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Conclusions: Despite the limitations, our study suggests that our educational campaign to train population in BLS-D may be efficacious.
In this study, we did not attempt to estimate founder population frequencies, and pj was calculated as the allele frequencies of the loci in the TRAIN population.
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In order to identify the impacts of training population size and marker number on predictability, we used different subsets of training population and markers to evaluate the predictability.
The relatedness between training population and test populations is also a key factor for predictability.
All fitness values calculated are stored in an archive to be reused as training population.
With respect to the size of training population, it has strong effect on the predictability.
In DE-kNN, all re-evaluated samples are stored and the training population gradually increases.
Fivefold cross-validations are repeated 100 times for each subset of the training population.
Furthermore, this article discusses potential breeding schemes for GS, genotyping considerations, and methods for effective training population design.
The size of training population had greater influence on prediction for rice hybrid performance compared with the marker density.
Therefore, increasing the size of training population rather than increasing the marker number can be preferable for rice hybrid prediction.
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