Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview
Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework.
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Format: | artículo biblioteca |
Language: | English |
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Frontiers Media
2021-09-09
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Subjects: | Training set optimization, Genomic selection, Genome-wide markers, Statistical design, Sparse phenotyping, Genomic prediction, Mixed models, |
Online Access: | http://hdl.handle.net/20.500.12792/6153 http://hdl.handle.net/10261/289749 |
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dig-inia-es-10261-2897492023-02-16T14:55:33Z Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview Sanchez, Julio Isidro Akdemir, Deniz Training set optimization Genomic selection Genome-wide markers Statistical design Sparse phenotyping Genomic prediction Mixed models Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework. 2023-02-16T12:23:50Z 2023-02-16T12:23:50Z 2021-09-09 artículo Frontiers in Plant Science 12: e715910 (2021) 1664-462X http://hdl.handle.net/20.500.12792/6153 http://hdl.handle.net/10261/289749 10.3389/fpls.2021.715910 1664-462X en open Frontiers Media |
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Training set optimization Genomic selection Genome-wide markers Statistical design Sparse phenotyping Genomic prediction Mixed models Training set optimization Genomic selection Genome-wide markers Statistical design Sparse phenotyping Genomic prediction Mixed models |
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Training set optimization Genomic selection Genome-wide markers Statistical design Sparse phenotyping Genomic prediction Mixed models Training set optimization Genomic selection Genome-wide markers Statistical design Sparse phenotyping Genomic prediction Mixed models Sanchez, Julio Isidro Akdemir, Deniz Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview |
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Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework. |
format |
artículo |
topic_facet |
Training set optimization Genomic selection Genome-wide markers Statistical design Sparse phenotyping Genomic prediction Mixed models |
author |
Sanchez, Julio Isidro Akdemir, Deniz |
author_facet |
Sanchez, Julio Isidro Akdemir, Deniz |
author_sort |
Sanchez, Julio Isidro |
title |
Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview |
title_short |
Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview |
title_full |
Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview |
title_fullStr |
Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview |
title_full_unstemmed |
Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview |
title_sort |
training set optimization for sparse phenotyping in genomic selection: a conceptual overview |
publisher |
Frontiers Media |
publishDate |
2021-09-09 |
url |
http://hdl.handle.net/20.500.12792/6153 http://hdl.handle.net/10261/289749 |
work_keys_str_mv |
AT sanchezjulioisidro trainingsetoptimizationforsparsephenotypingingenomicselectionaconceptualoverview AT akdemirdeniz trainingsetoptimizationforsparsephenotypingingenomicselectionaconceptualoverview |
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