Title:	Structural equation modeling and genome-wide selection for multiple traits to enhance arabica coffee breeding programs.
Authors:	SUELA, M. M. AZEVEDO, C. F. NASCIMENTO, A. C. C. CAIXETA, E. T. OLIVEIRA, A. C. B. de MOROTA, G. NASCIMENTO, M.
Affiliation:	MATHEUS MASSARIOL SUELA, UNIVERSIDADE FEDERAL DE VIÇOSA; CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; EVELINE TEIXEIRA CAIXETA MOURA, CNPCA; ANTONIO CARLOS BAIAO DE OLIVEIRA, CNPCA; GOTA MOROTA, THE UNIVERSITY OF TOKYO; MOYSÉS NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA.
Date Issued:	2025
Citation:	Agronomy, v. 15, n. 7, 1686, 2025.
Pages:	22 p.
Description:	Recognizing the interrelationship among variables becomes critical in genetic breeding programs, where the goal is often to optimize selection for multiple traits. Conventional multi-trait models face challenges such as convergence issues, and they fail to account for cause-and-effect relationships. To address these challenges, we conducted a comprehensive analysis involving confirmatory factor analysis (CFA), Bayesian networks (BN), structural equation modeling (SEM), and genome-wide selection (GWS) using data from 195 arabica coffee plants. These plants were genotyped with 21,211 single nucleotide polymorphism markers as part of the Coffea arabica breeding program at UFV/EPAMIG/EMBRAPA. Traits included vegetative vigor (VV), canopy diameter (CD), number of vegetative nodes (NVN), number of reproductive nodes (NRN), leaf length (LL), and yield (Y). CFA established the following latent variables: vigor latent (VL) explaining VV and CD; nodes latent (NL) explaining NVN and NRN; leaf length latent (LLL) explaining LL; and yield latent (YL) explaining Y. These were integrated into the BN model, revealing the following key interrelationships: LLL → VL, LLL → NL, LLL → YL, VL → NL, and NL → YL. SEM estimated structural coefficients, highlighting the biological importance of VL → NL and NL → YL connections. Genomic predictions based on observed and latent variables showed that using VL to predict NVN and NRN traits resulted in similar gains to using NL. Predicting gains in Y using NL increased selection gains by 66.35% compared to YL. The SEM-GWS approach provided insights into selection strategies for traits linked with vegetative vigor, nodes, leaf length, and coffee yield, offering valuable guidance for advancing Arabica coffee breeding programs.
Thesagro:	Coffea Arábica
NAL Thesaurus:	Genome Plant breeding Structural equation modeling Bayesian theory Nucleotides
DOI:	https://doi.org/10.3390/agronomy15071686
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (SAPC)