Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability

Abstract The accuracy of genomic prediction increases with increasing heritability, and thus the challenge of optimizing the design of multienvironment yield trials under a limited budget arises. With this in mind, we aimed to find the best of several options to sparsely distribute a fixed number of...

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Autores principales:	Moritz Lell, Jochen Reif, Yusheng Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/caa1fad15d38456daf8d33697a0927cf
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spelling	oai:doaj.org-article:caa1fad15d38456daf8d33697a0927cf2021-12-05T07:50:11ZOptimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability1940-337210.1002/tpg2.20150https://doaj.org/article/caa1fad15d38456daf8d33697a0927cf2021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20150https://doaj.org/toc/1940-3372Abstract The accuracy of genomic prediction increases with increasing heritability, and thus the challenge of optimizing the design of multienvironment yield trials under a limited budget arises. With this in mind, we aimed to find the best of several options to sparsely distribute a fixed number of plots across different environments to increase the accuracy of hybrid performance prediction. We used a comprehensive published genomic and phenotypic data set of 1,604 winter wheat (Triticum aestivum L.) hybrids and compared several commonly used biometric models for phenotypic data analysis in a resampling study to identify the one that most accurately estimated the hybrid performance in different imbalanced trials. Our results showed that when using information about genotypic relationships, genotypic values were more strongly associated with the reference values than when this information was ignored. In addition, a balanced environmental sampling resulted in an adequate characterization of each environment and increased the accuracy for estimating the hybrid performance. One promising design involved dividing the genotypes into equally sized subgroups that were tested in a subset of environments, with the constraint that the subgroups overlapped with respect to the environments. This scenario appears to be particularly appropriate, as it provided both high accuracies in the estimates of genotypic values and had low variability resulting from the data sample used. Thus, we were able to clearly demonstrate the utility for optimizing the design of multienvironment hybrid wheat yield trials in times of genomic selection.Moritz LellJochen ReifYusheng ZhaoWileyarticlePlant cultureSB1-1110GeneticsQH426-470ENThe Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Plant culture SB1-1110 Genetics QH426-470
spellingShingle	Plant culture SB1-1110 Genetics QH426-470 Moritz Lell Jochen Reif Yusheng Zhao Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability
description	Abstract The accuracy of genomic prediction increases with increasing heritability, and thus the challenge of optimizing the design of multienvironment yield trials under a limited budget arises. With this in mind, we aimed to find the best of several options to sparsely distribute a fixed number of plots across different environments to increase the accuracy of hybrid performance prediction. We used a comprehensive published genomic and phenotypic data set of 1,604 winter wheat (Triticum aestivum L.) hybrids and compared several commonly used biometric models for phenotypic data analysis in a resampling study to identify the one that most accurately estimated the hybrid performance in different imbalanced trials. Our results showed that when using information about genotypic relationships, genotypic values were more strongly associated with the reference values than when this information was ignored. In addition, a balanced environmental sampling resulted in an adequate characterization of each environment and increased the accuracy for estimating the hybrid performance. One promising design involved dividing the genotypes into equally sized subgroups that were tested in a subset of environments, with the constraint that the subgroups overlapped with respect to the environments. This scenario appears to be particularly appropriate, as it provided both high accuracies in the estimates of genotypic values and had low variability resulting from the data sample used. Thus, we were able to clearly demonstrate the utility for optimizing the design of multienvironment hybrid wheat yield trials in times of genomic selection.
format	article
author	Moritz Lell Jochen Reif Yusheng Zhao
author_facet	Moritz Lell Jochen Reif Yusheng Zhao
author_sort	Moritz Lell
title	Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability
title_short	Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability
title_full	Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability
title_fullStr	Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability
title_full_unstemmed	Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability
title_sort	optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/caa1fad15d38456daf8d33697a0927cf
work_keys_str_mv	AT moritzlell optimizingthesetupofmultienvironmentalhybridwheatyieldtrialsforboostingtheselectioncapability AT jochenreif optimizingthesetupofmultienvironmentalhybridwheatyieldtrialsforboostingtheselectioncapability AT yushengzhao optimizingthesetupofmultienvironmentalhybridwheatyieldtrialsforboostingtheselectioncapability
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Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability

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