Genomic Selection Strategies for Wheat Improvement.

Plant breeding strategies are driven by new methods and technologies. Over time breeders have not abandoned old breeding methods but instead have adapted them to new technologies and integrated them into new strategies. In fact, Fischers 1918 theory of infinitesimal variation is still applicable to breeding populations today. Knowledge of the level of genetic diversity and historical relationships among cultivated wheat germplasm can be effectively exploited for the assessment of genetic variation, association breeding, marker-assisted recurrent selection (MARS) and genomic selection (GS) for wheat improvement. Advancements in genotyping technologies are rapidly reducing marker costs and increasing genome coverage allowing the widespread use of molecular markers and methods in plant breeding. Association breeding and MARS are are based on the selection of statistically significant, marker-trait associations. Association breeding facilitates the discovery of novel alleles whose relative allelic value can be assessed as often as necessary. However, MAS strategies are not well suited for agronomically important complex traits controlled by many genes. Genomic selection incorporates genome-wide marker information in a breeding value prediction model, thereby avoiding biased marker effect estimates and capturing more of the variation due to small effect QTL.  In GS, a training population representative of the breeding germplasm is genotyped with genome-wide markers and phenotyped in a target set of environments. That data is used to train a prediction model that is used to estimate the breeding values of lines in a population using only the marker scores.  Prediction models can incorporate performance over multiple environments, G x E effects, specific screening techniques, and novel traits.  Because of reduced generation time, annual genetic gain for GS is predicted to be two to three fold greater than for a conventional phenotypic selection program, even with only modest prediction accuracy of 0.50. We developed an analytical framework to compare gains from conventional breeding and GS for complex traits with equal budgets. Results indicate that GS can outperform conventional breeding on a per year basis even at low accuracies.  This new approach to crop improvement will facilitate a better understanding of the dynamic genome processes that generate and maintain new genetic variation?