Integrative multi-environmental genomic prediction in apple
Jung, Michaela (ETH Zürich. Institute of Agricultural Sciences)
Quesada-Traver, Carles (ETH Zürich. Institute of Agricultural Sciences)
Roth, Morgane (Institut National de Recherche sur l'Agriculture, l'Alimentation et l'Environnement)
Aranzana, Maria José (Centre de Recerca en Agrigenòmica)
Muranty, Hélène (Institut National de Recherche sur l'Agriculture, l'Alimentation et l'Environnement)
Rymenants, Marijn (University of Leuven. Department of Biosystems)
Guerra, Walter (Institute for Fruit Growing and Viticulture)
Holzknecht, Elias (Institute for Fruit Growing and Viticulture)
Pradas, Nicole (Centre de Recerca en Agrigenòmica)
Lozano, Lidia (Institut de Recerca i Tecnologia Agroalimentàries)
Didelot, Frédérique (Institut National de Recherche sur l'Agriculture, l'Alimentation et l'Environnement)
Laurens, François (Institut National de Recherche sur l'Agriculture, l'Alimentation et l'Environnement)
Yates, Steven (ETH Zuric. Institute of Agricultural Sciences)
Studer, Bruno (ETH Zuric. Institute of Agricultural Sciences)
Broggini, Giovanni A. L. (ETH Zuric. Institute of Agricultural Sciences)
Patocchi, Andrea (Agroscope (Switzerland))

Fecha:	2025
Resumen:	Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, nonadditive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning. However, the application of multi-environmental genomic prediction in apple remained limited, likely due to the challenge of building multi-environmental datasets and structurally complex models. Here, we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic- and enviromic-based model components. Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0. 08 for nine traits compared to the benchmark model. This outcome, based on Gaussian and Deep kernels, shows these alternatives can effectively substitute the standard genomic best linear unbiased predictor (G-BLUP). Including nonadditive and enviromic-based effects resulted in a predictive ability very similar to the benchmark model. The deep learning approach achieved the highest predictive ability for three traits with oligogenic genetic architectures, outperforming the benchmark by up to 0. 10. Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well, and the deep learning models efficiently integrate data from diverse sources. This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions, providing an opportunity to address climate change impacts.
Ayudas:	European Commission 817970 European Commission 847585
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Publicado en:	Horticulture research, Vol. 12, Issue 2 (February 2025) , art. uhae319, ISSN 2052-7276

DOI: 10.1093/hr/uhae319
PMID: 40041603

15 p, 2.4 MB

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