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ARS Home » Midwest Area » Madison, Wisconsin » Vegetable Crops Research » Research » Publications at this Location » Publication #320189

Research Project: Potato Genetic Resource Management, Characterization, and Evaluation

Location: Vegetable Crops Research

Title: Intuitive visual impressions (cogs) for identifying clusters of diversity within potato species

Author
item Bamberg, John
item Del Rio, A - University Of Wisconsin
item Navarre, Duroy - Roy

Submitted to: American Journal of Potato Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/17/2016
Publication Date: 4/29/2016
Publication URL: http://handle.nal.usda.gov/10113/63215
Citation: Bamberg, J., del Rio, A., Navarre, D.A. 2016. Intuitive visual impressions (cogs) for identifying clusters of diversity within potato species. American Journal of Potato Research. 93(4):350-359. doi: 10.1007/s12230-016-9508-6.

Interpretive Summary: Potato is the most important vegetable crop in the US and world, but could be improved in many ways. Stocks in the US Potato Genebank have a great breadth of genetic diversity for traits, so provide opportunities for genetic improvement that may not exist in the potato crop breeding genepool. However, there are over 5,000 populations in the genebank to study, so more efficient ways to categorize them are needed. Humans have a remarkable ability to rapidly visually interpret subtle patterns without any conscious study (you are doing that when reading this text). We sought to apply it to grouping forms within a certain species of wild potato, Solanum okadae. When staff members performed repeated rapid visual categorization of coded plants grown in different environments, a pattern emerged of two groups that corresponded to differences in genetic markers, disease resistance, tuber components, and leaf hairiness. We invented the term "cog" for this process of rapid visual grouping. This fast, simple and inexpensive technique is being applied to other potato species in the genebank in an effort to detect natural groups with traits in common. That will promote more efficient management of the stocks in the genebank and more efficient use by potato breeders.

Technical Abstract: One of the basic research activities of genebanks is to partition stocks into groups that facilitate the efficient preservation and evaluation of the full range of useful phenotype diversity. We sought to test the usefulness of making of infra-specific groups by replicated rapid visual intuitive impressions of coded plants by multiple un-coached observers. We invented the term "cog" (shorthand for cognate = "born together") to indicate assumed genetic relatedness of cog members. All of the 16 populations of the wild potato species Solanum okadae in the genebank were thus examined in four separate grow-outs by up to 7 genebank staff members, a total of 26 times. They were instructed to place them into two cogs defined only as big and not-big. Four populations were placed in the big cog for 70-90% of observations, while all remaining populations but one were placed in the big cog for less than 5% of observations. All populations were then assessed for DNA markers and various empirical traits. AFLP and SNP markers clearly distinguished the two cogs. The big cog populations were also distinguished from the others by virtue of having less foliar late blight resistance, more leaf hairiness, and lower tuber tomatine content. SNP similarity suggests one population of reputed Bolivian origin is really a mislabeled duplicate of another from Argentina. If so, the two cogs also perfectly align with country of natural origin, with big originating exclusively from Bolivia. This study shows that rapid, simple, and inexpensive visual intuitive cogs reliably predict genetic and phenotypic differences. Thus we propose that the two cogs of okadae be assigned different identifiers that alert germplasm users to their distinctions. Cogs, groups identified by repeated intuitive visual clustering of coded samples, appear to have utility as a simple first step for detecting groups of diversity in the genebank.