Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
Open accessAbstract
Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.
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WGP models including envirotyping analysis can be used to mimic the linear response of the phenotypic performance of genotypes over a certain type of environmental gradient (envirotype), i.e., the reaction norm, in which the GE effects are studied as an extension of the GBLUP.
“WGP models including the so-called envirotyping (Xu 2016) analysis can be used to mimic the linear response of the phenotypic performance of genotypes over a certain type of environmental gradient (envirotype), i.e., the reaction norm (Jarquín et al. 2014; Crossa et al. 2017), in which the GE effects are studied as an extension of the GBLUP.”
From Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2021) · cites this paper as Crossa et al. 2017
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Experimental — accuracy not yet measuredPrediction-based strategies employing genomic-assisted data are responsible for the greatest leaps in genetic gain and reduction of time between selection cycles in both animal- and plant-breeding programs.
“Prediction-based strategies employing genomic-assisted data (Meuwissen et al. 2001) are responsible for the greatest leaps in genetic gain and reduction of time between selection cycles in both animal- and plant-breeding programs (Crossa et al. 2017; Voss-Fels et al. 2019).”
From Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2021) · cites this paper as Crossa et al. 2017
The Trends section reports that genomic selection can halve the breeding-cycle interval in CIMMYT maize, and the Concluding Remarks say it can enhance genetic gains in plant breeding. The paper does not establish that these strategies produced the greatest gains across breeding methods, or substantiate the claim for animal-breeding programs. (LLM, conf=0.86)
Experimental — accuracy not yet measuredOther methods consider the complete genetic values of individuals, including both additive and nonadditive (dominance and epistasis) effects, thereby estimating the genetic performance of the lines or hybrids.
“Other methods consider the complete genetic values of individuals, including both additive and nonadditive (dominance and epistasis) effects, thereby estimating the genetic performance of the lines or hybrids (Crossa et al. 2017).”
From Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2021) · cites this paper as Crossa et al. 2017
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Montesinos‐López, A., Montesinos-López, O. A., Crossa, J. L., Burgueño, J. A., Eskridge, K. M., Falconí-Castillo, E., He, X., Singh, P. K., & Cichy, K. A. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.028118 · Cited by 32 · see citing works
Self-citationGenomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression
Montesinos-López, O. A., Montesinos‐López, A., Crossa, J. L., Burgueño, J. A., & Eskridge, K. M. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.115.021154 · Cited by 30 · see citing works
Self-citationBernardo, R., Cunha, D. V., Wang, C., Pereira, L., Silva, S. M., Salazar, S. B., Schröder, M., Okamoto, M., Takahashi‐Nakaguchi, A., Chibana, H., et al. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.034660 · Cited by 30 · see citing works
Montesinos-López, O. A., Montesinos‐López, A., Crossa, J., et al. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.039974 · Cited by 21 · see citing works
Self-citationGenomic Prediction Models for Count Data
Montesinos-López, O. A., Montesinos‐López, A., Pérez‐Rodríguez, P., Eskridge, K. M., He, X., Juliana, P., Singh, P. K., & Crossa, J. L. (2015) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-015-0223-4 · Cited by 20 · see citing works
Self-citationBayesian Genomic-Enabled Prediction as an Inverse Problem
Cuevas, J., Pérez-Elizalde, S., Soberanis, V., Pérez-Rodríguez, P., Gianola, D., & Crossa, J. (2014) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.114.013094 · Cited by 10 · see citing works
Self-citation
Flagged first, then by citations
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Harnessing genomic information for livestock improvement
Georges, M., Charlier, C., & Hayes, B. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0082-2 · Cited by 384 · see citing works
DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants
Wang, K., Abid, M. A., Rasheed, A., Crossa, J., Hearne, S., & Li, H. (2023) · Molecular Plant · doi:10.1016/j.molp.2022.11.004 · Cited by 278 · see citing works
Self-citationCosta-Neto, G., Fritsche-Neto, R., & Crossa, J. (2021) · Heredity · doi:10.1038/s41437-020-00353-1 · Cited by 153 · see citing works
Self-citationMachine learning: a powerful tool for gene function prediction in plants
Mahood, E. H., Kruse, L. H., & Moghe, G. D. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11376 · Cited by 153 · see citing works
Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials
Cuevas, J., Montesinos-López, O., Juliana, P., Guzmán, C., Pérez-Rodríguez, P., González-Bucio, J., Burgueño, J., Montesinos-López, A., & Crossa, J. (2019) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.119.400493 · Cited by 97 · see citing works
Self-citationJarquin, D., de Leon, N., Romay, C., Bohn, M., Buckler, E. S., Ciampitti, I., Edwards, J., Ertl, D., Flint-Garcia, S., Gore, M. A., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.592769 · Cited by 86 · see citing works
Crossa, J., Martini, J. W. R., Gianola, D., Pérez-Rodríguez, P., Jarquin, D., Juliana, P., Montesinos-López, O., & Cuevas, J. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01168 · Cited by 79 · see citing works
Self-citationAdvanced domestication: harnessing the precision of gene editing in crop breeding
Lyzenga, W. J., Pozniak, C. J., & Kagale, S. (2021) · Plant Biotechnology Journal · doi:10.1111/pbi.13576 · Cited by 85 · see citing works
Hsu, Y.-C., Chiu, C.-H., Yap, R., Tseng, Y.-C., & Wu, Y.-P. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21041281 · Cited by 69 · see citing works
Washburn, J. D., Burch, M. B., & Franco, J. A. V. (2020) · Crop Science · doi:10.1002/csc2.20052 · Cited by 65 · see citing works
Predicting phenotypes from genetic, environment, management, and historical data using CNNs
Washburn, J. D., Cimen, E., Ramstein, G., Reeves, T., O’Briant, P., McLean, G., Cooper, M., Hammer, G., & Buckler, E. S. (2021) · Theoretical and Applied Genetics · doi:10.1007/s00122-021-03943-7 · Cited by 57 · see citing works
Cooper, M., Messina, C. D., Tang, T., Gho, C., Powell, O. M., Podlich, D. W., Technow, F., & Hammer, G. L. (2022) · Plant Breeding Reviews · doi:10.1002/9781119874157.ch8 · Cited by 56 · see citing works
Togninalli, M., Wang, X., Kucera, T., Shrestha, S., Juliana, P., Mondal, S., Pinto, F., Govindan, V., Crespo-Herrera, L., Huerta-Espino, J., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad336 · Cited by 49 · see citing works
Self-citationGenome-based prediction of Bayesian linear and non-linear regression models for ordinal data
Pérez‐Rodríguez, P., Flores‐Galarza, S., Vaquera‐Huerta, H., del Valle‐Paniagua, D. H., Montesinos‐López, O. A., & Crossa, J. (2020) · The Plant Genome · doi:10.1002/tpg2.20021 · Cited by 24 · see citing works
Self-citationMelsen, K. A. C., Gezan, S., Tolhurst, D. J., van Eeuwijk, F. A., & Peeters, C. F. W. (2025) · arXiv · doi:10.48550/arxiv.2501.06844 · Cited by 1 · see citing works
“Genomic selection, i.e., selection based on GEBVs rather than phenotypes, typically allows for higher selection intensities due to larger populations, higher selection accuracies, and possibly shorter generation intervals (Crossa et al. , 2017).”
- Authors
- Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de los Campos, G., Burgueño, J., González-Camacho, J. M., Pérez-Elizalde, S., Beyene, Y., Dreisigacker, S., Singh, R., Zhang, X., Gowda, M., Roorkiwal, M., Rutkoski, J., & Varshney, R. K.
- Affiliations
- Centro Internacional de Mejoramiento de Maíz Y Trigo (MX)
- University of Quintana Roo (MX)
- Universidad de Colima (MX)
- University of Nebraska–Lincoln (US)
- International Crops Research Institute for the Semi-Arid Tropics (IN)
- International Rice Research Institute (PH)
- Corresponding author
- Crossa, José
- Varshney, Rajeev K.
- ORCID
- Venue
- Trends in Plant Science
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 22
- Issue
- 11
- Pages
- 961-975
- ISSN
- 1360-1385 (ISSN-L)
- OpenAlex
- W2760544538
- Cited by
- 1,839
- References
- 85
- Citations by year
- 2026: 373 · 2025: 271 · 2024: 243 · 2023: 190 · 2022: 204 · 2021: 195 · 2020: 184 · 2019: 115 · 2018: 63 · 2017: 1
- FWCI
- 12.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Trends in Plant Science (publishedVersion)
- PubMed (publishedVersion)
- Open Access Repository of ICRISAT (International Crops Research Institute for the Semi-Arid Tropics) (acceptedVersion)
- Murdoch University Research Portal (submittedVersion)
- CGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
- Keywords
- genomic selection, gene-environment interaction, prediction accuracy, maize breeding, cereals, legume crops, pre-breeding, germplasm improvement, hyperspectral imaging, plant breeding
- MeSH terms
- Machine Learning, Plant Breeding, Zea mays, Models, Genetic, Selection, Genetic, Crops, Agricultural, Genome, Plant, Polymorphism, Single Nucleotide, Quantitative Trait Loci, High-Throughput Nucleotide Sequencing, Gene-Environment Interaction
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