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Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping
Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, bu...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8526887/ https://www.ncbi.nlm.nih.gov/pubmed/34691111 http://dx.doi.org/10.3389/fpls.2021.735285 |
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author | He, Sang Jiang, Yong Thistlethwaite, Rebecca Hayden, Matthew J. Trethowan, Richard Daetwyler, Hans D. |
author_facet | He, Sang Jiang, Yong Thistlethwaite, Rebecca Hayden, Matthew J. Trethowan, Richard Daetwyler, Hans D. |
author_sort | He, Sang |
collection | PubMed |
description | Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs. |
format | Online Article Text |
id | pubmed-8526887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85268872021-10-21 Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping He, Sang Jiang, Yong Thistlethwaite, Rebecca Hayden, Matthew J. Trethowan, Richard Daetwyler, Hans D. Front Plant Sci Plant Science Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs. Frontiers Media S.A. 2021-10-06 /pmc/articles/PMC8526887/ /pubmed/34691111 http://dx.doi.org/10.3389/fpls.2021.735285 Text en Copyright © 2021 He, Jiang, Thistlethwaite, Hayden, Trethowan and Daetwyler. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science He, Sang Jiang, Yong Thistlethwaite, Rebecca Hayden, Matthew J. Trethowan, Richard Daetwyler, Hans D. Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping |
title | Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping |
title_full | Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping |
title_fullStr | Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping |
title_full_unstemmed | Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping |
title_short | Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping |
title_sort | improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8526887/ https://www.ncbi.nlm.nih.gov/pubmed/34691111 http://dx.doi.org/10.3389/fpls.2021.735285 |
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