Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat

Abstract End‐use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end‐use quality traits in soft white wheat (Triticum aestivum L.) and tested...

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Autores principales:	Meriem Aoun, Arron Carter, Yvonne A. Thompson, Brian Ward, Craig F. Morris
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/9600ff77b7a645f196a3cfb3097eccc1
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spelling	oai:doaj.org-article:9600ff77b7a645f196a3cfb3097eccc12021-12-05T07:50:12ZEnvironment characterization and genomic prediction for end‐use quality traits in soft white winter wheat1940-337210.1002/tpg2.20128https://doaj.org/article/9600ff77b7a645f196a3cfb3097eccc12021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20128https://doaj.org/toc/1940-3372Abstract End‐use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end‐use quality traits in soft white wheat (Triticum aestivum L.) and tested the effectiveness of implementing genomic selection to optimize breeding for these traits. We used a multi‐environment unbalanced dataset comprised of 672 breeding lines and cultivars adapted to the Pacific Northwest region of the United States, which were evaluated for 14 end‐use quality traits. Genetic correlations between environments based on factor analytic models showed low‐to‐moderate G × E interaction for most traits but high G × E interaction for grain and flour protein. A total of 40,518 single‐nucleotide polymorphism markers were used for genomic prediction. Genomic prediction accuracies were high for most traits thereby justifying the use of genomic selection to assist breeding for superior end‐use quality in soft white wheat. Excluding outlier environments based on genetic correlations between environments was more effective in increasing genomic prediction accuracies compared with that based on environment clustering analysis. For kernel size, kernel weight, milling score, ash, and flour swelling volume, excluding outlier environments increased prediction accuracies by 1–11%. However, for grain and flour protein, flour yield, and cookie diameter, excluding outlier environments did not improve genomic prediction performance.Meriem AounArron CarterYvonne A. ThompsonBrian WardCraig F. MorrisWileyarticlePlant 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 Meriem Aoun Arron Carter Yvonne A. Thompson Brian Ward Craig F. Morris Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat
description	Abstract End‐use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end‐use quality traits in soft white wheat (Triticum aestivum L.) and tested the effectiveness of implementing genomic selection to optimize breeding for these traits. We used a multi‐environment unbalanced dataset comprised of 672 breeding lines and cultivars adapted to the Pacific Northwest region of the United States, which were evaluated for 14 end‐use quality traits. Genetic correlations between environments based on factor analytic models showed low‐to‐moderate G × E interaction for most traits but high G × E interaction for grain and flour protein. A total of 40,518 single‐nucleotide polymorphism markers were used for genomic prediction. Genomic prediction accuracies were high for most traits thereby justifying the use of genomic selection to assist breeding for superior end‐use quality in soft white wheat. Excluding outlier environments based on genetic correlations between environments was more effective in increasing genomic prediction accuracies compared with that based on environment clustering analysis. For kernel size, kernel weight, milling score, ash, and flour swelling volume, excluding outlier environments increased prediction accuracies by 1–11%. However, for grain and flour protein, flour yield, and cookie diameter, excluding outlier environments did not improve genomic prediction performance.
format	article
author	Meriem Aoun Arron Carter Yvonne A. Thompson Brian Ward Craig F. Morris
author_facet	Meriem Aoun Arron Carter Yvonne A. Thompson Brian Ward Craig F. Morris
author_sort	Meriem Aoun
title	Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat
title_short	Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat
title_full	Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat
title_fullStr	Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat
title_full_unstemmed	Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat
title_sort	environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/9600ff77b7a645f196a3cfb3097eccc1
work_keys_str_mv	AT meriemaoun environmentcharacterizationandgenomicpredictionforendusequalitytraitsinsoftwhitewinterwheat AT arroncarter environmentcharacterizationandgenomicpredictionforendusequalitytraitsinsoftwhitewinterwheat AT yvonneathompson environmentcharacterizationandgenomicpredictionforendusequalitytraitsinsoftwhitewinterwheat AT brianward environmentcharacterizationandgenomicpredictionforendusequalitytraitsinsoftwhitewinterwheat AT craigfmorris environmentcharacterizationandgenomicpredictionforendusequalitytraitsinsoftwhitewinterwheat
_version_	1718372569904578560

Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat

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