Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices.

ABSTRACT: Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (

SUBMITTER: Lopez-Cruz M 

PROVIDER: S-EPMC8551287 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature