Estimation of variance for reciprocal general and specific combining ability effects by EM-AI algorithm

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorMarçal, Tiago de S.-
Autor(es): dc.creatorRocha, João R. do A. S. de C.-
Autor(es): dc.creatorSalvador, Felipe V.-
Autor(es): dc.creatorAnjos, Rafael S. R. dos-
Autor(es): dc.creatorSilva, Adriel C. da-
Autor(es): dc.creatorCarneiro, Pedro C. S.-
Autor(es): dc.creatorCarneiro, José E. de S.-
Data de aceite: dc.date.accessioned2026-02-09T12:18:32Z-
Data de disponibilização: dc.date.available2026-02-09T12:18:32Z-
Data de envio: dc.date.issued2020-04-03-
Data de envio: dc.date.issued2020-04-03-
Data de envio: dc.date.issued2019-07-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/39750-
Fonte completa do material: dc.identifierhttps://dl.sciencesocieties.org/publications/cs/abstracts/59/4/1494-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1158686-
Descrição: dc.descriptionIn diallel analysis, using mixed models, it is possible to estimate the variance components of general combining ability (GCA), specific combining ability (SCA), reciprocal GCA (RGCA), reciprocal SCA (RSCA), and parents, even in unbalanced datasets. These variances can be estimated by likelihood maximization via numerical algorithms (e.g., expectation maximization [EM] and average information [AI]), which have different advantages. Thus, the objective of this study was to describe and implement the EM-AI algorithm (combination of EM and AI) in R software to estimate the variance components of RGCA, RSCA, and parents effects in diallel analysis. Two real datasets and three diallel models (Griffing’s Model 1, Griffing’s Model 1 + RGCA, and Model 3, a general diallel model with the effects of GCA, SCA, RGCA, RSCA, and parents) were used to evaluate the efficiency of the algorithms AI, EM, and EM-AI. The AI algorithm failed to converge for the three diallel models in both datasets. The other algorithms (EM and EM-AI) converged normally, and the estimated variance components with these algorithms were similar for the three diallel models in both datasets. However, the EM-AI algorithm was more efficient than the EM algorithm, and the general diallel model (Model 3) provided more accurate estimates of variance parameters. Thus, the EM-AI algorithm with routine implemented in R has potential for use in diallel analyses in plant breeding.-
Idioma: dc.languageen-
Publicador: dc.publisherAmerican Society of Agronomy-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceCrop Science-
Palavras-chave: dc.subjectGeneral combining ability-
Palavras-chave: dc.subjectSpecific combining ability-
Palavras-chave: dc.subjectDiallel analyses-
Palavras-chave: dc.subjectPlant breeding-
Palavras-chave: dc.subjectCapacidade geral de combinação-
Palavras-chave: dc.subjectCapacidade de combinação específica-
Palavras-chave: dc.subjectAnálises dialélicas-
Palavras-chave: dc.subjectMelhoramento de plantas-
Título: dc.titleEstimation of variance for reciprocal general and specific combining ability effects by EM-AI algorithm-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

Não existem arquivos associados a este item.