Maximizing efficiency in sunflower breeding through historical data optimization.

ABSTRACT: Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS ye

SUBMITTER: Fernandez-Gonzalez J 

PROVIDER: S-EPMC10943787 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature