Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations.

The efficient acquisition and transport of nutrients by plants largely depend on the root architecture. Due to the absence of complex microbial network interactions and soil heterogeneity in a restricted soilless medium, the architecture of roots is a function of genetics defined by the soilless matrix and exogenously supplied nutrients such as nitrogen (N). The knowledge of root trait combinations that offer the optimal nitrogen use efficiency (NUE) is far from being conclusive. The objective of this study was to define the root trait(s) that best predicts and correlates with vegetative biomass under differed N treatments. We used eight image-derived root architectural traits of 202 diverse spinach lines grown in two N concentrations (high N, HN, and low N, LN) in randomized complete blocks design. Supervised random forest (RF) machine learning augmented by ranger hyperparameter grid search was used to predict the variable importance of the root traits. We also determined the broad-sense heritability (H) and genetic (rg) and phenotypic (rp) correlations between root traits and the vegetative biomass (shoot weight, SWt). Each root trait was assigned a predicted importance rank based on the trait's contribution to the cumulative reduction in the mean square error (MSE) in the RF tree regression models for SWt. The root traits were further prioritized for potential selection based on the rg and SWt correlated response (CR). The predicted importance of the eight root traits showed that the number of root tips (Tips) and root length (RLength) under HN and crossings (Xsings) and root average diameter (RAvdiam) under LN were the most relevant. SWt had a highly antagonistic rg (- 0.83) to RAvdiam, but a high predicted indirect selection efficiency (- 112.8%) with RAvdiam under LN; RAvdiam showed no significant rg or rp to SWt under HN. In limited N availability, we suggest that selecting against larger RAvdiam as a secondary trait might improve biomass and, hence, NUE with no apparent yield penalty under HN.

Phenotypic Diversity and Association Mapping of Ascorbic Acid Content in Spinach.

Ascorbic acid (AsA), or vitamin C, is an essential nutrient for humans. In plants, AsA functions as an antioxidant during normal metabolism or in response to stress. Spinach is a highly nutritious green leafy vegetable that is consumed fresh, cooked or as a part of other dishes. One current goal in spinach breeding programs is to enhance quality and nutritional content. However, little is known about the diversity of nutritional content present in spinach germplasm, especially for AsA content. In this study, a worldwide panel of 352 accessions was screened for AsA content showing that variability in spinach germplasm is high and could be utilized for cultivar improvement. In addition, a genome-wide association study for marker-trait association was performed using three models, and associated markers were searched in the genome for functional annotation analysis. The generalized linear model (GLM), the compressed mixed linear model (CMLM) based on population parameters previously determined (P3D) and the perMarker model together identified a total of 490 significant markers distributed across all six spinach chromosomes indicating the complex inheritance of the trait. The different association models identified unique and overlapping marker sets, where 27 markers were identified by all three models. Identified high AsA content accessions can be used as parental lines for trait introgression and to create segregating populations for further genetic analysis. Bioinformatic analysis indicated that identified markers can differentiate between high and low AsA content accessions and that, upon validation, these markers should be useful for breeding programs.

Minor alleles are associated with white rust (Albugo occidentalis) susceptibility in spinach (Spinacia oleracea).

Minor alleles (MA) have been associated with disease incidence in human studies, enabling the identification of diagnostic risk factors for various diseases. However, allelic mapping has rarely been performed in plant systems. The goal of this study was to determine whether a difference in MA prevalence is a strong enough risk factor to indicate a likely significant difference in disease resistance against white rust (WR; Albugo occidentalis) in spinach (Spinacia oleracea). We used WR disease severity ratings (WR-DSRs) in a diversity panel of 267 spinach accessions to define resistant- and susceptibility-associated groups within the distribution scores and then tested the single-nucleotide polymorphism (SNP) variants to interrogate the MA prevalence in the most susceptible (MS) vs. most resistant (MR) individuals using permutation-based allelic association tests. A total of 448 minor alleles associated with WR severity were identified in the comparison between the 25% MS and the 25% MR accessions, while the MA were generally similar between the two halves of the interquartile range. The minor alleles in the MS group were distributed across all six chromosomes and made up ~71% of the markers that were also strongly associated with WR in parallel performed genome-wide association study. These results indicate that susceptibility may be highly determined by the disproportionate overrepresentation of minor alleles, which could be used to select for resistant plants. Furthermore, by focusing on the distribution tails, allelic mapping could be used to identify plant markers associated with quantitative traits on the most informative segments of the phenotypic distribution.

Developing Growth-Associated Molecular Markers Via High-Throughput Phenotyping in Spinach.

CORE IDEAS: High-throughput imaging and genomic information can be combined to optimize marker development. Genome-wide association studies identified loci associated with plant growth traits. We identified candidate genes associated with plant growth and development. Despite advances in sequencing for genotyping, the lack of rapid, accurate, and reproducible phenotyping platforms has hampered efforts to use genetic analysis to predict traits of interest. Therefore, the use of high-throughput systems to phenotype traits related to crop growth, yield, quality, and resistance to biotic and abiotic stresses has become a major asset for breeding. Here, we assessed the efficacy of unmanned aircraft system (UAS)-based high-throughput phenotyping to obtain data for molecular marker development for spinach (Spinacia oleracea L.) improvement. We used a UAS equipped with a red-green-blue sensor to capture raw images of 284 spinach accessions throughout the crop cycle. Processed images generated orthomosaic and digital surface models for estimating canopy cover, canopy volume, and excess greenness index models. In addition, we manually recorded the number of days to bolting. Genome-wide association studies against a single-nucleotide polymorphism (SNP) panel obtained by ddRADseq identified 99 SNPs significantly associated with growth parameters. Some of these SNPs are in transcription factor and stress-response genes with possible roles in plant growth and development. The results underscore the utility of combining aerial imaging and genomic data analysis to optimize marker development. This study lays the foundation for the use of UAS-based high-throughput phenotyping for the molecular breeding of spinach.