Last updated: 2021-03-24

Checks: 2 0

Knit directory: PredictOutbredCrossVar/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 45e6b20. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  Icon
    Untracked:  PredictOutbredCrossVarMS_ResponseToReviews_R1.gdoc
    Untracked:  figure/
    Untracked:  manuscript/
    Untracked:  output/crossPredictions/
    Untracked:  output/gblups_DirectionalDom_parentwise_crossVal_folds.rds
    Untracked:  output/gblups_geneticgroups.rds
    Untracked:  output/gblups_parentwise_crossVal_folds.rds
    Untracked:  output/mtMarkerEffects/

Unstaged changes:
    Modified:   analysis/NGCleadersCall.Rmd
    Modified:   code/fitDirectionalDomMtBRR.R
    Modified:   code/fitmtBRR.R
    Modified:   code/getDirectionalDomGenomicBLUPs.R
    Modified:   code/getDirectionalDomMtCrossMeanPreds.R
    Modified:   code/getDirectionalDomMtCrossVarBVpreds.R
    Modified:   code/getDirectionalDomMtCrossVarTGVpreds.R
    Modified:   code/getDirectionalDomVarComps.R
    Modified:   code/getGenomicBLUPs.R
    Modified:   code/getMtCrossMeanPreds.R
    Modified:   code/getMtCrossVarPreds.R
    Modified:   code/getUntestedMtCrossVarPreds.R
    Modified:   code/getVarComps.R
    Modified:   data/blups_forawcdata.rds
    Modified:   data/genmap_awc_May2020.rds
    Modified:   data/parentwise_crossVal_folds.rds
    Modified:   data/ped_awc.rds
    Modified:   data/selection_index_weights_4traits.rds
    Modified:   output/CrossesToPredict_top100stdSI_and_209originalParents.rds
    Modified:   output/accuraciesMeans.rds
    Modified:   output/accuraciesUC.rds
    Modified:   output/accuraciesVars.rds
    Modified:   output/crossRealizations/realizedCrossMeans.rds
    Modified:   output/crossRealizations/realizedCrossMeans_BLUPs.rds
    Modified:   output/crossRealizations/realizedCrossMetrics.rds
    Modified:   output/crossRealizations/realizedCrossVars.rds
    Modified:   output/crossRealizations/realizedCrossVars_BLUPs.rds
    Modified:   output/crossRealizations/realized_cross_means_and_covs_traits.rds
    Modified:   output/crossRealizations/realized_cross_means_and_vars_selindices.rds
    Modified:   output/ddEffects.rds
    Modified:   output/gebvs_ModelA_GroupAll_stdSI.rds
    Modified:   output/obsVSpredMeans.rds
    Modified:   output/obsVSpredUC.rds
    Modified:   output/obsVSpredVars.rds
    Modified:   output/pmv_DirectionalDom_varcomps_geneticgroups.rds
    Modified:   output/pmv_varcomps_geneticgroups.rds
    Modified:   output/pmv_varcomps_geneticgroups_tidy_includingSIvars.rds
    Modified:   output/propHomozygous.rds
    Modified:   output/top100stdSI.rds

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/about.Rmd) and HTML (docs/about.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html be1e9fc wolfemd 2021-03-24 Build site.
Rmd 5018bd0 wolfemd 2021-03-24 Start workflowr project.
html 4de1330 wolfemd 2021-02-01 Build site.
Rmd 883b1d4 wolfemd 2021-02-01 Update the syntax highlighting and code-block formatting throughout for
Rmd 6a10c30 wolfemd 2021-01-04 Submission and GitHub ready version.
html 6a10c30 wolfemd 2021-01-04 Submission and GitHub ready version.

Abstract, acknowledgements and funding sources for the project here.

Abstract

Diverse crops ranging from staples (e.g., cassava) to cash crops (e.g., cacao) are both outbred and clonally propagated. In these crops, exceptional genotypes can be immortalized and commercialized as clonal varieties. To take advantage of this, genomic prediction can incorporate both additive and non-additive effects in clonal crops to select candidates with high breeding value as parents for crossing and candidates with high total genetic merit as varieties for release to farmers. It is possible to predict not only the mean breeding value but also the additive genetic variance and trait covariance in a cross using genome-wide phased parental haplotypes, marker effects estimates, and a recombination map. Several recent studies in both animal and plant breeding have demonstrated improved short and long term genetic gain using optimized parent selection and mate allocation, enabled by predictions of cross variances. In this study, we extend cross (co)variance prediction to include non-additive (namely dominance) effects. We present an empirical study of cassava (Manihot esculenta), a staple root crop essential to food security throughout the tropics. We analyze 462 outbred cassava families (209 parents total) derived from a genomic selection program, part of the Next Generation Cassava Breeding Project (www.nextgencassava.org). We assess the practical potential to predict the multivariate genetic distribution (means, variances and trait covariances) in untested cassava crosses in terms of both general and specific combining ability using cross-validation. Ultimately, we hope to enable breeders of outbred clonal crops to consider the potential of crosses to produce future parents (progeny with excellent breeding values) as well as potential varieties (progeny with top performance) on a multi-trait selection index.

Acknowledgements

We are grateful to the entire Next Generation Cassava Breeding team and especially the International Institute of Tropical Agriculture Cassava Breeding team, so many of whom have contributed to this study in the field, in the lab and beyond. We appreciate Christian Werner for pointing us towards directional dominance models, and the Jean-Luc Jannink and Mark Sorrells research groups for fruitful discussions and comments along the way. Thanks to Lukas Mueller and Prasad Peteti for data hosting and curation respectively.

Funding

We acknowledge the Bill & Melinda Gates Foundation and UK Foreign, Commonwealth & Development Office (FCDO) (Grant 1048542) and support from the CGIAR Research Program on Roots, Tubers and Bananas.