Limits of artificial selection under unbalanced mating systems

Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean

Plant breeding is a decision-making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize the rate of genetic improvement and minimize the loss of useful genetic variance. For commercial plant breeders, competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast, public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing the loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short-term genetic gains from genomic selection are much greater than phenotypic selection, while phenotypic selection provides better long-term genetic gains because it retains useful genetic diversity during the early cycles of selection. With limited resources, must a soybean breeder choose between the two extreme responses provided by genomic selection or phenotypic selection? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs, and whether the breeding population should be organized as family islands. For breeding populations organized into islands, decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to 10 cycles using genomic selection and a hub network mating design, where the hub parents with the largest selection metric make large parental contributions. It also requires that the breeding populations be organized as fully connected family islands, where every island is connected to every other island, and migration rules allow the exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except that the mating design could be hub network, chain rule, or a multi-objective optimization method-based mating design. Weighted genomic selection applied to centralized populations also resulted in the realization of the greatest proportion of the genetic potential of the founders but required more cycles than the best compromise strategy.

Impact of mating design on selection response in Brassica rapa L

The impact of four mating designs on selection response for leaf area was assessed at four different population sizes, using fast-cycling Brassica rapa L. Mating designs were either balanced (partial diallel or pair mating) or unbalanced (factorial mating designs with either one or two testers). When balanced, the mating designs required different numbers of crossings for the same number of parents: the partial diallel design, in the configuration retained here, required three times as many crossings as pair mating. Population sizes were 4, 8, 16, and 32. The percentage of selected individuals was kept constant at 25%. Despite an average estimated heritability around 0.4, the overall response to selection after five generations was fairly weak in all three replicates. For a given population size, selection response was larger under balanced mating designs than under unbalanced ones. There was no difference among balanced mating designs. Both results indicate that effective population size is more important than population size or the number of crossings in maintaining genetic gain.