4eb57feb9943f4efc4dd23ec8b20e0e7 horres_a2025m2v12n2puhae319.pdf 0d7de470770fdbf91e92c29070ef16dfa36d2dc3 horres_a2025m2v12n2puhae319.pdf 478796469687e5f279e08c1b2c9df143ffe0c8efe7c117af63724fad67863f12 horres_a2025m2v12n2puhae319.pdf Title: Integrative multi-environmental genomic prediction in apple Subject: DOI: 10.1093/hr/uhae319, Horticulture Research, 12, 00, 1 1 2025. Abstract: 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. 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Abstract: 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. 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ToUnicode: true Font: BaseFont: PLNAOM+CaeciliaLTStd-Roman FontSubset: true FirstChar: 40 LastChar: 169 FontDescriptor: FontName: PLNAOM+CaeciliaLTStd-Roman Flags: Serif, Symbolic FontBBox: -167, -270, 1136, 926 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: EPHIHG+CaeciliaLTStd-Italic FontSubset: true FirstChar: 40 LastChar: 121 FontDescriptor: FontName: EPHIHG+CaeciliaLTStd-Italic Flags: Serif, Symbolic, Italic FontBBox: -166, -270, 1053, 915 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true TrueType: Font: BaseFont: QYWMWU+ArialMT FontSubset: true FirstChar: 32 LastChar: 121 FontDescriptor: FontName: QYWMWU+ArialMT Flags: Nonsymbolic FontBBox: -664, -324, 2000, 1039 FontFile2: true Encoding: WinAnsiEncoding XMP: application/pdf Integrative multi-environmental genomic prediction in apple Quesada-Traver Carles Roth Morgane Aranzana Maria José Muranty Hélène Rymenants Marijn Guerra Walter Holzknecht Elias Pradas Nicole Lozano Lidia Didelot Frédérique Laurens François Yates Steven Studer Bruno Broggini Giovanni A.L. Patocchi Andrea DOI: 10.1093/hr/uhae319, Horticulture Research, 12, 00, 1 1 2025. Abstract: 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. © The Author(s) 2025. Published by Oxford University Press on behalf of Nanjing Agricultural University. 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