Calibration and validation of predicted genomic breeding values in an advanced cycle maize population.

ABSTRACT:

Key message

Model training on data from all selection cycles yielded the highest prediction accuracy by attenuating specific effects of individual cycles. Expected reliability was a robust predictor of accuracies obtained with different calibration sets. The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers. We evaluated genomic prediction accuracies in two ind

SUBMITTER: Auinger HJ 

PROVIDER: S-EPMC8354938 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature