Last updated: 2020-10-09
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Knit directory: NRCRI_2020GS/
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Unstaged changes:
Modified: analysis/04-CrossValidation.Rmd
Modified: data/NRCRI_CleanedTrialData_2020April21.rds
Modified: data/NRCRI_ExptDesignsDetected_2020April21.rds
Modified: output/NRCRI_CuratedTrials_2020April27.rds
Modified: output/nrcri_blupsForModelTraining_2020April27.rds
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/ImputeDCas20_5440.Rmd) and HTML (docs/ImputeDCas20_5440.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 | 63fe5fa | wolfemd | 2020-10-09 | Publish imputations for 2020 of DCAs20_5440 and restructure page for |
DArTseqLD (DCas20-5440). From Princess Onyyegbule on Sep 22, 2020: “These samples are from materials for inbreeding depression after one self-pollinated generation in two elite cassava varieties (TMS980581 and TMS070337). We want to assess the possibility of obtaining genetic gain by selecting transgressive individuals based on several productive traits, mostly high dry matter content.”
Impute with RefPanelWA (W. Africa RefPanel)
Copy the imputation reference panel from 2019 to the data/ folder.
cp -r /home/jj332_cas/marnin/NRCRI_2020GS /workdir/mw489/
cp -r /home/jj332_cas/marnin/NRCRI_2020GS/data/Report-DCas20-5440 /workdir/mw489/NRCRI_2020GS/data/
cp -r /home/jj332_cas/CassavaGenotypeData/CassavaGeneticMap /workdir/mw489/NRCRI_2020GS/data/
cp /home/jj332_cas/CassavaGenotypeData/nextgenImputation2019/ImputationStageII_71219/chr*_ImputationReferencePanel_StageIIpartI_72219.vcf.gz /workdir/mw489/NRCRI_2020GS/data/Impute with Beagle V5.0.
Use the “imputation reference panel” dataset from 2019, e.g. chr1_ImputationReferencePanel_StageIIpartI_72219.vcf.gz as reference.
Used 1 large memory Cornell CBSU machine (e.g. cbsulm16; 112 cores, 512 GB RAM), running 1 chromosome at a time.
R functions are stored in the code/ sub-directory. Functions sourced from e.g. imputationFunctions.R are wrappers around e.g. Beagle, and other command line programs.
#library(tidyverse); library(magrittr);
source(here::here("code","imputationFunctions.R"))
targetVCFpath<-here::here("data/Report-DCas20-5440/") # location of the targetVCF
refVCFpath<-here::here("data/")
mapPath<-here::here("data/CassavaGeneticMap/")
outPath<-here::here("output/")
outSuffix<-"DCas20_5440"
purrr::map(1:18,~runBeagle5(targetVCF=paste0(targetVCFpath,"chr",.,"_DCas20_5440.vcf.gz"),
refVCF=paste0(refVCFpath,"chr",.,"_ImputationReferencePanel_StageIIpartI_72219.vcf.gz"),
mapFile=paste0(mapPath,"chr",.,"_cassava_cM_pred.v6_91019.map"),
outName=paste0(outPath,"chr",.,"_DCas20_5440_WA_REFimputed"),
nthreads=112))Clean up Beagle log files after run. Move to sub-directory output/BeagleLogs/.
For now, the function will just do a fixed filter: AR2>0.75 (DR2>0.75 as of Beagle5.0), P_HWE>1e-20, MAF>0.005 [0.5%].
It can easily be modified in the future to include parameters to vary the filter specifications.
Input parameters
#' @inPath path to input VCF-to-be-filtered, can be left null if path included in @inName . Must end in "/"
#' @inName name of input VCF file EXCLUDING file extension. Assumes .vcf.gz
#' @outPath path where filtered VCF and related are to be stored.Can be left null if path included in @outName . Must end in "/".
#' @outName name desired for output EXCLUDING extension. Output will be .vcf.gz Loop to filter all 18 VCF files in parallel
inPath<-here::here("output/")
outPath<-here::here("output/")
source(here::here("code","imputationFunctions.R"))
require(furrr); options(mc.cores=ncores); plan(multiprocess)
future_map(1:18,~postImputeFilter(inPath=inPath,
inName=paste0("chr",.,"_DCas20_5440_WA_REFimputed"),
outPath=outPath,
outName=paste0("chr",.,"_DCas20_5440_WA_REFimputedAndFiltered")))Check what’s left
library(tidyverse); library(magrittr);
# Make binary plink
pathIn<-"/home/jj332_cas/marnin/NRCRI_2020GS/output/"
require(furrr); options(mc.cores=ncores); plan(multiprocess)
future_map(1:18,~system(paste0("export PATH=/programs/plink-1.9-x86_64-beta3.30:$PATH;",
"plink --vcf ",pathIn,"chr",.,
"_DCas20_5440_WA_REFimputedAndFiltered.vcf.gz ",
"--make-bed --const-fid ",
"--out ",pathIn,"chr",.,
"_DCas20_5440_WA_REFimputedAndFiltered")))
# Recode to dosage
future_map(1:18,~system(paste0("export PATH=/programs/plink-1.9-x86_64-beta3.30:$PATH;",
"plink --bfile ",pathIn,"chr",.,
"_DCas20_5440_WA_REFimputedAndFiltered ",
"--recode A ",
"--out ",pathIn,"chr",.,
"_DCas20_5440_WA_REFimputedAndFiltered")))
# Genome-wide dosage (for use in R)
snps<-future_map(1:18,~read.table(paste0(pathIn,"chr",.,"_DCas20_5440_WA_REFimputedAndFiltered.raw"), stringsAsFactor=F, header = T) %>%
dplyr::select(-FID,-PAT,-MAT,-SEX,-PHENOTYPE) %>%
column_to_rownames(var = "IID") %>%
as.matrix()) %>%
reduce(.,cbind)
dim(snps)
# [1] 251 44930
saveRDS(snps,file = paste0(pathIn,"DosageMatrix_DCas20_5440_WA_REFimputedAndFiltered.rds"))