CRISPR/Cas9 gene editing is now revolutionizing the ability to effectively modify plant genomes in the absence of efficient homologous recombination mechanisms that exist in other organisms. However, soybean is allotetraploid and is commonly viewed as difficult and inefficient to transform. In this study, we demonstrate the utility of CRISPR/Cas9 gene editing in soybean at relatively high efficiency. This was shown by specifically targeting the Fatty Acid Desaturase 2 (GmFAD2) that converts the monounsaturated oleic acid (C18:1) to the polyunsaturated linoleic acid (C18:2), therefore, regulating the content of monounsaturated fats in soybean seeds. We designed two gRNAs to guide Cas9 to simultaneously cleave two sites, spaced 1Kb apart, within the second exons of GmFAD2–1A and GmFAD2–1B. In order to test whether the Cas9 and gRNAs would perform properly in transgenic soybean plants, we first tested the CRISPR construct we developed by transient hairy root transformation using Agrobacterium rhizogenesis strain K599. Once confirmed, we performed stable soybean transformation and characterized ten, randomly selected T0 events. Genotyping of CRISPR/Cas9 T0 transgenic lines detected a variety of mutations including large and small DNA deletions, insertions and inversions in the GmFAD2 genes. We detected CRISPR- edited DNA in all the tested T0 plants and 77.8% of the events transmitted the GmFAD2 mutant alleles to T1 progenies. More importantly, null mutants for both GmFAD2 genes were obtained in 40% of the T0 plants we genotyped. The fatty acid profile analysis of T1 seeds derived from CRISPR-edited plants homozygous for both GmFAD2 genes showed dramatic increases in oleic acid content to over 80%, whereas linoleic acid decreased to 1.3–1.7%. In addition, transgene-free high oleic soybean homozygous genotypes were created as early as the T1 generation. Overall, our data showed that dual gRNA CRISPR/Cas9 system offers a rapid and highly efficient method to simultaneously edit homeologous soybean genes, which can greatly facilitate breeding and gene discovery in this important crop plant.

Although modern soybean cultivars feature yellow seed coats, with the only color variation found at the hila, the ancestral condition is black seed coats. Both seed coat and hila coloration are due to the presence of phenylpropanoid pathway derivatives, principally anthocyanins. The genetics of soybean seed coat and hilum coloration were first investigated during the resurgence of genetics during the 1920s, following the rediscovery of Mendel's work. Despite the inclusion of this phenotypic marker into the extensive genetic maps developed for soybean over the last twenty years, the genetic basis behind the phenomenon of brown seed coats (the R locus) has remained undetermined until now.

Background: Genomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures. Results: Correlations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions. Conclusions: We conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.

Background: Soybean (Glycine max [L] Merr.) seed isoflavones have long been considered a desirable trait to target in selection programs for their contribution to human health and plant defense systems. However, attempts to modify seed isoflavone contents have not always produced the expected results because their genetic basis is polygenic and complex. Undoubtedly, the extreme variability that seed isoflavones display over environments has obscured our understanding of the genetics involved. Results: In this study, a mapping population of RILs with three replicates was analyzed in four different environments (two locations over two years). We found a total of thirty-five main-effect genomic regions and many epistatic interactions controlling genistein, daidzein, glycitein and total isoflavone accumulation in seeds. The use of distinct environments permitted detection of a great number of environment-modulated and minor-effect QTL. Our findings suggest that isoflavone seed concentration is controlled by a complex network of multiple minor-effect loci interconnected by a dense epistatic map of interactions. The magnitude and significance of the effects of many of the nodes and connections in the network varied depending on the environmental conditions. In an attempt to unravel the genetic architecture underlying the traits studied, we searched on a genome-wide scale for genomic regions homologous to the most important identified isoflavone biosynthetic genes. We identified putative candidate genes for several of the main-effect and epistatic QTL and for QTL reported by other groups. Conclusions: To better understand the underlying genetics of isoflavone accumulation, we performed a large scale analysis to identify genomic regions associated with isoflavone concentrations. We not only identified a number of such regions, but also found that they can interact with one another and with the environment to form a complex adaptable network controlling seed isoflavone levels. We also found putative candidate genes in several regions and overall we advanced the knowledge of the genetics underlying isoflavone synthesis.

High growth temperatures negatively affect soybean (Glycine max (L.) Merr) yields and seed quality. Soybean plants, heat stressed during seed development, produce seed that exhibit wrinkling, discoloration, poor seed germination, and have an increased potential for incidence of pathogen infection and an overall decrease in economic value. Soybean breeders have identified a heat stress tolerant exotic landrace genotype, which has been used in traditional hybridization to generate experimental genotypes, with improved seed yield and heat tolerance. Here, we have investigated the seed protein composition and ultrastructure of cotyledonary parenchyma cells of soybean genotypes that are either susceptible or tolerant to high growth temperatures. Biochemical analyses of seed proteins isolated from heat-tolerant and heat-sensitive genotypes produced under 28/22 °C (control), 36/24 °C (moderate), and 42/26 °C (extreme) day/night temperatures revealed that the accumulation in soybean seeds of lipoxygenase, the β-subunit of β-conglycinin, sucrose binding protein and Bowman-Birk protease inhibitor were negatively impacted by extreme heat stress in both genotypes, but these effects were less pronounced in the heat-tolerant genotype. Western blot analysis showed elevated accumulation of heat shock proteins (HSP70 and HSP17.6) in both lines in response to elevated temperatures during seed fill. Transmission electron microscopy showed that heat stress caused dramatic structural changes in the storage parenchyma cells. Extreme heat stress disrupted the structure and the membrane integrity of protein storage vacuoles, organelles that accumulate seed storage proteins. The detachment of the plasma membrane from the cell wall (plasmolysis) was commonly observed in the cells of the sensitive line. In contrast, these structural changes were less pronounced in the tolerant genotype, even under extreme heat stress, cells, for the most part, retained their structural integrity. The results of our study demonstrate the contrasting effects of heat stress on the seed protein composition and ultrastructural alterations that contribute to the tolerant genotype’s ability to tolerate high temperatures during seed development.