MORPHOLOGICAL AND NUTRITIONAL VARIATION IN A SET OF TOMATO GERMPLASM WITH HIGHLY VARIABLE FRUIT SHAPES AND COLORS
Location: Plant Genetic Resources
Project Number: 1910-21000-019-18
Nonfunded Cooperative Agreement
Start Date: Jul 01, 2010
End Date: Sep 03, 2012
Refine the classification of cultivated tomato based on fruit morphology and genotype. Tomato fruit morphology categories will be studied using the program Tomato Analyzer (TA) and the effect of each fruit morphology gene will be estimated. First the tomato fruit classification scheme will be refined using 51 accessions that vary in longitudinal fruit shape. Data on transverse shape attributes such as pericarp and placenta thickness, and locule number which are features implemented in the latest version of TA will be added.
The Plant Genetic Resources Unit will collect data on nutritional traits for all three locations of the trials for the two years. Nutritional data will include lycopene, vitamin C, brix, and titratable acids. Data on nutritional traits and morphological traits will be analyzed for diversity and genetic relationships.
This collection will be grown at three different locations over two growing seasons: Wooster OH, Mills River NC, and Wellington Farm in Geneva, NY (Plant Genetic Resources Unit). This agreement covers the field grow out for these two years at Geneva, NY. From each accession, we will analyze 48 fruit (N=4992, i.e. 52 lines x 4 fruit/plant x 4 plants x 3 locations x 2 years) by scanning using the TA program.
Nutritional data will be collected from frozen fruit homogenates from the three locations. An indirect measure of citric acid, titratable acidity (TA), will be determined by titrating samples with NaOH. Ascorbic acid (vitamin C) will be estimated using a commercial kit (COSMO BIO CO, Japan) that measures both oxidized and reduced forms. Lycopene will be estimated using a Minolta Chroma Meter CR-300 that records L*a*b* color space. Each L*a*b* value represents the average of three measurements. Lycopene will be estimated using a regression model based on the transformed a*. Degrees Brix data will be collected using a Model DR103L digital refractometer (QA Supplies, Norfolk, VA). Juice from thawed homogenates will be place upon the refractometer. For each sample degrees Brix will be calculated as a mean of three readings for each replication for each accession.
Hierarchical cluster analyses will be performed interactively using different combinations of similarity/dissimilarity indices and linkage algorithms to reveal the most appropriate number of shape classes. This approach will also reveal the subset of TA-defined attributes that contribute most to the classification, and establish standardized tolerances for key attributes such that each shape class can be objectively defined regardless of source. The net result will be a uniquely semi-automatic and objective classification system useful for categorization based on fruit morphology. By growing the collection in three different environments over two years we will be able to establish the degree to which genotype x environment interaction affect fruit shape based on multi-way analyses of variance of each attribute and shape class. Importantly, these analyses will determine the variability in fruit morphology for certain genotypes and categories.