Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

Abstract excerpt

KEY MESSAGE: Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction...

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