Simulations of rate of genetic gain in dry bean breeding programs

Jennifer Lin, Vivi Arief, Zulfi Jahufer, Juan Osorno, Phil McClean, Diego Jarquin & Valerio Hoyos-Villegas

Theoretical and Applied Genetics January 2023; vol. 136: 1–22

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.

See https://link.springer.com/article/10.1007/s00122-023-04244-x

Figure 1: Selection strategies simulated in QuLinePlus. (a) Mass selection, (b) Bulk breeding, (c) Single seed descent, (d) Pedigree and (e) Modified pedigree.