Strategies to assure optimal trade-offs among competing objectives for genetic improvement of soybean

Abstract

Plant breeding is a decision making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize rate of genetic improvement and minimize 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 loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short term genetic gains from Genomic Selection (GS) are much greater than Phenotypic Selection (PS), while PS provides better long term genetic gains because PS 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 GS or PS? 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 ten cycles using GS, a hub network mating design in breeding populations organized as fully connected family islands and migration rules allowing 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 a genomic mating design, instead of a hub networked mating design, is used. This strategy also resulted in realizing the greatest proportion of genetic potential of the founder populations. Weighted genomic selection applied to both non-isolated and island populations also resulted in realization of the greatest proportion of genetic potential of the founders, but required more cycles than the best compromise strategy.

Weighted Kernels Improve Multi-Environment Genomic Prediction

“…That will essentially reduce selection accuracy and genetic gain in the long term. Demonstrated in simulation studies, up-weighting such alleles would provide 8 to 30.8% greater long-term gain than that of un-weighted prediction methods (Jannink 2010; Liu et al 2015), further advocating the WK approaches proposed in this study for the long-term reliability of GS (Rutkoski et al 2015; Zhang et al 2018; Ramasubramanian and Beavis 2021).…”

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

“…For example, using an NK landscape model, O’Hagan et al evaluated the potential of elite selection, tournament selection, fitness sharing, and two rule-based learning selection schemes for selective breeding applications (O’Hagan et al, 2012). Inspired by genetic algorithms, island model approaches (Tanese, 1989) have been proposed for improving plant and animal breeding programs (Ramasubramanian & Beavis, 2021; Yabe et al, 2016), and Akdemir et al applied multi-objective selection algorithms like non-dominated selection to plant and animal breeding (Akdemir et al, 2019). In each of these applications, however, artificial selection acted as screens on individuals and not whole populations; therefore, our work focuses on screening at the population-level in order to test the applicability of evolutionary computing selection algorithms as general-purpose screening methods for directed microbial evolution.…”

“…For example, multi-objective evolutionary algorithms have been applied to DNA sequence design (Chaves-González, 2015; Shin et al, 2005); however, these applications are treated as computational optimization problems. A range of selection schemes from evolutionary computing have also been proposed for both biomolecule engineering (Currin et al, 2015; Handl et al, 2007) and agricultural selective breeding (especially for scenarios where genetic data can be exploited) (Ramasubramanian & Beavis, 2021). For example, using an NK landscape model, O’Hagan et al evaluated the potential of elite selection, tournament selection, fitness sharing, and two rule-based learning selection schemes for selective breeding applications (O’Hagan et al, 2012).…”