Background
A set of superior parental lines is the key to high-performing recombinant inbred lines (RILs) for biparental crossing in a rice breeding program. The number of possible crosses in such a breeding program is often far greater than the number that breeders can handle in the field. A practical parental selection method via genomic prediction (GP) is therefore developed to help breeders identify a set of superior parental lines from a candidate population before field trials.
Results
The parental selection via GP often involves truncation selection, selecting the top fraction of accessions based on their genomic estimated breeding values (GEBVs). However, the truncation selection inevitably causes a loss of genomic diversity in the breeding population. To preserve genomic variation, the selection of closely related accessions should be avoided. We first proposed a new index to quantify the genomic diversity for a set of candidate accessions. Then, we compared the performance of three classes of strategy for the parental selection, including those consider (a) GEBV only, (b) genomic diversity only, and (c) both GEBV and genomic diversity. We analyzed two rice (Oryza sativa L.) genome datasets for the comparison. The results show that the strategies considering both GEBV and genomic diversity have the best or second-best performance for all the traits analyzed in this study.
Conclusion
Combining GP with Monte Carlo simulation can be a useful means of parental selection for rice pre-breeding programs. Different strategies can be implemented to identify a set of superior parental lines from a candidate population. In consequence, the strategies considering both GEBV and genomic diversity that can balance the starting GEBV average with maintenance of genomic diversity should be recommended for practical use.