Last updated: 2021-06-18

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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:

Phasing error
Use of a (or various) genetic map(s) to predict recombination frequencies
Inherent uncertainty in marker effects and their effect on variance prediction

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.

Cross-validation study

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.

Copy gsFunctions.R from code/ of most recent NextGen prediction, NRCRI C3b predicted April 2021.
Reference previous analysis for IITA (2020) in case there are variations.

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.
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.
Preprocess data files: Prepare haplotype and dosage matrices, pedigree and BLUPs, genetic map and recombination frequency matrix, for use in predictions.
Parent-wise 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.
Results: Home for plots and summary tables.

Additional future analyses to do:

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

Impact of phasing switch errors
- Quality and quantity of data available on parents
- Relatedness of parents
- What is the relationship between the true accuracy and estimate of accuracy?

Implementation Tests for Genomic Mate Selection in Cassava

Cross-validation study