Improving the use of plant genetic resources to sustain breeding programs’ efficiency

Abstract

Genetic progress of crop plants is required to face human population growth and guarantee production stability in increasingly unstable environmental conditions. Breeding is accompanied by a loss in genetic diversity, which hinders sustainable genetic gain. Methodologies based on molecular marker information have been developed to manage diversity and proved effective in increasing long-term genetic gain. However, with realistic plant breeding population sizes, diversity depletion in closed programs appears ineluctable, calling for the introduction of relevant diversity donors. Although maintained with significant efforts, genetic resource collections remain underutilized, due to a large performance gap with elite germplasm. Bridging populations created by crossing genetic resources to elite lines prior to introduction into elite programs can manage this gap efficiently. To improve this strategy, we explored with simulations different genomic prediction and genetic diversity management options for a global program involving a bridging and an elite component. We analyzed the dynamics of quantitative trait loci fixation and followed the fate of allele donors after their introduction into the breeding program. Allocating 25% of total experimental resources to create a bridging component appears highly beneficial. We showed that potential diversity donors should be selected based on their phenotype rather than genomic predictions calibrated with the ongoing breeding program. We recommend incorporating improved donors into the elite program using a global calibration of the genomic prediction model and optimal cross selection maintaining a constant diversity. These approaches use efficiently genetic resources to sustain genetic gain and maintain neutral diversity, improving the flexibility to address future breeding objectives.

Evolutionary computing to assemble standing genetic diversity and achieve long-term genetic gain

“…This study’s simulation show a similar number of generations for truncation selection conditions to reach their plateau, but the OCS conditions in this study exhaust their diversity much faster than they do in Allier et al (2019). Allier et al (2019) produced doubled haploids from full-sib families from each suggested mating, and Sanchez et al (2023) produced 80 doubled haploids each generation from each of the 20 pairs in the mating plan, rather than producing only one progeny for each suggested pair in the mating plan, as was done in this study. This study’s results potentially do not show OCS at its best-performing.…”

“…Like the simulations in this paper, Labroo & Rutkoski (2022) produced a single progeny from each pair in the OCS crossing plan. Simulation of recurrent truncation selection and OCS on closed elite populations in Sanchez et al (2023), however, showed OCS maintaining rates of genetic gain on par with truncation selection, and continuing to improve after generation 20 even as the rates of gain of truncation selection fell off and reached a plateau. Even after 60 generations, the OCS populations had not exhausted their genetic diversity.…”

“…Potentially, the loss of diversity caused by selecting this way could be mitigated by introgressing genetically distant germplasm (Gorjanc et al 2016, Allier et al 2020, Sanchez et al 2023). However, truncation selection is not well suited to introgression scenarios.Diverse or landrace germplasm often lacks important agronomic traits compared to elite varieties that have been developed through generations of targeted selection.…”

“…A similar strategy is Allier et al (2019)’s usefulness criterion parental contribution (UCPC) method, which uses OCS-like parental contribution constraints but defines the genetic value objective using expected progeny mean and variation. Both GOCS, over 20 generations of recurrent selection (Tiret et al 2021), Genomic Mating, over 20 cycles of recurrent selection (Akdemir & Sánchez 2016), and UCPC, over 60 generations of recurrent selection (Allier et al 2019, Sanchez et al 2023), slightly outperformed OCS for genetic gain. Recurrent OCS itself can provide rates of gain in line with or outperforming truncation selection within 15-20 generations of simulation, after a slight lag for the first few generations (Akdemir & Sánchez 2016, Sanchez et al 2023).…”

Cross Potential Selection: A Proposal for Optimizing Crossing Combinations in Recurrent Selection Based on the Ability of Future Inbred Lines

“…As crossing pairs directly influence the genotypic values of the progeny, various crossing strategies have been devised to select crossing pairs from the current population. Genetic diversity plays a crucial role in driving genetic improvements in breeding programs (Sanchez et al, 2023(Sanchez et al, , 2024. Some crossing strategies consider factors, such as the expected genotypic values of the next generation and the degree of kinship among the selected individuals (Akdemir & Sánchez, 2016;Kinghorn, 2011).…”