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For many horticultural crops, selection is based on quality as well as yield. To investigate the distribution of trait variation and identify those attributes appropriate for developing selection indices, we collected and organized information related to fruit size, shape, color, soluble solids, acid, and yield traits for 143 processing tomato (Solanum lycopersicum L.) lines from North America. Evaluation of the germplasm panel was conducted in a multiyear, multilocation trial. Data were stored in a flat-file format and in a trait ontology database, providing a public archive. We estimated variance components and proportion of variance resulting from genetics for each trait. Genetic variance was low to moderate (range, 0.03–0.51) for most traits, indicating high environmental influence on trait expression and/or complex genetic architecture. Phenotypic values for each line were estimated across environments as best linear unbiased predictors (BLUPs). Principal components (PC) analysis using the trait BLUPs provided a means to assess which traits explained variation in the germplasm. The first two PCs explained 28.0% and 16.2% of the variance and were heavily weighted by measures of fruit shape and size. The third PC explained 12.9% of the phenotypic variance and was determined by fruit color and yield components. Trait BLUPs and the first three PCs were also used to explore the relationship between phenotypes and the origin of the accessions. We were able to differentiate germplasm for fruit size, fruit shape, yield, soluble solids, and color based on origin, indicating regional breeding programs provide a source of trait variation. These analyses suggest that multitrait selection indices could be established that encompass quality traits in addition to yield. However, such indices will need to balance trait correlations and be consistent with market valuation.

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Late blight (LB), caused by Phytophthora infestans, is a destructive disease of tomato (Solanum lycopersicum). To gain adequate control of LB in tomato, new sources of resistance are being identified and characterized with the aim of introducing resistance genes to elite tomato germplasm. In this study, heritability (h 2 ) of LB resistance conferred by a S. pimpinellifolium resistant accession (PI 270443) was estimated using populations developed from crosses with a LBsusceptible tomato breeding line. In the F 2 population, 986 seedlings were evaluated for LB, and 50 resistant and 40 susceptible individuals were selected and self-pollinated to generate F 3 progeny. Seedlings of the F 3 progeny were evaluated for LB in two separate experiments. Using parent-offspring correlation analysis, h 2 of LB resistance was estimated to be 0.86, indicating that this resistance was highly heritable and could be transferred to the cultivated tomato via phenotypic selection. The number of resistance genes involved was estimated to be two. Breeding efforts to transfer this resistance to elite tomato lines and genetic mapping to identify the underlying resistance genes are underway.

In plant breeding and genetics research, plant breeders establish a hypothesis to explain how they think a particular trait is inherited, such as if it is due to one gene with complete dominance, an interaction of more than one gene, or quantitative inheritance, with many genes contributing, etc. Next the breeder sets up some crosses and observes the resulting progeny to test that inheritance hypothesis. However, when the data is collected, oftentimes the breeder discovers the number of plants observed in each class is not exactly what was expected from the hypothesis. The question then is how do plant breeders determine if the data supports their hypothesis or not? Following a tomato disease resistance example in this lesson, you will learn a simple statistical test that breeders can use to conclude if the experimental data supports their hypothesis. This lesson is written for undergraduate and graduate students studying plant breeding, as well as agriculture professionals unfamiliar with the use of the chi‐square analysis. After completing this lesson module you should be able to: Calculate expected phenotypic and genotypic ratios and the number of plants expected in each class for a given plant breeding scheme. Calculate chi‐square values for plant genetics data sets from both phenotypic and genotypic observations. Calculate degrees of freedom. Accurately interpret results from a chi‐square test. Identify appropriate uses and limitations of the chi‐square test in plant breeding and genetics research.