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Image-based phenomic prediction can provide valuable decision support in wheat breeding

KEY MESSAGE: Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. ABSTRACT: Traditionally, breeders’ selection decisions in early ge...

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Autores principales: Roth, Lukas, Fossati, Dario, Krähenbühl, Patrick, Walter, Achim, Hund, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299972/
https://www.ncbi.nlm.nih.gov/pubmed/37368140
http://dx.doi.org/10.1007/s00122-023-04395-x
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author Roth, Lukas
Fossati, Dario
Krähenbühl, Patrick
Walter, Achim
Hund, Andreas
author_facet Roth, Lukas
Fossati, Dario
Krähenbühl, Patrick
Walter, Achim
Hund, Andreas
author_sort Roth, Lukas
collection PubMed
description KEY MESSAGE: Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. ABSTRACT: Traditionally, breeders’ selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders’ ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text] E interactions of secondary traits (i.e., growth dynamics’ traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text] E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose–response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text] E interactions. In contrast, the modeling of G[Formula: see text] E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-023-04395-x.
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spelling pubmed-102999722023-06-29 Image-based phenomic prediction can provide valuable decision support in wheat breeding Roth, Lukas Fossati, Dario Krähenbühl, Patrick Walter, Achim Hund, Andreas Theor Appl Genet Original Article KEY MESSAGE: Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. ABSTRACT: Traditionally, breeders’ selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders’ ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text] E interactions of secondary traits (i.e., growth dynamics’ traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text] E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose–response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text] E interactions. In contrast, the modeling of G[Formula: see text] E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-023-04395-x. Springer Berlin Heidelberg 2023-06-27 2023 /pmc/articles/PMC10299972/ /pubmed/37368140 http://dx.doi.org/10.1007/s00122-023-04395-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Roth, Lukas
Fossati, Dario
Krähenbühl, Patrick
Walter, Achim
Hund, Andreas
Image-based phenomic prediction can provide valuable decision support in wheat breeding
title Image-based phenomic prediction can provide valuable decision support in wheat breeding
title_full Image-based phenomic prediction can provide valuable decision support in wheat breeding
title_fullStr Image-based phenomic prediction can provide valuable decision support in wheat breeding
title_full_unstemmed Image-based phenomic prediction can provide valuable decision support in wheat breeding
title_short Image-based phenomic prediction can provide valuable decision support in wheat breeding
title_sort image-based phenomic prediction can provide valuable decision support in wheat breeding
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299972/
https://www.ncbi.nlm.nih.gov/pubmed/37368140
http://dx.doi.org/10.1007/s00122-023-04395-x
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