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Trait variation and genetic diversity in a banana genomic selection training population
Banana (Musa spp.) is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency a...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460855/ https://www.ncbi.nlm.nih.gov/pubmed/28586365 http://dx.doi.org/10.1371/journal.pone.0178734 |
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author | Nyine, Moses Uwimana, Brigitte Swennen, Rony Batte, Michael Brown, Allan Christelová, Pavla Hřibová, Eva Lorenzen, Jim Doležel, Jaroslav |
author_facet | Nyine, Moses Uwimana, Brigitte Swennen, Rony Batte, Michael Brown, Allan Christelová, Pavla Hřibová, Eva Lorenzen, Jim Doležel, Jaroslav |
author_sort | Nyine, Moses |
collection | PubMed |
description | Banana (Musa spp.) is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency are being investigated to breed for resistant, high yielding hybrids of East African Highland banana (EAHB). These include genomic selection (GS), which will benefit breeding through increased genetic gain per unit time. Understanding trait variation and the correlation among economically important traits is an essential first step in the development and selection of suitable GS models for banana. In this study, we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype. A training population created using EAHB breeding material and its progeny was phenotyped in two contrasting conditions. A high level of correlation among vegetative and yield related traits was observed. Therefore, genomic selection models could be developed for traits that are easily measured. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Genotype response to cycle and field management practices varied greatly with respect to traits. Yield related traits accounted for 31–35% of principal component variation under low and high input field management conditions. Resistance to Black Sigatoka was stable across cycles but varied under different field management depending on the genotype. The best cross combination was 1201K-1xSH3217 based on selection response (R) of hybrids. Genotyping using simple sequence repeat (SSR) markers revealed that the training population was genetically diverse, reflecting a complex pedigree background, which was mostly influenced by the male parents. |
format | Online Article Text |
id | pubmed-5460855 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54608552017-06-15 Trait variation and genetic diversity in a banana genomic selection training population Nyine, Moses Uwimana, Brigitte Swennen, Rony Batte, Michael Brown, Allan Christelová, Pavla Hřibová, Eva Lorenzen, Jim Doležel, Jaroslav PLoS One Research Article Banana (Musa spp.) is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency are being investigated to breed for resistant, high yielding hybrids of East African Highland banana (EAHB). These include genomic selection (GS), which will benefit breeding through increased genetic gain per unit time. Understanding trait variation and the correlation among economically important traits is an essential first step in the development and selection of suitable GS models for banana. In this study, we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype. A training population created using EAHB breeding material and its progeny was phenotyped in two contrasting conditions. A high level of correlation among vegetative and yield related traits was observed. Therefore, genomic selection models could be developed for traits that are easily measured. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Genotype response to cycle and field management practices varied greatly with respect to traits. Yield related traits accounted for 31–35% of principal component variation under low and high input field management conditions. Resistance to Black Sigatoka was stable across cycles but varied under different field management depending on the genotype. The best cross combination was 1201K-1xSH3217 based on selection response (R) of hybrids. Genotyping using simple sequence repeat (SSR) markers revealed that the training population was genetically diverse, reflecting a complex pedigree background, which was mostly influenced by the male parents. Public Library of Science 2017-06-06 /pmc/articles/PMC5460855/ /pubmed/28586365 http://dx.doi.org/10.1371/journal.pone.0178734 Text en © 2017 Nyine et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Nyine, Moses Uwimana, Brigitte Swennen, Rony Batte, Michael Brown, Allan Christelová, Pavla Hřibová, Eva Lorenzen, Jim Doležel, Jaroslav Trait variation and genetic diversity in a banana genomic selection training population |
title | Trait variation and genetic diversity in a banana genomic selection training population |
title_full | Trait variation and genetic diversity in a banana genomic selection training population |
title_fullStr | Trait variation and genetic diversity in a banana genomic selection training population |
title_full_unstemmed | Trait variation and genetic diversity in a banana genomic selection training population |
title_short | Trait variation and genetic diversity in a banana genomic selection training population |
title_sort | trait variation and genetic diversity in a banana genomic selection training population |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460855/ https://www.ncbi.nlm.nih.gov/pubmed/28586365 http://dx.doi.org/10.1371/journal.pone.0178734 |
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