Last updated: 2021-08-19
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Knit directory: IITA_2021GS/
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| File | Version | Author | Date | Message |
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| html | 1c03315 | wolfemd | 2021-08-11 | Build site. |
| 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
RtargetVCFpath<-here::here("data/Report-DCas21-6038/") # location of the targetVCF
refVCFpath<-here::here("data/")
mapPath<-here::here("data/CassavaGeneticMap/")
outPath<-here::here("output/")
outSuffix<-"DCas21_6038"library(tidyverse); library(magrittr);
library(genomicMateSelectR)
purrr::map(1:18,
~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
inPath<-here::here("output/")
outPath<-here::here("output/")
require(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
purrr::map(1:18,~system(paste0("zcat ",here::here("output/"),"chr",.,"_DCas21_6038_WA_REFimputedAndFiltered.vcf.gz | wc -l")))
# 7580
# 3604
# 3685
# 3411
# 3721
# 3349
# 1716
# 3151
# 3286
# 2635
# 2897
# 2745
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# 2612
# 2913cd /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)
inPath<-here::here("output/")
outPath<-here::here("output/")
future_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)
vcfName<-"AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08"
system(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 SitesPrepare 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()