Simulations of rate of genetic gain in dry bean breeding programs

Abstract

Key message A reference study for breeders aiming at maximizing genetic gain in common bean. Depending on trait heritability and genetic architecture, conventional approaches may provide an advantage over other frameworks. 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 selection 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

“…1 in Lin et al. (2022). In mass selection, top candidates are identified from an entire population (as opposed to a single family) and, for ease of implementation, advanced to the next generation without maintaining family structure.…”

“…In a previous study, Lin et al. (2022) obtained GS accuracies from simulations among various selection strategies. However, the model reported in Lin et al.…”

“…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 first three cycles by using SimuPOP (Peng & Kimmel, 2005).…”

“…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.…”

“…As in Lin et al. (2022), five breeding strategies were simulated with the selection on three traits. The following hypotheses were tested: Selection strategy influences GS accuracy and genetic gain. GS model updating influences genetic gain and prediction accuracy to varying degrees depending on selection strategy, trait heritability, and number of initial parents in the breeding program. By accounting for population size, heritability, and number of independent loci, GS accuracy can be predicted prior to implementation. …”