Optimizing simultaneous selection in long-term breeding: a stochastic simulation study for a tropical corn haploid inducer population

Abstract

Abstract Plant breeders widely use recurrent selection schemes to increase the frequency of favorable alleles for quantitative traits in a population. Although simultaneous selection is complex because it involves several traits combined with selection cycles, the use of selection indexes (SI) is applied to increase the chance of success of the breeding program. Moreover, many indices are available in the literature; therefore, simulations can help breeders determine which selection index can be adjusted better considering the selection goals, intensity, and genetic correlation among traits over breeding cycles. In this context, we aimed to optimize the simultaneous selection in long-term breeding programs via stochastic simulations using as an example a tropical maize inducer breeding. Furthermore, we proposed a new approach to optimize the initial weights for the Smith-Hazel method to maximize the genetic gains for all traits in a balanced way. Finally, our results confirm that the traditional Smith and Hazel approach outperformed other methods for the total and balanced response to selection for important traits in a tropical corn haploid inducer breeding population.