Genomic selection: Essence, applications, and prospects
Diana M. Escamilla, Dongdong Li, Karlene L. Negus, Kiara L. Kappelmann, Aaron Kusmec, Adam Vanous, Patrick S. Schnable, Xianran Li, Jianming Yu
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.