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.
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Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values.
The size and shape of carrot roots are the primary determinants not only of yield, but also market class. These quantitative phenotypes have historically been challenging to objectively evaluate, and thus subjective visual assessment of market class remains the primary method by which selection for these traits is performed. However, advancements in digital image analysis have recently made possible the high-throughput quantification of size and shape attributes. It is therefore now feasible to utilize modern methods of genetic analysis to investigate the genetic control of root morphology. To this end, this study utilized both genome wide association analysis (GWAS) and genomic-estimated breeding values (GEBVs) and demonstrated that the components of market class are highly polygenic traits, likely under the influence of many small effect QTL. Relatively large proportions of additive genetic variance for many of the component phenotypes support high predictive ability of GEBVs; average prediction ability across underlying market class traits was 0.67. GWAS identified multiple QTL for four of the phenotypes which compose market class: length, aspect ratio, maximum width, and root fill, a previously uncharacterized trait which represents the size-independent portion of carrot root shape. By combining digital image analysis with GWAS and GEBVs, this study represents a novel advance in our understanding of the genetic control of market class in carrot. The immediate practical utility and viability of genomic selection for carrot market class is also described, and concrete guidelines for the design of training populations are provided.
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- Award ID(s):
- 2048425
- NSF-PAR ID:
- 10315268
- Date Published:
- Journal Name:
- Theoretical and applied genetics
- ISSN:
- 0040-5752
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract This study investigated the genetic basis of carrot root shape traits using composite interval mapping in two biparental populations (n = 119 and n = 128). The roots of carrot F2:3 progenies were grown over 2 years and analyzed using a digital imaging pipeline to extract root phenotypes that compose market class. Broad-sense heritability on an entry-mean basis ranged from 0.46 to 0.80 for root traits. Reproducible quantitative trait loci (QTL) were identified on chromosomes 2 and 6 on both populations. Colocalization of QTLs for phenotypically correlated root traits was also observed and coincided with previously identified QTLs in published association and linkage mapping studies. Individual QTLs explained between 14 and 27% of total phenotypic variance across traits, while four QTLs for length-to-width ratio collectively accounted for up to 73% of variation. Predicted genes associated with the OFP-TRM (OVATE Family Proteins—TONNEAU1 Recruiting Motif) and IQD (IQ67 domain) pathway were identified within QTL support intervals. This observation raises the possibility of extending the current regulon model of fruit shape to include carrot storage roots. Nevertheless, the precise molecular mechanisms through which this pathway operates in roots characterized by secondary growth originating from cambium layers remain unknown.
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Carrot (
Daucus carota var.sativus ) cultivars with common root shape, appearance, and end-use are grouped and commercialized in market classes. The shape of the carrot storage root is the result of growth and development, which is highly influenced by genotype; however, the extent to which planting density affects root shape traits and its interaction with genotype remains unexplored. To observe the effects of market class and density on carrot root shape characteristics, five cultivars classified in five different market classes, including Imperator, Nantes, Danvers, Chantenay, and Ball, were each grown at five planting densities ranging from 0.5 million to 4.5 million plants/ha. A generalized complete block design with a two-way factorial treatment arrangement of the two factors, density and genotype, was used in three environments. Roots were phenotyped using a digital imaging pipeline and scored for root size (length, maximum width) and compound root shape traits including traits derived from the principal component analysis of root contour profiles like root fill and tip and shoulder curvature. The results suggest that planting density had minimal impact on the shape of carrot roots, and the expected shape for each market class was maintained regardless of planting density; however, the analysis was constrained by the presence of interactions among genotype, density, and environment, which influence the contribution of main effects to shape. For the Nantes, Danvers, Chantenay, and Imperator market classes, planting density influenced the size of the carrot root, with size decreasing by up to 50% in length and width at high planting densities. We found high estimates of broad-sense heritability for traits that determine the shape of the carrot root, such as root fill and length-to-width ratio, which capture size-independent variation of the storage root. Although environmental signals play a role, our results suggested that the shape of the carrot root is primarily determined by genotype, and that planting density generally does not have a significant effect on its shape. -
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Abstract Background Genome wide association (GWA) studies demonstrate linkages between genetic variants and traits of interest. Here, we tested associations between single nucleotide polymorphisms (SNPs) in rice (
Oryza sativa ) and two root hair traits, root hair length (RHL) and root hair density (RHD). Root hairs are outgrowths of single cells on the root epidermis that aid in nutrient and water acquisition and have also served as a model system to study cell differentiation and tip growth. Using lines from the Rice Diversity Panel-1, we explored the diversity of root hair length and density across four subpopulations of rice (aus ,indica ,temperate japonica , andtropical japonica ). GWA analysis was completed using the high-density rice array (HDRA) and the rice reference panel (RICE-RP) SNP sets.Results We identified 18 genomic regions related to root hair traits, 14 of which related to RHD and four to RHL. No genomic regions were significantly associated with both traits. Two regions overlapped with previously identified quantitative trait loci (QTL) associated with root hair density in rice. We identified candidate genes in these regions and present those with previously published expression data relevant to root hair development. We re-phenotyped a subset of lines with extreme RHD phenotypes and found that the variation in RHD was due to differences in cell differentiation, not cell size, indicating genes in an associated genomic region may influence root hair cell fate. The candidate genes that we identified showed little overlap with previously characterized genes in rice and
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Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s00122-021-03988-8
