Gene Expression Profiling of Iron Deficiency Chlorosis Sensitive and Tolerant Soybean Indicates Key Roles for Phenylpropanoids under Alkalinity Stress

Alkaline soils comprise 30% of the earth and have low plant-available iron (Fe) concentration, and can cause iron deficiency chlorosis (IDC). IDC causes soybean yield losses of $260 million annually. However, it is not known whether molecular responses to IDC are equivalent to responses to low iron supply. IDC tolerant and sensitive soybean lines provide a contrast to identify specific factors associated with IDC.We used RNA-seq to compare gene expression under combinations of normal pH (5.7) or alkaline pH (7.7, imposed by 2.5mM bicarbonate, or pH 8.2 imposed by 5mM bicarbonate) and normal (25μM) or low (1μM) iron conditions from roots of these lines. Thus, we were able to treat pH and Fe supply as separate variables. We also noted differential gene expression between IDC sensitive and tolerant genotypes in each condition. Classical iron uptake genes, including ferric-chelate reductase (FCR) and ferrous transporters, were upregulated by both Fe deficiency and alkaline stress, however, their gene products did not function well at alkaline pH. In addition, genes in the phenylpropanoid synthesis pathway were upregulated in both alkaline and low Fe conditions. These genes lead to the production of fluorescent root exudate (FluRE) compounds, such as coumarins. Fluorescence of nutrient solution increased with alkaline treatment, and was higher in the IDC tolerant line. Some of these genes also localized to previously identified QTL regions associated with IDC. We hypothesize that FluRE become essential at alkaline pH where the classical iron uptake system does not function well. This work could result in new strategies to screen for IDC tolerance, and provide breeding targets to improve crop alkaline stress tolerance

Response Surface Analysis of Genomic Prediction Accuracy Values Using Quality Control Covariates in Soybean

An important and broadly used tool for selection purposes and to increase yield and genetic gain in plant breeding programs is genomic prediction (GP). Genomic prediction is a technique where molecular marker information and phenotypic data are used to predict the phenotype (eg, yield) of individuals for which only marker data are available. Higher prediction accuracy can be achieved not only by using efficient models but also by using quality molecular marker and phenotypic data. The steps of a typical quality control (QC) of marker data include the elimination of markers with certain level of minor allele frequency (MAF) and missing marker values and the imputation of missing marker values. In this article, we evaluated how the prediction accuracy is influenced by the combination of 12 MAF values, 27 different percentages of missing marker values, and 2 imputation techniques (IT; naïve and Random Forest (RF)). We constructed a response surface of prediction accuracy values for the two ITs as a function of MAF and percentage of missing marker values using soybean data from the University of Nebraska–Lincoln Soybean Breeding Program. We found that both the genetic architecture of the trait and the IT affect the prediction accuracy implying that we have to be careful how we perform QC on the marker data. For the corresponding combinations MAF-percentage of missing values we observed that implementing the RF imputation increased the number of markers by 2 to 5 times than the simple naïve imputation method that is based on the mean allele dosage of the non-missing values at each loci. We conclude that there is not a unique strategy (combination of the QCs and imputation method) that outperforms the results of the others for all traits

Generating High Density, Low Cost Genotype Data in Soybean [\u3ci\u3eGlycine max\u3c/i\u3e (L.) Merr.]

Obtaining genome-wide genotype information for millions of SNPs in soybean [Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage. Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI Short Read Archive. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using GATK’s Haplotype Caller tool. Whole genome resequencing at approximately 1X depth was performed on 114 previously ungenotyped experimental soybean lines. Coverages down to 0.1X were analyzed by randomly subsetting raw reads from the original 1X sequence data. SNPs discovered in the reference panel were genotyped in the experimental lines after aligning to the soybean reference genome, and missing markers imputed using Beagle 4.1. Sequencing depth of the experimental lines could be reduced to 0.3X while still retaining an accuracy of 97.8%. Accuracy was inversely related to minor allele frequency, and highly correlated with marker linkage disequilibrium. The high accuracy of skim sequencing combined with imputation provides a low cost method for obtaining dense genotypic information that can be used for various genomics applications in soybean

Identification of new QTLs for seed mineral, cysteine, and methionine concentrations in soybean [Glycine max (L.) Merr.]

Increased concentrations of important nutrients in edible parts of plants could result in biofortified foods. Soybean [Glycine max (L.) Merr.] is a major legume crop and an important source of certain nutrients, including protein and minerals, in human and animal diets. Understanding the underlying genetic basis of seed composition is crucial to improving seed nutrient composition. In this study we used three soybean recombinant inbred line mapping populations derived from the crosses Williams 82 × DSR-173, Williams 82 × NKS19-90 and Williams 82 × Vinton 81, and constructed a joint linkage map from these populations. Forty quantitative trait loci (QTLs) were detected for 18 traits: seed weight, seed magnesium, sulfur, calcium, manganese, potassium, iron, cobalt, nickel, copper, zinc, selenium, molybdenum, cadmium and arsenic concentrations, total nitrogen:total sulfur (N:S) ratio, cysteine, and methionine concentrations. Using the joint linkage map, we detected nine QTLs that were not identified in the individual populations. We identified several candidate genes that might contribute to these traits, including transporters and genes involved in nitrogen and amino acid metabolism. Some strong QTLs had no obvious candidate genes, offering the possibility that subsequent confirmation of these QTLs may result in identification of new genes affecting seed nutrients in soybean. Seed weight and seed mineral concentrations were not highly correlated, suggesting the possibility of improving seed mineral concentrations without significant changes in seed weight. An inverse relationship between N:S ratio and most other minerals suggests the possibility of using N:S ratio as an indirect measure of seed mineral concentration in soybean breeding programs

Field-Based Scoring of Soybean Iron Deficiency Chlorosis Using RGB Imaging and Statistical Learning

Iron deficiency chlorosis (IDC) is an abiotic stress in soybean that can cause significant biomass and yield reduction. IDC is characterized by stunted growth and yellowing and interveinal chlorosis of early trifoliate leaves. Scoring IDC severity in the field is conventionally done by visual assessment. The goal of this study was to investigate the usefulness of Red Green Blue (RGB) images of soybean plots captured under the field condition for IDC scoring. A total of 64 soybean lines with four replicates were planted in 6 fields over 2 years. Visual scoring (referred to as Field Score, or FS) was conducted at V3–V4 growth stage; and concurrently RGB images of the field plots were recorded with a high-throughput field phenotyping platform. A second set of IDC scores was done on the plot images (displayed on a computer screen) consistently by one person in the office (referred to as Office Score, or OS). Plot images were then processed to remove weeds and extract six color features, which were used to train computer-based IDC scoring models (referred to as Computer Score, or CS) using linear discriminant analysis (LDA) and support vector machine (SVM). The results showed that, in the fields where severe IDC symptoms were present, FS and OS were strongly positively correlated with each other, and both of them were strongly negatively correlated with yield. CS could satisfactorily predict IDC scores when evaluated using FS and OS as the reference (overall classification accuracy \u3e 81%). SVM models appeared to outperform LDA models; and the SVM model trained to predict IDC OS gave the highest prediction accuracy. It was anticipated that coupling RGB imaging from the high-throughput field phenotyping platform with real-time image processing and IDC CS models would lead to a more rapid, cost-effective, and objective scoring pipeline for soybean IDC field screening and breeding

Stacking of a stearoyl-ACP thioesterase with a dual-silenced palmitoyl-ACP thioesterase and Δ12 fatty acid desaturase in transgenic soybean

Soybean (Glycine max (L.) Merr) is valued for both its protein and oil, whose seed is composed of 40% and 20% of each component, respectively. Given its high percentage of polyunsaturated fatty acids, linoleic acid and linolenic acid, soybean oil oxidative stability is relatively poor. Historically food processors have employed a partial hydrogenation process to soybean oil as a means to improve both the oxidative stability and functionality in end-use applications. However, the hydrogenation process leads to the formation of trans-fats, which are associated with negative cardiovascular health. As a means to circumvent the need for the hydrogenation process, genetic approaches are being pursued to improve oil quality in oilseeds. In this regard, we report here on the introduction of the mangosteen (Garcinia mangostana) stearoyl-ACP thioesterase into soybean and the subsequent stacking with an event that is dual-silenced in palmitoyl-ACP thioesterase and Δ12 fatty acid desaturase expression in a seed-specific fashion. Phenotypic analyses on transgenic soybean expressing the mangosteen stearoyl-ACP thioesterase revealed increases in seed stearic acid levels up to 17%. The subsequent stacked with a soybean event silenced in both palmitoyl-ACP thioesterase and Δ12 fatty acid desaturase activity, resulted in a seed lipid phenotype of approximately 11%–19% stearate and approximately 70% oleate. The oil profile created by the stack was maintained for four generations under greenhouse conditions and a fifth generation under a field environment. However, in generation six and seven under field conditions, the oleate levels decreased to 30%–40%, while the stearic level remained elevated

A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding

Collecting plant phenotypic data with sufficient resolution (in both space and time) and accuracy represents a long standing challenge in plant science research, and has been a major limiting factor for the effective use of genomic data for crop improvement. This is particularly true in plant breeding where collecting large-scale field-based plant phenotypes can be very labor intensive and costly. In this paper we reported a multi-sensor system for high throughput phenotyping in plant breeding. The system comprised five sensor modules (ultrasonic distance sensors, thermal infrared radiometers, NDVI sensors, portable spectrometers, and RGB web cameras) to measure crop canopy traits from field plots. A GPS was used to geo-reference the sensor measurements. Two environmental sensors (a solar radiation sensor and air temperature/relative humidity sensor) were also integrated into the system to collect simultaneous environmental data. A LabVIEW program was developed to control and synchronize measurements from all sensor modules and stored sensor readings in the host computer. Canopy reflectance spectra (by portable spectrometers) were post processed to extract NDVI and red-edge NDVI spectral indices; and RGB images were post processed to extract canopy green pixel fraction (as a proxy for biomass). The sensor system was tested in a soybean and wheat field trial. The results showed strong correlations among the sensor-based plant traits at both early and late growing season. Significant correlations were also found between the sensor-based traits and final grain yield at the early season (Pearson’s correlation coefficient r ranged from 0.41 to 0.55) and late season (r from 0.55 to 0.70), suggesting the potential use of the sensor system to assist in phenotypic selection for plant breeding. The sensor system performed satisfactorily and robustly in the field tests. It was concluded that the sensor system could be a powerful tool for plant breeders to collect field-based, high throughput plant phenotyping data

Genome-wide Association Mapping of Qualitatively Inherited Traits in a Germplasm Collection

Genome-wide association (GWA) has been used as a tool for dissecting the genetic architecture of quantitatively inherited traits. We demonstrate here that GWA can also be highly useful for detecting many major genes governing categorically defined phenotype variants that exist for qualitatively inherited traits in a germplasm collection. Genome-wide association mapping was applied to categorical phenotypic data available for 10 descriptive traits in a collection of ~13,000 soybean [Glycine max (L.) Merr.] accessions that had been genotyped with a 50,000 single nucleotide polymorphism (SNP) chip. A GWA on a panel of accessions of this magnitude can offer substantial statistical power and mapping resolution, and we found that GWA mapping resulted in the identification of strong SNP signals for 24 classical genes as well as several heretofore unknown genes controlling the phenotypic variants in those traits. Because some of these genes had been cloned, we were able to show that the narrow GWA mapping SNP signal regions that we detected for the phenotypic variants had chromosomal bp spans that, with just one exception, overlapped the bp region of the cloned genes, despite local variation in SNP number and nonuniform SNP distribution in the chip set

An epigenetic breeding system in soybean for increased yield and stability

Epigenetic variation has been associated with a wide range of adaptive phenotypes in plants, but there exist few direct means for exploiting this variation. RNAi suppression of the plant-specific gene, MutS HOMOLOG1 (MSH1), in multiple plant species produces a range of developmental changes accompanied by modulation of defence, phytohormone and abiotic stress response pathways along with methylome repatterning. This msh1-conditioned developmental reprogramming is retained independent of transgene segregation, giving rise to transgene-null ‘memory’ effects. An isogenic memory line crossed to wild type produces progeny families displaying increased variation in adaptive traits that respond to selection. This study investigates amenability of the MSH1 system for inducing agronomically valuable epigenetic variation in soybean. We developed MSH1 epi-populations by crossing with msh1-acquired soybean memory lines. Derived soybean epi-lines showed increase in variance for multiple yield-related traits including pods per plant, seed weight and maturity time in both glasshouse and field trials. Selected epi-F2:4 and epi-F2:5 lines showed an increase in seed yield over wild type. By epi-F2:6, we observed a return of MSH1-derived enhanced growth back to wild-type levels. Epi-populations also showed evidence of reduced epitype-by-environment (e 9 E) interaction, indicating higher yield stability. Transcript profiling of epi-lines identified putative signatures of enhanced growth behaviour across generations. Genes related to cell cycle, abscisic acid biosynthesis and auxin response, particularly SMALL AUXIN UP RNAs (SAURs), were differentially expressed in epi-F2:4 lines that showed increased yield when compared to epi-F2:6. These data support the potential of MSH1-derived epigenetic variation in plant breeding for enhanced yield and yield stability

Dissecting the Genetic Basis of Local Adaptation in Soybean

Soybean (Glycine max) is the most widely grown oilseed in the world and is an important source of protein for both humans and livestock. Soybean is widely adapted to both temperate and tropical regions, but a changing climate demands a better understanding of adaptation to specific environmental conditions. Here, we explore genetic variation in a collection of 3,012 georeferenced, locally adapted landraces from a broad geographical range to help elucidate the genetic basis of local adaptation. We used geographic origin, environmental data and dense genome-wide SNP data to perform an environmental association analysis and discover loci displaying steep gradients in allele frequency across geographical distance and between landrace and modern cultivars. Our combined application of methods in environmental association mapping and detection of selection targets provide a better understanding of how geography and selection may have shaped genetic variation among soybean landraces. Moreover, we identified several important candidate genes related to drought and heat stress, and revealed important genomic regions possibly involved in the geographic divergence of soybean