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ARS Home » Midwest Area » Ames, Iowa » Plant Introduction Research » Research » Publications at this Location » Publication #423813
Research Project: Germplasm Adaptation and Genetic Enhancement of Maize for Resiliency in the United States

Location: Plant Introduction Research

Title: Genomic selection: essence, applications, and prospects

Author
item ESCAMILLA, DIANA - Iowa State University
item LI, DONGDONG - Iowa State University
item NEGUS, KARLENE - Iowa State University
item KAPPELMANN, KIARA - Iowa State University
item KUSMEC, AARON - Iowa State University
item Vanous, Adam
item SCHNABLE, PATRICK - Iowa State University
item Li, Xianran
item YU, JIANMING - Iowa State University

Submitted to: The Plant Genome
Publication Type: Review Article
Publication Acceptance Date: 4/9/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary: Genomic selection (GS) is a strategy used in plant breeding to predict measurable traits of plants. GS exploits the relationship between plant's genetic makeups and a measurable phenotype to build a prediction model. With the prediction mode, the genotype information of untested plants is used to predict their potential performance for selection. GS increases the evaluation capacity of breeding programs, and reduces the time needed to develop new cultivars, leading to faster crop improvement. GS can be used for exploring genetic variation, selecting breeding parents, or selecting individuals at different stages of the breeding cycle. GS can incorporate other data types such as environmental variables, gene expression, and metabolic profile. Advances in artificial intelligence are expected to facilitate the integration of multiple data types in prediction models. GS is continuously evolving due to technological advances, research innovations, and emerging challenges in agriculture.

Technical Abstract: Genomic selection (GS) emerged as a key part of the solution to ensure 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 a wider 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 (AI), 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.