Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the 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 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.

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 8–30.8% 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

“…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).…”

“…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.…”

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

“…For example, multiobjective evolutionary algorithms have been applied to DNA sequence design ( Shin et al, 2005 ; Chaves-González, 2015 ); 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 and 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 ).…”

“…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 and Beavis, 2021 ; Yabe et al, 2016 ), and Akdemir et al, 2019 applied multiobjective selection algorithms like non-dominated selection to plant and animal breeding. 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.…”