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Last updated: 2022-06-02
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Knit directory: EMBRAPAImputation2022/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 95ddcaf | LucianoRogerio | 2022-05-05 | Imputation of DArT Genotypic data |
| html | 95ddcaf | LucianoRogerio | 2022-05-05 | Imputation of DArT Genotypic data |
| Rmd | 8204f0e | LucianoRogerio | 2022-03-03 | Start workflowr project. |
New Imputation for EMBRAPA with DArT Marker only and DArT plus GBS Markers
Diversity Array Technology LTDA joint all the genotyping data from the four Genotyping Orders that EMBRAPA requested during these six or seven years in one huge file.
So let’s check what we got at the DArT report for EMBRAPA DArT genotyping of 2022
library(genomicMateSelectR)
dir("data/DArT2022")
nskipvcf <- 2
nskipcounts <- 2
VCF2022 <- read.table(here::here("data", "DArT2022", "Report_6902_VCF_Ref_Version6.txt"),
sep = "\t", header = T, skip = nskipvcf, comment.char = "",
check.names = F)
Counts2022 <- read.table(here::here("data", "DArT2022",
"SEQ_SNPs_counts_0_Target_extend_Ref.csv"),
sep = ",", header = T, skip = nskipcounts, check.names = F)
Counts2022[1:10,1:50]
VCF2022[1:10,1:30]
genomicMateSelectR::convertDart2vcf(dartvcfInput = here::here("data", "DArT2022", "Report_6902_VCF_Ref_Version6.txt"),
dartcountsInput = here::here("data", "DArT2022", "SEQ_SNPs_counts_0_Target_extend_Ref.csv"),
nskipvcf = 2, nskipcounts = 2,
outName = "output/DCas22_6902", ncores = 1)Add the VCF header to the DArT VCF file
system(paste0("bgzip -d -@ 7 output/DArT2022/DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.vcf.gz > ",
"output/DArT2022/DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.vcf"))
DArTVCFFileSel <- read.table(file = here::here("output", "DArT2022",
"DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.vcf"),
sep = "\t", header = T, comment.char = "", check.names = F)
header <- c("##fileformat=VCFv4.0",
"##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">",
"##FORMAT=<ID=AD,Number=.,Type=Integer,Description=\"Allelic depths for the reference and alternate alleles in the order listed\">",
"##FORMAT=<ID=DP,Number=1,Type=Integer,Description=\"Read Depth (only filtered reads used for calling)\">",
"##FORMAT=<ID=PL,Number=3,Type=Float,Description=\"Normalized, Phred-scaled likelihoods for AA,AB,BB genotypes where A=ref and B=alt; not applicable if site is not biallelic\">")
write_lines(header, file = here::here("output", "DCas22_6902",
"DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.vcf"),
append = F)
write.table(x = DArTVCFFileSel, file = here::here("output", "DCas22_6902",
"DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.vcf"),
quote = F, row.names = F, append = T, col.names = T, sep = "\t")
system(paste0("bgzip -c -@ 7 output/DCas22_6902/DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.vcf > ",
"output/DCas22_6902/DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.vcf.gz"))Filtering the DArT markers previously the Beagle imputation
library(here); library(tidyverse)
library(magrittr); library(dplyr)
## Parameters for the Filter function
inPath <- "output/"
inName <- "DCas22_6902_DArTseqLD_AllSites_AllChrom_raw"
outPath <- "output/"
outName <- "DCas22_6902_DArTseqLD_AllSites_AllChrom_rawFiltered"
FilterLuc <- function(inPath = NULL, inName, outPath = NULL, outName, CRthresh = 0.6){
system(paste0("vcftools --gzvcf ", inPath, inName, ".vcf.gz --freq2 --out ",
outPath, inName))
system(paste0("vcftools --gzvcf ", inPath, inName, ".vcf.gz --missing-site --out ",
outPath, inName))
INFO <- read.table(paste0(outPath, inName, ".frq"), stringsAsFactors = F,
header = F, skip = 1) %>%
rename(CHROM = V1, POS = V2, N_ALLELES = V3,
N_CHR = V4, FREQ1 = V5, FREQ2 = V6)
callrate <- read.table(paste0(outPath, inName, ".lmiss"), stringsAsFactors = F,
header = T) %>% dplyr::select(CHR, POS, N_DATA, F_MISS) %>%
mutate(CHROM = CHR,
CR = 1 - F_MISS,
.keep = "unused")
stats2filterOn <- left_join(INFO, callrate)
stats2filterOn %<>% dplyr::mutate(FREQ2 = as.numeric(FREQ2)) %>%
dplyr::mutate(MAF = ifelse(FREQ2 > 0.5,
yes = 1 - FREQ2, no = FREQ2)) %>%
dplyr::select(-FREQ1, -FREQ2)
MAFthresh <- (1/max(stats2filterOn$N_DATA, na.rm = T))**2
sitesPassingFilters <- stats2filterOn %>%
dplyr::filter(MAF >= MAFthresh, CR >= CRthresh) %>%
dplyr::select(CHROM, POS)
print(paste0(nrow(sitesPassingFilters), " sites passing filter"))
write.table(sitesPassingFilters, file = paste0(outPath, inName,
".sitesPassing"), row.names = F, col.names = F, quote = F)
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))
}
FilterLuc(inPath=inPath, inName=inName,
outPath=outPath, outName=outName,
CRthresh = 0.6)Download of the Raw Filtered VCF
cd output/DArT2022
scp lbraatz@cbsulm35.biohpc.cornell.edu:/workdir/lbraatz/DCas22_6902/output/DCas22_6902_DArTseqLD_AllSites_AllChrom_rawFiltered.vcf.gz .
scp lbraatz@cbsulm35.biohpc.cornell.edu:/workdir/lbraatz/DCas22_6902/output/DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.lmiss .
scp lbraatz@cbsulm35.biohpc.cornell.edu:/workdir/lbraatz/DCas22_6902/output/DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.frq .
cd ../..MAF and Call Rate of the Raw data
library(tidyverse); library(here)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.7 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 2.1.2 ✔ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()here() starts at /Users/lbd54/Documents/GitHub/EMBRAPAImputation2022library(reactable)
INFO <- read.table(here::here("output", "DArT2022", "DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.frq"),
stringsAsFactors = F,
header = F, skip = 1) %>%
rename(CHROM = V1, POS = V2, N_ALLELES = V3,
N_CHR = V4, FREQ1 = V5, FREQ2 = V6)
callrate <- read.table(here::here("output", "DArT2022", "DCas22_6902_DArTseqLD_AllSites_AllChrom_raw.lmiss"),
stringsAsFactors = F,
header = T) %>% dplyr::select(CHR, POS, N_DATA, F_MISS) %>%
mutate(CHROM = CHR,
CR = 1 - F_MISS,
.keep = "unused")
stats2filterOn <- left_join(INFO, callrate)Joining, by = c("CHROM", "POS")stats2filterOn %<>% dplyr::mutate(FREQ2 = as.numeric(FREQ2)) %>%
dplyr::mutate(MAF = ifelse(FREQ2 > 0.5,
yes = 1 - FREQ2, no = FREQ2)) %>%
dplyr::select(-FREQ1, -FREQ2)
MAFthresh <- (1/max(stats2filterOn$N_DATA, na.rm = T))**2
stats2filterOn %<>% filter(!is.na(CHROM), CR >= 0.6, MAF >= MAFthresh) %>%
select(CR, MAF) %>% rename(CallRate = CR) %>% reshape2::melt(.)No id variables; using all as measure variablesstats2filterOn %>% ggplot(aes(x= value)) +
geom_density() + facet_grid(~variable, scales = "free_x") + theme_minimal()
| Version | Author | Date |
|---|---|---|
| 95ddcaf | LucianoRogerio | 2022-05-05 |
Split the VCF per chromosome
require(furrr); plan(multisession, workers = 18)
options(future.globals.maxSize=+Inf); options(future.rng.onMisuse="ignore")
vcfIn<-here::here("output/","DCas22_6902_DArTseqLD_AllSites_AllChrom_rawFiltered.vcf.gz")
filters<-"--minDP 4 --maxDP 50" # because using GT not PL for impute (Beagle5)
outPath<-here::here("output/")
outSuffix<-"DCas22_6902_DArTseqLD_AllSites_AllChrom_rawFiltered"
future_map(1:18,
~genomicMateSelectR::splitVCFbyChr(Chr=.,
vcfIn=vcfIn,filters=filters,
outPath=outPath,
outSuffix=outSuffix))
plan(sequential)Check for Duplicates in DArT report and GBS VCF file - link
Imputation DArT markers only - link
Quality control
| Pre | Post | ||
|---|---|---|---|
| MAF | \(≥(2/7827)\) | MAF | \(≥(2/7827)\) |
| CR | \(≥0.6\) | HWE | \(P_{HWE}≥10^{-20}\) |
SNPs Markers
5,914 SNPs Markers from 7,827 clones
| Chr | N˚Mkrs | Chr | N˚Mkrs | Chr | N˚Mkrs |
|---|---|---|---|---|---|
| Chr 1 | 674 | Chr 7 | 216 | Chr 13 | 261 |
| Chr 2 | 369 | Chr 8 | 294 | Chr 14 | 380 |
| Chr 3 | 372 | Chr 9 | 262 | Chr 15 | 297 |
| Chr 4 | 371 | Chr 10 | 301 | Chr 16 | 219 |
| Chr 5 | 337 | Chr 11 | 317 | Chr 17 | 305 |
| Chr 6 | 379 | Chr 12 | 285 | Chr 18 | 275 |
Imputation GBS + DArT Markers - link
Quality control
| Pre | Post | ||
|---|---|---|---|
| MAF | \(≥(2/7827)\) | MAF | \(≥(2/7827)\) |
| CR | \(≥0.6\) | HWE | \(P_{HWE}≥10^{-20}\) |
| HWE | \(P_{HWE}≥10^{-20}\) | ||
SNPs Markers
37,877 SNPs Markers from 7,827 clones
| Chr | N˚Mkrs | Chr | N˚Mkrs | Chr | N˚Mkrs |
|---|---|---|---|---|---|
| Chr 1 | 3,669 | Chr 7 | 1,256 | Chr 13 | 1,812 |
| Chr 2 | 2,717 | Chr 8 | 2,015 | Chr 14 | 2,105 |
| Chr 3 | 2,547 | Chr 9 | 2,117 | Chr 15 | 2,428 |
| Chr 4 | 1,982 | Chr 10 | 1,519 | Chr 16 | 1,692 |
| Chr 5 | 2,607 | Chr 11 | 2,109 | Chr 17 | 1,542 |
| Chr 6 | 2,497 | Chr 12 | 1,733 | Chr 18 | 1,530 |
Results of the Imputations - link
Genomic Prediction Analysis for Yield traits
Genotypic data preparation - link
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] reactable_0.2.3 here_1.0.1 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 lubridate_1.8.0 assertthat_0.2.1 rprojroot_2.0.3
[5] digest_0.6.29 utf8_1.2.2 plyr_1.8.7 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 whisker_0.4 jquerylib_0.1.4
[21] rmarkdown_2.14 labeling_0.4.2 htmlwidgets_1.5.4 munsell_0.5.0
[25] broom_0.8.0 compiler_4.1.2 httpuv_1.6.5 modelr_0.1.8
[29] xfun_0.30 pkgconfig_2.0.3 htmltools_0.5.2 tidyselect_1.1.2
[33] workflowr_1.7.0 fansi_1.0.3 crayon_1.5.1 tzdb_0.3.0
[37] dbplyr_2.1.1 withr_2.5.0 later_1.3.0 grid_4.1.2
[41] jsonlite_1.8.0 gtable_0.3.0 lifecycle_1.0.1 DBI_1.1.2
[45] git2r_0.30.1 magrittr_2.0.3 scales_1.2.0 cli_3.3.0
[49] stringi_1.7.6 farver_2.1.0 reshape2_1.4.4 fs_1.5.2
[53] promises_1.2.0.1 xml2_1.3.3 bslib_0.3.1 ellipsis_0.3.2
[57] generics_0.1.2 vctrs_0.4.1 tools_4.1.2 glue_1.6.2
[61] hms_1.1.1 fastmap_1.1.0 yaml_2.3.5 colorspace_2.0-3
[65] rvest_1.0.2 knitr_1.38 haven_2.5.0 sass_0.4.1