Abstract
BackgroundCore 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.ResultsPrincipal 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0343-z) contains supplementary material, which is available to authorized users.
Highlights
Core collections are important tools in genetic resources research and administration
The use of TEs as an alternative to locus-specific molecular marker systems is based on the assumption that a significant fraction of plant genomes comprise TEs [37], i.e., recently active display higher polymorphisms [38]
We proposed a method that does not require genomic information, or a large number of locus-specific genetic markers, which is based on an amplified fragment length polymorphism (AFLP)-like technique that could be transferred to other biological systems
Summary
Core collections are important tools in genetic resources research and administration. 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. 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. Most researchers must address the problem of data mining to obtain collections of an appropriate size [5]. Due to the size of some collections, complete collection (MC) data mining may sometimes be too expensive (both operative and monetary); core collections (CC) [6] and mini-core collections have emerged in recent decades [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
