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Abstract

The persistent challenge of genotype-by-environment (G × E) interactions has hindered the development of high-performing and stable cultivars in plant breeding. This chapter provides a critical review on the genomic selection (GS) strategies to address the G × E interactions complexity. It outlines the theoretical foundations of G × E interactions, early models of its interaction with genetic traits, and the subsequent emergence of quantitative trait loci (QTL)-based and reaction norm approaches. The integration of high-dimensional genomic data with environmental covariates has revolutionized prediction accuracy, especially through models such as genomic best linear unbiased prediction (GBLUP), Bayesian methods, reaction norms, and factor-analytic mixed models. Emphasis is placed on the synergy between genomic prediction and multi-environment trials (METs), as well as the role of sparse testing and enviromics in optimizing breeding pipelines. Additionally, this chapter presents case studies from major crops including cassava, maize, potato, rice, sorghum, and wheat, illustrating trait prediction improvement under variable environmental conditions. It also highlights future directions, emphasizing the need for high-quality phenotypic and environmental data, multi-omics integration, and ensemble-based modeling approaches to ensure robust and scalable breeding solutions in the face of global environmental variability.

Published in

Title: Genotype x Environment Interactions and its Implications for Plant Breeding
Publisher: Springer Singapore

SLU Authors

UKÄ Subject classification

Agricultural Science
Genetics and Breeding in Agricultural Sciences

Publication identifier

  • DOI: https://doi.org/10.1007/978-981-95-5664-9_12
  • ISBN: 978-981-95-5663-2
  • eISBN: 978-981-95-5664-9

Permanent link to this page (URI)

https://res.slu.se/id/publ/146183