Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection
The objective of this project is to determine the dynamics of updating a training population and the long-term impact on a closed breeding program. We aimed to compare several methods of training population updating in terms of prediction accuracy, response to selection, and decay of genetic variance. This R package provide the code and data to run the simulation and create the figures used in the paper.
Please read our pre-print on biorXiv.
To install, use the
devtools package like so:
We used data from The Triticeae Toolbox data base in our simulations. A step-by- step set of instructions for obtaining and downloading this data is provided in a package vignette. Use the following code to access the vignette:
The simulations were run on a supercomputing platform that accepts batch submissions. The file
inst/scripts/run_simulations_use.job is a
Bash script that calls upon
R and the script
inst/scripts/run_experiment_use.R to run the simulations. Both of these scripts need to be edited (i.e. with different directory paths) before they can be used.
Tables and figures can be replicated by using the procedure outlined in the Markdown file
Please open an issue for support with the package or to add comments.
To cite the pre-print, please use the following (formatting for Genetics):
Neyhart, J. L., T. Tiede, A. J. Lorenz, and K. P. Smith. 2016 Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection. bioRxiv. doi: http://dx.doi.org/10.1101/087163.
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