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

Abstract excerpt

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...

Topics

Share this publication in a Topic to start or enrich a Post.

• Share to Genome
• Share to Genomics
• Share to Models, Genetic