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Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored th...

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Autores principales: Sandhu, Karansher S., Patil, Shruti Sunil, Aoun, Meriem, Carter, Arron H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841657/
https://www.ncbi.nlm.nih.gov/pubmed/35173770
http://dx.doi.org/10.3389/fgene.2022.831020
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author Sandhu, Karansher S.
Patil, Shruti Sunil
Aoun, Meriem
Carter, Arron H.
author_facet Sandhu, Karansher S.
Patil, Shruti Sunil
Aoun, Meriem
Carter, Arron H.
author_sort Sandhu, Karansher S.
collection PubMed
description Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait–based GS models. This study’s main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait– and multi-trait–based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait–based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.
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spelling pubmed-88416572022-02-15 Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat Sandhu, Karansher S. Patil, Shruti Sunil Aoun, Meriem Carter, Arron H. Front Genet Genetics Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait–based GS models. This study’s main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait– and multi-trait–based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait–based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner. Frontiers Media S.A. 2022-01-31 /pmc/articles/PMC8841657/ /pubmed/35173770 http://dx.doi.org/10.3389/fgene.2022.831020 Text en Copyright © 2022 Sandhu, Patil, Aoun and Carter. 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 Genetics
Sandhu, Karansher S.
Patil, Shruti Sunil
Aoun, Meriem
Carter, Arron H.
Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat
title Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat
title_full Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat
title_fullStr Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat
title_full_unstemmed Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat
title_short Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat
title_sort multi-trait multi-environment genomic prediction for end-use quality traits in winter wheat
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841657/
https://www.ncbi.nlm.nih.gov/pubmed/35173770
http://dx.doi.org/10.3389/fgene.2022.831020
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