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Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat

Multi-trait and multi-environment analyses can improve genomic prediction by exploiting between-trait correlations and genotype-by-environment interactions. In the context of reaction norm models, genotype-by-environment interactions can be described as functions of high-dimensional sets of markers...

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Autores principales: Raffo, Miguel Angel, Sarup, Pernille, Andersen, Jeppe Reitan, Orabi, Jihad, Jahoor, Ahmed, Jensen, Just
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481302/
https://www.ncbi.nlm.nih.gov/pubmed/36119585
http://dx.doi.org/10.3389/fpls.2022.939448
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author Raffo, Miguel Angel
Sarup, Pernille
Andersen, Jeppe Reitan
Orabi, Jihad
Jahoor, Ahmed
Jensen, Just
author_facet Raffo, Miguel Angel
Sarup, Pernille
Andersen, Jeppe Reitan
Orabi, Jihad
Jahoor, Ahmed
Jensen, Just
author_sort Raffo, Miguel Angel
collection PubMed
description Multi-trait and multi-environment analyses can improve genomic prediction by exploiting between-trait correlations and genotype-by-environment interactions. In the context of reaction norm models, genotype-by-environment interactions can be described as functions of high-dimensional sets of markers and environmental covariates. However, comprehensive multi-trait reaction norm models accounting for marker × environmental covariates interactions are lacking. In this article, we propose to extend a reaction norm model incorporating genotype-by-environment interactions through (co)variance structures of markers and environmental covariates to a multi-trait reaction norm case. To do that, we propose a novel methodology for characterizing the environment at different growth stages based on growth degree-days (GDD). The proposed models were evaluated by variance components estimation and predictive performance for winter wheat grain yield and protein content in a set of 2,015 F6-lines. Cross-validation analyses were performed using leave-one-year-location-out (CV1) and leave-one-breeding-cycle-out (CV2) strategies. The modeling of genomic [SNPs] × environmental covariates interactions significantly improved predictive ability and reduced the variance inflation of predicted genetic values for grain yield and protein content in both cross-validation schemes. Trait-assisted genomic prediction was carried out for multi-trait models, and it significantly enhanced predictive ability and reduced variance inflation in all scenarios. The genotype by environment interaction modeling via genomic [SNPs] × environmental covariates interactions, combined with trait-assisted genomic prediction, boosted the benefits in predictive performance. The proposed multi-trait reaction norm methodology is a comprehensive approach that allows capitalizing on the benefits of multi-trait models accounting for between-trait correlations and reaction norm models exploiting high-dimensional genomic and environmental information.
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spelling pubmed-94813022022-09-17 Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat Raffo, Miguel Angel Sarup, Pernille Andersen, Jeppe Reitan Orabi, Jihad Jahoor, Ahmed Jensen, Just Front Plant Sci Plant Science Multi-trait and multi-environment analyses can improve genomic prediction by exploiting between-trait correlations and genotype-by-environment interactions. In the context of reaction norm models, genotype-by-environment interactions can be described as functions of high-dimensional sets of markers and environmental covariates. However, comprehensive multi-trait reaction norm models accounting for marker × environmental covariates interactions are lacking. In this article, we propose to extend a reaction norm model incorporating genotype-by-environment interactions through (co)variance structures of markers and environmental covariates to a multi-trait reaction norm case. To do that, we propose a novel methodology for characterizing the environment at different growth stages based on growth degree-days (GDD). The proposed models were evaluated by variance components estimation and predictive performance for winter wheat grain yield and protein content in a set of 2,015 F6-lines. Cross-validation analyses were performed using leave-one-year-location-out (CV1) and leave-one-breeding-cycle-out (CV2) strategies. The modeling of genomic [SNPs] × environmental covariates interactions significantly improved predictive ability and reduced the variance inflation of predicted genetic values for grain yield and protein content in both cross-validation schemes. Trait-assisted genomic prediction was carried out for multi-trait models, and it significantly enhanced predictive ability and reduced variance inflation in all scenarios. The genotype by environment interaction modeling via genomic [SNPs] × environmental covariates interactions, combined with trait-assisted genomic prediction, boosted the benefits in predictive performance. The proposed multi-trait reaction norm methodology is a comprehensive approach that allows capitalizing on the benefits of multi-trait models accounting for between-trait correlations and reaction norm models exploiting high-dimensional genomic and environmental information. Frontiers Media S.A. 2022-09-02 /pmc/articles/PMC9481302/ /pubmed/36119585 http://dx.doi.org/10.3389/fpls.2022.939448 Text en Copyright © 2022 Raffo, Sarup, Andersen, Orabi, Jahoor and Jensen. 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 Plant Science
Raffo, Miguel Angel
Sarup, Pernille
Andersen, Jeppe Reitan
Orabi, Jihad
Jahoor, Ahmed
Jensen, Just
Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat
title Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat
title_full Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat
title_fullStr Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat
title_full_unstemmed Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat
title_short Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat
title_sort integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481302/
https://www.ncbi.nlm.nih.gov/pubmed/36119585
http://dx.doi.org/10.3389/fpls.2022.939448
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