Reproducible documentation: Wolfe et al. Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices (PREPRINT)

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Last updated: 2021-03-24

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Knit directory: PredictOutbredCrossVar/

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PREPRINT

This repository and website documents all analyses, summary, tables and figures associated with the following PREPRINT: Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices!

Abstract

Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. We were able to predict all genetic variances and most covariances with non-zero accuracy. We also tested a directional dominance model and found significant inbreeding depression for most traits and a boost in total merit accuracy for root yield. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with excellent breeding values) and varieties (progeny with top performance).

Manuscript

1. Supplementary Tables
2. Results to make numeric summaries for the text, based on e.g. the Sup. Tables. Most supplementary figures appear here first.
3. Figures
4. Supplementary Figures

See also drop-down navigation menu “Manuscript”.

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/manuscripts/Wolfe_et_al_2020

or until publication here.

Analyses

1. Preprocess data files: Initial processing and subsetting of all input files.
2. Fit multi-trait Bayesian ridge regressions (MtBRRs): We fit the following MtBRR models: (1) additive-only (A), (2) additive plus dominance (AD or ClassicAD) and (3) additive plus directional dominance (DirDom or DirDomAD). First, we define parent-wise cross-validation folds. Next, we fit MtBRRs for both the parent-wise cross-validation and each of the genetic groups in the pedigree. Finally, using MtBRR marker-effects, we compute genomic BLUPs (i.e. GEBV and GETGV) for later use.
3. Predict cross means, variances and co-variances: Using training-set marker effects obtained in the previous step, we predict (co)variances and means for each cross according to the cross-validation scheme.
4. Compute observed cross means, variances and covariances: This step is to create validation data to which predictions of cross means and (co)variances will be correlated. We compute the empirical means and variances for each validation-family in two ways. First, we use GEBV and GETGV of actual offspring based on test-set marker effeects. Second, and for comparison only, we use phenotypic BLUPs of the offspring, where available. Lastly, we measure the realized selection intensity for each cross and compute the mean GEBV / GETGV of selected family-members as validation data for predicted cross “usefulness criteria”.
5. Compute prediction accuracy: Format predicted and observed values for each cross specified by the cross-validation scheme.
6. Exploratory analysis: predict the means and variances of previously untested crosses: Using marker-effects obtained from fitting All data, predict all possible crosses between (1) the 100 clones with top rank on the “StdSI” and (2) the 209 clones already used as parents.
7. Compute posterior mean (co)variance parameters from MtBRRs: For comparison and context, compute genetic variances and covariances accounting for LD (Lehermeier M2) for each of the genetic groups (“All”,“GG”,“TMS13”,“TMS14”,“TMS15”).

Supporting R package predCrossVar

In addition, we combined many of the core (and useful) support functions for predicting crosses into an R package predCrossVar, which is available on GitHub and can be installed with, e.g.:

devtools::install_github("wolfemd/predCrossVar", ref = 'master') 

The functions in predCrossVar are used throughout.

Supporting functions code/

The analyses in the html / Rmd files referenced above often source R scripts in the code/ sub-folder. These are wrapper functions around the packaged core functions in predCrossVar, to do the specific analyses for this paper.

Session information

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
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[29] rmarkdown_2.7     stringi_1.5.3     bslib_0.2.4       compiler_4.0.3   
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