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

Autor(es)

Aono, Alexandre Hild ; Francisco, Felipe Roberto ; Souza, Livia Moura ; Goncalves, Paulo de Souza ; Scaloppi Junior, Erivaldo J. ; Le Guen, Vincent ; Fritsche-Neto, Roberto ; Gorjanc, Gregor ; Quiles, Marcos Goncalves ; de Souza, Anete Pereira

• Tipo de documento: Artigo Científico
• Fonte: SCIENTIFIC REPORTS; v. 12, n. 1, p. 14-pg., 2022-10-26.

Resumo

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. (AU)

• Processo FAPESP: 19/26858-8 - Simulação de arquiteturas de características complexas de plantas usando redes neurais profundas
• Beneficiário: Alexandre Hild Aono
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado Direto
• Processo FAPESP: 18/18985-7 - Mapa genético integrado e seleção genômica ampla visando caracteres de importância econômica em seringueira
• Beneficiário: Felipe Roberto Francisco
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 19/03232-6 - Seleção genômica ampla em cana-de-açúcar via aprendizado de máquina e redes complexas para caracteres de importância econômica
• Beneficiário: Alexandre Hild Aono
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto