Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets
To enhance the implementation of genomic selection (GS) in plant breeding, we conducted a comprehensive comparative analysis of deep learning (DL) models and genomic best linear unbiased predictor (GBLUP) methods across 14 real-world datasets derived from diverse plant breeding programs. We evaluate...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1568705/full |
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| author | Abelardo Montesinos-López Osval A. Montesinos-López Sofia Ramos-Pulido Brandon Alejandro Mosqueda-González Edgar Alejandro Guerrero-Arroyo José Crossa José Crossa Rodomiro Ortiz |
| author_facet | Abelardo Montesinos-López Osval A. Montesinos-López Sofia Ramos-Pulido Brandon Alejandro Mosqueda-González Edgar Alejandro Guerrero-Arroyo José Crossa José Crossa Rodomiro Ortiz |
| author_sort | Abelardo Montesinos-López |
| collection | DOAJ |
| description | To enhance the implementation of genomic selection (GS) in plant breeding, we conducted a comprehensive comparative analysis of deep learning (DL) models and genomic best linear unbiased predictor (GBLUP) methods across 14 real-world datasets derived from diverse plant breeding programs. We evaluated model performance by meticulously tuning hyperparameters specific to each dataset, aiming to maximize predictive accuracy and reliability. Our results demonstrated that DL models effectively captured complex, non-linear genetic patterns, frequently providing superior predictive performance compared to GBLUP, especially in smaller datasets. However, neither method consistently outperformed the other across all evaluated traits and scenarios. The analysis revealed that the success of DL models significantly depended on careful parameter optimization, reinforcing the importance of rigorous model tuning procedures. In the discussion, we emphasize the complementary nature of DL and GBLUP methods, highlighting that the choice between these models should be driven by the specific characteristics of the traits under study and the evaluation metrics prioritized in breeding programs. These insights contribute practical guidelines for selecting and optimizing genomic prediction models to achieve robust outcomes in plant breeding contexts. |
| format | Article |
| id | doaj-art-1ce264fbc9954dddbba6aa31689fd4e1 |
| institution | OA Journals |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-1ce264fbc9954dddbba6aa31689fd4e12025-08-20T02:19:37ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-04-011610.3389/fgene.2025.15687051568705Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasetsAbelardo Montesinos-López0Osval A. Montesinos-López1Sofia Ramos-Pulido2Brandon Alejandro Mosqueda-González3Edgar Alejandro Guerrero-Arroyo4José Crossa5José Crossa6Rodomiro Ortiz7Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, MexicoFacultad de Telemática, Universidad de Colima, Colima, MexicoDepartamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, MexicoInstitut National des Sciences Appliquées de Lyon: Lyon, Villeurbanne, FranceDepartamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, MexicoInternational Maize and Wheat Improvement Center (CIMMYT), Texcoco, MexicoColegio de Postgraduate Program in Socioeconomics, Statistics, and Informatics (PSEI) at the Montecillo Campus, Texcoco, MexicoDepartment of Plant Breeding at SLU, Swedish University of Agricultural Sciences, Uppsala, SwedenTo enhance the implementation of genomic selection (GS) in plant breeding, we conducted a comprehensive comparative analysis of deep learning (DL) models and genomic best linear unbiased predictor (GBLUP) methods across 14 real-world datasets derived from diverse plant breeding programs. We evaluated model performance by meticulously tuning hyperparameters specific to each dataset, aiming to maximize predictive accuracy and reliability. Our results demonstrated that DL models effectively captured complex, non-linear genetic patterns, frequently providing superior predictive performance compared to GBLUP, especially in smaller datasets. However, neither method consistently outperformed the other across all evaluated traits and scenarios. The analysis revealed that the success of DL models significantly depended on careful parameter optimization, reinforcing the importance of rigorous model tuning procedures. In the discussion, we emphasize the complementary nature of DL and GBLUP methods, highlighting that the choice between these models should be driven by the specific characteristics of the traits under study and the evaluation metrics prioritized in breeding programs. These insights contribute practical guidelines for selecting and optimizing genomic prediction models to achieve robust outcomes in plant breeding contexts.https://www.frontiersin.org/articles/10.3389/fgene.2025.1568705/fullbenchmarkingdeep learningGBLUPgenomic selectionplant breeding |
| spellingShingle | Abelardo Montesinos-López Osval A. Montesinos-López Sofia Ramos-Pulido Brandon Alejandro Mosqueda-González Edgar Alejandro Guerrero-Arroyo José Crossa José Crossa Rodomiro Ortiz Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets Frontiers in Genetics benchmarking deep learning GBLUP genomic selection plant breeding |
| title | Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets |
| title_full | Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets |
| title_fullStr | Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets |
| title_full_unstemmed | Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets |
| title_short | Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets |
| title_sort | artificial intelligence meets genomic selection comparing deep learning and gblup across diverse plant datasets |
| topic | benchmarking deep learning GBLUP genomic selection plant breeding |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1568705/full |
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