Title:	Enhancing genomic prediction with stacking ensemble learning in arabica coffee.
Authors:	NASCIMENTO, M. NASCIMENTO, A. C. C. AZEVEDO, C. F. OLIVEIRA, A. C. B. de CAIXETA, E. T. JARQUIN, D.
Affiliation:	MOYSES NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANTONIO CARLOS BAIAO DE OLIVEIRA, CNPCA; EVELINE TEIXEIRA CAIXETA MOURA, CNPCA; DIEGO JARQUIN, UNIVERSITY OF FLORIDA.
Date Issued:	2024
Citation:	Frontiers in Plant Science, v. 15, 2024.
Pages:	14 p.
Description:	Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits.
Thesagro:	Coffea Arábica
NAL Thesaurus:	Plant breeding Genomics Genetic traits
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (SAPC)