Inference of population effect and progeny selection via a multi-trait index in soybean breeding
The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Eduem (Editora da Universidade Estadual de Maringá)
2020-08-01
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| Series: | Acta Scientiarum: Agronomy |
| Subjects: | |
| Online Access: | https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/44623 |
| _version_ | 1818973415537639424 |
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| author | Leonardo Volpato João Romero do Amaral Santos de Carvalho Rocha Rodrigo Silva Alves Willian Hytalo Ludke Aluízio Borém Felipe Lopes Silva |
| author_facet | Leonardo Volpato João Romero do Amaral Santos de Carvalho Rocha Rodrigo Silva Alves Willian Hytalo Ludke Aluízio Borém Felipe Lopes Silva |
| author_sort | Leonardo Volpato |
| collection | DOAJ |
| description |
The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.
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| first_indexed | 2024-12-20T15:23:48Z |
| format | Article |
| id | doaj.art-b0d91572097c4806a6a20ff472b916ff |
| institution | Directory Open Access Journal |
| issn | 1679-9275 1807-8621 |
| language | English |
| last_indexed | 2024-12-20T15:23:48Z |
| publishDate | 2020-08-01 |
| publisher | Eduem (Editora da Universidade Estadual de Maringá) |
| record_format | Article |
| series | Acta Scientiarum: Agronomy |
| spelling | doaj.art-b0d91572097c4806a6a20ff472b916ff2022-12-21T19:35:56ZengEduem (Editora da Universidade Estadual de Maringá)Acta Scientiarum: Agronomy1679-92751807-86212020-08-0143110.4025/actasciagron.v43i1.4462344623Inference of population effect and progeny selection via a multi-trait index in soybean breedingLeonardo Volpato0João Romero do Amaral Santos de Carvalho Rocha1Rodrigo Silva Alves2Willian Hytalo Ludke3Aluízio Borém4Felipe Lopes Silva5Universidade Federal de ViçosaUniversidade Federal de ViçosaUniversidade Federal de ViçosaUniversidade Federal de ViçosaUniversidade Federal de ViçosaFederal University of Viçosa The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding. https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/44623mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design. |
| spellingShingle | Leonardo Volpato João Romero do Amaral Santos de Carvalho Rocha Rodrigo Silva Alves Willian Hytalo Ludke Aluízio Borém Felipe Lopes Silva Inference of population effect and progeny selection via a multi-trait index in soybean breeding Acta Scientiarum: Agronomy mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design. |
| title | Inference of population effect and progeny selection via a multi-trait index in soybean breeding |
| title_full | Inference of population effect and progeny selection via a multi-trait index in soybean breeding |
| title_fullStr | Inference of population effect and progeny selection via a multi-trait index in soybean breeding |
| title_full_unstemmed | Inference of population effect and progeny selection via a multi-trait index in soybean breeding |
| title_short | Inference of population effect and progeny selection via a multi-trait index in soybean breeding |
| title_sort | inference of population effect and progeny selection via a multi trait index in soybean breeding |
| topic | mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design. |
| url | https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/44623 |
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