Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 12/1/1999
Publication Date: N/A
Citation: N/A Interpretive Summary: Core collections are a subset of a larger collection of plant material and provide a way of identifying the maximum diversity possible in a germplasm collection. They are useful for improving the efficiency of the germplasm evaluation process and selection for improved types. Both agronomic and molecular based data can be used to develop core collections but little information is available regarding which type of data might provide the best core collection. This study showed that both agronomic and molecular data could be used to produce high quality core collections in Kentucky bluegrass. However, it appeared that the agronomic data, when used with the proper statistical approach, produced a core that maximized both agronomic and molecular diversity and gave the highest overall diversity.
Technical Abstract: Core collections offer a way to improve access to germplasm collections by providing a highly diverse, representative subsample of the total collection. Our objective was to compare methods of developing core collections from the USDA Poa pratensis L. (Kentucky bluegrass) collection using agronomic and molecular genetic (RAPD) data. In replicated experiments, data for 17 agronomic attributes and 88 RAPD markers were collected on the USDA Poa pratensis L. collection. For all 228 accessions evaluated, the distance matrix for agronomic data, based on Euclidian distance, and the matrix for RAPD data, using the Sorenson coefficient, were significantly correlated, showing a degree of correspondence between agronomic and RAPD data. Cores representing approximately 10% of theaccessions in the collection, and based on agronomic or RAPD data, were developed using random sampling, UPGMA hierarchal cluster analysis, and stratification by broad geographic regions. Stratification over broad geographic areas did not improve core quality. But, both agronomic and RAPD data, when used with cluster analysis, produced highly diverse cores.