Bowman-Birk Inhibitor Mutants of Soybean Generated by CRISPR-Cas9 Reveal Drastic Reductions in Trypsin and Chymotrypsin Inhibitor Activities.

Despite the high quality of soybean protein, raw soybeans and soybean meal cannot be directly included in animal feed mixtures due to the presence of Kunitz (KTi) and Bowman-Birk protease inhibitors (BBis), which reduces animal productivity. Heat treatment can substantially inactivate trypsin and chymotrypsin inhibitors (BBis), but such treatment is energy-intensive, adds expense, and negatively impacts the quality of seed proteins. As an alternative approach, we have employed CRISPR/Cas9 gene editing to create mutations in BBi genes to drastically lower the protease inhibitor content in soybean seed. Agrobacterium-mediated transformation was used to generate several stable transgenic soybean events. These independent CRISPR/Cas9 events were examined in comparison to wild-type plants using Sanger sequencing, proteomic analysis, trypsin/chymotrypsin inhibitor activity assays, and qRT-PCR. Collectively, our results demonstrate the creation of an allelic series of loss-of-function mutations affecting the major BBi gene in soybean. Mutations in two of the highly expressed seed-specific BBi genes lead to substantial reductions in both trypsin and chymotrypsin inhibitor activities.

Development of a Standardized Soybean Cyst Nematode Screening Assay in Pennycress and Identification of Resistant Germplasm.

The prospect of incorporating pennycress as an oilseed cover crop in the Midwest's corn-soybean rotation system has drawn researcher and farmer attention. The inclusion of pennycress will be beneficial as it provides an excellent soil cover to reduce soil erosion and nutrient leaching while serving as an additional source for oilseed production and income. However, pennycress is an alternative host for soybean cyst nematode (SCN), which is a major biological threat to soybean that needs to be addressed for sustainable pennycress adoption into our current production systems. To develop a standardized SCN resistance screening strategy in pennycress, we tested and optimized five parameters: (i) germination stimulants, (ii) inoculation timing, (iii) inoculation rate, (iv) experimental incubation time, and (v) susceptible checks. The standardized SCN resistance screening protocol includes the following: (i) treating pennycress seeds with gibberellic acid for 24 h, (ii) transplanting seedlings 12 to 15 days after initiating germination and inoculating 10 to 12 days after transplantation, (iii) inoculating at a rate of 1,500 eggs/100 cc soil (1,500 eggs per plant), (iv) processing roots at 30 days after inoculation, and (v) using susceptible pennycress accession Ames 32869 to calculate the female index. The standardized protocol was used to quantify the response of a diverse set of pennycress accessions for response against SCN HG type 1.2.5.7 and HG type 7. While there were no highly resistant pennycress lines identified, 15 were rated as moderately resistant to HG type 1.2.5.7, and eight were rated moderately resistant to HG type 7. The resistant lines identified in this study could be utilized to develop SCN-resistant pennycress cultivars. The study also opens a new avenue for research to understand SCN-pennycress interactions through molecular and genomic studies. This knowledge could aid in the successful inclusion of pennycress as a beneficial cover/oilseed crop in the United States Midwest.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Loss-of-function of an α-SNAP gene confers resistance to soybean cyst nematode.

Plant-parasitic nematodes are one of the most economically impactful pests in agriculture resulting in billions of dollars in realized annual losses worldwide. Soybean cyst nematode (SCN) is the number one biotic constraint on soybean production making it a priority for the discovery, validation and functional characterization of native plant resistance genes and genetic modes of action that can be deployed to improve soybean yield across the globe. Here, we present the discovery and functional characterization of a soybean resistance gene, GmSNAP02. We use unique bi-parental populations to fine-map the precise genomic location, and a combination of whole genome resequencing and gene fragment PCR amplifications to identify and confirm causal haplotypes. Lastly, we validate our candidate gene using CRISPR-Cas9 genome editing and observe a gain of resistance in edited plants. This demonstrates that the GmSNAP02 gene confers a unique mode of resistance to SCN through loss-of-function mutations that implicate GmSNAP02 as a nematode virulence target. We highlight the immediate impact of utilizing GmSNAP02 as a genome-editing-amenable target to diversify nematode resistance in commercially available cultivars.

Identification of genomic regions associated with the plasticity of carbon 13 ratio in soybean.

Improving water use efficiency (WUE) for soybean [Glycine max (L.) Merr.] through selection for high carbon isotope (C13) ratio may increase drought tolerance, but increased WUE may limit growth in productive environments. An ideal genotype would be plastic for C13 ratio; that is, be able to alter C13 ratio in response to the environment. Our objective was to identify genomic regions associated with C13 ratio plasticity, C13 ratio stability, and overall C13 ratio in two panels of diverse Maturity Group IV soybean accessions. A second objective was to identify accessions that differed in their C13 ratio plasticity. Panel 1 (205 accessions) was evaluated in seven irrigated and four drought environments, and Panel 2 (373 accessions) was evaluated in four environments. Plasticity was quantified as the slope from regressing C13 ratio of individual genotypes against an environmental index calculated based on the mean within and across environments. The regression intercept was considered a measure of C13 ratio over all environments, and the root mean square error was considered a measure of stability. Combined over both panels, genome-wide association mapping (GWAM) identified 19 single nucleotide polymorphisms (SNPs) for plasticity, 39 SNPs for C13 ratio, and 16 SNPs for stability. Among these SNPs, 71 candidate genes had annotations associated with transpiration or water conservation and transport, root development, root hair elongation, and stomatal complex morphogenesis. The genomic regions associated with plasticity and stability identified in the current study will be a useful resource for implementing genomic selection for improving drought tolerance in soybean.

Linkage analysis and residual heterozygotes derived near isogenic lines reveals a novel protein quantitative trait loci from a Glycine soja accession.

Modern soybean [Glycine max (L.) Merr] cultivars have low overall genetic variation due to repeated bottleneck events that arose during domestication and from selection strategies typical of many soybean breeding programs. In both public and private soybean breeding programs, the introgression of wild soybean (Glycine soja Siebold and Zucc.) alleles is a viable option to increase genetic diversity and identify new sources for traits of value. The objectives of our study were to examine the genetic architecture responsible for seed protein and oil using a recombinant inbred line (RIL) population derived from hybridizing a G. max line ('Osage') with a G. soja accession (PI 593983). Linkage mapping identified a total of seven significant quantitative trait loci on chromosomes 14 and 20 for seed protein and on chromosome 8 for seed oil with LOD scores ranging from 5.3 to 31.7 for seed protein content and from 9.8 to 25.9 for seed oil content. We analyzed 3,015 single F4:9 soybean plants to develop two residual heterozygotes derived near isogenic lines (RHD-NIL) populations by targeting nine SNP markers from genotype-by-sequencing, which corresponded to two novel quantitative trait loci (QTL) derived from G. soja: one for a novel seed oil QTL on chromosome 8 and another for a novel protein QTL on chromosome 14. Single marker analysis and linkage analysis using 50 RHD-NILs validated the chromosome 14 protein QTL, and whole genome sequencing of RHD-NILs allowed us to reduce the QTL interval from ∼16.5 to ∼4.6 Mbp. We identified two genomic regions based on recombination events which had significant increases of 0.65 and 0.72% in seed protein content without a significant decrease in seed oil content. A new Kompetitive allele-specific polymerase chain reaction (KASP) assay, which will be useful for introgression of this trait into modern elite G. max cultivars, was developed in one region. Within the significantly associated genomic regions, a total of eight genes are considered as candidate genes, based on the presence of gene annotations associated with the protein or amino acid metabolism/movement. Our results provide better insights into utilizing wild soybean as a source of genetic diversity for soybean cultivar improvement utilizing native traits.

Epistatic interaction between Rhg1-a and Rhg2 in PI 90763 confers resistance to virulent soybean cyst nematode populations.

KEY MESSAGE: An epistatic interaction between SCN resistance loci rhg1-a and rhg2 in PI 90763 imparts resistance against virulent SCN populations which can be employed to diversify SCN resistance in soybean cultivars. With more than 95% of the $46.1B soybean market dominated by a single type of genetic resistance, breeding for soybean cyst nematode (SCN)-resistant soybean that can effectively combat the widespread increase in virulent SCN populations presents a significant challenge. Rhg genes (for Resistance to Heterodera glycines) play a key role in resistance to SCN; however, their deployment beyond the use of the rhg1-b allele has been limited. In this study, quantitative trait loci (QTL) were mapped using PI 90763 through two biparental F3:4 recombinant inbred line (RIL) populations segregating for rhg1-a and rhg1-b alleles against a SCN HG type 1.2.5.7 (Race 2) population. QTL located on chromosome 18 (rhg1-a) and chromosome 11 (rhg2) were determined to confer SCN resistance in PI 90763. The rhg2 gene was fine-mapped to a 169-Kbp region pinpointing GmSNAP11 as the strongest candidate gene. We demonstrated a unique epistatic interaction between rhg1-a and rhg2 loci that not only confers resistance to multiple virulent SCN populations. Further, we showed that pyramiding rhg2 with the conventional mode of resistance, rhg1-b, is ineffective against these virulent SCN populations. This highlights the importance of pyramiding rhg1-a and rhg2 to maximize the impact of gene pyramiding strategies toward management of SCN populations virulent on rhg1-b sources of resistance. Our results lay the foundation for the next generation of soybean resistance breeding to combat the number one pathogen of soybean.

Genomic prediction models for traits differing in heritability for soybean, rice, and maize.

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.

Identification and Confirmation of Loci Associated With Canopy Wilting in Soybean Using Genome-Wide Association Mapping.

Drought causes significant soybean [Glycine max (L.) Merr.] yield losses each year in rain-fed production systems of many regions. Genetic improvement of soybean for drought tolerance is a cost-effective approach to stabilize yield under rain-fed management. The objectives of this study were to confirm previously reported soybean loci and to identify novel loci associated with canopy wilting (CW) using a panel of 200 diverse maturity group (MG) IV accessions. These 200 accessions along with six checks were planted at six site-years using an augmented incomplete block design with three replications under irrigated and rain-fed treatments. Association mapping, using 34,680 single nucleotide polymorphisms (SNPs), identified 188 significant SNPs associated with CW that likely tagged 152 loci. This includes 87 SNPs coincident with previous studies that likely tagged 68 loci and 101 novel SNPs that likely tagged 84 loci. We also determined the ability of genomic estimated breeding values (GEBVs) from previous research studies to predict CW in different genotypes and environments. A positive relationship (P ≤ 0.05;0.37 ≤ r ≤ 0.5) was found between observed CW and GEBVs. In the vicinity of 188 significant SNPs, 183 candidate genes were identified for both coincident SNPs and novel SNPs. Among these 183 candidate genes, 57 SNPs were present within genes coding for proteins with biological functions involved in plant stress responses. These genes may be directly or indirectly associated with transpiration or water conservation. The confirmed genomic regions may be an important resource for pyramiding favorable alleles and, as candidates for genomic selection, enhancing soybean drought tolerance.

Phenotyping and Quantitative Trait Locus Analysis for the Limited Transpiration Trait in an Upper-Mid South Soybean Recombinant Inbred Line Population ("Jackson" × "KS4895"): High Throughput Aquaporin Inhibitor Screening.

Soybean is most often grown under rainfed conditions and negatively impacted by drought stress in the upper mid-south of the United States. Therefore, identification of drought-tolerance traits and their corresponding genetic components are required to minimize drought impacts on productivity. Limited transpiration (TRlim) under high vapor pressure deficit (VPD) is one trait that can help conserve soybean water-use during late-season drought. The main research objective was to evaluate a recombinant inbred line (RIL) population, from crossing two mid-south soybean lines ("Jackson" × "KS4895"), using a high-throughput technique with an aquaporin inhibitor, AgNO3, for the TRlim trait. A secondary objective was to undertake a genetic marker/quantitative trait locus (QTL) genetic analysis using the AgNO3 phenotyping results. A set of 122 soybean genotypes (120-RILs and parents) were grown in controlled environments (32/25-d/n °C). The transpiration rate (TR) responses of derooted soybean shoots before and after application of AgNO3 were measured under 37°C and >3.0 kPa VPD. Then, the decrease in transpiration rate (DTR) for each genotype was determined. Based on DTR rate, a diverse group (slow, moderate, and high wilting) of 26 RILs were selected and tested for the whole plant TRs under varying levels of VPD (0.0-4.0 kPa) at 32 and 37°C. The phenotyping results showed that 88% of slow, 50% of moderate, and 11% of high wilting genotypes expressed the TRlim trait at 32°C and 43, 10, and 0% at 37°C, respectively. Genetic mapping with the phenotypic data we collected revealed three QTL across two chromosomes, two associated with TRlim traits and one associated with leaf temperature. Analysis of Gene Ontologies of genes within QTL regions identified several intriguing candidate genes, including one gene that when overexpressed had previously been shown to confer enhanced tolerance to abiotic stress. Collectively these results will inform and guide ongoing efforts to understand how to deploy genetic tolerance for drought stress.

Genomic prediction using training population design in interspecific soybean populations.

Agronomically important traits generally have complex genetic architecture, where many genes have a small and largely additive effect. Genomic prediction has been demonstrated to increase genetic gain and efficiency in plant breeding programs beyond marker-assisted selection and phenotypic selection. The objective of this study was to evaluate the impact of allelic origin, marker density, training population size, and cross-validation schemes on the accuracy of genomic prediction models in an interspecific soybean nested association mapping (NAM) panel. Three cross-validation schemes were used: (a) Within-Family (WF): training population and predictions are made exclusively within each family; (b) Across All families (AF): all the individuals from the three families were randomly assigned to either the training or validation set; (c) Leave one Family out (LFO): each family is predicted using a training set that contains the other two families. Predictive abilities increased with training population size up to 350 individuals, but no significant gains were noted beyond 250 individuals in the training population. The number of markers had a limited impact on the observed predictive ability across traits; increasing markers used in the model above 1000 revealed no significant increases in prediction accuracy. Predictive abilities for AF were not significantly different from the WF method, and predictive abilities across populations for the WF method had a range of 0.58 to 0.70 for maturity, protein, meal, and oil. Our results also showed encouraging prediction accuracies for grain yield (0.58-0.69) using the WF method. Partitioning genomic prediction between G. max and G. soja alleles revealed useful information to select material with a larger allele contribution from both parents and could accelerate allele introgression from exotic germplasm into the elite soybean gene pool. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01203-6.

Effect of Heat Stress on Seed Protein Composition and Ultrastructure of Protein Storage Vacuoles in the Cotyledonary Parenchyma Cells of Soybean Genotypes That Are Either Tolerant or Sensitive to Elevated Temperatures.

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 ß-subunit of ß-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.

Genome-Wide Association Mapping of Dark Green Color Index using a Diverse Panel of Soybean Accessions.

Nitrogen (N) plays a key role in plants because it is a major component of RuBisCO and chlorophyll. Hence, N is central to both the dark and light reactions of photosynthesis. Genotypic variation in canopy greenness provides insights into the variation of N and chlorophyll concentration, photosynthesis rates, and N2 fixation in legumes. The objective of this study was to identify significant loci associated with the intensity of greenness of the soybean [Glycine max (L.) Merr.] canopy as determined by the Dark Green Color Index (DGCI). A panel of 200 maturity group IV accessions was phenotyped for canopy greenness using DGCI in three environments. Association mapping identified 45 SNPs that were significantly (P ≤ 0.0003) associated with DGCI in three environments, and 16 significant SNPs associated with DGCI averaged across all environments. These SNPs likely tagged 43 putative loci. Out of these 45 SNPs, eight were present in more than one environment. Among the identified loci, 21 were located in regions previously reported for N traits and ureide concentration. Putative loci that were coincident with previously reported genomic regions may be important resources for pyramiding favorable alleles for improved N and chlorophyll concentrations, photosynthesis rates, and N2 fixation in soybean.

Nested association mapping of important agronomic traits in three interspecific soybean populations.

KEY MESSAGE: Glycine soja germplasm can be used to successfully introduce new alleles with the potential to add valuable new genetic diversity to the current elite soybean gene pool. Given the demonstrated narrow genetic base of the US soybean production, it is essential to identify beneficial alleles from exotic germplasm, such as wild soybean, to enhance genetic gain for favorable traits. Nested association mapping (NAM) is an approach to population development that permits the comparison of allelic effects of the same QTL in multiple parents. Seed yield, plant maturity, plant height and plant lodging were evaluated in a NAM panel consisting of 392 recombinant inbred lines derived from three biparental interspecific soybean populations in eight environments during 2016 and 2017. Nested association mapping, combined with linkage mapping, identified three major QTL for plant maturity in chromosomes 6, 11 and 12 associated with alleles from wild soybean resulting in significant increases in days to maturity. A significant QTL for plant height was identified on chromosome 13 with the allele increasing plant height derived from wild soybean. A significant grain yield QTL was detected on chromosome 17, and the allele from Glycine soja had a positive effect of 166 kg ha-1; RIL's with the wild soybean allele yielded on average 6% more than the lines carrying the Glycine max allele. These findings demonstrate the usefulness and potential of alleles from wild soybean germplasm to enhance important agronomic traits in a soybean breeding program.

Proteomic Comparison of Three Extraction Methods Reveals the Abundance of Protease Inhibitors in the Seeds of Grass Pea, a Unique Orphan Legume.

Grass pea is an orphan legume that is grown in many places in the world. It is a high-protein, drought-tolerant legume that is capable of surviving extreme environmental challenges and can be a sole food source during famine. However, grass pea produces the neurotoxin ß-N-oxalyl-L-α,ß-diaminopropionic acid (ß-ODAP), which can cause a neurological disease. This crop is promising as a food source for both animals and humans if ß-ODAP levels and other antinutritional factors such as protease inhibitors are lowered or removed. To understand more about these proteins, a proteomic analysis of grass pea was conducted using three different extraction methods to determine which was more efficient at isolating antinutritional factors. Seed proteins extracted with Tris-buffered saline (TBS), 30% ethanol, and 50% isopropanol were identified by mass spectrometry, resulting in the documentation of the most abundant proteins for each extraction method. Mass spectrometry spectral data and BLAST2GO analysis led to the identification of 1376 proteins from all extraction methods. The molecular function of the extracted proteins revealed distinctly different protein functional profiles. The majority of the TBS-extracted proteins were annotated with nutrient reservoir activity, while the isopropanol extraction yielded the highest percentage of endopeptidase proteinase inhibitors. Our results demonstrate that the 50% isopropanol extraction method was the most efficient at isolating antinutritional factors including protease inhibitors.

A seed germination transcriptomic study contrasting two soybean genotypes that differ in terms of their tolerance to the deleterious impacts of elevated temperatures during seed fill.

OBJECTIVE: Soybean seed development is negatively impacted by elevated temperatures during seed fill, which can decrease seed quality and economic value. Prior germplasm screens identified an exotic landrace able to maintain ~ 95% seed germination under stress conditions that reduce germination dramatically (> 50%) for typical soybean seeds. Seed transcriptomic analysis was performed for two soybean lines (a heat-tolerant landrace and a typical high-yielding adapted line) for dry, mature seed, 6-h imbibed seed and germinated seed. Seeds were produced in two environments: a typical Midwestern field and a heat stressed field located in the Midsouth soybean production region. RESULTS: Transcriptomic analysis revealed 23-30K expressed genes in each seed tissue sample, and differentially expressed genes (DEGs) with ≥ twofold gene expression differences (at q-value < 0.05) comprised ~ 5-44% of expressed genes. Gene ontology (GO) enrichment analysis on DEGs revealed enrichment in heat-tolerant seeds for genes annotated for general and temperature-specific stress, as well as protein-refolding. DEGs were also clustered in modules using weighted co-expressed gene network analysis, which were examined for enrichment of GO biological process terms. Collectively, our results provide new and valuable insights into this unique form of genetic abiotic stress tolerance and to soybean seed physiological responses to elevated temperatures.

Demonstration of highly efficient dual gRNA CRISPR/Cas9 editing of the homeologous GmFAD2-1A and GmFAD2-1B genes to yield a high oleic, low linoleic and α-linolenic acid phenotype in soybean.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize.

Association mapping (AM) is a powerful tool for fine mapping complex trait variation down to nucleotide sequences by exploiting historical recombination events. A major problem in AM is controlling false positives that can arise from population structure and family relatedness. False positives are often controlled by incorporating covariates for structure and kinship in mixed linear models (MLM). These MLM-based methods are single locus models and can introduce false negatives due to over fitting of the model. In this study, eight different statistical models, ranging from single-locus to multilocus, were compared for AM for three traits differing in heritability in two crop species: soybean (Glycine max L.) and maize (Zea mays L.). Soybean and maize were chosen, in part, due to their highly differentiated rate of linkage disequilibrium (LD) decay, which can influence false positive and false negative rates. The fixed and random model circulating probability unification (FarmCPU) performed better than other models based on an analysis of Q-Q plots and on the identification of the known number of quantitative trait loci (QTLs) in a simulated data set. These results indicate that the FarmCPU controls both false positives and false negatives. Six qualitative traits in soybean with known published genomic positions were also used to compare these models, and results indicated that the FarmCPU consistently identified a single highly significant SNP closest to these known published genes. Multiple comparison adjustments (Bonferroni, false discovery rate, and positive false discovery rate) were compared for these models using a simulated trait having 60% heritability and 20 QTLs. Multiple comparison adjustments were overly conservative for MLM, CMLM, ECMLM, and MLMM and did not find any significant markers; in contrast, ANOVA, GLM, and SUPER models found an excessive number of markers, far more than 20 QTLs. The FarmCPU model, using less conservative methods (false discovery rate, and positive false discovery rate) identified 10 QTLs, which was closer to the simulated number of QTLs than the number found by other models.

Genomewide association study of ionomic traits on diverse soybean populations from germplasm collections.

The elemental content of a soybean seed is a determined by both genetic and environmental factors and is an important component of its nutritional value. The elemental content is chemically stable, making the samples stored in germplasm repositories an intriguing source of experimental material. To test the efficacy of using samples from germplasm banks for gene discovery, we analyzed the elemental profile of seeds from 1,653 lines in the USDA Soybean Germplasm Collection. We observed large differences in the elemental profiles based on where the lines were grown, which lead us to break up the genetic analysis into multiple small experiments. Despite these challenges, we were able to identify candidate single nucleotide polymorphisms (SNPs) controlling elemental accumulation as well as lines with extreme elemental accumulation phenotypes. Our results suggest that elemental analysis of germplasm samples can identify SNPs in linkage disequilibrium to genes, which can be leveraged to assist in crop improvement efforts.

Whole-Genome Resequencing Identifies the Molecular Genetic Cause for the Absence of a Gy5 Glycinin Protein in Soybean PI 603408.

During ongoing proteomic analysis of the soybean (Glycine max (L.) Merr) germplasm collection, PI 603408 was identified as a landrace whose seeds lack accumulation of one of the major seed storage glycinin protein subunits. Whole genomic resequencing was used to identify a two-base deletion affecting glycinin 5 The newly discovered deletion was confirmed to be causative through immunological, genetic, and proteomic analysis, and no significant differences in total seed protein content were found to be due to the glycinin 5 loss-of-function mutation per se In addition to focused studies on this one specific glycinin subunit-encoding gene, a total of 1,858,185 nucleotide variants were identified, of which 39,344 were predicted to affect protein coding regions. In order to semiautomate analysis of a large number of soybean gene variants, a new SIFT 4G (Sorting Intolerant From Tolerated 4 Genomes) database was designed to predict the impact of nonsynonymous single nucleotide soybean gene variants, potentially enabling more rapid analysis of soybean resequencing data in the future.

Genetic Analysis of Teosinte Alleles for Kernel Composition Traits in Maize.

Teosinte (Zea mays ssp. parviglumis) is the wild ancestor of modern maize (Zea mays ssp. mays). Teosinte contains greater genetic diversity compared with maize inbreds and landraces, but its use is limited by insufficient genetic resources to evaluate its value. A population of teosinte near isogenic lines (NILs) was previously developed to broaden the resources for genetic diversity of maize, and to discover novel alleles for agronomic and domestication traits. The 961 teosinte NILs were developed by backcrossing 10 geographically diverse parviglumis accessions into the B73 (reference genome inbred) background. The NILs were grown in two replications in 2009 and 2010 in Columbia, MO and Aurora, NY, respectively, and near infrared reflectance spectroscopy and nuclear magnetic resonance calibrations were developed and used to rapidly predict total kernel starch, protein, and oil content on a dry matter basis in bulk whole grains of teosinte NILs. Our joint-linkage quantitative trait locus (QTL) mapping analysis identified two starch, three protein, and six oil QTL, which collectively explained 18, 23, and 45% of the total variation, respectively. A range of strong additive allelic effects for kernel starch, protein, and oil content were identified relative to the B73 allele. Our results support our hypothesis that teosinte harbors stronger alleles for kernel composition traits than maize, and that teosinte can be exploited for the improvement of kernel composition traits in modern maize germplasm.