gle variable (i.e., soybean allergen) aims to describe the measurement dispersion of the variable in a way that does not depend on the variable’s measurement unit. The higher the %CV is, the greater the measurement dispersion is in the variable. For each soybean allergen, %CV was calculated for each cultivar in each site using the following formula:
where %CVij is the %CV for the ith cultivar at the jth site (i = 1, 2, 3, 4, j = 1, 2, …, 6), and s(yij) and y¯yij are sample standard deviation and sample mean of four replicate individual data values yijk (k = 1, 2, 3, 4), respectively.
For a given variable, genotypic variation and environmental variation were quantified relative to residual variation using ANOVA-based F-test. Genotypic variation and environmental variation were also assessed using %CV, which represents normalized variation to the overall mean of the variable. ANOVA was conducted using the following statistical model:
where yijk denotes the kth measurement for the ith cultivar at the jth site (i = 1, 2, 3, 4, j = 1, 2, …, 6, k = 1, 2, 3, 4); U denotes the overall mean (fixed effect); Gi denotes the ith genotype effect (fixed effect); Ej denotes the jth environment effect (fixed effect); and eijk denotes the residual term associated with yijk (random error). The assumption for residual terms is eijk ∼ MVN(0, Σ) where MVN denotes multivariate normal and Σ denotes variance-covariance matrix. The Compound Symmetry model was utilized to fit the residual variance-covariance structure for repeated measurements on the same subject (a subject was defined as a unique cultivar by site combination).
A PCA analysis was conducted to explore the patterns of multivariate data and a biplot (Gabriel, 1971) was utilized to provide results in graphic presentation (Figure 5). The biplot visualizes the relationships among allergens and identifies allergens that are positively or negatively associated. It is also useful to visualize the allergen profiles of samples (site and cultivar combinations).
Small technical variation was observed for Glycinin G1, Glycinin G2, and KTI 1 with an average %CV less than 10 and a majority of %CV values being 5–15. Medium technical variation was observed for Beta conglycinin α subunit, Gly m Bd 28k, Glycinin G3, Glycinin G4, and KTI 3 with average %CV of less than 15 and a majority of the %CV values being 5–25.
Genotypic and environmental variation assessed using %CV are shown in Figure 2. From this plot, the amount of variation observed was larger between locations than between cultivars, with KTI 1 being the only exception, showing over onefold greater variation from the cultivar than the location. KTI 3 showed a similar differential in variation but in an opposite direction, with the location providing over threefold greater variation than the cultivar. KTI 1 expression was influenced more by the genotype and KTI 3 was affected more by the environment based on the F statistics and P values (Table 1). KTI 1 expression was the only allergen clearly influenced by the cultivar in this study compared to the other allergens with an F-value of 61.57 (Table 1). Cultivar 92B12 had the highest amount of KTI 1 protein and cultivar 93B15 had the lowest (Figure 3). The expression trend of KTI 3 was similar among the cultivars. The environment had the greatest impact on KTI 3 expression in this study compared to the other allergens, with an F-value of 70.54. Cultivar 92B12 had higher levels of KTI 3 compared to the other three commercial cultivars (Figure 3). However, all the cultivars grown in Ontario had low levels of KTI 3 compared to the other locations. KTI 3 levels were twofold to fourfold– reduced for each cultivar grown in Ontario. These drastic differences in variation between the two variables (cultivar and location) were unique to the KTIs. Four paralogs for the seed storage protein glycinin were individually quantified based upon unique peptides, which revealed a surprising range of expression variation. The most prominently expressed paralog, Glycinin G1, showed 5% variation with respect to cul