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      Characterizing stay-green in barley across diverse environments: unveiling novel haplotypes

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          Abstract

          Key message

          There is variation in stay-green within barley breeding germplasm, influenced by multiple haplotypes and environmental conditions. The positive genetic correlation between stay-green and yield across multiple environments highlights the potential as a future breeding target.

          Abstract

          Barley is considered one of the most naturally resilient crops making it an excellent candidate to dissect the genetics of drought adaptive component traits. Stay-green, is thought to contribute to drought adaptation, in which the photosynthetic machinery is maintained for a longer period post-anthesis increasing the photosynthetic duration of the plant. In other cereal crops, including wheat, stay-green has been linked to increased yield under water-limited conditions. Utilizing a panel of diverse barley breeding lines from a commercial breeding program we aimed to characterize stay-green in four environments across two years. Spatiotemporal modeling was used to accurately model senescence patterns from flowering to maturity characterizing the variation for stay-green in barley for the first time. Environmental effects were identified, and multi-environment trait analysis was performed for stay-green characteristics during grain filling. A consistently positive genetic correlation was found between yield and stay-green. Twenty-two chromosomal regions with large effect haplotypes were identified across and within environment types, with ten being identified in multiple environments. In silico stacking of multiple desirable haplotypes showed an opportunity to improve the stay-green phenotype through targeted breeding. This study is the first of its kind to model barley stay-green in a large breeding panel and has detected novel, stable and environment specific haplotypes. This provides a platform for breeders to develop Australian barley with custom senescence profiles for improved drought adaptation.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00122-024-04612-1.

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          Most cited references39

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          A new look at the statistical model identification

          IEEE Transactions on Automatic Control, 19(6), 716-723
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            A decimal code for the growth stages of cereals

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              Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

              Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
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                Author and article information

                Contributors
                l.hickey@uq.edu.au
                h.robinson1@uq.edu.au
                Journal
                Theor Appl Genet
                Theor Appl Genet
                TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0040-5752
                1432-2242
                6 May 2024
                6 May 2024
                2024
                : 137
                : 6
                : 120
                Affiliations
                [1 ]Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, ( https://ror.org/00rqy9422) St Lucia, QLD Australia
                [2 ]InterGrain Pty Ltd, Perth, WA 6163 Australia
                [3 ]School of Agriculture and Food Sustainability, The University of Queensland, ( https://ror.org/00rqy9422) Gatton, QLD Australia
                Author notes

                Communicated by Jiankang Wang.

                Author information
                http://orcid.org/0000-0001-6909-7101
                Article
                4612
                10.1007/s00122-024-04612-1
                11074220
                38709310
                39d3527f-a3a3-416a-980e-606b20424028
                © The Author(s) 2024

                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/.

                History
                : 14 December 2023
                : 23 March 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000923, Australian Research Council;
                Award ID: LP200200927
                Award Recipient :
                Funded by: The University of Queensland
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2024

                Genetics
                Genetics

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