Last updated: 2021-08-11
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Knit directory: IITA_2021GS/
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Unstaged changes:
Modified: analysis/07-Results.Rmd
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Deleted: code/parentWiseCrossVal.R
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Deleted: output/estimateSelectionError.rds
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/ImputeDCas21_6038.Rmd
) and HTML (docs/ImputeDCas21_6038.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 | e4df79f | wolfemd | 2021-08-11 | Completed IITA_2021GS pipeline including imputation and genomic prediction. Last bit of cross-validation and cross-prediction finishes in 24 hrs. |
html | a3150ab | wolfemd | 2021-08-09 | Build site. |
Rmd | 6f2057f | wolfemd | 2021-08-09 | Publish project. Imputation completed. Run and complete ‘cleanTPdata’ step. |
Copy the imputation reference panel from 2019 to the data/
folder.
mkdir /workdir/mw489/;
cp -r ~/IITA_2021GS /workdir/mw489/;
cp -r /home/jj332_cas/CassavaGenotypeData/CassavaGeneticMap /workdir/mw489/IITA_2021GS/data/;
cp /home/jj332_cas/CassavaGenotypeData/nextgenImputation2019/ImputationStageIII_72619/chr*_RefPanelAndGSprogeny_ReadyForGP_72719.vcf.gz /workdir/mw489/IITA_2021GS/data/;
Impute with Beagle V5.0.
Use the “imputation reference panel” dataset from 2019 merged with the imputed GS progeny TMS13-14-15 + TMS18, e.g. chr1_RefPanelAndGSprogeny_ReadyForGP_72719.vcf.gz
as reference for the current imputation.
Used 1 large memory Cornell CBSU machine (e.g. cbsulm17; 112 cores, 512 GB RAM), running 1 chromosome at a time.
# 1) start a screen shell
screen; # or screen -r if re-attaching...
# Project directory, so R will use as working dir.
cd /workdir/mw489/IITA_2021GS/
# 3) Start R
R
<-here::here("data/Report-DCas21-6038/") # location of the targetVCF
targetVCFpath<-here::here("data/")
refVCFpath<-here::here("data/CassavaGeneticMap/")
mapPath<-here::here("output/")
outPath<-"DCas21_6038" outSuffix
library(tidyverse); library(magrittr);
library(genomicMateSelectR)
::map(1:18,
purrr~genomicMateSelectR::runBeagle5(targetVCF=paste0(targetVCFpath,"chr",.,
"_DCas21_6038.vcf.gz"),
refVCF=paste0(refVCFpath,"chr",.,
"_RefPanelAndGSprogeny_ReadyForGP_72719.vcf.gz"),
mapFile=paste0(mapPath,"chr",.,
"_cassava_cM_pred.v6_91019.map"),
outName=paste0(outPath,"chr",.,
"_DCas21_6038_WA_REFimputed"),
nthreads=112))
Clean up Beagle log files after run. Move to sub-directory output/BeagleLogs/
.
cd /workdir/mw489/IITA_2021GS/output/;
mkdir BeagleLogs;
cp *_DCas21_6038_WA_REFimputed.log BeagleLogs/
cp -r BeagleLogs ~/IITA_2021GS/output/
cp *_DCas21_6038_WA_REFimputed* ~/IITA_2021GS/output/
cp *_DCas21_6038_WA_REFimputed.vcf.gz ~/IITA_2021GS/output/
Standard post-imputation filter: AR2>0.75 (DR2>0.75 as of Beagle5.0), P_HWE>1e-20, MAF>0.005 [0.5%].
Loop to filter all 18 VCF files in parallel
<-here::here("output/")
inPath<-here::here("output/")
outPathrequire(furrr); plan(multisession, workers = 18)
future_map(1:18,
~genomicMateSelectR::postImputeFilter(inPath=inPath,
inName=paste0("chr",.,"_DCas21_6038_WA_REFimputed"),
outPath=outPath,
outName=paste0("chr",.,"_DCas21_6038_WA_REFimputedAndFiltered")))
plan(sequential)
Check what’s left
::map(1:18,~system(paste0("zcat ",here::here("output/"),"chr",.,"_DCas21_6038_WA_REFimputedAndFiltered.vcf.gz | wc -l")))
purrr# 7580
# 3604
# 3685
# 3411
# 3721
# 3349
# 1716
# 3151
# 3286
# 2635
# 2897
# 2745
# 2625
# 5219
# 3519
# 2751
# 2612
# 2913
cd /workdir/mw489/IITA_2021GS/output/;
cp -r *_DCas21_6038_WA_REFimputed* ~/IITA_2021GS/output/
Need to create a genome-wide VCF with the RefPanel + DCas21_6038 VCFs merged.
The downstream preprocessing steps in the pipeline will take that as input to create haplotype and dosage matrices, etc.
cd /workdir/mw489/IITA_2021GS/
R;
require(furrr); plan(multisession, workers = 18)
# 1. Subset RefPanel to sites remaining after post-impute filter of DCas21_6038
future_map(1:18,~system(paste0("vcftools --gzvcf ",
"/workdir/mw489/IITA_2021GS/data/chr",
"_RefPanelAndGSprogeny_ReadyForGP_72719.vcf.gz"," ",
.,"--positions ","/workdir/mw489/IITA_2021GS/output/chr",.,
"_DCas21_6038_WA_REFimputed.sitesPassing"," ",
"--recode --stdout | bgzip -c -@ 24 > ",
"/workdir/mw489/IITA_2021GS/output/chr",.,
"_RefPanelAndGSprogeny72719_SubsetAndReadyToMerge.vcf.gz")))
plan(sequential)
# 2. Merge RefPanel and DCas21_6038
library(tidyverse); library(magrittr); library(genomicMateSelectR)
<-here::here("output/")
inPath<-here::here("output/")
outPathfuture_map(1:18,~mergeVCFs(inPath=inPath,
inVCF1=paste0("chr",.,"_RefPanelAndGSprogeny72719_SubsetAndReadyToMerge"),
inVCF2=paste0("chr",.,"_DCas21_6038_WA_REFimputedAndFiltered"),
outPath=outPath,
outName=paste0("chr",.,"_RefPanelAndGSprogeny_ReadyForGP_2021Aug08")))
# 3. Concatenate chromosomes
## Index with tabix first
future_map(1:18,~system(paste0("tabix -f -p vcf ",inPath,
"chr",.,"_RefPanelAndGSprogeny_ReadyForGP_2021Aug08.vcf.gz")))
plan(sequential)
## bcftools concat
system(paste0("bcftools concat ",
"--output ",outPath,
"AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08.vcf.gz ",
"--output-type z --threads 18 ",
paste0(inPath,"chr",1:18,
"_RefPanelAndGSprogeny_ReadyForGP_2021Aug08.vcf.gz",
collapse = " ")))
## Convert to binary blink (bed/bim/fam)
<-"AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08"
vcfNamesystem(paste0("export PATH=/programs/plink-1.9-x86_64-beta3.30:$PATH;",
"plink --vcf ",inPath,vcfName,".vcf.gz ",
"--make-bed --const-fid --keep-allele-order ",
"--out ",outPath,vcfName))
cd /workdir/mw489/IITA_2021GS/output/
cp *_RefPanelAndGSprogeny_ReadyForGP_2021Aug08* ~/IITA_2021GS/output/
# vcftools --gzvcf AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08.vcf.gz
# After filtering, kept 23332 out of 23332 Individuals
# After filtering, kept 61239 out of a possible 61239 Sites
Prepare training dataset: Download data from DB, “Clean” and format DB data.
Get BLUPs combining all trial data: Combine data from all trait-trials to get BLUPs for downstream genomic prediction. Fit mixed-model to multi-trial dataset and extract BLUPs, de-regressed BLUPs and weights. Include two rounds of outlier removal.
[uses imputed data as input] 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 relationships or otherwise correct the pedigree. Assess evidence that relationship is correct, remove if incorrect.
[uses imputed data as input] Preprocess data files: Prepare haplotype and dosage matrices, GRMs, pedigree and BLUPs, genetic map and recombination frequency matrix, for use in predictions.
sessionInfo()