Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population.

ABSTRACT: Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S₀ plants were all genotyped and advanced by selfing and bulk seed harvest to the S_0:2, S_0:3, and S_0:4 generations. The PCT27 was then divided into two sets. The S_0:2 and S_0:3 progenies for PCT27A and the S_0:4 progenies for PCT27B we

SUBMITTER: de Verdal H 

PROVIDER: S-EPMC10533757 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature