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

Alexandre Hild Aono^1,2, Felipe Roberto Francisco¹, Livia Moura Souza^1,3

¹Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil.

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|October 26, 2022

Summary

This study introduces a new machine learning method to predict rubber tree growth using molecular markers. This approach significantly improves the accuracy of genomic selection (GS) for Hevea breeding programs.

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Area of Science:

Plant breeding
Genomics
Machine learning

Background:

Rubber tree (Hevea brasiliensis) breeding is slow due to long cycles.
Genomic data and marker-assisted selection show promise but require enhancement.
Genomic selection (GS) can accelerate breeding, but predictive models need improvement.

Purpose of the Study:

To develop a novel machine learning-based approach for predicting rubber tree stem circumference.
To enhance the accuracy and practical application of genomic selection (GS) in Hevea breeding.
To reduce the breeding cycle time for developing improved rubber tree varieties.

Main Methods:

A two-stage neural network prediction system was developed.
The system utilizes a divide-and-conquer strategy: subpopulation prediction and phenotype estimation.
Molecular markers were used as input for predicting stem circumference.

Main Results:

The proposed machine learning approach achieved higher prediction accuracies than traditional statistical models.
Significant accuracy improvements were observed in a single-environment scenario.
The methodology demonstrates a powerful tool for Hevea GS strategies.

Conclusions:

Machine learning integration offers a robust method for optimizing rubber tree breeding programs.
The novel approach enhances the predictive capabilities of GS in Hevea.
This facilitates faster development of more productive rubber tree varieties.