Seed Composition Traits Research Articles

A Preliminary Evaluation of Faba Bean as a Green Shell Bean in Virginia, USA

Even though faba bean (Vicia faba L.) is an important food crop on worldwide basis, its use as a vegetable i.e. use of green seeds as food is limited. We were interested in characterizing production and food quality traits of faba bean grown in eastern USA. Our main objective is to develop faba bean as a winter alternative crop for this region to diversify cropping system that currently mainly depends upon cereal grain crops. This necessitates existence of winter-hardy faba bean varieties. We conducted a preliminary experiment with two winter-hardy faba bean breeding lines to record production and seed composition traits. Two faba bean lines (VSX-BL and VSX-BH) were planted in the field in November 2022 and green pods were harvested in June 2023 at physiological maturity. Values of most of the traits under study were statistically similar between the two lines except for concentration of phosphorus. Results demonstrated that faba bean green pod and green seeds yields could be approximately 5700 and 2100 kg ha-1. The shelling percent in green faba bean pods was approximately 37 whereas seed number per pod was approximately 2.6. Concentrations (g 100 g-1) of fructose, glucose, and sucrose were 0.245, 0.668, 2.9021, respectively whereas concentrations of Fe and Zn (mg kg-1) were 103.7 and 69.7, respectively. Green faba bean seeds contained small amounts of RFO carbohydrates. Seed composition traits of green faba bean seeds compare well with green seeds of pea, soybean, and white lupin. Our results provide a positive proof of concept that production of green faba bean as an alternate winter food legume crop in eastern USA is possible.

Soybean test weight in relation to genotype, environment, and genotype × environment interaction in the Southern United States

AbstractTest weight (TW) is a unit of bulk density that is influenced by several factors, including both genetics and environment. A subset of the trials in the Uniform Soybean Tests—Southern States was used to correlate seed composition traits of oil and protein percentages, and seed weight (SW) with TW to investigate the interaction of these traits in multi‐environments for four different maturity group (MG) tests. The results indicate that environment (location and year combination) had a larger impact on TW than genetic effect in all MG tests. This difference was greater in the earlier MGs, compared to MG VI and MG VII. In all tests across four MGs, the genotype interaction with environment effect was significant, but practically was not as important as genotype or environment effects. Significant negative correlations between TW and SW (MG V, VI, and VII tests), and TW and oil (MG V and VI tests) were observed but not consistent in all environments within these tests. TW and protein correlations were inconsistent with a negative relationship in MG V but a positive relationship in MG VII. As anticipated, the inconsistencies in the relationships across MGs for TW and seed composition traits suggest that selection strategies for improving TW will differ across soybean breeding programs. In the future, a similar study using a diverse panel of cultivars that do not change from year to year may shed more light on the influence of seed composition and environmental conditions on TW.

Identification of High-Yielding Soybean Lines with Exceptional Seed Composition Qualities

In current markets, the primary uses for soybean seeds are in products derived from their oil or protein content. However, growers are compensated based on seed yield, so a more valuable crop is one that does not compromise on yield when compared with existing options, with an optimum combination of protein and oil. A negative correlation of seed protein with seed yield and oil makes the simultaneous improvement of these traits difficult but not impossible through conventional breeding. Selections of lines with exceptional yield and seed composition were made from two recombinant inbred line (RIL) soybean mapping populations to identify high protein and/or high oil lines with yields comparable to elite cultivars. The performance of these RILs was evaluated in multiple environments, and several genotypes were identified with yields comparable to those of high-yielding check cultivars with seed protein and/or oil content superior to the checks. These genotypes will provide breeders with additional sources of germplasm for continuing efforts to improve seed composition traits without compromising seed yield and provide growers with more profitable cultivars.

Soybean seed composition prediction from standing crops using PlanetScope satellite imagery and machine learning

Soybean is a pivotal agricultural commodity around the world, primarily because of its high seed protein and oil concentration. Therefore, farmers, breeders and end-users are highly interested in understanding and predicting the soybean seed composition traits from the individual field level or agroecosystem. Seed composition traits are the proportions of different chemical and physical makeup of soybean seeds. Frequent daily coverage of PlanetScope (PS) satellite provides a unique opportunity of estimating seed composition due to its ability to track crop growth and development with its unique combination of high spatial and temporal resolution. We aim to predict six different soybean seed composition traits (i.e., protein, oil, sucrose, fiber, ash, starch) using PS imagery of standing soybean crops and machine learning algorithms. We developed multi-stream deep neural network which is based on two types of recurrent neural networks, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) that utilize temporal phenology observed from PS. Four statistical machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVR) were used for comparison. Our results show that GRU worked well for protein (R2 0.36, NRMSE 3.62%) and oil (R2 0.53, NRMSE 4.78%), SVR showed the best results for sucrose (R2 0.74, NRMSE 8.34%), fiber (R2 0.21, NRMSE 4.20%), and starch (R2 0.15, NRMSE 16.84%), and PLSR provided the best result for ash (R2 0.60, NRMSE 1.70%). Among the features, vegetation indices at later reproductive stages were found as the most important variables compared to texture features. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation.

Determining Genetic Markers and Seed Compositions Related to High Test Weight in Glycine max.

Test weight, one of the primary indicators of soybean seed quality, is measured as the amount of soybean seeds in kilograms that can fit into one hectoliter. The price that growers receive for their soybean is dependent on test weight. Over the past 50 years, growers have observed a decreasing trend in test weight. Therefore, it is imperative to understand better the relationship between soybean test weight and other traits to enable breeders to select parental lines with high test weights in breeding programs to ensure the grower's profitability. The objectives of the study were to identify genetic markers associated with high test weight in soybean and to determine the correlation between high test weight and five important seed composition traits (protein, oil, sucrose, raffinose, and stachyose content). Maturity group IV and V germplasms from the USDA soybean germplasm collection were grown in Blacksburg and Warsaw in Virginia from 2019 to 2021 and were measured for all of the above traits. Results show that test weight values ranged from 62-77 kg/hL over the three years. Multiple single-nucleotide polymorphisms (SNPs) significantly associated with high test weight were found on chromosome (Chr.) 15 along with a couple on chromosome 14, and 11 candidate genes were found near these SNPs. Test weight was found to be significantly negatively correlated with oil content, inconsistently correlated with protein content in all environments, and negatively correlated but not significantly with all three sugars except for raffinose in Blacksburg 2019. We concluded that the genes that underlie test weight might be on chromosome 15, and the validated associated SNPs might be used to assist breeding selection of test weight. Breeders should pay special attention to test weight while selecting for high oil content in soybean due to their negative correlation.

Selecting recombinants to stack high protein with high oleic acid and low linoleic acid in soybean (Glycine max)

AbstractSoybean accounts for over a quarter of the world's oilseed consumption and over 70% of the world's protein meal consumption. The separate development of high oleic, low linolenic acid (HOLL) soybean and high‐protein (HP) soybean means that no soybean cultivar on the market has an optimal fatty acid profile and increased protein. The objective of this study was to develop and evaluate high protein, high oleic acid, and low linolenic acid (HP‐HOLL) soybean. A five‐gene stack was created using a two‐phase forward breeding scheme and marker‐assisted selection method. Forty‐six HP‐HOLL lines from three genetic backgrounds were grown in six environments in the Southeast United States. Although genotype‐by‐environment interaction was significant for seed composition traits, lines met the >75% and <3% cutoffs for oleic acid and linolenic acid, respectively, and met or exceeded the protein concentration of the HP parent. No negative interaction could be detected between the HP and HOLL traits. Additionally, yield testing in four environments indicated yield parity for some lines, suggesting HP and HOLL soybean cultivars with high yield could be selected.

Genetic Diversity of Korean Black Soybean (Glycine max L.) Germplasms with Green Cotyledons Based on Seed Composition Traits

The demand for black soybeans (Glycine max (L.) Merr.) with green cotyledons is increasing because of their health benefits. Therefore, it is important to understand the genetic diversity of black soybean germplasms and to develop a new soybean cultivar. This study aimed to evaluate genetic diversity among 469 black soybean germplasms with green cotyledons based on seed composition traits. Twenty seed composition traits were analyzed to conduct correlation analysis, principal component analysis (PCA), and cluster analysis, which indicated that black soybean germplasms were divided into four clusters. Black soybean germplasms in cluster 1 had higher crude fat, lutein, chlorophyll a, chlorophyll b, and total chlorophyll contents, but lower cyanidin-3-glucoside content than those in clusters 2 and 3. However, germplasms in clusters 2 and 3 had the highest cyanidin-3-glucoside content. Moreover, germplasms in cluster 1 had significantly higher palmitic acid content than those in clusters 2 and 3. Germplasms in clusters 2 and 3 had relatively high α-linolenic acid content. Germplasms in cluster 4 had the highest oleic acid content. This study highlights the genetic diversity of black soybean germplasms with different seed composition traits, and the results of this study can be beneficial for soybean breeding programs, enabling them to develop new black soybean cultivars with green cotyledons and improved seed composition traits.

Soybean Protein and Oil Variants Identified through a Forward Genetic Screen for Seed Composition.

Mutagenesis remains an important tool in soybean biology. In classical plant mutation breeding, mutagenesis has been a trusted approach for decades, creating stable non-transgenic variation, and many mutations have been incorporated into germplasm for several crops, especially to introduce favorable seed composition traits. We performed a genetic screen for aberrant oil or protein composition of soybean seeds, and as a result isolated over 100 mutant lines for seed composition phenotypes, with particular interest in high protein or high oil phenotypes. These lines were followed for multiple seasons and generations to select the most stable traits for further characterization. Through backcrossing and outcrossing experiments, we determined that a subset of the lines showed recessive inheritance, while others showed a dominant inheritance pattern that suggests the involvement of multiple loci and genetic mechanisms. These lines can be used as a resource for future studies of the genetic control of seed protein and oil content in soybean.

A Study of Application and Comparison of Thermal Drying and Freeze Drying of Fresh Edamame Seeds in the Analysis of Seed Composition

Edamame is a vegetable soybean (Glycine max) with high nutritional and market value. It is mainly grown in Asia and has expanded to North America and Africa. Freeze and low- and high-heat drying methods were used to dry fresh edamame seeds of 20 soybean lines and cultivars for analysis of their composition using near-infrared reflectance (NIR) technology. The results indicated that significant differences existed between years of samplings for all seed composition traits investigated. Differences between drying methods were significant for all the traits with whole-seed samples, while they were not significant in protein, raffinose and linoleic acid for flour samples. Ground flour and whole-seed samples were similar or comparable in most cases for freeze and low-heat drying methods, but differences in seed composition between sample types were observed for high-heat drying. Among the traits investigated, protein content was the most consistent for all three drying methods, with an over 91% estimate of repeatability, showing high stability during drying. Oil content also showed a repeatability estimate of over 86% for all three drying methods. Low-heat drying generated results more comparable to freeze drying, while high-heat drying exhibited larger differences in most cases. Estimates of repeatability and correlation coefficients further confirmed that, low-heat drying, similar to freeze drying, was appropriate for drying fresh edamame seeds, but high-heat drying was not. Therefore, low-heat drying is a suitable method for drying fresh edamame and rapid analysis of seed composition, and it can be used as an alternative of freeze-drying method.

SoyMAGIC: An Unprecedented Platform for Genetic Studies and Breeding Activities in Soybean.

Multi-Parent Advanced Generation Inter-Cross (MAGIC) populations are emerging genetic platforms for high-resolution and fine mapping of quantitative traits, such as agronomic and seed composition traits in soybean (Glycine max L.). We have established an eight-parent MAGIC population, comprising 721 recombinant inbred lines (RILs), through conical inter-mating of eight soybean lines. The parental lines were genetically diverse elite cultivars carrying different agronomic and seed composition characteristics, including amino acids and fatty acids, as well as oil and protein concentrations. This study aimed to introduce soybean MAGIC (SoyMAGIC) population as an unprecedented platform for genotypic and phenotypic investigation of agronomic and seed quality traits in soybean. The RILs were evaluated for important seed composition traits using replicated field trials during 2020 and 2021. To measure the seed composition traits, near-infrared reflectance (NIR) was employed. The RILs were genotyped using genotyping-by-sequencing (GBS) method to decipher the genome and discover single-nucleotide polymorphic (SNP) markers among the RILs. A high-density linkage map was constructed through inclusive composite interval mapping (ICIM). The linkage map was 3,770.75 cM in length and contained 12,007 SNP markers. Chromosomes 11 and 18 were recorded as the shortest and longest linkage groups with 71.01 and 341.15 cM in length, respectively. Observed transgressive segregation of the selected traits and higher recombination frequency across the genome confirmed the capability of MAGIC population in reshuffling the diversity in the soybean genome among the RILs. The assessment of haplotype blocks indicated an uneven distribution of the parents’ genomes in RILs, suggesting cryptic influence against or in favor of certain parental genomes. The SoyMAGIC population is a recombined genetic material that will accelerate further genomic studies and the development of soybean cultivars with improved seed quality traits through the development and implementation of reliable molecular-based toolkits.

Advances in improvement of soybean seed composition traits using genetic, genomic and biotechnological approaches

Advances in improvement of soybean seed composition traits using genetic, genomic and biotechnological approaches

Exploring the Phenotypic Stability of Soybean Seed Compositions Using Multi-Trait Stability Index Approach

In order to ensure an ongoing and long-term breeding progress of soybean, stable sources of major quality traits across multi-environments need to be identified. Here, a panel of 135 soybean genotypes was tested in three different Chinese environments, including Beijing, Anhui, and Hainan during the 2017 and 2018 growing seasons to identify stable genotypes for cultivation under varying environmental conditions. The weighted average of absolute scores biplot (WAASB) for the best linear unbiased predictions of the genotype-environment interaction and multi-trait stability index (MTSI) were utilized to determine the stability of the soybeans for seven seed composition traits viz; protein content, oil content, and five fatty acids (palmitic, stearic, oleic, linoleic, and linolenic acids). Based on the WAASB index, the following genotypes were identified as stable genotypes for some specific traits; ZDD12828 and ZDD12832 for protein content, WDD01583 and WDD03025 for oil content, ZDD23040 for palmitic acid, WDD00033 for stearic acid, ZDD23822 for oleic acid, ZDD11183 for linoleic acid, and ZDD08489 for linolenic acid. Furthermore, based on MTSI at a selection intensity of 10%, 14 soybean genotypes were selected for their average performance and stability. Overall, the MTSI was shown to be a powerful and simple tool for identifying superior genotypes in terms of both performance and stability, hence, identifying stable soybean genotypes for future breeding programs of quality traits.

Comparative prediction accuracy of hyperspectral bands for different soybean crop variables: From leaf area to seed composition

Prediction of soybean seed yield and seed composition at a plot scale before harvesting has potential uses in breeding programs for early-season selection and harvesting decisions. Reflectance information from hyperspectral bands have been mainly used for predicting yield and other crop variables. However, an analysis comparing the prediction accuracy among different crop variables such as LAI, biomass, seed yield and seed protein and oil, when using hyperspectral bands as predictors, is lacking. Our objective is to rank the prediction accuracy among different crop variables using hyperspectral bands captured at different timepoints during the growing season. Our hypothesis is based on a physiological framework where crop variables that are closely associated with light interception (i.e., LAI) would be best predicted by the hyperspectral signal (350 nm–2500 nm) than variables that involve more physiological processes (i.e., biomass, seed yield and seed protein and oil) for their determination. The dataset used for testing this hypothesis involved different genotypes, environments, and management practices. We used Partial Least Squares regression with cross-validation to test the association between the observed variables and the hyperspectral bands. Our results showed that LAI can be best predicted using reflectance information, and suggest that hyperspectral bands are necessary but not sufficient to improve the prediction of other crop variables such as biomass, seed yield, and seed composition traits.

Mining QTLs for elevated protein and other major seed composition traits from diverse soybean germplasm.

The online version contains supplementary material available at 10.1007/s11032-021-01242-z.

Modeling spatial trends and enhancing genetic selection: An approach to soybean seed composition breeding

AbstractSpatial variation is a recurrent issue in field trials and can cause obstacles in terms of genetic selection. Analyses that account for spatial variation within location can lead breeders to predict genetic values accurately across locations in multi‐environment trials (METs). The present study aims to fit spatial models for analyzing soybean [Glycine max (L.) Merr.] seed composition traits using a two‐stage analysis pipeline and to assess its efficiency relative to a single‐stage analysis setting. Seed protein content (SPC), seed oil content (SOC), and seed storage protein content (SSP) data were collected from 283 soybean genotypes tested in four environments (C1, C2, V1, and V2). In Stage 1 of the two‐stage analysis, a randomized complete block (RCB) design model as well as four two‐dimensional first‐order (AR1 ⊗ AR1) spatial models were fit in each dataset to determine the most suitable model for genetic prediction. Predicted genetic values were used as input data for Stage 2. The most used spatial model [5] in Stage 1 of this study had accommodated local and global residuals. The autocorrelation estimates depicted spatial trends, especially in terms of rows, while column autocorrelation coefficients were low for C1 and C2 because of the limited number of blocks and their short length. Broad‐sense heritability, mean accuracy, and selection gains were greater for all traits in the two‐stage analysis than in the single‐stage analysis. The two‐stage analysis leveraged the spatial model fitting in the Stage 1 and proved to be advantageous for soybean seed composition breeding.

Effects of type I Diacylglycerol O-acyltransferase (DGAT1) genes on soybean (Glycine max L.) seed composition

Type I Diacylglycerol acyltransferase (DGAT1) catalyzes the final step of the biosynthesis process of triacylglycerol (TAG), the major storage lipids in plant seeds, through the esterification of diacylglycerol (DAG). To characterize the function of DGAT1 genes on the accumulation of oil and other seed composition traits in soybean, transgenic lines were generated via trans-acting siRNA technology, in which three DGAT1 genes (Glyma.13G106100, Glyma.09G065300, and Glyma.17G053300) were downregulated. The simultaneous downregulation of the three isoforms in transgenic lines was found to be associated with the reduction of seed oil concentrations by up to 18 mg/g (8.3%), which was correlated with increases in seed protein concentration up to 42 mg/g (11%). Additionally, the downregulations also influenced the fatty acid compositions in the seeds of transgenic lines through increasing the level of oleic acid, up to 121 mg/g (47.3%). The results of this study illustrate the importance of DGAT1 genes in determining the seed compositions in soybean through the development of new potential technology for manipulating seed quality in soybean to meet the demands for its various food and industrial applications.

Identification of potential QTLs and genes associated with seed composition traits in peanut (Arachis hypogaea L.) using GWAS and RNA-Seq analysis

Cultivated peanut (Arachis hypogaea L.) is a major oilseed crop providing edible oil and protein. Oil quality is determined by fatty acid composition including the ratio of oleic acid (C18:1) and linoleic acid (C18:2). A genome-wide association study with 13,382 single nucleotide polymorphisms (SNPs) was conducted to investigate the genetics basis of oil, protein, eight fatty acid concentrations, and O/L ratio (ratio of oleic and linoleic acid) using a diverse panel of 120 genotypes mainly selected from the U.S. peanut mini core collection grown in two years. A total of 178 significant quantitative trait loci (QTLs) associated with those seed composition traits were identified with phenotypic variation explained (PVE) from 18.35% to 27.56%. RNA-Seq analysis identified 282 DEGs (differentially expressed genes) within the 1Mb of the significant QTLs for seed composition traits. Among those 282 genes, sixteen candidate genes for seed fatty acid metabolism and protein synthesis were screened according to the gene functions.

Similar Seed Composition Phenotypes Are Observed From CRISPR-Generated In-Frame and Knockout Alleles of a Soybean KASI Ortholog.

The β-ketoacyl-[acyl carrier protein] synthase 1 (KASI) gene has been shown in model plant systems to be critical for the conversion of sucrose to oil. A previous study characterized the morphological and seed composition phenotypes associated with a reciprocal chromosomal translocation that disrupted one of the KASI genes in soybean. The principle findings of this work included a wrinkled seed phenotype, an increase in seed sucrose, a decrease in seed oil, and a low frequency of transmission of the translocation. However, it remained unclear which, if any, of these phenotypes were directly caused by the loss of KASI gene function, as opposed to the chromosomal translocation or other associated factors. In this study, CRISPR/Cas9 mutagenesis was used to generate multiple knockout alleles for this gene, and also one in-frame allele. These soybean plants were evaluated for morphology, seed composition traits, and genetic transmission. Our results indicate that the CRISPR/Cas9 mutants exhibited the same phenotypes as the chromosomal translocation mutant, validating that the observed phenotypes are caused by the loss of gene function. Furthermore, the plants harboring homozygous in-frame mutations exhibited similar phenotypes compared to the plants harboring homozygous knockout mutations. This result indicates that the amino acids lost in the in-frame mutant are essential for proper gene function. In-frame edits for this gene may need to target less essential and/or evolutionarily conserved domains in order to generate novel seed composition phenotypes.

Assessment of Phenotypic Variations and Correlation among Seed Composition Traits in Mutagenized Soybean Populations.

Soybean [Glycine max (L.) Merr.] seed is a valuable source of protein and oil worldwide. Traditionally, the natural variations were heavily used in conventional soybean breeding programs to select desired traits. However, traditional plant breeding is encumbered with low frequencies of spontaneous mutations. In mutation breeding, genetic variations from induced mutations provide abundant sources of alterations in important soybean traits; this facilitated the development of soybean germplasm with modified seed composition traits to meet the different needs of end users. In this study, a total of 2366 ‘Forrest’-derived M2 families were developed for both forward and reverse genetic studies. A subset of 881 M3 families was forward genetically screened to measure the contents of protein, oil, carbohydrates, and fatty acids. A total of 14 mutants were identified to have stable seed composition phenotypes observed in both M3 and M4 generations. Correlation analyses have been conducted among ten seed composition traits and compared to a collection of 103 soybean germplasms. Mainly, ethyl methanesulfonate (EMS) mutagenesis had a strong impact on the seed-composition correlation that was observed among the 103 soybean germplasms, which offers multiple benefits for the soybean farmers and industry to breed for desired multiple seed phenotypes.

Impact of genotype, seed composition, agronomic trait, and environment on soybean test weight

ABSTRACTTest weight (TW) is a primary indicator of soybean grain quality and measure of bulk density (mass/volume). The objectives of this research were to evaluate the effects of genotype, environment and planting date on TW, and determine the relationship of TW with agronomic and seed composition traits. Six experiments (Exp) were conducted using diverse sets of cultivars/experimental lines. Significant negative linear correlations between TW and seed moisture in each of five genotypes were observed (P < 0.0001). A linear regression model using both measured TW and moisture were developed to normalize TW on a 13% moisture basis for comparison across genotypes and environments. Significant genetic variations (P < 0.01) of TW among genotypes were observed, ranging from 70.0 to 75.3; 70.3 to 74.3; and 66.4 to 74.3 kg hL−1 in Exp-1, 2, and 3, respectively. Locations and G x E interactions also had significant effects on TW (P < 0.01) in these EXPs. Soybean yield was significantly negatively correlated with TW in Exp-1 and 3 (P < 0.01). Seed protein and sucrose contents, and maturity days were significantly, positively correlated with TW, while seed oil content and seed size were significantly, negatively correlated with TW in most or all experiments (P < 0.05). Effects of planting dates on TW were inconsistent for both Exp-4 and 5 between 2 years. There was no correlation between TW of the cultivars and their releasing years in MG VII. To our knowledge, this is the first report on test weight in soybean and the results could be used to guide future genetic improvement of TW.