Last updated: 2020-09-01
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Knit directory: EMBRAPA_2020GS/
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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).
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.
Imputation 2020: DArTseqLD (DCas20-5360) arrived on Aug. 22, 2020. Contains GS C2 for EMBRAPA.
Two main steps:
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:
chr*_ImputationReferencePanel_EMBRAPA_Phased_102619.vcf.gz
chr*_DCas20_5360_REFimputed.vcf.gz
chr*_DCas20_5360_REFimputedAndFiltered.vcf.gz
chr*_ImputationReferencePanel_C1progenyAdded_EMBRAPA.vcf.gz
chr*_DCas20_5360_REFimputedWithC1unfiltered.vcf.gz
chr*_DCas20_5360_REFimputedWithC1unfiltered_PostImputeFiltered.vcf.gz
chr*_ImputationReferencePanel_C1progenyAddedFilteredSites_EMBRAPA.vcf.gz
chr*_DCas20_5360_REFimputedWithC1filtered.vcf.gz
chr*_DCas20_5360_REFimputedWithC1filtered_PostImputeFiltered.vcf.gz
SUGGESTION: Use combination PCA, prediction, correlation of kinship matrices (off-diagonals and diagonals) to compare these datasets.
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.
NOTICE: data/
and output/
are empty on GitHub. Please see ftp://ftp.cassavabase.org/marnin_datasets/EMBRAPA_2020GS/ for access.
data/
: raw data (e.g. unimputed SNP data)output/
: outputs (e.g. imputed SNP data)analysis/
: most code and workflow documented in .Rmd filesdocs/
: compiled .html, “knitted” from .RmdSupporting 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.