Accounting for spatial trends in multi-environment diallel analysis in maize breeding.

Spatial trends represent an obstacle to genetic evaluation in maize breeding. Spatial analyses can correct spatial trends, which allow for an increase in selective accuracy. The objective of this study was to compare the spatial (SPA) and non-spatial (NSPA) models in diallel multi-environment trial...

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Autores principales:	Igor Ferreira Coelho, Marco Antônio Peixoto, Tiago de Souza Marçal, Arthur Bernardeli, Rodrigo Silva Alves, Rodrigo Oliveira de Lima, Edésio Fialho Dos Reis, Leonardo Lopes Bhering
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/66ca8ee00f484478861cdfcbc40a6efe
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id	oai:doaj.org-article:66ca8ee00f484478861cdfcbc40a6efe
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spelling	oai:doaj.org-article:66ca8ee00f484478861cdfcbc40a6efe2021-12-02T20:16:40ZAccounting for spatial trends in multi-environment diallel analysis in maize breeding.1932-620310.1371/journal.pone.0258473https://doaj.org/article/66ca8ee00f484478861cdfcbc40a6efe2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258473https://doaj.org/toc/1932-6203Spatial trends represent an obstacle to genetic evaluation in maize breeding. Spatial analyses can correct spatial trends, which allow for an increase in selective accuracy. The objective of this study was to compare the spatial (SPA) and non-spatial (NSPA) models in diallel multi-environment trial analyses in maize breeding. The trials consisted of 78 inter-populational maize hybrids, tested in four environments (E1, E2, E3, and E4), with three replications, under a randomized complete block design. The SPA models accounted for autocorrelation among rows and columns by the inclusion of first-order autoregressive matrices (AR1 ⊗ AR1). Then, the rows and columns factors were included in the fixed and random parts of the model. Based on the Bayesian information criteria, the SPA models were used to analyze trials E3 and E4, while the NSPA model was used for analyzing trials E1 and E2. In the joint analysis, the compound symmetry structure for the genotypic effects presented the best fit. The likelihood ratio test showed that some effects changed regarding significance when the SPA and NSPA models were used. In addition, the heritability, selective accuracy, and selection gain were higher when the SPA models were used. This indicates the power of the SPA model in dealing with spatial trends. The SPA model exhibits higher reliability values and is recommended to be incorporated in the standard procedure of genetic evaluation in maize breeding. The analyses bring the parents 2, 10 and 12, as potential parents in this microregion.Igor Ferreira CoelhoMarco Antônio PeixotoTiago de Souza MarçalArthur BernardeliRodrigo Silva AlvesRodrigo Oliveira de LimaEdésio Fialho Dos ReisLeonardo Lopes BheringPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258473 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Igor Ferreira Coelho Marco Antônio Peixoto Tiago de Souza Marçal Arthur Bernardeli Rodrigo Silva Alves Rodrigo Oliveira de Lima Edésio Fialho Dos Reis Leonardo Lopes Bhering Accounting for spatial trends in multi-environment diallel analysis in maize breeding.
description	Spatial trends represent an obstacle to genetic evaluation in maize breeding. Spatial analyses can correct spatial trends, which allow for an increase in selective accuracy. The objective of this study was to compare the spatial (SPA) and non-spatial (NSPA) models in diallel multi-environment trial analyses in maize breeding. The trials consisted of 78 inter-populational maize hybrids, tested in four environments (E1, E2, E3, and E4), with three replications, under a randomized complete block design. The SPA models accounted for autocorrelation among rows and columns by the inclusion of first-order autoregressive matrices (AR1 ⊗ AR1). Then, the rows and columns factors were included in the fixed and random parts of the model. Based on the Bayesian information criteria, the SPA models were used to analyze trials E3 and E4, while the NSPA model was used for analyzing trials E1 and E2. In the joint analysis, the compound symmetry structure for the genotypic effects presented the best fit. The likelihood ratio test showed that some effects changed regarding significance when the SPA and NSPA models were used. In addition, the heritability, selective accuracy, and selection gain were higher when the SPA models were used. This indicates the power of the SPA model in dealing with spatial trends. The SPA model exhibits higher reliability values and is recommended to be incorporated in the standard procedure of genetic evaluation in maize breeding. The analyses bring the parents 2, 10 and 12, as potential parents in this microregion.
format	article
author	Igor Ferreira Coelho Marco Antônio Peixoto Tiago de Souza Marçal Arthur Bernardeli Rodrigo Silva Alves Rodrigo Oliveira de Lima Edésio Fialho Dos Reis Leonardo Lopes Bhering
author_facet	Igor Ferreira Coelho Marco Antônio Peixoto Tiago de Souza Marçal Arthur Bernardeli Rodrigo Silva Alves Rodrigo Oliveira de Lima Edésio Fialho Dos Reis Leonardo Lopes Bhering
author_sort	Igor Ferreira Coelho
title	Accounting for spatial trends in multi-environment diallel analysis in maize breeding.
title_short	Accounting for spatial trends in multi-environment diallel analysis in maize breeding.
title_full	Accounting for spatial trends in multi-environment diallel analysis in maize breeding.
title_fullStr	Accounting for spatial trends in multi-environment diallel analysis in maize breeding.
title_full_unstemmed	Accounting for spatial trends in multi-environment diallel analysis in maize breeding.
title_sort	accounting for spatial trends in multi-environment diallel analysis in maize breeding.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/66ca8ee00f484478861cdfcbc40a6efe
work_keys_str_mv	AT igorferreiracoelho accountingforspatialtrendsinmultienvironmentdiallelanalysisinmaizebreeding AT marcoantoniopeixoto accountingforspatialtrendsinmultienvironmentdiallelanalysisinmaizebreeding AT tiagodesouzamarcal accountingforspatialtrendsinmultienvironmentdiallelanalysisinmaizebreeding AT arthurbernardeli accountingforspatialtrendsinmultienvironmentdiallelanalysisinmaizebreeding AT rodrigosilvaalves accountingforspatialtrendsinmultienvironmentdiallelanalysisinmaizebreeding AT rodrigooliveiradelima accountingforspatialtrendsinmultienvironmentdiallelanalysisinmaizebreeding AT edesiofialhodosreis accountingforspatialtrendsinmultienvironmentdiallelanalysisinmaizebreeding AT leonardolopesbhering accountingforspatialtrendsinmultienvironmentdiallelanalysisinmaizebreeding
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Accounting for spatial trends in multi-environment diallel analysis in maize breeding.

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