A0837
Title: Future-oriented strategy via simulations optimizes breeding schemes with selection indices
Authors: Kosuke Hamazaki - Center for Advanced Intelligence Project (AIP), RIKEN (Japan) [presenting]
Hiroyoshi Iwata - The University of Tokyo (Japan)
Abstract: In recent years, genomic selection using prediction values based on genomic prediction models has been contributing to more efficient and rapid breeding. Although various models have been proposed to improve the selection accuracy, in breeding programs, it is known that the decision for selection and crossing based on the genomic prediction has a greater impact on the final genetic gain than the accuracy of the models themselves. This study proposes a framework to optimize decision-making in breeding programs by utilizing numerical optimization approaches. We focused on the optimal mating combination of parental candidates in each generation, including the allocation of progenies for crosses, and parameterized it based on a soft-max function that combines multiple selection indices. To improve genetic gain while maintaining the genetic diversity of the breeding population, predicted breeding values and genetic diversity of the progenies in a subsequent generation were used as indices. We then proceeded with simulation-based breeding, giving a parameter to weight these indices, and optimized the parameters using a numerical optimization algorithm called StoSOO. The results showed that the breeding conducted under the scheme optimized based on the proposed method showed a higher genetic gain in the final generation compared to the non-optimized breeding.
