Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values

Linkage mapping of root shape traits in two carrot populations

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.

A Genome-Wide Association Study Reveals the Genetic Mechanisms of Nutrient Accumulation in Spinach

Spinach is a significant source of vitamins, minerals, and antioxidants. These nutrients make it delicious and beneficial for human health. However, the genetic mechanism underlying the accumulation of nutrients in spinach remains unclear. In this study, we analyzed the content of chlorophyll a, chlorophyll b, oxalate, nitrate, crude fiber, soluble sugars, manganese, copper, and iron in 62 different spinach accessions. Additionally, 3,356,182 high-quality, single-nucleotide polymorphisms were found using resequencing and used in a genome-wide association study. A total of 2077 loci were discovered that significantly correlated with the concentrations of the nutritional elements. Data mining identified key genes in these intervals for four traits: chlorophyll, oxalate, soluble sugar, and Fe. Our study provides insights into the genetic architecture of nutrient variation and facilitates spinach breeding for good nutrition.

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

BACKGROUND

Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA.

RESULTS

Accuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection.

CONCLUSIONS

Accuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models.

Form and contour: breeding and genetics of organ shape from wild relatives to modern vegetable crops

Shape is a primary determinant of consumer preference for many horticultural crops and it is also associated with many aspects of marketing, harvest mechanics, and postharvest handling. Perceptions of quality and preference often map to specific shapes of fruits, tubers, leaves, flowers, roots, and other plant organs. As a result, humans have greatly expanded the palette of shapes available for horticultural crops, in many cases creating a series of market classes where particular shapes predominate. Crop wild relatives possess organs shaped by natural selection, while domesticated species possess organs shaped by human desires. Selection for visually-pleasing shapes in vegetable crops resulted from a number of opportunistic factors, including modification of supernumerary cambia, allelic variation at loci that control fundamental processes such as cell division, cell elongation, transposon-mediated variation, and partitioning of photosynthate. Genes that control cell division patterning may be universal shape regulators in horticultural crops, influencing the form of fruits, tubers, and grains in disparate species. Crop wild relatives are often considered less relevant for modern breeding efforts when it comes to characteristics such as shape, however this view may be unnecessarily limiting. Useful allelic variation in wild species may not have been examined or exploited with respect to shape modifications, and newly emergent information on key genes and proteins may provide additional opportunities to regulate the form and contour of vegetable crops.

Image-Based High-Throughput Phenotyping in Horticultural Crops

Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.