Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

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Autor(es): dc.creatorDias, Kaio Olímpio das Graças-
Autor(es): dc.creatorGezan, Salvador Alejandro-
Autor(es): dc.creatorGuimarães, Claudia Teixeira-
Autor(es): dc.creatorNazarian, Alireza-
Autor(es): dc.creatorSilva, Luciano da Costa e-
Autor(es): dc.creatorParentoni, Sidney Netto-
Autor(es): dc.creatorGuimarães, Paulo Evaristo de Oliveira-
Autor(es): dc.creatorAnoni, Carina de Oliveira-
Autor(es): dc.creatorPádua, José Maria Villela-
Autor(es): dc.creatorPinto, Marcos de Oliveira-
Autor(es): dc.creatorNoda, Roberto Willians-
Autor(es): dc.creatorRibeiro, Carlos Alexandre Gomes-
Autor(es): dc.creatorMagalhães, Jurandir Vieira de-
Autor(es): dc.creatorGarcia, Antonio Augusto Franco-
Autor(es): dc.creatorSouza, João Cândido de-
Autor(es): dc.creatorGuimarães, Lauro José Moreira-
Autor(es): dc.creatorPastina, Maria Marta-
Data de aceite: dc.date.accessioned2026-02-09T12:10:16Z-
Data de disponibilização: dc.date.available2026-02-09T12:10:16Z-
Data de envio: dc.date.issued2019-03-22-
Data de envio: dc.date.issued2019-03-22-
Data de envio: dc.date.issued2018-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/33242-
Fonte completa do material: dc.identifierhttps://www.nature.com/articles/s41437-018-0053-6-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1155835-
Descrição: dc.descriptionBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids’ genotypes were inferred based on their parents’ genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceHeredity-
Palavras-chave: dc.subjectAgricultural genetics-
Palavras-chave: dc.subjectPlant breeding-
Título: dc.titleImproving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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