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

Roberto Fritsche-Neto, Jauhar Ali, Erik Jon De Asis, Mehrzad Allahgholipour, Marlee Rose Labroo
2023 · doi:10.21203/rs.3.rs-2860585/v1
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Abstract

Abstract 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 and recover higher-performing hybrids. The impact of the latter method on genetic gain has not been previously reported. Therefore, our study compared 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 phenotypic schemes. We used stochastic simulation to compare compared five RRS breeding schemes in terms of genetic gain and best hybrid performance: Traditional (TRAD_RRS), drift (DRIFT_RRS), Traditional but updating testers every cycle (TRAD_RRS_ UP), Genomic Additive (GS_A_RRS), and Genomic Additive+Dominace (GS_AD_RRS). We also compared three breeding sizes which varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of the number of phenotyped hybrids, and the number of genomic predicted hybrids. Schemes which used genomic prediction of hybrid performance outperformed the others for both the average interpopulation hybrid population performance and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. Overall, the largest breeding size tested had the highest rates of genetic gain and in the lowest decrease in additive genetic variance due to drift, although cost was not considered. This study demonstrates the usefulness of single-cross prediction, which initially may be easier to implement than rapid-cycling RRS, and cyclical updating of testers. We also demonstrate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance, disregarding cost.

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