Last updated: 2020-10-09

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Knit directory: NRCRI_2020GS/

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Unstaged changes:
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    Modified:   output/nrcri_blupsForModelTraining_2020April27.rds

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File	Version	Author	Date	Message
Rmd	8bff9f0	wolfemd	2020-10-09	Start workflowr project.
html	f3f6163	wolfemd	2020-04-28	Build site.
Rmd	5389b1f	wolfemd	2020-04-28	Start workflowr project.
Rmd	b7a6ef4	wolfemd	2020-04-21	Start workflowr project.

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

April Genomic Prediction

Re-prediction of NRCRI germplasm. Updating available training data as of April 2020. Produce GEBV and GETGV.

Prepare a training dataset: Download data from DB, “Clean” and format DB data.
Curate by trait-trial: Model each trait-trial separately, remove outliers, get BLUPs.
Get BLUPs combining all trial data: Combine data from all trait-trials to get BLUPs for downstream genomic prediction.
Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
Genomic prediction of GS C2: Predict genomic BLUPs (GEBV and GETGV) for all selection candidates using all available data.
Estimate genetic gain

October Imputation

DCas20-5440 From Princess Onyyegbule on Sep 22, 2020: “These samples are from materials for inbreeding depression after one self-pollinated generation in two elite cassava varieties (TMS980581 and TMS070337). We want to assess the possibility of obtaining genetic gain by selecting transgressive individuals based on several productive traits, mostly high dry matter content.”

Steps:

Convert DCas20-5440 report to VCF for imputation:
Impute DCas20-5440: with West Africa reference panel

Files:

RefPanel VCF filename: chr*_ImputationReferencePanel_StageIIpartI_72219.vcf.gz
Imputed filename: chr*_DCas20_5440_WA_REFimputed.vcf.gz
Post-impute filtered filename: chr*_DCas20_5440_WA_REFimputedAndFiltered.vcf.gz
Genome-wide dosage matrix format for use in R: DosageMatrix_DCas20_5440_WA_REFimputedAndFiltered.rds

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/NRCRI_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/NRCRI_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 files
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.

NRCRI 2020 GS and related procedures

Marnin Wolfe

April Genomic Prediction

October Imputation

Data availability and reproducibility

Directory structure of this repository