Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets

Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program

The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

Use of simulation to optimize a sweet corn breeding program: implementing genomic selection and doubled haploid technology

Genomic selection and doubled haploids hold significant potential to enhance genetic gains and shorten breeding cycles across various crops. Here, we utilized stochastic simulations to investigate the best strategies for optimize a sweet corn breeding program. We assessed the effects of incorporating varying proportions of old and new parents into the crossing block (3:1, 1:1, 1:3, and 0:1 ratio, representing different degrees of parental substitution), as well as the implementation of genomic selection in two distinct pipelines: one calibrated using the phenotypes of testcross parents (GSTC scenario) and another using F1 individuals (GSF1). Additionally, we examined scenarios with doubled haploids, both with (DH) and without (DHGS) genomic selection. Across 20 years of simulated breeding, we evaluated scenarios considering traits with varying heritabilities, the presence or absence of genotype-by-environment effects, and two program sizes (50 vs 200 crosses per generation). We also assessed parameters such as parental genetic mean, average genetic variance, hybrid mean, and implementation costs for each scenario. Results indicated that within a conventional selection program, a 1:3 parental substitution ratio (replacing 75% of parents each generation with new lines) yielded the highest performance. Furthermore, the GSTC model outperformed the GSF1 model in enhancing genetic gain. The DHGS model emerged as the most effective, reducing cycle time from 5 to 4 years and enhancing hybrid gains despite increased costs. In conclusion, our findings strongly advocate for the integration of genomic selection and doubled haploids into sweet corn breeding programs, offering accelerated genetic gains and efficiency improvements.

Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance

KEY MESSAGE

Schemes that use genomic prediction outperform others, updating testers increases hybrid genetic gain, and larger population sizes tend to have higher genetic gain and less depletion of genetic variance One of the most common methods to improve hybrid performance is reciprocal recurrent selection (RRS). Genomic prediction (GP) can be used to increase genetic gain in RRS by reducing cycle length, but it is also possible to use GP to predict single-cross hybrid performance. The impact of the latter method on genetic gain has yet to be previously reported. Therefore, we compared via stochastic simulations various phenotypic and genomics-assisted RRS breeding schemes which used GP to predict hybrid performance rather than reducing cycle length, which allows minimal changes to traditional breeding schemes. We also compared three breeding sizes scenarios that varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of hybrids evaluated, and the number of genomic predicted hybrids. Our results demonstrated that schemes that used genomic prediction of hybrid performance outperformed the others for the average interpopulation hybrid population and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. As expected, the largest breeding size tested had the highest rates of genetic improvement and the lowest decrease in additive genetic variance due to the drift. Therefore, this study demonstrates the usefulness of single-cross prediction, which may be easier to implement than rapid-cycling RRS and cyclical updating of testers. We also reiterate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance.