Abstract
Root shape in carrot (Daucus carota subsp. sativus), which ranges from long and tapered to short and blunt, has been used for at least several centuries to classify carrot cultivars. The subjectivity involved in determining market class hinders the establishment of metric-based standards and is ill-suited to dissecting the genetic basis of such quantitative phenotypes. Advances in digital image acquisition and analysis has enabled new methods for quantifying sizes of plant structures and shapes, but in order to dissect the genetic control of the shape features that define market class in carrot, a tool is required that quantifies the specific shape features used by humans in distinguishing between classes. This study reports the construction and demonstration of the first such platform, which facilitates rapid phenotyping of traits that are measurable by hand, such as length and width, as well as principal component analysis (PCA) of the root contour and its curvature. This latter approach is of particular interest, as it enabled the detection of a novel and significant quantitative trait, defined here as root fill, which accounts for 85% of the variation in root shape. Curvature analysis was demonstrated to be an effective method for precise measurement of the broadness of the carrot shoulder, and degree of tip fill; the first principal component of the respective curvature profiles captured 87% and 84% of the total variance. This platform’s performance was validated in two experimental panels. First, a diverse, global collection of germplasm was used to assess its capacity to identify market classes through clustering analysis. Second, a diallel mating design between inbred breeding lines of differing market classes was used to estimate the heritability of the key phenotypes that define market class, which revealed significant variation in the narrow-sense heritability of size and shape traits, ranging from 0.14 for total root size, to 0.84 for aspect ratio. These results demonstrate the value of high-throughput digital phenotyping in characterizing the genetic control of complex quantitative phenotypes.
Highlights
Carrot (Daucus carota subsp. sativus) is an economically and nutritionally important vegetable crop
The subjectivity involved in assessing market class at present hinders the establishment of metric-based standards and are ill-suited to dissecting the genetic basis of such quantitative phenotypes
Applying principal component analysis (PCA) to this set of contours showed that the first principal component (PC1) explained 84.9% of the total variance in the contours, with PC2 and PC3 explaining only 9.6 and 2.5% of the variance, respectively
Summary
Carrot (Daucus carota subsp. sativus) is an economically and nutritionally important vegetable crop. Carrot root shape, which ranges from long and tapered to short and blunt, has been used for at least several centuries to classify carrot cultivars (Banga, 1957; Simon et al, 2008). Culinary practices and horticultural traditions have led to the establishment of the modern market classes based primarily on variation in root dimensions and shape differences, giving these component traits increasing economic importance. The distinctions between market classes frequently depend on subjective assessment of the curvature of the shoulder at the crown of the root, the variable degrees of tip fill, and specific combinations of these shape parameters and root dimensions. The subjectivity involved in assessing market class at present hinders the establishment of metric-based standards and are ill-suited to dissecting the genetic basis of such quantitative phenotypes. While advances in genetic resources in carrot have improved researchers ability to investigate the genetic architecture of key traits (Iorizzo et al, 2016; Que et al, 2019), this phenotyping bottleneck remains a key limitation in the study of root morphology
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