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Genomic selection (GS) is the process by which the genetic improvement of plants or animals is accomplished using the genomic prediction (GP) of additive genetic merits [known as genomic estimated breeding values (GEBVs)] of selection candidates. Alternative statistical models for GP were first described in 2001 by Meuwissen et al. (1), who used simulation to evaluate the performance of linear mixed models and Bayesian mixture models for the prediction of marker effects and GEBVs. This work was truly visionary, because it was not until January 2008 that the first high-density genotyping chip for an agricultural species, the Illumina BovineSNP50 (2), became publically available, allowing the generation of datasets that would enable GP and facilitate the deployment of GS (Fig. 1). In PNAS, Garcia-Ruiz et al. (3) characterize the impact of 7 y of implementation of GS on a national breeding program and remarkably demonstrate that rates of annual genetic improvement in US Holstein dairy cows have increased from 50% to 100% for moderately heritable yield traits and from 300% to 400% for lowly heritable fitness traits. These increases in response to selection come with little evidence of any increase in rates of inbreeding that can lead to reductions in population fitness. Moreover, the rate of adoption of GS within the US dairy industry has been astounding. Although Garcia-Ruiz et al. (3) analyzed data from over 25 million US Holstein cows born since 1975 and 316,485 bulls born since 1950, almost 1.2 million of these animals have now been chip-genotyped (Fig. 1), representing an industry investment of at least $50 million. Garcia-Ruiz et al. (3) demonstrate that GS is the most important technology adopted by the US dairy industry since artificial insemination (AI) 75 y ago and that this industry has become the GS poster child as agriculture attempts to feed … [↵][1]1To whom correspondence should be addressed. Email: taylorjerr{at}missouri.edu. [1]: #xref-corresp-1-1

Holsteins are the genomic selection poster cows