Genomic Prediction: Progress and Perspectives for Rice Improvement
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (<jats:italic>Oryza sativa</jats:italic>) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage “<jats:italic>To someone with a hammer, everything looks like a nail</jats:italic>” describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Format: | Book Chapter biblioteca |
Language: | English |
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Springer
2022
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Subjects: | genomics, oryza sativa, rice, breeding programmes, |
Online Access: | https://hdl.handle.net/10568/128168 https://doi.org/10.1007/978-1-0716-2205-6_21 |
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dig-cgspace-10568-1281682023-12-08T19:36:04Z Genomic Prediction: Progress and Perspectives for Rice Improvement Bartholomé, Jérôme Thathapalli Prakash, Parthiban Cobb, Joshua N. genomics oryza sativa rice breeding programmes Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (<jats:italic>Oryza sativa</jats:italic>) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage “<jats:italic>To someone with a hammer, everything looks like a nail</jats:italic>” describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context. 2022 2023-01-25T08:27:02Z 2023-01-25T08:27:02Z Book Chapter Bartholomé, J., Prakash, P.T. and Cobb, J.N. 2022. Genomic Prediction: Progress and Perspectives for Rice Improvement. IN: Ahmadi, N., Bartholomé, J. (eds) Genomic Prediction of Complex Traits. Methods in Molecular Biology 2467:569–617. Humana, New York, NY. 9781071622049 9781071622056 1064-3745 1940-6029 https://hdl.handle.net/10568/128168 https://doi.org/10.1007/978-1-0716-2205-6_21 en CC-BY-4.0 Open Access 569-617 application/pdf Springer Genomic Prediction of Complex Traits: Methods and Protocols, Methods in Molecular Biology |
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genomics oryza sativa rice breeding programmes genomics oryza sativa rice breeding programmes Bartholomé, Jérôme Thathapalli Prakash, Parthiban Cobb, Joshua N. Genomic Prediction: Progress and Perspectives for Rice Improvement |
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Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (<jats:italic>Oryza sativa</jats:italic>) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage “<jats:italic>To someone with a hammer, everything looks like a nail</jats:italic>” describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context. |
format |
Book Chapter |
topic_facet |
genomics oryza sativa rice breeding programmes |
author |
Bartholomé, Jérôme Thathapalli Prakash, Parthiban Cobb, Joshua N. |
author_facet |
Bartholomé, Jérôme Thathapalli Prakash, Parthiban Cobb, Joshua N. |
author_sort |
Bartholomé, Jérôme |
title |
Genomic Prediction: Progress and Perspectives for Rice Improvement |
title_short |
Genomic Prediction: Progress and Perspectives for Rice Improvement |
title_full |
Genomic Prediction: Progress and Perspectives for Rice Improvement |
title_fullStr |
Genomic Prediction: Progress and Perspectives for Rice Improvement |
title_full_unstemmed |
Genomic Prediction: Progress and Perspectives for Rice Improvement |
title_sort |
genomic prediction: progress and perspectives for rice improvement |
publisher |
Springer |
publishDate |
2022 |
url |
https://hdl.handle.net/10568/128168 https://doi.org/10.1007/978-1-0716-2205-6_21 |
work_keys_str_mv |
AT bartholomejerome genomicpredictionprogressandperspectivesforriceimprovement AT thathapalliprakashparthiban genomicpredictionprogressandperspectivesforriceimprovement AT cobbjoshuan genomicpredictionprogressandperspectivesforriceimprovement |
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1787231043042934784 |