Last updated: 2020-02-14

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Knit directory: IITA_2019GS/

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Rmd 6a4d569 wolfemd 2020-02-14 Add box and barplots of genetic gain based on GEBVs.
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Rmd bfffb51 wolfemd 2019-11-21 Publish the first set of analyses and files for IITA 2019 GS,

Prep. data for genomic prediction

Load snps

rm(list=ls()); gc()
library(tidyverse); library(magrittr); 
snps<-readRDS(file=paste0("/workdir/ImputationStageIII_72619/",
                          "DosageMatrix_RefPanelAndGSprogeny_ReadyForGP_73019.rds"))

Load blups

blupsNoOut<-readRDS(file="data/iita_blupsForCrossVal_outliersRemoved_73019.rds")
blups<-readRDS(file="data/iita_blupsForCrossVal_72619.rds")

blups<-blupsNoOut %>% 
  dplyr::select(Trait,blups) %>% 
  mutate(OutliersRemoved=TRUE) %>% 
  bind_rows(
    blups %>% 
      filter(Dataset=="HistoricalDataIncluded" | Trait %in% c("BRNHT1","PLTHT")) %>% 
      dplyr::select(Trait,blups) %>% 
      mutate(OutliersRemoved=FALSE))

Match TMS18F germNames to DArT genos

For the TMS18F, there are some phenotypes available now.

gidsInPhenos<-tibble(GID=blups %>% unnest() %$% GID %>% unique)
table(gidsInPhenos$GID %in% rownames(snps)) # 6629 (that only includes GG through C3)

# gidsInPhenos$GID %>% grep("TMS18",.,value=T) %>% length # 1452
# rownames(snps) %>% grep("TMS18",.,value=T) %>% length # 2420
gidsInPhenos %<>% 
  left_join(
    tibble(dartName=rownames(snps) %>% grep("TMS18",.,value=T)) %>% 
      separate(dartName,c("GID","dartID"),"_",extra = 'merge',remove = F) %>% 
      semi_join(gidsInPhenos) %>% 
      group_by(GID) %>% 
      slice(1) %>% 
      ungroup()) %>% 
  mutate(NewName=ifelse(!is.na(dartName),dartName,GID))

blups %<>% 
  unnest(blups) %>% 
  left_join(gidsInPhenos) %>% 
  mutate(GID=NewName) %>% 
  dplyr::select(-dartName,-dartID,-NewName) %>% 
  group_by(Trait,OutliersRemoved) %>% 
  nest(.key = "trainingData")

Make a kinship matrix including C4

Subset snps

table(gidsInPhenos$NewName %in% rownames(snps))
iitaSamples2keep<-union(rownames(snps) %>% .[. %in% gidsInPhenos$NewName],
                        rownames(snps) %>% grep("TMS18",.,value = T)) #%>% length # 9049
snps<-snps[iitaSamples2keep,]

MAF>1% filter

maf_filter<-function(snps,thresh){
  freq<-colMeans(snps, na.rm=T)/2; maf<-freq;
  maf[which(maf > 0.5)]<-1-maf[which(maf > 0.5)]
  snps1<-snps[,which(maf>thresh)];
  return(snps1) }
snps %<>% maf_filter(.,0.01)
dim(snps) # [1]  9049 68029

Make kinships

library(tidyverse); library(magrittr); library(rrBLUP)
K<-rrBLUP::A.mat(snps-1)

saveRDS(K,file=paste0("/workdir/marnin/IITA_2019GS/",
                      "Kinship_IITA_SamplesForGP_73119.rds"))
saveRDS(snps,file=paste0("/workdir/marnin/IITA_2019GS/",
                         "DosageMatrix_IITA_SamplesForGP_73119.rds"))
rm(snps); gc()

PCA

On cbsurobbins

pca<-prcomp(snps, scale=T, center=T) 
pc_scores<-pca$x %>% 
  .[,1:15] %>% 
  as.data.frame %>% 
  rownames_to_column(var="FullSampleName") %>% 
  mutate(GeneticGroup=NA,
         GeneticGroup=ifelse(grepl("TMS13F|2013_",FullSampleName),"TMS13F",
                             ifelse(grepl("TMS14F",FullSampleName),"TMS14F",
                                    ifelse(grepl("TMS15F",FullSampleName),"TMS15F",
                                           ifelse(grepl("TMS18F",FullSampleName),"TMS18F","GGetc")))))
summary(pca)$importance[,1:10]

First two PCs only explain about 9% of the genetic variance.

Save PCA results

saveRDS(pc_scores,file="output/PCscores_IITA_SamplesForGP_73119.rds")
saveRDS(pca,file="/workdir/marnin/IITA_2019GS/PCA_IITA_SamplesForGP_73119.rds")
rm(pca); gc()
rm(list=ls());gc()
          used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells  605648 32.4    1271975   68         NA  1062694 56.8
Vcells 1157548  8.9    8388608   64     102400  2071651 15.9
library(tidyverse); library(magrittr); library(cowplot); 
pc_scores<-readRDS("output/PCscores_IITA_SamplesForGP_73119.rds")
library(viridis)
pc1v2<-pc_scores %>% 
  ggplot(.,aes(x=PC1,y=PC2,color=GeneticGroup)) + 
  geom_point() + 
  theme_bw() + scale_color_viridis_d() + 
  labs(x="PC1 (6%)",y="PC2 (3%)") + theme(legend.position = 'none')
pc3v4<-pc_scores %>% 
  ggplot(.,aes(x=PC3,y=PC4,color=GeneticGroup)) + 
  geom_point() + 
  theme_bw() + scale_color_viridis_d() + 
  labs(x="PC3 (3%)",y="PC4 (3%)")
plot_grid(pc1v2,pc3v4)

Version Author Date
a869b9e wolfemd 2019-11-21

Prediction

On cbsulm16

Load data for pred

rm(list=ls()); gc()
library(tidyverse); library(magrittr); 
K<-readRDS(file=paste0("/workdir/IITA_2019GS/Kinship_IITA_SamplesForGP_73119.rds"))
blupsNoOut<-readRDS(file="data/iita_blupsForCrossVal_outliersRemoved_73019.rds")
blups<-readRDS(file="data/iita_blupsForCrossVal_72619.rds")

blups<-blupsNoOut %>% 
  dplyr::select(Trait,blups) %>% 
  mutate(OutliersRemoved=TRUE) %>% 
  bind_rows(
    blups %>% 
      filter(Dataset=="HistoricalDataIncluded" | Trait %in% c("BRNHT1","PLTHT")) %>% 
      dplyr::select(Trait,blups) %>% 
      mutate(OutliersRemoved=FALSE))
gidsInPhenos<-tibble(GID=blups %>% unnest() %$% GID %>% unique)
gidsInPhenos %<>% 
  left_join(
    tibble(dartName=rownames(K) %>% grep("TMS18",.,value=T)) %>% 
      separate(dartName,c("GID","dartID"),"_",extra = 'merge',remove = F) %>% 
      semi_join(gidsInPhenos) %>% 
      group_by(GID) %>% 
      slice(1) %>% 
      ungroup()) %>% 
  mutate(NewName=ifelse(!is.na(dartName),dartName,GID))
blups %<>% 
  unnest(blups) %>% 
  left_join(gidsInPhenos) %>% 
  mutate(GID=NewName) %>% 
  dplyr::select(-dartName,-dartID,-NewName) %>% 
  filter(GID %in% rownames(K)) %>% 
  group_by(Trait,OutliersRemoved) %>% 
  nest(.key = "trainingData")

Get GEBVs

require(sommer)
require(furrr)
options(future.globals.maxSize= 1500*1024^2)
options(mc.cores=18)
plan(multiprocess)
proctime<-proc.time()
blups %>% 
  mutate(GEBV=future_pmap(.,function(Trait,trainingData,OutliersRemoved,...){
    trndata<-trainingData %>% 
      mutate(GID=factor(GID,levels=rownames(K)))
    fit <- mmer(fixed = drgBLUP ~1,
                random = ~vs(GID,Gu=K),
                weights = WT,
                data=trndata)
    x<-fit$U$`u:GID`$drgBLUP 
    gebvs<-tibble(GID=names(x),
                  GEBV=as.numeric(x))
    saveRDS(gebvs,file=paste0("output/",
                              "GEBV_",Trait,"_OutliersEqual",OutliersRemoved,"_IITA_73119.rds"))
    rm(fit); gc()}))
proc.time() - proctime

Format GEBV

gebvs<-list.files(path = "output/") %>% 
  grep("GEBV_",.,value = T) %>% 
  grep("_IITA_73119.rds",.,value = T) %>% 
  tibble(Files=paste0("output/",.)) %>% 
  mutate(Trait=gsub("output/GEBV_","",Files),
         Trait=gsub("_IITA_73119.rds","",Trait)) %>% 
  separate(Trait,c("Trait","OutliersRemoved"),"_OutliersEqual") %>% 
  mutate(Files=map(Files,~readRDS(.))) %>% 
  unnest(Files) %>% 
  mutate(GeneticGroup=NA,
         GeneticGroup=ifelse(grepl("TMS13F|2013_",GID),"TMS13F",
                             ifelse(grepl("TMS14F",GID),"TMS14F",
                                    ifelse(grepl("TMS15F",GID),"TMS15F",
                                           ifelse(grepl("TMS18F",GID),"TMS18F","GGetc")))))

Write GEBVs

gebvs %>% 
  spread(Trait,GEBV) %>% 
  group_by(OutliersRemoved) %>% 
  nest() %>% 
  mutate(data=map2(data,OutliersRemoved,function(data,OutliersRemoved){
    write.csv(data,
              file=paste0("output/GEBV_IITA_OutliersRemoved",OutliersRemoved,"_73119.csv"),
              row.names = F) }))

Next step

Get Genetic Gain Estimate


sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] viridis_0.5.1     viridisLite_0.3.0 cowplot_1.0.0     magrittr_1.5     
 [5] forcats_0.4.0     stringr_1.4.0     dplyr_0.8.3       purrr_0.3.3      
 [9] readr_1.3.1       tidyr_1.0.0       tibble_2.1.3      ggplot2_3.2.1    
[13] tidyverse_1.2.1  

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5     xfun_0.11            haven_2.2.0         
 [4] lattice_0.20-38      colorspace_1.4-1     vctrs_0.2.0         
 [7] generics_0.0.2       htmltools_0.4.0      yaml_2.2.0          
[10] rlang_0.4.1          later_1.0.0          pillar_1.4.2        
[13] withr_2.1.2          glue_1.3.1           modelr_0.1.5        
[16] readxl_1.3.1         lifecycle_0.1.0      munsell_0.5.0       
[19] gtable_0.3.0         workflowr_1.5.0.9000 cellranger_1.1.0    
[22] rvest_0.3.5          evaluate_0.14        labeling_0.3        
[25] knitr_1.26           httpuv_1.5.2         broom_0.5.2         
[28] Rcpp_1.0.3           promises_1.1.0       backports_1.1.5     
[31] scales_1.1.0         jsonlite_1.6         farver_2.0.1        
[34] fs_1.3.1             gridExtra_2.3        hms_0.5.2           
[37] digest_0.6.22        stringi_1.4.3        grid_3.6.1          
[40] rprojroot_1.3-2      cli_1.1.0            tools_3.6.1         
[43] lazyeval_0.2.2       crayon_1.3.4         whisker_0.4         
[46] pkgconfig_2.0.3      zeallot_0.1.0        xml2_1.2.2          
[49] lubridate_1.7.4      assertthat_0.2.1     rmarkdown_1.17      
[52] httr_1.4.1           rstudioapi_0.10      R6_2.4.1            
[55] nlme_3.1-142         git2r_0.26.1         compiler_3.6.1