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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8530354/?tool=EBI |
| _version_ | 1831717831335477248 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-21T01:06:16Z |
| format | Article |
| id | doaj.art-106ac149ede0474f9b5c9fe13bc44965 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T01:06:16Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-106ac149ede0474f9b5c9fe13bc449652022-12-21T19:21:02ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-011610Accounting for spatial trends in multi-environment diallel analysis in maize breedingIgor Ferreira CoelhoMarco Antônio PeixotoTiago de Souza MarçalArthur BernardeliRodrigo Silva AlvesRodrigo Oliveira de LimaEdésio Fialho dos ReisLeonardo Lopes BheringSpatial 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.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8530354/?tool=EBI |
| spellingShingle | 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 PLoS ONE |
| title | 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_short | 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8530354/?tool=EBI |
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