TY  - JA
AU  - Fritsche-Neto,R.
AU  - Ali,J.
AU  - De Asis,E.J.
AU  - Allahgholipour,M.
AU  - Labroo,M.
TI  - Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance
SN  - 0040-5752
PY  - 2024///
CY  - Berlin (Germany)
PB  - Springer
KW  - AGROVOC
KW  - Stochastic models
KW  - Rice
KW  - Hybrids
KW  - Genetic improvement
KW  - Genetic gain
KW  - Breeding programmes
N1  - Peer review; Open Access
N2  - 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 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. 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
UR  - https://hdl.handle.net/10883/22884
DO  - https://doi.org/10.1007/s00122-023-04508-6
T2  - Theoretical and Applied Genetics
ER  -