Last updated: 2021-07-14

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Previous step

Validate the pedigree obtained from cassavabase: Before setting up a cross-validation scheme for predictions that depend on a correct pedigree, add a basic verification step to the pipeline. Not trying to fill unknown or otherwise correct the pedigree. Assess evidence that relationship is correct, remove if incorrect.

Haplotype matrix from phased VCF

Extract haps from VCF with bcftools

library(tidyverse); library(magrittr)
pathIn<-"/home/jj332_cas/marnin/implementGMSinCassava/data/"
pathOut<-pathIn
vcfName<-"AllChrom_RefPanelAndGSprogeny_ReadyForGP_72719"
system(paste0("bcftools convert --hapsample ",
              pathOut,vcfName," ",
              pathIn,vcfName,".vcf.gz "))

Read haps to R

library(data.table)
haps<-fread(paste0(pathIn,vcfName,".hap.gz"),
            stringsAsFactors = F,header = F) %>% 
  as.data.frame
sampleids<-fread(paste0(pathIn,vcfName,".sample"),
                 stringsAsFactors = F,header = F,skip = 2) %>% 
  as.data.frame

Extract needed GIDs from BLUPs and pedigree: Subset to: (1) genotyped-plus-phenotyped and/or (2) in verified pedigree.

blups<-readRDS(file=here::here("output",
                               "IITA_blupsForModelTraining_twostage_asreml_2021May10.rds"))
blups %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  distinct(GID) %$% GID -> gidWithBLUPs

genotypedWithBLUPs<-gidWithBLUPs[gidWithBLUPs %in% sampleids$V1]
length(genotypedWithBLUPs) # 7960

ped<-read.table(here::here("output","verified_ped.txt"),
                header = T, stringsAsFactors = F)

pednames<-union(ped$FullSampleName,
                union(ped$SireID,ped$DamID))
length(pednames) # 4384

samples2keep<-union(genotypedWithBLUPs,pednames)
length(samples2keep) # 8013

# write a sample list to disk for downstream purposes
# format suitable for subsetting with --keep in plink
write.table(tibble(FID=0,IID=samples2keep),
            file=here::here("output","samples2keep_IITA_2021May13.txt"),
            row.names = F, col.names = F, quote = F)

Add sample ID’s

hapids<-sampleids %>% 
  select(V1,V2) %>% 
  mutate(SampleIndex=1:nrow(.)) %>% 
  rename(HapA=V1,HapB=V2) %>% 
  pivot_longer(cols=c(HapA,HapB),
               names_to = "Haplo",values_to = "SampleID") %>% 
  mutate(HapID=paste0(SampleID,"_",Haplo)) %>% 
  arrange(SampleIndex)
colnames(haps)<-c("Chr","HAP_ID","Pos","REF","ALT",hapids$HapID)

Subset haps

hapids2keep<-hapids %>% filter(SampleID %in% samples2keep)
hapids2keep$HapID
dim(haps) # [1] 68814 43717
haps<-haps[,c("Chr","HAP_ID","Pos","REF","ALT",hapids2keep$HapID)]
dim(haps) # [1] 68814 16031

Format, transpose, convert to matrix and save!

haps %<>% 
  mutate(HAP_ID=gsub(":","_",HAP_ID)) %>% 
  column_to_rownames(var = "HAP_ID") %>% 
  select(-Chr,-Pos,-REF,-ALT)
haps %<>% t(.) %>% as.matrix(.)
saveRDS(haps,file=here::here("data","haps_IITA_2021May13.rds")

Make dosages from haps

To ensure consistency in allele counting, create dosage from haps manually.

dosages<-haps %>%
  as.data.frame(.) %>% 
  rownames_to_column(var = "GID") %>% 
  separate(GID,c("SampleID","Haplo"),"_Hap",remove = T) %>% 
  select(-Haplo) %>% 
  group_by(SampleID) %>% 
  summarise(across(everything(),~sum(.))) %>% 
  ungroup() %>% 
  column_to_rownames(var = "SampleID") %>% 
  as.matrix
saveRDS(dosages,file=here::here("data","dosages_IITA_2021May13.rds"))
# > dim(dosages)
# [1]  8013 68814

Variant filters

Apply a MAF filter and lightly LD prune: The number of markers in the “raw” dataset (~68K) is ~3X the number used in the mate selection paper and I think more than is necessary. There is a burden incurred because we have to compute and store in memory (and on disk) \(N_{snp} \times N_{snp}\) recombination frequency matrices.

# library(tidyverse); library(magrittr)
# pathIn<-"/home/jj332_cas/marnin/implementGMSinCassava/data/"
# pathOut<-pathIn
# vcfName<-"AllChrom_RefPanelAndGSprogeny_ReadyForGP_72719"
# 
# write.table(tibble(FID=0,IID=samples2keep),
#             file=here::here("output","samples2keep_IITA_2021May13.txt"),
#             row.names = F, col.names = F, quote = F)
# 
# ped2check<-read.table(file=here::here("output","ped2genos.txt"),
#                       header = F, stringsAsFactors = F)
# 
# # pednames<-union(ped2check$V1,union(ped2check$V2,ped2check$V3)) %>% 
# #   tibble(FID=0,IID=.)
# # write.table(pednames,file=here::here("output","pednames2keep.txt"), 
# #             row.names = F, col.names = F, quote = F)

cd /home/jj332_cas/marnin/implementGMSinCassava/
export PATH=/programs/plink-1.9-x86_64-beta3.30:$PATH;
plink --bfile data/AllChrom_RefPanelAndGSprogeny_ReadyForGP_72719 \
  --keep output/samples2keep_IITA_2021May13.txt \
  --maf 0.01 \
  --indep-pairwise 50 25 0.98 \
  --out output/samples2keep_IITA_MAFpt01_prune50_25_pt98;

Used plink to output a list of pruned SNPs.

Next, subset the columns of haps and dosages in R.

library(tidyverse); library(magrittr); 
haps<-readRDS(file=here::here("data","haps_IITA_2021May13.rds"))
dosages<-readRDS(file=here::here("data","dosages_IITA_2021May13.rds"))
snps2keep<-read.table(here::here("output",
                      "samples2keep_IITA_MAFpt01_prune50_25_pt98.prune.in"),
           header = F, stringsAsFactors = F)
snps2keep<-tibble(HapSNP_ID=colnames(haps)) %>% 
  separate(HapSNP_ID,c("Chr","Pos","Ref","Alt"),remove = F) %>% 
  mutate(SNP_ID=paste0("S",Chr,"_",Pos)) %>% 
  filter(SNP_ID %in% snps2keep$V1)

haps<-haps[,snps2keep$HapSNP_ID]
dosages<-dosages[,snps2keep$HapSNP_ID]

# dim(haps); dim(dosages); haps[1:5,1:10]

saveRDS(haps,file=here::here("data","haps_IITA_filtered_2021May13.rds"))
saveRDS(dosages,file=here::here("data","dosages_IITA_filtered_2021May13.rds"))

Make Add and Dom GRMs from dosages

# activate multithread OpenBLAS for fast matrix algebra
export OMP_NUM_THREADS=56

dosages<-readRDS(file=here::here("data","dosages_IITA_filtered_2021May13.rds"))
A<-predCrossVar::kinship(dosages,type="add")
D<-predCrossVar::kinship(dosages,type="dom")
saveRDS(A,file=here::here("output","kinship_A_IITA_2021May13.rds"))
saveRDS(D,file=here::here("output","kinship_D_IITA_2021May13.rds"))

cd /home/mw489/implementGMSinCassava/;
screen; 
singularity shell rocker.sif; R

dosages<-readRDS(file=here::here("data","dosages_IITA_filtered_2021May13.rds"))
source(here::here("code","gsFunctions.R"))
RhpcBLASctl::blas_set_num_threads(56)
D<-kinship(dosages,type="domGenotypic")
saveRDS(D,file=here::here("output","kinship_domGenotypic_IITA_2021July5.rds"))

Genetic Map

cp -r /home/jj332_cas/CassavaGenotypeData/CassavaGeneticMap /home/jj332_cas/marnin/implementGMSinCassava/data/

# activate multithread OpenBLAS for fast matrix algebra
export OMP_NUM_THREADS=56

Creating the map used for Beagle-imputation in 2019: In 2019, I obtained a ICGMC-derived genetic map, I think from Guillaume Bauchet and used it to create a map I’ve been using for imputation, which has 25K markers (Beagle interpolates the map to the markers genotyped in the panel).

However, the recombination frequency matrix and thus cross-variance predictions needs to have all positions for which we have marker effects. It means I have to interpolate a map from the original file cassava_cM_pred.v6.allchr.txt. See below:

library(tidyverse); library(magrittr)
dosages<-readRDS(file=here::here("data","dosages_IITA_filtered_2021May13.rds"))
# genmap<-tibble(Chr=1:18) %>% 
#   mutate(geneticMap=map(Chr,~read.table(here::here("data/CassavaGeneticMap",
#                                                    paste0("chr",.,"_cassava_cM_pred.v6_91019.map")),
#                                         header = F, stringsAsFactors = F)))

genmap<-read.table(here::here("data/CassavaGeneticMap",
                              "cassava_cM_pred.v6.allchr.txt"),
           header = F, stringsAsFactors = F,sep=';') %>% 
  rename(SNP_ID=V1,Pos=V2,cM=V3) %>% 
  as_tibble

snps_genmap<-tibble(DoseSNP_ID=colnames(dosages)) %>% 
  separate(DoseSNP_ID,c("Chr","Pos","Ref","Alt"),remove = F) %>% 
  mutate(SNP_ID=paste0("S",Chr,"_",Pos)) %>% 
  left_join(genmap %>% mutate(across(everything(),as.character)))
# snps_genmap %>% 
#   ggplot(.,aes(x=as.integer(Pos),y=as.numeric(cM))) + 
#   geom_point() + 
#   theme_bw() + 
#   facet_wrap(~Chr)

interpolate_genmap<-function(data){
  # for each chromosome map
  # find and _decrements_ in the genetic map distance
  # fix them to the cumulative max to force map to be only increasing
  # fit a spline for each chromosome
  # Use it to predict values for positions not previously on the map
  # fix them AGAIN (in case) to the cumulative max, forcing map to only increase
  data_forspline<-data %>% 
    filter(!is.na(cM)) %>% 
    mutate(cumMax=cummax(cM),
           cumIncrement=cM-cumMax) %>% 
    filter(cumIncrement>=0) %>% 
    select(-cumMax,-cumIncrement)
  
  spline<-data_forspline %$% smooth.spline(x=Pos,y=cM,spar = 0.75)
  
  splinemap<-predict(spline,x = data$Pos) %>% 
    as_tibble(.) %>% 
    rename(Pos=x,cM=y) %>% 
    mutate(cumMax=cummax(cM),
           cumIncrement=cM-cumMax) %>% 
    mutate(cM=cumMax) %>% 
    select(-cumMax,-cumIncrement)
  
  return(splinemap) 
}

splined_snps_genmap<-snps_genmap %>% 
  select(-cM) %>% 
  mutate(Pos=as.numeric(Pos)) %>% 
  left_join(snps_genmap %>% 
              mutate(across(c(Pos,cM),as.numeric)) %>% 
              arrange(Chr,Pos) %>% 
              nest(-Chr) %>% 
              mutate(data=map(data,interpolate_genmap)) %>% 
              unnest(data)) %>% 
  distinct

all(splined_snps_genmap$DoseSNP_ID == colnames(dosages))

[1] TRUE

# [1] TRUE

saveRDS(splined_snps_genmap,file=here::here("data","genmap_2021May13.rds"))

splined_snps_genmap %>% 
  mutate(Map="Spline") %>% 
  bind_rows(snps_genmap %>% 
              mutate(across(c(Pos,cM),as.numeric)) %>% 
              arrange(Chr,Pos) %>% mutate(Map="Data")) %>% 
  ggplot(.,aes(x=Pos,y=cM,color=Map),alpha=0.5,size=0.75) + 
  geom_point() + 
  theme_bw() + facet_wrap(~as.integer(Chr), scales='free_x')

Recomb. freq. matrix

Construct a matrix of recombination frequencies at loci for all study loci. Pre-compute 1-2c to save time predicting cross variance.

library(predCrossVar)
genmap<-readRDS(file=here::here("data","genmap_2021May13.rds"))
m<-genmap$cM;
names(m)<-genmap$DoseSNP_ID
recombFreqMat<-1-(2*genmap2recombfreq(m,nChr = 18))
saveRDS(recombFreqMat,file=here::here("data","recombFreqMat_1minus2c_2021May13.rds"))

Pick traits to cross-validate

# This list from Dec. 2020 GeneticGain rate estimation
# These were what Ismail/IITA/BMGF wanted to see
# Will cross-validate these traits
traits<-c("logDYLD","logFYLD","logRTNO","logTOPYLD","MCMDS","DM","BCHROMO",
          "PLTHT","BRLVLS","BRNHT1","HI")

# Full trait list = 14:
## traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI",
##           "logDYLD", # <-- logDYLD now included.
##           "logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")

Pedigree

Validate the pedigree obtained from cassavabase: Before setting up a cross-validation scheme for predictions that depend on a correct pedigree, add a basic verification step to the pipeline. Not trying to fill unknown or otherwise correct the pedigree. Assess evidence that relationship is correct, remove if incorrect.

ped<-read.table(here::here("output","verified_ped.txt"),
                header = T, stringsAsFactors = F)

BLUPs

Select traits and data to be analyzed.

library(tidyverse); library(magrittr);
blups<-readRDS(file=here::here("output",
                               "IITA_blupsForModelTraining_twostage_asreml_2021May10.rds"))
dosages<-readRDS(file=here::here("data","dosages_IITA_filtered_2021May13.rds"))


blups %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  distinct(GID) %$% GID -> gidWithBLUPs

genotypedWithBLUPs<-gidWithBLUPs[gidWithBLUPs %in% rownames(dosages)]
length(genotypedWithBLUPs) # 7960

blups %<>% 
  filter(Trait %in% traits) %>% 
  select(Trait,blups,varcomp) %>% 
  mutate(blups=map(blups,~filter(.,GID %in% genotypedWithBLUPs)))

saveRDS(blups,file=here::here("data","blups_forCrossVal.rds"))

Index weights [get from Ismail]

# library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
#   select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
#   unnest(blups) %>% 
#   select(Trait,germplasmName,drgBLUP) %>% 
#   spread(Trait,drgBLUP)
# 
# indices<-blups %>% 
#   summarize_if(is.numeric,sd, na.rm=T) %>% 
#   pivot_longer(cols = everything(), names_to = "Trait", values_to = "blupSD") %>% 
#   left_join(tibble(Trait=c("DM","logFYLD","MCMDS","TCHART"), 
#                    stdSI_unscaled=c(5, 10, -10, -5),
#                    biofortSI_unscaled=c(10, 5, -5,10))) %>% 
#   mutate(stdSI=stdSI_unscaled/blupSD,
#          biofortSI=biofortSI_unscaled/blupSD)
# indices %>% mutate_if(is.numeric,~round(.,2))
# saveRDS(indices,file=here::here("data","selection_index_weights_4traits.rds"))

Next step

Parent-wise cross-validation: Compute parent-wise cross-validation folds using the validated pedigree. Fit models to get marker effects and make subsequent predictions of cross means and (co)variances.

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] magrittr_2.0.1  forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7    
 [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.3     tibble_3.1.2   
 [9] ggplot2_3.3.5   tidyverse_1.3.1 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7        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_1.6.1      rlang_0.4.11     
[17] readxl_1.3.1      rstudioapi_0.13   whisker_0.4       jquerylib_0.1.4  
[21] rmarkdown_2.9     labeling_0.4.2    munsell_0.5.0     broom_0.7.8      
[25] compiler_4.1.0    httpuv_1.6.1      modelr_0.1.8      xfun_0.24        
[29] pkgconfig_2.0.3   htmltools_0.5.1.1 tidyselect_1.1.1  fansi_0.5.0      
[33] crayon_1.4.1      dbplyr_2.1.1      withr_2.4.2       later_1.2.0      
[37] grid_4.1.0        jsonlite_1.7.2    gtable_0.3.0      lifecycle_1.0.0  
[41] DBI_1.1.1         git2r_0.28.0      scales_1.1.1      cli_3.0.0        
[45] stringi_1.6.2     farver_2.1.0      fs_1.5.0          promises_1.2.0.1 
[49] xml2_1.3.2        bslib_0.2.5.1     ellipsis_0.3.2    generics_0.1.0   
[53] vctrs_0.3.8       tools_4.1.0       glue_1.4.2        hms_1.1.0        
[57] yaml_2.2.1        colorspace_2.0-2  rvest_1.0.0       knitr_1.33       
[61] haven_2.4.1       sass_0.4.0

Pre-process data files for downstream cross-validation

Marnin Wolfe

2021-May-13