DArT Markers Imputation
LucianoRogerio
2022-05-09
Last updated: 2022-06-02
Checks: 6 1
Knit directory: EMBRAPAImputation2022/
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Imputation of the DArT Markers only
Imputation is performed by chromosome
java -Xms2g -Xmx [maxmem] -jar /programs/beagle/beagle.jar gt= [targetVCF] map= [mapFile] out= [outName] nthreads= [nthreads] impute= [impute] ne= [ne]runBeagle5Luc <- function(targetVCF, mapFile, outName, nthreads, maxmem = "500g",
impute = TRUE, ne = 1e+05, samplesToExclude = NULL){
system(paste0("java -Xms2g -Xmx", maxmem, " -jar /programs/beagle/beagle.jar ",
"gt=", targetVCF, " ", "map=", mapFile, " ",
"out=", outName, " ", "nthreads=", nthreads,
" impute=", impute, " ne=", ne,
ifelse(!is.null(samplesToExclude),
paste0(" excludesamples=", samplesToExclude), "")))}
targetVCFpath<-here::here("output/") # location of the targetVCF
mapPath<-here::here("data", "CassavaGeneticMapV6updated/")
outPath<-here::here("output/")
outSuffix<-"DCas22_6902"
library(tidyverse); library(magrittr);
purrr::map(1:18,
~runBeagle5Luc(targetVCF=paste0(targetVCFpath,"chr",.,
"_DCas22_6902_DArTseqLD_AllSites_AllChrom_rawFiltered.vcf.gz"),
mapFile=paste0(mapPath,"chr",.,
"_cassava_cM_pred.v6_91019.map"),
outName=paste0(outPath,"chr",.,
"_DCas22_6902_DArT_imputed"),
nthreads=110))Organize the Beagle logs in a directory
cd ~/Desktop/Genotyping/DArT/EMBRAPA/DCas22_6902/output/
mkdir BeagleLogs
cp *_DCas22_6902_DArT_imputed.log BeagleLogs/.
rm *_DCas22_6902_DArT_imputed.logPost Imputation Filter
Standard post-imputation filter: \(CR≥0.6\), \(MAF≥(1/7827)^2\).
Loop to filter all 18 VCF files in parallel
inPath<-here::here("output/")
outPath<-here::here("output/")
require(furrr); plan(multisession, workers = 18)
future_map(1:18,
~FilterLuc(inPath=inPath,
inName=paste0("chr",.,"_DCas22_6902_DArT_imputed"),
outPath=outPath,
outName=paste0("chr",.,"_DCas22_6902_DArT_imputedAndFiltered"),
CRthresh = 0.6))
plan(sequential)Let’s check what we got
purrr::map(1:18,~system(paste0("zcat ",here::here("output/"),"chr",.,"_DCas22_6902_DArT_imputedAndFiltered.vcf.gz | wc -l")))Chr 1 - 1064
Chr 2 - 696
Chr 3 - 682
Chr 4 - 682
Chr 5 - 634
Chr 6 - 656
Chr 7 - 428
Chr 8 - 532
Chr 9 - 520
Chr 10 - 664
Chr 11 - 591
Chr 12 - 468
Chr 13 - 508
Chr 14 - 707
Chr 15 - 516
Chr 16 - 437
Chr 17 - 543
Chr 18 - 480Formats for GS and GWAS Analysis
library(tidyverse); library(magrittr)
### Joint all the 18 Chromosome VCF to one unique file
inPath <- here::here("output/")
future_map(1:18,~system(paste0("tabix -f -p vcf ",inPath,
"chr",.,"_DCas22_6902_DArT_imputedAndFiltered.vcf.gz")))
system(paste0("bcftools concat ",
"--output ",
"AllChrom_DArT_ReadyForGP_2022May05.vcf.gz ",
"--output-type z --threads 7 ",
paste0("chr",1:18,
"_DCas22_6902_DArT_imputedAndFiltered.vcf.gz",
collapse = " ")))
### Post Imputation Filter function
postImputeFilterLuc <- function(inPath=NULL,inName,outPath=NULL,outName,HWEthresh=1e-20){
require(magrittr); require(dplyr)
# Extract imputation quality scores (DR2 and AF) from VCF
system(paste0("vcftools --gzvcf ",inPath,inName,".vcf.gz --hardy --out ",outPath,inName))
# Read scores into R
hwe<-read.table(paste0(outPath,inName,".hwe"),
stringsAsFactors = F, header = T)
stats2filterOn<-hwe %>% rename(CHROM=CHR)
# Compute MAF from AF and make sure numeric
# Identify sites passing filter
sitesPassingFilters<-stats2filterOn %>%
dplyr::filter(P_HWE>HWEthresh) %>%
dplyr::select(CHROM,POS)
print(paste0(nrow(sitesPassingFilters)," sites passing filter"))
# Write a list of positions passing filter to disk
write.table(sitesPassingFilters,
file = paste0(outPath,inName,".sitesPassing"),
row.names = F, col.names = F, quote = F)
# Apply filter to vcf file with vcftools
system(paste0("vcftools --gzvcf ",inPath,inName,".vcf.gz"," ",
"--positions ",outPath,inName,".sitesPassing"," ",
"--recode --stdout | bgzip -c -@ 24 > ",
outPath,outName,".vcf.gz"))
print(paste0("Filtering Complete: ",outName))
}
inPath<-here::here("")
outPath<-here::here("")
ncores <- 7
nclones <- 7827
require(furrr); options(mc.cores=ncores); plan(multiprocess)
postImputeFilterLuc(inPath=inPath,
inName=paste0("AllChrom_DArT_ReadyForGP_2022May05"),
outPath=outPath,
outName=paste0("AllChrom_DArT_ReadyForGP_2022May30"))
system(paste0("vcftools --gzvcf output/AllChrom_DArT_ReadyForGP_2022May30.vcf.gz --maf 0.00025",
" --recode --stdout | bgzip -c -@ 7 > output/AllChrom_DArT_ReadyForGPFil_2022May30.vcf.gz"))dgenomicMateSelectR::convertVCFtoDosage(pathIn = "output/", pathOut = "output/",
vcfName = "AllChrom_DArT_ReadyForGPFil_2022May30")
snps <- read.table(file = here::here("output", "AllChrom_DArT_ReadyForGPFil_2022May30.raw"),
stringsAsFactor=F, header = T) %>%
dplyr::select(-FID,-PAT,-MAT,-SEX,-PHENOTYPE) %>%
column_to_rownames(var = "IID") %>%
as.matrix()
saveRDS(snps,file = here::here("output", "DCas22_DArT_ReadyForGP_Dos.rds"))Markers Density
library(tidyverse); library(CMplot)
snps <- readRDS(file = here::here("output", "DArT2022", "DCas22_DArt_ReadyForGP_Dos.rds"))
CMsnps <- tibble(SNP = colnames(snps),
chr = substring(SNP,1,3),
pos = substring(SNP,4)) %>%
mutate(chr = gsub(pattern = "_", replacement = "", x = chr) %>%
gsub(pattern = "S", replacement = "") %>% as.integer,
pos = gsub(pattern = "_[A-Z]", replacement = "", x = pos) %>%
gsub(pattern = "_", replacement = "", x = .) %>% as.integer)
CMplot(CMsnps, plot.type = "d", bin.size = 1e6, col = c("darkgreen", "yellow", "red"),
file = "jpg", memo = "DArTDensityMkrs", dpi = 500, file.output = T, verbose = TRUE)Fig 1. Density Markers for DArT genotyping
Principal Components Analysis
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.6.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/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_2.0.3 data.table_1.14.3 forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.9 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[9] tibble_3.1.7 ggplot2_3.3.6 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 here_1.0.1 lubridate_1.8.0 assertthat_0.2.1
[5] rprojroot_2.0.3 digest_0.6.29 utf8_1.2.2 R6_2.5.1
[9] cellranger_1.1.0 backports_1.4.1 reprex_2.0.1 evaluate_0.15
[13] highr_0.9 httr_1.4.3 pillar_1.7.0 rlang_1.0.2
[17] readxl_1.4.0 rstudioapi_0.13 jquerylib_0.1.4 rmarkdown_2.14
[21] labeling_0.4.2 munsell_0.5.0 broom_0.8.0 compiler_4.1.2
[25] httpuv_1.6.5 modelr_0.1.8 xfun_0.30 pkgconfig_2.0.3
[29] htmltools_0.5.2 tidyselect_1.1.2 workflowr_1.7.0 fansi_1.0.3
[33] crayon_1.5.1 tzdb_0.3.0 dbplyr_2.1.1 withr_2.5.0
[37] later_1.3.0 grid_4.1.2 jsonlite_1.8.0 gtable_0.3.0
[41] lifecycle_1.0.1 DBI_1.1.2 git2r_0.30.1 scales_1.2.0
[45] cli_3.3.0 stringi_1.7.6 farver_2.1.0 fs_1.5.2
[49] promises_1.2.0.1 xml2_1.3.3 bslib_0.3.1 ellipsis_0.3.2
[53] generics_0.1.2 vctrs_0.4.1 tools_4.1.2 glue_1.6.2
[57] hms_1.1.1 fastmap_1.1.0 yaml_2.3.5 colorspace_2.0-3
[61] rvest_1.0.2 knitr_1.38 haven_2.5.0 sass_0.4.1