Last updated: 2020-11-11

Checks: 7 0

Knit directory: IITA_EA_2020/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200826)

The command set.seed(20200826) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 74febf4

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 74febf4. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


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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/ImputeDCas20_5261.Rmd) and HTML (docs/ImputeDCas20_5261.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 74febf4 wolfemd 2020-11-11 Imputation and processing complete.
html a58160e wolfemd 2020-11-11 Build site.
Rmd 8903c1c wolfemd 2020-11-11 All code for processing and imputing DCas20_5251 for IITA-EA is ready to run.

DArTseqLD (DCas20-5419). Contains pre-breeding materials of combined African and L. American descent.

  1. 2019 East Africa Reference panel: 56250 SNP, 19136 clones included W. Africa landraces
  2. Latin America Ref (4101 cclones, 65886 SNP)

Suggest comparing the results using PCA, prediction, correlation of kinship matrices, etc.

Copy data

Copy the imputation reference panel from 2019 to the data/ folder.

# mkdir /workdir/mw489/
cp -r /home/jj332_cas/marnin/IITA_EA_2020 /workdir/mw489/
cp -r /home/jj332_cas/CassavaGenotypeData/CassavaGeneticMap /workdir/mw489/IITA_EA_2020/data/
cp /home/jj332_cas/CassavaGenotypeData/nextgenImputation2019/ImputationEastAfrica_StageII_90919/chr*_ImputationReferencePanel_StageVI_91119.vcf.gz /workdir/mw489/IITA_EA_2020/data/

Impute

Impute with Beagle V5.0.

Use the “imputation reference panel” dataset from 2019, e.g. chr1_ImputationReferencePanel_StageVI_91119.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.

cd /workdir/mw489/IITA_EA_2020/
targetVCFpath<-here::here("data/OrderAppendix_1_DCas20-5261/") # location of the targetVCF
refVCFpath<-here::here("data/")
mapPath<-here::here("data/CassavaGeneticMap/")
outPath<-here::here("output/")
outSuffix<-"DCas20_5261"
source(here::here("code","imputationFunctions.R"))

purrr::map(1:18,~runBeagle5(targetVCF=paste0(targetVCFpath,"chr",.,"_DCas20_5261.vcf.gz"),
                            refVCF=paste0(refVCFpath,"chr",.,"_ImputationReferencePanel_StageVI_91119.vcf.gz"),
                            mapFile=paste0(mapPath,"chr",.,"_cassava_cM_pred.v6_91019.map"),
                            outName=paste0(outPath,"chr",.,"_DCas20_5261_EA_REFimputed"),
                            nthreads=112))

Clean up Beagle log files after run. Move to sub-directory output/BeagleLogs/.

cd /workdir/mw489/IITA_EA_2020/output/; 
mkdir BeagleLogs;
cp *_DCas20_5261_EA_REFimputed.log BeagleLogs/
cp -r BeagleLogs /home/jj332_cas/marnin/IITA_EA_2020/output/
cp *_DCas20_5261_EA_REFimputed* /home/jj332_cas/marnin/IITA_EA_2020/output/

Post-impute filter

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_5261_EA_REFimputed"),
                                  outPath=outPath,
                                  outName=paste0("chr",.,"_DCas20_5261_EA_REFimputedAndFiltered")))

Check what’s left

purrr::map(1:18,~system(paste0("zcat ",here::here("output/"),"chr",.,"_DCas20_5261_EA_REFimputedAndFiltered.vcf.gz | wc -l")))
# 5506
# 2181
# 2402
# 2352
# 2325
# 2134
# 1023
# 2094
# 2147
# 1587
# 1743
# 2118
# 1441
# 3211
# 2409
# 1709
# 1622
# 1687
cd /workdir/mw489/IITA_EA_2020/output/;
cp -r *_DCas20_5261_EA_REFimputed* /home/jj332_cas/marnin/IITA_EA_2020/output/

Formats for downstream analysis

The function below will (1) convert the input VCF to plink1.9 binary format and (2) convert the plink binary to a dosage (0,1,2) matrix with special attention to which allele gets counted in the file.

NOTICE: I was worried about plink1.9 changing allele codes between files. There is some risk the counted allele could switch between e.g. the reference panel and the progeny files because of allele freq. (see plink documentation). To avoid this, went to extra trouble: write a file suffixed *.alleleToCount listing SNP ID (column 1) and the ALT allele from the VCF (column 2). Pass the file to plink1.9 using the --recode-allele flag to ensure all output dosages count the ALT allele consistent with the VCFs. The reason to use plink1.9 is that Beagle5 imputed files don’t have a DS (dosage) field that can be directly extracted. Instead, phased genotypes e.g. 0|1 need to be converted to dosages (e.g. 0|1 --> 1, 1|1 --> 2). An alternative might be to extract the haplotypes using vcftools and manually (in R) computed the dosages; that would give most control but is slow.

cd /home/jj332_cas/marnin/IITA_EA_2020/;
library(tidyverse); library(magrittr);
source(here::here("code","imputationFunctions.R"))
require(furrr); options(mc.cores=18); plan(multiprocess)
pathOut<-here::here("output/")

# Imputation reference panel
future_map(1:18,~convertVCFtoDosage(pathIn="/home/jj332_cas/CassavaGenotypeData/nextgenImputation2019/ImputationEastAfrica_StageII_90919/",
                                    pathOut=pathOut,
                                    vcfName = paste0("chr",.,"_ImputationReferencePanel_StageVI_91119")))
# DCas20_5261
future_map(1:18,~convertVCFtoDosage(pathIn=here::here("output/"),pathOut=pathOut,
                                    vcfName = paste0("chr",.,"_DCas20_5261_EA_REFimputedAndFiltered")))

# Genome-wide dosage (for use in R) for each dataset
# Imputation reference panels
createGenomewideDosage(pathIn = here::here("output/"), chroms=1:18, "_ImputationReferencePanel_StageVI_91119")
# DCas20_5261
createGenomewideDosage(pathIn = here::here("output/"), chroms=1:18, "_DCas20_5261_EA_REFimputedAndFiltered")

sessionInfo()