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Research article - Peer-reviewed, 2022

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


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 x 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] x 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] x 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.


genomic prediction; multi-trait; multi-environment; reaction norm; genotype by environment interaction

Published in

Frontiers in Plant Science
2022, volume: 13, article number: 939448

Authors' information

Raffo, Miguel Angel
Aarhus University
Sarup, Pernille
Aarhus University
Andersen, Jeppe Reitan
Nordic Seed A/S
Orabi, Jihad
Nordic Seed A/S
Nordic Seed A/S
Swedish University of Agricultural Sciences, Department of Plant Breeding
Jensen, Just
Aarhus University

UKÄ Subject classification

Agricultural Science
Bioinformatics (Computational Biology)

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