Genomic selection: Essence, applications, and prospects
Abstract Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype–phenotype relationship in quantitative genetics. GS is a breedi...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.70053 |
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| author | Diana M. Escamilla Dongdong Li Karlene L. Negus Kiara L. Kappelmann Aaron Kusmec Adam E. Vanous Patrick S. Schnable Xianran Li Jianming Yu |
| author_facet | Diana M. Escamilla Dongdong Li Karlene L. Negus Kiara L. Kappelmann Aaron Kusmec Adam E. Vanous Patrick S. Schnable Xianran Li Jianming Yu |
| author_sort | Diana M. Escamilla |
| collection | DOAJ |
| description | Abstract Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype–phenotype relationship in quantitative genetics. GS is a breeding strategy to predict the genotypic values of individuals for selection using their genotypic data and a trained model. It includes four major steps: training population design, model building, prediction, and selection. GS revises the traditional breeding process by assigning phenotyping a new role of generating data for the building of prediction models. The increased capacity of GS to evaluate more individuals, in combination with shorter breeding cycle times, has led to wide adoption in plant breeding. Research studies have been conducted to implement GS with different emphases in crop‐ and trait‐specific applications, prediction models, design of training populations, and identifying factors influencing prediction accuracy. GS plays different roles in plant breeding such as turbocharging of gene banks, parental selection, and candidate selection at different stages of the breeding cycle. It can be enhanced by additional data types such as phenomics, transcriptomics, metabolomics, and enviromics. In light of the rapid development of artificial intelligence, GS can be further improved by either upgrading the entire framework or individual components. Technological advances, research innovations, and emerging challenges in agriculture will continue to shape the role of GS in plant breeding. |
| format | Article |
| id | doaj-art-cc72f91312cc49bf9ecb02f5d9d55a3a |
| institution | Kabale University |
| issn | 1940-3372 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj-art-cc72f91312cc49bf9ecb02f5d9d55a3a2025-08-20T03:30:36ZengWileyThe Plant Genome1940-33722025-06-01182n/an/a10.1002/tpg2.70053Genomic selection: Essence, applications, and prospectsDiana M. Escamilla0Dongdong Li1Karlene L. Negus2Kiara L. Kappelmann3Aaron Kusmec4Adam E. Vanous5Patrick S. Schnable6Xianran Li7Jianming Yu8Department of Agronomy Iowa State University Ames Iowa USADepartment of Agronomy Iowa State University Ames Iowa USADepartment of Agronomy Iowa State University Ames Iowa USADepartment of Agronomy Iowa State University Ames Iowa USADepartment of Agronomy Kansas State University Manhattan Kansas USAUSDA‐ARS North Central Regional Plant Introduction Station Ames Iowa USADepartment of Agronomy Iowa State University Ames Iowa USAUSDA‐ARS, Wheat Health Genetics, and Quality Research Unit Pullman Washington USADepartment of Agronomy Iowa State University Ames Iowa USAAbstract Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype–phenotype relationship in quantitative genetics. GS is a breeding strategy to predict the genotypic values of individuals for selection using their genotypic data and a trained model. It includes four major steps: training population design, model building, prediction, and selection. GS revises the traditional breeding process by assigning phenotyping a new role of generating data for the building of prediction models. The increased capacity of GS to evaluate more individuals, in combination with shorter breeding cycle times, has led to wide adoption in plant breeding. Research studies have been conducted to implement GS with different emphases in crop‐ and trait‐specific applications, prediction models, design of training populations, and identifying factors influencing prediction accuracy. GS plays different roles in plant breeding such as turbocharging of gene banks, parental selection, and candidate selection at different stages of the breeding cycle. It can be enhanced by additional data types such as phenomics, transcriptomics, metabolomics, and enviromics. In light of the rapid development of artificial intelligence, GS can be further improved by either upgrading the entire framework or individual components. Technological advances, research innovations, and emerging challenges in agriculture will continue to shape the role of GS in plant breeding.https://doi.org/10.1002/tpg2.70053 |
| spellingShingle | Diana M. Escamilla Dongdong Li Karlene L. Negus Kiara L. Kappelmann Aaron Kusmec Adam E. Vanous Patrick S. Schnable Xianran Li Jianming Yu Genomic selection: Essence, applications, and prospects The Plant Genome |
| title | Genomic selection: Essence, applications, and prospects |
| title_full | Genomic selection: Essence, applications, and prospects |
| title_fullStr | Genomic selection: Essence, applications, and prospects |
| title_full_unstemmed | Genomic selection: Essence, applications, and prospects |
| title_short | Genomic selection: Essence, applications, and prospects |
| title_sort | genomic selection essence applications and prospects |
| url | https://doi.org/10.1002/tpg2.70053 |
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