Comparison of representative and custom methods of generating core subsets of a carrot germplasm collection

Authors: CORAK, K - University Of Wisconsin; Ellison, Shelby; Simon, Philipp; Spooner, David; DAWSON, J - University Of Wisconsin

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/11/2018
Publication Date: 5/16/2019
Citation: Corak, K.E., Ellison, S.L., Simon, P.W., Spooner, D.M., Dawson, J.C. 2019. Comparison of representative and custom methods of generating core subsets of a carrot germplasm collection. Crop Science. 59(3):1107-1121. https://doi.org/10.2135/cropsci2018.09.0602.
DOI: https://doi.org/10.2135/cropsci2018.09.0602

Interpretive Summary: Many crop breeding programs are interested in using wild or cultivated plants in their breeding programs, but have difficulty choosing which ones to use from large collections in genebanks. One method scientists use to narrow down collections to use from larger germplasm collections is to identify a subset of the collections that are "representative" of the larger collection. Methods to identify these subsets, which are called core collections, have never been rigorously tested to see if the core collections actually meet their goals. We perform such a test by evaluating a large germplasm collection of carrots by various criteria that traditionally have been used to construct core collections, with the use of high-density molecular data. We find that in carrot, the traditional core collection strategies are no better than random sampling. These results are useful to breeders and genebank managers in bringing into question these traditional core collection strategies.

Technical Abstract: Many crop breeding programs are interested in using genetic resources but have difficulty identifying useful accessions from germplasm collections. To efficiently use the diversity present in large germplasm collections, breeders often identify a subset of accessions that represent the larger collection. Methods to identify these subsets, which are called core collections, do not consistently capture functional diversity and breeders would benefit from methods, which help create custom core collections using existing data from variety trials or breeding programs. Making use of the availability of high-density genomic data and existing phenotypic data for a collection of 433 domesticated carrot (Daucus carota) accessions, we test whether it is possible to develop custom subsets of accessions for specific breeding purposes. We find that for this collection, representative strategies are effective in developing core collections that capture the diversity of the collection, but are no better than random sampling, likely because the collection itself is not strongly subdivided. Custom strategies generate subsets that differed from the total collection with altered genetic, geographic and phenotypic compositions. When used as training populations for genomic prediction of the other accessions in the collection, however, these custom cores did not produce a substantial improvement over traditional core collections. Increasing the size of the core did improve prediction accuracy, suggesting that it is possible to improve the usefulness of core collections by identifying custom subsets that are large enough to represent the functional genetic diversity present in the collection.