A divide-and-conquer approach for genomic prediction in rubber tree using machine learning - Archive ouverte HAL Access content directly
Journal Articles Scientific Reports Year : 2022

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

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Abstract

Rubber tree ( Hevea brasiliensis ) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.
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Dates and versions

hal-03859812 , version 1 (18-11-2022)

Licence

Attribution - CC BY 4.0

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Alexandre Hild Aono, Felipe Roberto Francisco, Livia Moura Souza, Paulo de Souza Gonçalves, Erivaldo J Scaloppi Junior, et al.. A divide-and-conquer approach for genomic prediction in rubber tree using machine learning. Scientific Reports, 2022, 12 (1), pp.18023. ⟨10.1038/s41598-022-20416-z⟩. ⟨hal-03859812⟩
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