![]() ![]() Beauv.) is one of the oldest cereals consumed by people in Eurasia, America, Africa, and Australia. Several studies have utilized molecular markers in different collections, including the development of CCs based on widely used simple sequence repeats and restriction fragment length polymorphisms, which have demonstrated the great potential of using genetic data for CC selection.įoxtail millet ( Setaria italica subsp. As new genetic information becomes available, CC selection has increasingly used genotypic analysis as a good criterion, but the efficiency of specific molecular markers needs to be demonstrated for phenotypic traits of interest because both types of data are fundamental requirements of genetic breeding programs. Most CC-related studies are based on one or more of three principal characteristics: a) passport data, b) genotypic analysis, and c) morphological traits (). Methods for obtaining an optimal CC have been explored widely, and several algorithms and informatics tools have been developed, but CCs still have many different objectives and various evaluation criteria. ĭue to the size of some collections, complete collection (MC) data mining may sometimes be too expensive (both operative and monetary) therefore, core collections (CC) and mini-core collections have emerged in recent decades. However, most researchers must address the problem of data mining to obtain collections of an appropriate size. The exploitation of genetic resources has been a primary concern for several governmental and nongovernmental agricultural institutions around the world, where the interest may vary from economically exploitable variant crops, to sociocultural, health-related, and biological-related studies (phylogenetic relationships, phenotype-genotype relationships, and physiological-environmental behaviors ). italica as well as other genetic resources. This approach will be beneficial for genetic resources management and research activities for S. 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. However, core collection based on both genotype and agromorphological characteristics represented the overall diversity adequately. italica constructed using only genotype data demonstrated overall better validation scores than other core collections that we generated. 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. 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. Using a collection of ( Setaria italica sbsp. The development of a comprehensive methodology that includes as much element data as possible has been explored poorly. ![]() 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. ![]() Core collections are important tools in genetic resources research and administration. ![]()
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