Holsteins are the genomic selection poster cows.

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, Garcı́a-Ruiz et al. (3) characterize the impact of 7 y of implementation ofGS on a national breedingprogramand 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 Garcı́a-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. Garcı́a-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 theGS poster child as agriculture attempts to feed a growing population with increasing constraints on land and water availability and greenhouse gas emissions. The statistical methodologies underlying GP are now reasonably mature and build on Henderson’s mixed linear model equations for the best linear unbiased prediction (BLUP) of additive genetic merit using pedigree and phenotype data (4). When all animals are genotyped, BLUPs of additive genetic merits are obtained by replacing the pedigree relationship matrix with the genomic relationship matrix in Henderson’s mixed model equations, and the predicted additive genetic merits for genotyped animals that do not have phenotypes (generally young selection candidates) can be simply obtained by including them in themodel with null phenotypes (5). This formulation of the prediction problem (equation 13 in ref. 5) reveals that the reliability (precision of estimation) of GEBVs for animals without phenotype data depends on the extent of their relatedness to the animals with phenotypes and the number of animals with phenotypes. Therefore, the optimal design for a GS program requires (i) the ability to capture phenotypic data recurrently on genotyped animals to increase the reference population size and (ii) inclusion of parents and grandparents of selection candidates in the reference population to maximize the relatedness of reference individuals to selection candidates. As it turns out, GS was a technology waiting to be invented for the US dairy industry. More than 70%of all US dairy cows are bredbyAI, and because nearly all of the produced female calves have historically been retained as herd replacements, selection differentials and generation intervals for the sires of bulls and sires of cows pathways have contributed the most to selection response (3). Because milk yield is expressed only in females, bulls were historically progeny-tested to evaluate their ability to produce high-milk-yielding daughters. This process was very expensive, costing about $500,000 per selected AI bull; however, these bulls achieved reliabilities exceeding 80% for their estimated merits but were 7 y old when their semen was released (3). By 2009, a year after the release of the BovineSNP50 chip, 3,576 Holstein bulls with highly reliable pedigreeestimated additive genetic merits had been genotyped and GEBVs with reliabilities of 50% were estimated for the young bulls awaiting progeny test results. This accomplishment was equivalent to the reliability of a progeny-test estimate based on 11 daughters (6). By continuing to collect phenotypes on the daughters of all tested bulls and maintaining a single generation between relatives in the reference population and the selection candidates, the only limit to the GEBV reliabilities was the size of the reference population.