The article "Genomic Selection in Plant Breeding: From Theory to Practice" by Jean-Luc Jannink, Aaron J. Lorenz, and Hiroyoshi Iwata reviews the theoretical, simulation, and empirical aspects of genomic selection (GS) in plant breeding. GS has emerged as a powerful tool to accelerate the development of crop varieties with improved yield, quality, and stress tolerance by leveraging the vast amount of DNA marker data. Traditional marker-assisted selection (MAS) has been ineffective for complex traits due to the large number of genes with small effects, but GS addresses this by using all marker data to predict performance more accurately. The authors discuss the methods used in GS, including ridge regression, Bayesian regression, and machine learning techniques. They highlight the importance of high-density genotyping and the need for large training populations to achieve accurate predictions. The article also explores the impact of different marker types and densities on prediction accuracy, as well as the advantages of using low-density panels in biparental populations. Empirical studies in livestock, particularly dairy cattle, are reviewed to demonstrate the effectiveness of GS. The accuracy of GS methods is generally higher than that of pedigree information alone, and it increases with larger training populations. The article also discusses the potential for GS to manage inbreeding and optimize long-term selection gains by using marker data to reduce relatedness among selected individuals. Finally, the authors emphasize the need for further research to guide the design of training populations, predict accuracy, and combine different methods to maximize prediction accuracy. They conclude that GS will significantly change plant breeding practices and efficiency, and that ongoing scientific research is crucial to fully realize its potential.The article "Genomic Selection in Plant Breeding: From Theory to Practice" by Jean-Luc Jannink, Aaron J. Lorenz, and Hiroyoshi Iwata reviews the theoretical, simulation, and empirical aspects of genomic selection (GS) in plant breeding. GS has emerged as a powerful tool to accelerate the development of crop varieties with improved yield, quality, and stress tolerance by leveraging the vast amount of DNA marker data. Traditional marker-assisted selection (MAS) has been ineffective for complex traits due to the large number of genes with small effects, but GS addresses this by using all marker data to predict performance more accurately. The authors discuss the methods used in GS, including ridge regression, Bayesian regression, and machine learning techniques. They highlight the importance of high-density genotyping and the need for large training populations to achieve accurate predictions. The article also explores the impact of different marker types and densities on prediction accuracy, as well as the advantages of using low-density panels in biparental populations. Empirical studies in livestock, particularly dairy cattle, are reviewed to demonstrate the effectiveness of GS. The accuracy of GS methods is generally higher than that of pedigree information alone, and it increases with larger training populations. The article also discusses the potential for GS to manage inbreeding and optimize long-term selection gains by using marker data to reduce relatedness among selected individuals. Finally, the authors emphasize the need for further research to guide the design of training populations, predict accuracy, and combine different methods to maximize prediction accuracy. They conclude that GS will significantly change plant breeding practices and efficiency, and that ongoing scientific research is crucial to fully realize its potential.