate(Rank=1:nrow(.)) %>% 
  ggplot(aes(x = Rank, dist = "norm", 
             arg1 = predMean, arg2 = predSD, 
             fill = CrossGroup,
             label = CrossGroup)) +
  stat_dist_gradientinterval(n=100,side='top',position = "dodgejust",
                         aes(fill = stat(y < (arg1+arg2*qnorm(0.95))))) +
  stat_dist_dotsinterval(n=50,side='both',position = "dodgejust",
                         aes(fill = stat(y < (arg1+arg2*qnorm(0.95))))) +
  scale_fill_viridis_d() + scale_color_viridis_d() + 
  plottheme + theme(axis.text.x = element_text(face='bold'),
                    axis.title.x = element_text(face = 'bold'),
                    legend.position = 'none') + 
  labs(x = expression(bold("Rank on SELIND - UC"["variety"])),
       y = "Selection Index GETGV",
       title = "Predicted distribution of the top 50 crosses")

Or for more clarity, just the top 5 crosses:

forplot %>% 
  filter(predOf=="TGV") %>% 
  slice_max(order_by = predUsefulness, n = 5) %>% 
  arrange(desc(predUsefulness)) %>% 
  mutate(Rank=1:nrow(.)) %>% 
  ggplot(aes(y = Rank, dist = "norm", 
             arg1 = predMean, arg2 = predSD, 
             label = paste0(sireID,"\n x ",damID)),
         alpha=0.5) +
  stat_dist_dotsinterval(n=50,side='top',position = "dodgejust",scale=0.85,
                         aes(fill = stat(x < (arg1+arg2*qnorm(0.95))))) +
  stat_dist_halfeye(position = "dodgejust",scale=1.25, alpha=0.5,
                    aes(fill = stat(x < (arg1+arg2*qnorm(0.95))))) +
  geom_label(aes(x=predMean),size=3) + 
  scale_fill_viridis_d() + scale_color_viridis_d() + 
  plottheme + theme(axis.text.x = element_text(face='bold'),
                    axis.title.x = element_text(face = 'bold'),
                    legend.position = 'none') + 
  labs(y = expression(bold("Rank on SELIND - UC"["variety"])),
       x = "Selection Index GETGV",
       title = "Predicted distribution of the top 5 crosses")

Table of Top 50 Crosses: 2 rows for each cross, one for predOf=="BV" one for predOf=="TGV".

• Download the CSV for the top 50 crosses and their selection index means, SDs and UC here or
• Download the complete CSV of ALL genomic mate predictions here: for all crosses and both SELIND and component trait predictions.

top50crosses<-forplot %>% 
  group_by(predOf) %>% 
  slice_max(order_by = predUsefulness, n = 50) %>% 
  ungroup()
top50crosses %>% 
  write.csv(.,file = here::here("output","top50crosses.csv"), row.names = F)
top50crosses %>% 
  rmarkdown::paged_table()

[FUTURE] Compare PHG-to-Beagle data

[TO DO] PCA

[TO DO] Compare kinships

[TO DO] Prediction accuracy - PHG

[TO DO] Compare PHG-to-Beagle accuracy

Session information

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] ggdist_3.0.0    ragg_1.1.3      magrittr_2.0.1  forcats_0.5.1  
 [5] stringr_1.4.0   dplyr_1.0.7     purrr_0.3.4     readr_2.0.0    
 [9] tidyr_1.1.3     tibble_3.1.3    ggplot2_3.3.5   tidyverse_1.3.1
[13] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] viridis_0.6.1        httr_1.4.2           sass_0.4.0          
 [4] jsonlite_1.7.2       viridisLite_0.4.0    splines_4.1.0       
 [7] here_1.0.1           modelr_0.1.8         bslib_0.2.5.1       
[10] assertthat_0.2.1     distributional_0.2.2 highr_0.9           
[13] cellranger_1.1.0     yaml_2.2.1           lattice_0.20-44     
[16] pillar_1.6.2         backports_1.2.1      glue_1.4.2          
[19] digest_0.6.27        promises_1.2.0.1     rvest_1.0.1         
[22] colorspace_2.0-2     Matrix_1.3-4         htmltools_0.5.1.1   
[25] httpuv_1.6.1         pkgconfig_2.0.3      broom_0.7.9         
[28] haven_2.4.1          scales_1.1.1         whisker_0.4         
[31] later_1.2.0          tzdb_0.1.2           git2r_0.28.0        
[34] mgcv_1.8-36          generics_0.1.0       farver_2.1.0        
[37] ellipsis_0.3.2       withr_2.4.2          cli_3.0.1           
[40] crayon_1.4.1         readxl_1.3.1         evaluate_0.14       
[43] fs_1.5.0             fansi_0.5.0          nlme_3.1-152        
[46] xml2_1.3.2           textshaping_0.3.5    tools_4.1.0         
[49] hms_1.1.0            lifecycle_1.0.0      munsell_0.5.0       
[52] reprex_2.0.0         compiler_4.1.0       jquerylib_0.1.4     
[55] systemfonts_1.0.2    rlang_0.4.11         grid_4.1.0          
[58] rstudioapi_0.13      labeling_0.4.2       rmarkdown_2.9       
[61] gtable_0.3.0         DBI_1.1.1            R6_2.5.0            
[64] gridExtra_2.3        lubridate_1.7.10     knitr_1.33          
[67] utf8_1.2.2           rprojroot_2.0.2      stringi_1.7.3       
[70] Rcpp_1.0.7           vctrs_0.3.8          dbplyr_2.1.1        
[73] tidyselect_1.1.1     xfun_0.24