Genetic architecture of protein and oil content in soybean seed and meal.

Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker-trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker-assisted selection.

Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean.

Increasing the rate of genetic gain for seed yield remains the primary breeding objective in both public and private soybean [Glycine max (L.) Merr.] breeding programs. Genomic selection (GS) has the potential to accelerate the rate of genetic gain for soybean seed yield. Limited studies to date have validated GS accuracy and directly compared GS with phenotypic selection (PS), and none have been reported in soybean. This study conducted the first empirical validation of GS for increasing seed yield using over 1,500 lines and over 7 yr (2010-2016) of replicated experiments in the University of Nebraska-Lincoln soybean breeding program. The study was designed to capture the varying genetic relatedness of the training population to three validation sets: two large biparental populations (TBP-1 and TBP-2) and a large validation set comprised of 457 preselected advanced lines derived from 45 biparental populations (TMP). We found that prediction accuracy (.54) realized in our validation experiments was comparable with what we obtained from a series of cross-validation experiments (.64). Both GS and PS were more effective for increasing the population mean performance compared with random selection (RS). We found a selection advantage of GS over PS, where higher genetic gain and identification of top-performing lines was maximized at 10-20% selected proportion. Genomic selection led to small increases in genetic similarity when compared with PS and RS presumably because of a significant shift on allelic frequencies toward the extremes, suggesting that it could erode genetic diversity more quickly. Overall, we found that GS can perform as effectively as PS but that measures should be considered to protect against loss of genetic variance when using GS.

Comparing a Mixed Model Approach to Traditional Stability Estimators for Mapping Genotype by Environment Interactions and Yield Stability in Soybean [Glycine max (L.) Merr.].

Identifying genetic loci associated with yield stability has helped plant breeders and geneticists begin to understand the role and influence of genotype by environment (GxE) interactions in soybean [Glycine max (L.) Merr.] productivity, as well as other crops. Quantifying a genotype's range of performance across testing locations has been developed over decades with dozens of methodologies available. This includes directly modeling GxE interactions as part of an overall model for yield, as well as methods which generate overall yield "stability" values from multi-environment trial data. Correspondence between these methods as it pertains to the outcomes of genome wide association studies (GWAS) has not been well defined. In this study, the GWAS results for yield and yield stability were compared in 213 soybean lines across 11 environments to determine their utility and potential intersection. Both univariate and multivariate conventional stability estimates were considered alongside a mixed model for yield that fit marker by environment interactions as a random effect. One-hundred and six total QTL were discovered across all mapping results, however, genetic loci that were significant in the mixed model for grain yield that fit marker by environment interactions were completely distinct from those that were significant when mapping using traditional stability measures as a phenotype. Furthermore, 73.21% of QTL discovered in the mixed model were determined to cause a crossover interaction effect which cause genotype rank changes between environments. Overall, the QTL discovered via explicitly mapping GxE interactions also explained more yield variance that those QTL associated with differences in traditional stability estimates making their theoretical impact on selection greater. A lack of intersecting results between mapping approaches highlights the importance of examining stability in multiple contexts when attempting to manipulate GxE interactions in soybean.

Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery.

Global crop production is facing the challenge of a high projected demand, while the yields of major crops are not increasing at sufficient speeds. Crop breeding is an important way to boost crop productivity, however its improvement rate is partially hindered by the long crop generation cycles. If end-season crop traits such as yield can be predicted through early-season phenotypic measurements, crop selection can potentially be made before a full crop generation cycle finishes. This study explored the possibility of predicting soybean end-season traits through the color and texture features of early-season canopy images. Six thousand three hundred and eighty-three images were captured at V4/V5 growth stage over 6039 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix-based texture features were derived from each image. Another two variables were also introduced to account for location and timing differences between the images. Five regression and five classification techniques were explored. Best results were obtained using all 457 predictor variables, with Cubist as the regression technique and Random Forests as the classification technique. Yield (RMSE = 9.82, R2 = 0.68), Maturity (RMSE = 3.70, R2 = 0.76) and Seed Size (RMSE = 1.63, R2 = 0.53) were identified as potential soybean traits that might be early predictable.

Generating High Density, Low Cost Genotype Data in Soybean [Glycine max (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.

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 > 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.

Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population.

Genetic improvement toward optimized and stable agronomic performance of soybean genotypes is desirable for food security. Understanding how genotypes perform in different environmental conditions helps breeders develop sustainable cultivars adapted to target regions. Complex traits of importance are known to be controlled by a large number of genomic regions with small effects whose magnitude and direction are modulated by environmental factors. Knowledge of the constraints and undesirable effects resulting from genotype by environmental interactions is a key objective in improving selection procedures in soybean breeding programs. In this study, the genetic basis of soybean grain yield responsiveness to environmental factors was examined in a large soybean nested association population. For this, a genome-wide association to performance stability estimates generated from a Finlay-Wilkinson analysis and the inclusion of the interaction between marker genotypes and environmental factors was implemented. Genomic footprints were investigated by analysis and meta-analysis using a recently published multiparent model. Results indicated that specific soybean genomic regions were associated with stability, and that multiplicative interactions were present between environments and genetic background. Seven genomic regions in six chromosomes were identified as being associated with genotype-by-environment interactions. This study provides insight into genomic assisted breeding aimed at achieving a more stable agronomic performance of soybean, and documented opportunities to exploit genomic regions that were specifically associated with interactions involving environments and subpopulations.

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.

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 [ (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.

Multi-Population Selective Genotyping to Identify Soybean [Glycine max (L.) Merr.] Seed Protein and Oil QTLs.

Plant breeders continually generate ever-higher yielding cultivars, but also want to improve seed constituent value, which is mainly protein and oil, in soybean [Glycine max (L.) Merr.]. Identification of genetic loci governing those two traits would facilitate that effort. Though genome-wide association offers one such approach, selective genotyping of multiple biparental populations offers a complementary alternative, and was evaluated here, using 48 F2:3 populations (n = âˆ¼224 plants) created by mating 48 high protein germplasm accessions to cultivars of similar maturity, but with normal seed protein content. All F2:3 progeny were phenotyped for seed protein and oil, but only 22 high and 22 low extreme progeny in each F2:3 phenotypic distribution were genotyped with a 1536-SNP chip (ca 450 bimorphic SNPs detected per mating). A significant quantitative trait locus (QTL) on one or more chromosomes was detected for protein in 35 (73%), and for oil in 25 (52%), of the 48 matings, and these QTL exhibited additive effects of ≥ 4 g kg(-1) and R(2) values of 0.07 or more. These results demonstrated that a multiple-population selective genotyping strategy, when focused on matings between parental phenotype extremes, can be used successfully to identify germplasm accessions possessing large-effect QTL alleles. Such accessions would be of interest to breeders to serve as parental donors of those alleles in cultivar development programs, though 17 of the 48 accessions were not unique in terms of SNP genotype, indicating that diversity among high protein accessions in the germplasm collection is less than what might ordinarily be assumed.

Soy protein is beneficial but high-fat diet and voluntary running are detrimental to bone structure in mice.

Physical activity and soy protein isolate (SPI) augmentation have been reported to be beneficial for bone health. We hypothesized that combining voluntary running and SPI intake would alleviate detrimental changes in bone induced by a high-fat diet. A 2 × 2 × 2 experiment was designed with diets containing 16% or 45% of energy as corn oil and 20% SPI or casein fed to sedentary or running male C57BL/6 mice for 14 weeks. Distal femurs were assessed for microstructural changes. The high-fat diet significantly decreased trabecular number (Tb.N) and bone mineral density (BMD) and increased trabecular separation (Tb.Sp). Soy protein instead of casein, regardless of fat content, in the diet significantly increased bone volume fraction, Tb.N, connectivity density, and BMD and decreased Tb.Sp. Voluntary running, regardless of fat content, significantly decreased bone volume fraction, Tb.N, connectivity density, and BMD and increased Tb.Sp. The high-fat diet significantly decreased osteocalcin and increased tartrate-resistant acid phosphatase 5b (TRAP 5b) concentrations in plasma. Plasma concentrations of osteocalcin were increased by both SPI and running. Running alleviated the increase in TRAP 5b induced by the high-fat diet. These findings demonstrate that a high-fat diet is deleterious, and SPI is beneficial to trabecular bone properties. The deleterious effect of voluntary running on trabecular structural characteristics indicates that there may be a maximal threshold of running beyond which beneficial effects cease and detrimental effects occur. Increases in plasma osteocalcin and decreases in plasma TRAP 5b in running mice suggest that a compensatory response occurs to counteract the detrimental effects of excessive running.

Effects of voluntary running and soy supplementation on diet-induced metabolic disturbance and inflammation in mice.

We investigated the effects of diet (AIN93G or high-fat), physical activity (sedentary or voluntary running), and protein source (casein or soy protein isolate (SPI)) and their interactions on metabolic disturbance and inflammation in mice. After 14 weeks of feeding, the high-fat diet increased body weight gain by 34.5% (p < 0.01), whereas running reduced weight gain by 30.5% (p < 0.01) compared to their respective AIN93G and sedentary controls; SPI did not affect weight gain. The high-fat diet significantly increased plasma concentrations of insulin, glucose, triglycerides, leptin, and monocyte chemotactic protein-1 (MCP-1); running and SPI significantly reduced these parameters compared to their respective controls. The high-fat diet significantly increased and running significantly reduced plasma plasminogen activator inhibitor-1. A unique finding was that SPI supplementation to the high-fat diet reduced plasma insulin by 11% (p < 0.05), MCP-1 by 21% (p = 0.03), and tumor necrosis factor-α (TNF-α) by 50% (p = 0.05) compared to casein. As adipose tissues produce many adipocytokines, including MCP-1 and TNF-α, that contribute to a state of chronic low grade systemic inflammation and facilitate metabolic disturbance in obesity, further investigations are warranted into the roles of soy protein in reducing the risk of obesity.

Selenium bioavailability from soy protein isolate and tofu in rats fed a torula yeast-based diet.

Selenium (Se) is an essential nutrient, and soy is a major plant source of dietary protein to humans. The United States produces one-third of the world's soybeans, and the Se-rich Northern Plains produce a large share of the nation's soybeans. The present study used a rat model to determine the bioavailability of Se from a protein isolate and tofu (bean curd) prepared from a soybean cultivar we recently developed specifically for food grade markets. The soybean seeds contained 2.91 mg Se/kg. Male Sprague-Dawley rats were depleted of Se by feeding them a 30% Torula yeast-based diet containing 5 microg Se/kg; after 56 days, they were replenished of Se for an additional 50 days by feeding them the same diet supplemented with 20, 30, or 40 microg Se/kg from soy protein isolate or tofu. l-Selenomethionine (SeMet) was used as a reference. Selenium bioavailability was determined on the basis of the responses of Se-dependent enzyme activities and tissue Se contents, comparing those responses for each soy product to those for SeMet using a slope-ratio method. Dietary supplementation with the protein isolate or tofu resulted in dose-dependent increases in glutathione peroxidase activities in blood and liver and thioredoxin reductase activity in liver, as well as dose-dependent increases in the Se contents of plasma, liver, muscle, and kidneys. These responses indicated an overall bioavailability of approximately 97% for Se from both the protein isolate and tofu, relative to SeMet. These results demonstrate that Se from this soybean cultivar is highly bioavailable in this model and that high-Se soybeans can be good dietary sources of Se.

Response of Soybean Cultivars to Bean pod mottle virus Infection.

Bean pod mottle virus (BPMV) has become increasingly common in soybean throughout the north-central region of the United States. Yield loss assessments on southern soybean germplasm have reported reductions ranging from 3 to 52%. Currently, no soybean cultivars have been identified with resistance to BPMV. The objective of this study was to determine the impact of BPMV infection on soybean cultivars representing a broad range of northern soybean germ-plasm by comparing inoculated and noninoculated soybean plants in paired row studies. In all, 30 and 24 cultivars were evaluated in Nebraska (NE) in which soybean plants were inoculated at the V3 to V4 growth stage. Eleven cultivars from public and breeding lines were inoculated at the VC and R5 to R6 growth stages in Ohio (OH). Disease severity, yield, and percent seed coat mottling were assessed at both locations, whereas protein and oil content also were assessed at NE. Yield and percent seed coat mottling was significantly reduced following inoculation at the VC (OH) and V3 to V4 (NE) growth stages. In addition, seed oil and protein composition were impacted in 1 of the 2 years of the study. This study demonstrates that substantial yield losses can occur in soybean due to BPMV infection. In addition, protein and oil may be affected depending on the environment during the production season.

Dietary supplementation with high-selenium soy protein reduces pulmonary metastasis of melanoma cells in mice.

The effect of high-selenium (Se) soy protein on pulmonary metastasis of murine B16BL6 melanoma cells was investigated in male C57BL6 mice. Isolated soy proteins (ISP) from soybeans grown with and without Se foliar application during seed development were compared. Five diets were studied, a basal AIN-93G diet or a basal diet containing 10% low-Se ISP, 5% low-Se + 5% high-Se ISP, 10% high-Se ISP, or 10% low-Se ISP supplemented with Se equivalent to that of the 10% high-Se ISP diet. The Se concentrations of the 5 diets were 0.13, 0.13, 1.9, 3.6, and 3.0 microg/g, respectively. Mice were fed the diet for 2 wk before and 2 wk after an i.v. injection of 5 x 10(4) viable cells. At necropsy, the number and size of tumors that had developed in the lungs were determined. In the control group, 13/18 mice exhibited > or = 50 tumors. The numbers of mice with > or = 50 tumors were 8/18, 7/18, 3/18, and 6/17 in the ISP-fed groups, respectively. The differences between the 10% high-Se ISP group, the Se-supplemented 10% low-Se group, and the control were significant (P < 0.05). Dietary supplementation with 10% low-Se ISP significantly decreased the mean number of tumors per group and the tumor size compared with the control. A greater reduction in these variables occurred in mice fed the 10% high-Se ISP diet. The inhibition by the Se-supplemented 10% low-Se ISP diet was similar to that by the 10% high-Se ISP diet. The whole-blood Se concentration was inversely related to the tumor number (R = -0.87, P = 0.052), tumor cross-sectional area (R = -0.91, P < 0.05), and tumor volume (R = -0.93, P < 0.05). These findings suggest that Se is responsible for the greater antimetastatic effect of the high-Se ISP. We conclude that the high-Se soy protein has a greater inhibitory effect than the low-Se soy protein on pulmonary metastasis of melanoma cells in mice.