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

Author(s)

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

• Document type: Journal article
• Source: SCIENTIFIC REPORTS; v. 12, n. 1, p. 14-pg., 2022-10-26.

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. (AU)

• FAPESP's process: 19/26858-8 - Simulation of plant complex trait architectures using deep neural networks
• Grantee: Alexandre Hild Aono
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• FAPESP's process: 18/18985-7 - Integrated genetic map and wider genomic selection looking at characters of economic impotence in seringueira
• Grantee: Felipe Roberto Francisco
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• FAPESP's process: 19/03232-6 - Genome wide selection in sugarcane using machine learning and complex networks for economically important traits
• Grantee: Alexandre Hild Aono
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)