Technical Abstract: The use of marker-assisted selection (MAS) has stagnated for the improvement of quantitative traits. Experimental designs for the detection of loci affecting these traits impede their application, and the statistical methods used are ill-suited to the traits’ polygenic nature. Genomic selection (GS) has been proposed to address these deficiencies. Genomic selection encompasses methods to predict breeding values by analyzing phenotypes and high-density marker scores of lines in a breeding population. A key to the success of GS is that it incorporates all marker information in the prediction model, thereby avoiding bias associated with selecting model variables and capturing more of the variation due to small effect QTL. In simulations, the correlation between true breeding value and the genomic estimated breeding value from GS reaches levels of 0.85 even for highly polygenic low heritability traits. This level of prediction accuracy is sufficient to consider selecting for agronomic performance using marker information alone. Selection on markers alone would substantially accelerate the breeding cycle, enhancing gains per unit time. It would dramatically change the role of phenotyping, which would then serve to update prediction models and no longer to select lines. While research to date shows the promise of GS, work remains to be done to validate it empirically, to determine where it is best suited, and to incorporate it into breeding schemes.