Simulations of rate of genetic gain in dry bean breeding programs

Abstract

Dry beans (Phaseolus vulgaris L.) are a nutrient dense legume that is consumed by developed and developing nations around the world. The progress to improve this crop has been quite steady. However, with the continued rise in global populations, there are demands to expedite genetic gains. Plant breeders have been at the forefront at increasing yields in the common bean. As breeding programs are both time consuming and resource intensive, resource allocation must be carefully considered. To assist plant breeders, computer simulations can provide useful information that may then be applied to the real world. This study evaluated multiple breeding scenarios in the common bean and involved five breeding strategies, three breeding frameworks, and four different parental population sizes. In addition, the breeding scenarios were implemented in three different traits: days to flowering, white mold tolerance, and seed yield. Results from the study reflect the complexity of breeding programs, with the optimal breeding scenario varying based on trait being selected. Relative genetic gains per cycle of up to 8.69% for seed yield could be obtained under the use of the optimal breeding scenario. Principal component analyses revealed similarity between strategies, where single seed descent and the modified pedigree method would often aggregate. As well, clusters in the direction of the Hamming distance eigenvector are a good indicator of poor performance in a strategy.

Simulations of multiple breeding strategy scenarios in common bean for assessing genomic selection accuracy and model updating

“…If GS is going to be successfully implemented in a dry bean breeding program as a technique for increasing the rate of genetic gain, simulation studies will be an important first step. Lin et al (2022) carried out series of simulations to test five selection strategies (bulk breeding, single seed descent, mass selection, the pedigree method, and the modified pedigree method), three breeding frameworks (conventional, GS, and speed breeding), four parental population sizes (15, 30, 60 and 100), and three traits (seed yield [SY], days to flowering [DF], and white mold [WM] tolerance) in common bean. Expanding on the previous common bean simulation study, the objective of this study was to investigate the accuracy of GS in a simulation study in dry beans.…”

“…Expanding on the previous common bean simulation study, the objective of this study was to investigate the accuracy of GS in a simulation study in dry beans. As in Lin et al (2022), five breeding strategies were simulated with the selection on three traits. The following hypotheses were tested:…”

­­Simulations of genomic selection accuracy and model updating across multiple breeding strategy scenarios in common bean

“…A previously reported study Lin et al (2022) obtained GS accuracies from simulations among various selection strategies. However, the model reported in Lin et al (2022) was not updated during the simulation.…”

“…The construction of the various phenotypic selection simulation scenarios used for the experiments reported here can be found in further detail in Lin et al (2022). In this experiment, ve breeding strategies (mass selection, bulk breeding, single seed descent, pedigree method and modi ed pedigree method) with a parental population size of 30 were simulated using the QuLinePlus module (Hoyos-Villegas et al,…”

“…A previously reported study Lin et al (2022) obtained GS accuracies from simulations among various selection strategies. However, the model reported in Lin et al (2022) was not updated during the simulation. To simulate updating the GS model, a parental population was used as the reference population in the rst 3 cycles by using SimuPOP (Peng and Kimmel, 2005).…”