EMBRAPA 2020 GS and related procedures

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Last updated: 2020-09-01

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Knit directory: EMBRAPA_2020GS/

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File	Version	Author	Date	Message
Rmd	f3a0e16	wolfemd	2020-09-01	Added a link to cassavabase FTP destination for the data.
html	d29d605	wolfemd	2020-09-01	Build site.
Rmd	6e27397	wolfemd	2020-09-01	EMBRAPA C2 has been imputed. Publish report and share to cassavabase
html	ed70a5a	wolfemd	2020-08-26	Build site.
Rmd	9d4dfab	wolfemd	2020-08-26	Code organized into functions. Ready to run imputation of C2?
Rmd	4cc20db	wolfemd	2020-08-26	Start workflowr project.
Rmd	245ee4f	wolfemd	2020-08-26	Start workflowr project.

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This repository and website documents all analyses, summary, tables and figures associated with EMBRAPA genomic prediction and related procedures (e.g. imputation).

Analyses

Imputation conducted in 2019: Imputation of the L. America reference panel (GBS+DArTseqLD) and EMBRAPA GS C1 (DArTseqLD only) was done in October 2019. The codes for 2019 imputation were never published in a Git repository, though they were shared internally. The 2019 imputed VCFs will serve as imputation reference panel for 2020. Therefore, I am publishing the 2019 codes here as is for reference.

• Convert DCas19-4403 report to VCF for imputation: The report DCas19_4403 was converted to VCF and then included in the ImputationReferencePanel VCF and imputed along with the GBS/GBS+DArT samples.
• Verify GBS-to-DArTseqLD sample matches: Verify GBS-2-DArT matches by PLINK IBD was done on samples DCas19_4301.
• Imputation steps: this was a multi-stage process including assembly and filtering of VCFs, imputation with Beagle (4.1 and 5.0). The ultimate step of this pipeline was to impute the C1 progeny data, which were extracted from the DArTseqLD report “DCas19_4301”, with the imputation reference panel.

Imputation 2020: DArTseqLD (DCas20-5360) arrived on Aug. 22, 2020. Contains GS C2 for EMBRAPA.

Two main steps:

• Convert DCas20-5360 report to VCF for imputation:
• Impute DCas20-5360:

Last year’s reference panel for imputation had ~64K SNP. The C1 progeny imputed by it had <9K SNP after post-imputation filters.

Since imputation with Beagle5.0 is very fast, I imputed C2 (DCas20-5360) with three variants of the reference panel:

1. Last year’s reference panel, without C1 (64K SNP)

• RefPanel VCF filename: chr*_ImputationReferencePanel_EMBRAPA_Phased_102619.vcf.gz
• Imputed C2 filename: chr*_DCas20_5360_REFimputed.vcf.gz
• Post-impute filtered C2 filename: chr*_DCas20_5360_REFimputedAndFiltered.vcf.gz

2. Last year’s ref. panel + C1 (include unfiltered C1 data = 64K refpanel SNP)

• RefPanel VCF filename: chr*_ImputationReferencePanel_C1progenyAdded_EMBRAPA.vcf.gz
• Imputed C2 filename: chr*_DCas20_5360_REFimputedWithC1unfiltered.vcf.gz
• Post-impute filtered C2 filename: chr*_DCas20_5360_REFimputedWithC1unfiltered_PostImputeFiltered.vcf.gz

3. Last year’s reference panel + C1 (reduce refpanel to sites passing post-impute filter in C1 = 9K SNP)

• RefPanel VCF filename: chr*_ImputationReferencePanel_C1progenyAddedFilteredSites_EMBRAPA.vcf.gz
• Imputed C2 filename: chr*_DCas20_5360_REFimputedWithC1filtered.vcf.gz
• Post-impute filtered C2 filename: chr*_DCas20_5360_REFimputedWithC1filtered_PostImputeFiltered.vcf.gz

SUGGESTION: Use combination PCA, prediction, correlation of kinship matrices (off-diagonals and diagonals) to compare these datasets.

Data availability and reproducibility

The R package workflowr was used to document this study reproducibly.

Much of the supporting data and output from the analyses documented here are too large for GitHub.

The repository will be mirrored, here: ftp://ftp.cassavabase.org/marnin_datasets/EMBRAPA_2020GS/ with all data.

Directory structure of this repository

NOTICE: data/ and output/ are empty on GitHub. Please see ftp://ftp.cassavabase.org/marnin_datasets/EMBRAPA_2020GS/ for access.

1. data/: raw data (e.g. unimputed SNP data)
2. output/: outputs (e.g. imputed SNP data)
3. analysis/: most code and workflow documented in .Rmd files
4. docs/: compiled .html, “knitted” from .Rmd

Supporting functions code/

The analyses in the html / Rmd files referenced above often source R scripts in the code/ sub-folder. These are wrapper functions around the packaged core functions in predCrossVar, to do the specific analyses for this paper.