Last updated: 2020-10-16

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Knit directory: NRCRI_2020GS/

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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.

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

October Imputations

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:

  1. Convert DCas20-5440 report to VCF for imputation:
  2. 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

DCas20-5510

GS C3.

Steps:

  1. Convert DCas20-5510 report to VCF for imputation:
  2. Impute DCas20-5510: with West Africa reference panel

Files:

  • RefPanel VCF filename: chr*_ImputationReferencePanel_StageIIpartI_72219.vcf.gz
  • Imputed filename: chr*_DCas20_5510_WA_REFimputed.vcf.gz
  • Post-impute filtered filename: chr*_DCas20_5510_WA_REFimputedAndFiltered.vcf.gz
  • Genome-wide dosage matrix format for use in R: DosageMatrix_DCas20_5510_WA_REFimputedAndFiltered.rds

October Genomic Prediction

I will update the prediction done in April and predict GEBV/GETGV for all samples in the two new reports (DCas20-5440 and DCas20-5510). I learned some lessons doing a prediction for IITA in September.

To fit the mixed-model that I want, I am again resorting to asreml-R. I fit random effects for rep and block only where complete and incomplete blocks, respectively are indicated in the trial design variables. sommer should be able to fit the same model via the at() function, but I am having trouble with it and sommer is much slower even without a dense covariance (i.e. a kinship), compared to lme4::lmer() or asreml(). Note: For genomic predictions I do use sommer.

  1. Prepare training dataset: Download data from DB, “Clean” and format DB data.
  2. 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.
  3. Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
    • Compare prediction accuracy with vs. without IITA’s training data to augment.
  4. Genomic prediction: Predict genomic BLUPs (GEBV and GETGV) for all selection candidates using all available data.
  5. Results: New home for plots and other results.

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.

  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.