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

ABSTRACT:

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 scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant

SUBMITTER: Auinger HJ 

PROVIDER: S-EPMC5069347 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature