Last updated: 2021-08-12

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Knit directory: IITA_2021GS/

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Rmd efebeab wolfemd 2021-08-12 Cross-validation and genomic mate predictions complete. All results updated.
html 1c03315 wolfemd 2021-08-11 Build site.
Rmd e4df79f wolfemd 2021-08-11 Completed IITA_2021GS pipeline including imputation and genomic prediction. Last bit of cross-validation and cross-prediction finishes in 24 hrs.
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Rmd 772750a wolfemd 2021-07-14 DirDom model and selection index calc fully integrated functions.
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Rmd a8452ba wolfemd 2021-06-10 Initial build of the entire page upon completion of all
Rmd 8a0c50e wolfemd 2021-05-04 Start workflowr project.

See the Results here!

August 2021

Imputation of DCas21_6038

Steps:

  1. Convert DCas21-6038 report to VCF for imputation:
  2. Impute DCas21-6038: with West Africa reference panel merged with additional GS progeny (IITA TMS18)

Files: Access on Cassavabase FTP server here, use “Guest” credentials

Preliminary data steps

  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. Validate the pedigree obtained from cassavabase: Before setting up a cross-validation scheme for predictions that depend on a correct pedigree, add a basic verification step to the pipeline. Not trying to fill unknown relationships or otherwise correct the pedigree. Assess evidence that relationship is correct, remove if incorrect.

  4. Preprocess data files: Prepare haplotype and dosage matrices, GRMs, pedigree and BLUPs, genetic map and recombination frequency matrix, for use in predictions.

Genomic (mate) predictions

  1. Parent-wise and standard cross-validation: estimate selection index (and component trait) prediction accuracies using the direction-dominance (DirDom) model.

    • Additionally, check accuracy and similarity of predictions at reduced marker density: Cross-variance prediction is slow, but significant speed gains can be made by using fewer markers. Faster predictions will mean more crosses can be predicted and considered.

      • If, accuracy and \(cor_{preds}(All\_SNPs, Reduced\_Set)\) are similar based on both kinds of cross-validation, proceed to make cross-variance predictions with reduced marker set; possibly use full marker set for cross-mean predictions.

    • Click here to see the results!

  2. Genomic predictions: First, predict of individual GEBV/GETGV for all selection candidates using all available data and return marker effects for use downstream. Next, Select a top set of candidate parents, for whom we would like to predict cross performances. Finally, predict all pairwise crosses of candidate parents and evaluate them for genomic mate selection. Select the top crosses and plant a crossing nursery with the parents indicated.

  3. Results and recommendations: Home for all plots, summary tables, conclusions and recommendations.

Data and code repository access

CLICK HERE FOR ACCESS TO THE FULL REPOSITORY
(select “Guest” credentials when prompted by the Cassavabase FTP server)

or

DOWNLOAD FROM GitHub*

*GitHub only hosts files max 50 Mb.

Key directories and file names

  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
  5. code/: supporting functions sourced in analysis/*.Rmd’s.

FILES OF INTEREST: everything is in the output/ sub-directory (click here and select “Guest” credentials when prompted by the Cassavabase FTP server).