Structural equation modeling and genome-wide selection for multiple traits to enhance arabica coffee breeding programs.

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dc.contributorMATHEUS 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.
dc.creatorSUELA, M. M.
dc.creatorAZEVEDO, C. F.
dc.creatorNASCIMENTO, A. C. C.
dc.creatorCAIXETA, E. T.
dc.creatorOLIVEIRA, A. C. B. de
dc.creatorMOROTA, G.
dc.creatorNASCIMENTO, M.
dc.date2026-04-30T20:48:34Z
dc.date2026-04-30T20:48:34Z
dc.date2026-04-30
dc.date2025
dc.date.accessioned2026-07-01T00:27:39Z
dc.descriptionRecognizing 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.
dc.format22 p.
dc.identifierAgronomy, v. 15, n. 7, 1686, 2025.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186554
dc.identifierhttps://doi.org/10.3390/agronomy15071686
dc.identifier.urihttp://hdl.handle.net/123456789/404598
dc.languageeng
dc.rightsopenAccess
dc.subjectCoffea Arábica
dc.subjectGenome
dc.subjectPlant breeding
dc.subjectStructural equation modeling
dc.subjectBayesian theory
dc.subjectNucleotides
dc.titleStructural equation modeling and genome-wide selection for multiple traits to enhance arabica coffee breeding programs.
dc.typeArtigo de periódico

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