Last updated: 2021-07-14

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Knit directory: implementGMSinCassava/

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Available data and software make it hypothetically possible to predict cross variances (mendelian sampling variance) and use it to select mates, e.g. using the usefuleness criterion (UC).

New information also adds new sources of uncertainty:

We used cross-validation to estimate the accuracy of predicting means, variances and the usefulness of crosses on selection indices. That analysis leveraged a high-quality validated pedigree-based phasing pipeline. That pipeline is considerably more involved and may not be implementable on the entire breeding germplasm.

Here I test cross-variance prediction in our current breeding pipeline’s available data. We will assess the whether and how to start using cross variance predictions in practice.

See the Results here!

Analysis steps

  1. Prepare training dataset: Download data from DB, “Clean” and format DB data. Use the standard pipeline to obtain complete breeding trial data for IITA, generate de-regressed BLUPs for downstream analysis.

  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, pedigree and BLUPs, genetic map and recombination frequency matrix, for use in predictions.

  5. Parent-wise and standard cross-validation:

    • Compute parent-wise cross-validation folds using the validated pedigree. Fit models to get marker effects and make subsequent predictions of cross means and (co)variances.
    • Models “AD” (classic BV+DD partition of additive+dominance) and “DirDom” (genotypic add+dom partition with genome-wide homozygosity effect).
    • Include use of selection index weights to compute index accuracy.
    • Include new models and index predictions in standard fold cross-validation in addition to the parent-wise scheme / function.

  6. Genomic predictions:

      1. Standard genomic prediction of individual GEBV and GETGV for all selection candidates using all available data. (B) Predict cross means and variances for genomic mate selection.

    • Include models “AD” and “DirDom”

    • Include prediction of selection index GEBV/GETGV and \(UC^{SI}_{parent}\)/\(UC^{SI}_{variety}\).

    • New functions at gmsFunctions.R in code/

  7. Results: Home for plots and summary tables.

Data and code repository access

FULL REPOSITORY DOWNLOAD FROM CASSAVABASE FTP SERVER

or

DOWNLOAD FROM GitHub*

*GitHub only hosts files max 50 Mb.

Guide to 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.

Analyses To Do

Additional future analyses to do:

  1. PHG imputed and phased marker data
  2. AWC’s genetic map
  3. Multi-trait and/or Bayesian models
  4. Other efforts to improve variance prediction accuracy?
  5. Simulation to explore factors impacting estimate of accuracy