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REVIEW & INTERPRETATION

Genomic Selection for Crop Improvement

^a Dep. of Plant Breeding and Genetics, Cornell Univ., Bradfield Hall, Ithaca, NY 14853
^b USDA-ARS, R.W. Holley Center for Agriculture and Health, Cornell Univ., Ithaca, NY 14853

^* Corresponding author (jeanluc.jannink{at}ars.usda.gov).

Despite important strides in marker technologies, the use of marker-assisted selection has stagnated for the improvement of quantitative traits. Biparental mating designs for the detection of loci affecting these traits (quantitative trait loci [QTL]) 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 predicts the breeding values of lines in a population by analyzing their phenotypes and high-density marker scores. A key to the success of GS is that it incorporates all marker information in the prediction model, thereby avoiding biased marker effect estimates 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 has reached levels of 0.85 even for polygenic low heritability traits. This level of accuracy is sufficient to consider selecting for agronomic performance using marker information alone. Such selection 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 exceptional promise of GS, work remains to be done to validate it empirically and to incorporate it into breeding schemes.

Abbreviations: BLUP, best linear unbiased predictor • EBV, estimated breeding value • GxE, genotype x environment GEBV, genomic estimated breeding value • GS, genomic selection • LD, linkage disequilibrium • MAS, marker-assisted selection • QTL, quantitative trait locus • RR, ridge regression • SR, stepwise regression • TBV, true breeding value

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