Background: Core collections are important tools in genetic resources research and administration. At present, most core collection selection criteria are based on one of the following item characteristics: passport data, genetic markers, or morphological traits, which may lead to inadequate representations of variability in the complete collection. The development of a comprehensive methodology that includes as much element data as possible has been explored poorly. Using a collection of (Setaria italica sbsp. italica (L.) P. Beauv.) as a model, we developed a method for core collection construction based on genotype data and numerical representations of agromorphological traits, thereby improving the selection process.
Results: Principal component analysis allows the selection of the most informative discriminators among the various elements evaluated, regardless of whether they are genetic or morphological, thereby providing an adequate criterion for further K-mean clustering. Overall, the core collections of S. italica constructed using only genotype data demonstrated overall better validation scores than other core collections that we generated. However, core collection based on both genotype and agromorphological characteristics represented the overall diversity adequately.
Conclusions: The inclusion of both genotype and agromorphological characteristics as a comprehensive dataset in this methodology ensures that agricultural traits are considered in the core collection construction. This approach will be beneficial for genetic resources management and research activities for S. italica as well as other genetic resources.