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Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee.
Moyses Nascimento1,2, Ana Carolina Campana Nascimento1,2, Camila Ferreira Azevedo1
1Laboratory of Intelligence Computational and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Viçosa, Brazil.
Frontiers in Plant Science
|August 1, 2024
Summary
Stacking Ensemble Learning (SEL) significantly improves genomic selection accuracy in Coffea Arabica breeding. This DNA-based approach enhances prediction of key traits like yield and disease resistance, outperforming traditional methods.
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Area of Science:
- Plant Breeding and Genetics
- Bioinformatics and Computational Biology
- Agricultural Science
Background:
- Traditional coffee breeding relies on lengthy phenotypic observations, hindering rapid cultivar development.
- Genomic selection (GS) offers a faster, DNA-based alternative for identifying superior coffee (Coffea Arabica) genotypes.
- Ensemble learning methods, particularly Stacking Ensemble Learning (SEL), show potential for enhancing prediction accuracy in complex trait selection.
Purpose of the Study:
- To investigate the efficacy of Stacking Ensemble Learning (SEL) for improving prediction accuracy in Coffea Arabica genomic selection.
- To evaluate SEL's performance in predicting key agronomic and disease resistance traits: yield (YL), fruit number (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer).
Main Methods:
- Analysis of 195 Coffea Arabica individuals genotyped with 21,211 single-nucleotide polymorphism (SNP) markers.
- Implementation of a cross-validation (CV) scheme to assess model performance.
- Utilized Genomic Best Linear Unbiased Prediction (GBLUP), MARS, QRF, and RF as base learners within the SEL framework, with Ridge Regression, RF, GBLUP, and Single Average as meta-learners.
Main Results:
- Stacking Ensemble Learning (SEL) demonstrated superior predictive ability (PA) across all evaluated traits compared to individual base learner models.
- SEL achieved significant gains in PA over GBLUP: 87.44% for yield (YL), 37.83% for fruit number (NF), 199.82% for leaf miner infestation (LM), and 14.59% for cercosporiosis incidence (Cer).
- The study confirmed SEL's capability to accurately predict important traits in Coffea Arabica.
Conclusions:
- Stacking Ensemble Learning (SEL) represents a highly promising advancement for genomic selection in coffee breeding.
- By integrating predictions from multiple models, SEL effectively enhances the predictive accuracy for complex traits in Coffea Arabica.
- This approach accelerates the selection of superior coffee cultivars, addressing limitations of traditional breeding methods.