Evaluation of genomic and phenomic prediction for application in apple breeding.

ABSTRACT:

Background

Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. The predictive ability of these models depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the training set, and the validation method used. Alternative genotyping methods such as RADseq and complementary data from near-infrared spectroscopy could help improve the cost-effectiveness of genomic prediction. However, the impact of these factors and alternative approaches on predictive ability beyond experimental populations still need to be investigated. In this study, we evaluated 137 prediction scenarios varying the described factors and alternative approaches, offering recommendations for im

SUBMITTER: Jung M 

PROVIDER: S-EPMC11759423 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature