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Abstract This strategic plan summarizes the major accomplishments achieved in the last quinquennial by the soybean [Glycine max (L.) Merr.] genetics and genomics research community and outlines key priorities for the next 5 years (2024–2028). This work is the result of deliberations among over 50 soybean researchers during a 2‐day workshop in St Louis, MO, USA, at the end of 2022. The plan is divided into seven traditional areas/disciplines: Breeding, Biotic Interactions, Physiology and Abiotic Stress, Functional Genomics, Biotechnology, Genomic Resources and Datasets, and Computational Resources. One additional section was added, Training the Next Generation of Soybean Researchers, when it was identified as a pressing issue during the workshop. This installment of the soybean genomics strategic plan provides a snapshot of recent progress while looking at future goals that will improve resources and enable innovation among the community of basic and applied soybean researchers. We hope that this work will inform our community and increase support for soybean research.

We have created several high protein (HPBL) and low protein breeding lines (LPBL) by crossing between NC-Raleigh and PI407228. The NC-Raleigh is a low protein cultivar widely grown in North Carolina with excellent yield potential and a high protein wild soybean (Glycine soja) with low yield. We analyzed the F6 generation of this cross for protein and oil compositions using near-infrared spectroscopy (NIR). Two soybean lines HPBL and LPBL were selected for biochemical analysis using mass spectrometry-based proteomic and metabolomic techniques. We identified 4538 proteins and 267 metabolites in these lines. The HPBL showed the enrichment of proteins associated with protein synthesis, processing, and storage in addition to higher content of primary and secondary metabolites. The mapping of metabolites on the global metabolic pathways revealed the increased protein content in the HPBL that may have resulted from increased arginine content routed from citrulline-argininosuccinic acid. This information will be useful for breeders and biotechnologists to produce a value-added soybean trait.

Soybean is a major crop in the world and an essential source for minerals, including iron (Fe) and zin (Zn). Deficiency of Fe and Zn in soil and soybean plants result in yield loss and poor seed nutritional qualities. Information on genomic regions and candidate genes controlling seed Fe and Zn accumulation in soybean seed is limited. Therefore, The objective of this research was to identify genetic regions, known as quantitative trait loci (QTL), and candidate genes that control the accumulation of Fe and Zn in soybean mature seeds. A ‘Forrest’ by ‘Williams 82’ (F × W82) recombinant inbred line (RIL) population (n = 306) was used and genotyped using a total of 5405 single nucleotides polymorphism (SNP) markers using Infinium SNP6K BeadChips. A two-year experiment was conducted across two environments: North Carolina in 2018 (NC) and Illinois in 2020 (IL). Only QTL with LOD scores ≥ 2.5, as identified by the composite interval mapping (CIM) method, are reported here. In total, 6 QTL were identified for seed Fe; specifically, 3 QTL (qFe-01-[NC-2018], qFe-02-[NC-2018], and qFe-03-[NC-2018]) were located on chromosomes 1, 2, and 6, respectively, in the NC environment, and 3 QTL (qFe-01-[IL-2020], qFe-02-[IL-2020], and qFe-03-[IL-2020]) were positioned on chromosomes 1, 2, and 12, respectively, in the IL environment. A total of 6 QTL associated with seed Zn were also identified; 4 QTL (qZn-01-[NC-2018]; qZn-02-[NC-2018]; qZn-03-[NC-2018]; and qZn-04-[NC-2018]), respectively on Chr 2, 3, 7, and 19 in NC; and 2 QTL (qZn-01-[IL-2020] and qZn-02-[IL-2020]), respectively, on Chr 5 and 8 in IL. Several functional genes encode Fe- and Zn-proteins, transcription factors, proteins-zinc finger motifs (involved in DNA binding and transcriptional regulation; crosstalk between the regulatory pathways of Zn and Fe transporters) were identified and located within the QTL interval. To our knowledge, and based on the literature available, the QTL identified here on Chr 2 and Chr 6 are novel and were not previously identified. This current research provides a new knowledge of the genetic basis of seed Fe and Zn and the markers associated with QTL. The QTL identified here will contribute to efficient marker assisted selection for higher Fe and Zn content in soybean seeds. The candidate genes and metal-responsive transcription factors may coordinate the expression of both Zn and Fe transporters in response to changes in metal availability, providing new knowledge on minerals uptake and transport mechanisms, allowing for possible genetic engineering application.

Abstract SoySNP50K and SoySNP6K are commonly used for soybean (Glycine max) genotyping. The SoySNP50K assay has been used to genetically analyze the entire USDA Soybean Germplasm Collection, while the SoySNP6K assay, containing a subset of 6000 single‐nucleotide polymorphisms (SNPs) from SoySNP50K, has been used for quantitative trait loci mapping of different traits. To meet the needs for genomic selection, selection of parents for crosses, and characterization of breeding populations, especially early selection of ideal offspring from thousands of lines, we developed two assays, SoySNP3K and SoySNP1K, containing 3072 and 1252 SNPs, respectively, based on SoySNP50K and SoySNP6K mark sets. These two assays also contained the trait markers reported or contributed by soybean breeders. The SNPs in the SoySNP3K are a subset from SoySNP6K, while the SNPs in the SoySNP1K are a subset from SoySNP3K. These SNPs were chosen to reduce the SNP number in the large linkage blocks while capturing as much of the haplotype diversity as possible. They are highly polymorphic and of high quality. The mean minor allele frequencies of the SNPs in the southern and northern US elites were 0.25 and 0.27 for SoySNP3K, respectively, and 0.29 and 0.33 for SoySNP1K. The selected SNPs are a valuable source for developing targeted amplicon sequencing assay or beadchip assay in soybean. SoySNP3K and SoySNP1K assays are commercialized by Illumina Inc. and AgriPlex Genomics, respectively. Together with SoySNP50K and SoySNP6K, a series of nested assays with different marker densities will serve as additional low‐cost genomic tools for genetic, genomic, and breeding research.

For soybean, novel single dominant Resistance to Phytophthora sojae (Rps) genes are sought to manage Phytophthora root and stem rot. In this study, resistance to P. sojae was mapped individually in four recombinant inbred line (RIL) populations derived from crosses of the susceptible cultivar Williams with PI 407985, PI 408029, PI 408097, and PI424477 previously identified as putative novel sources of disease resistance. Each population was screened for resistance with five to seven isolates of P. sojae separately over multiple F7–F10 generations. Additionally, three of the populations were screened with inoculum from the combination of three P. sojae isolates (PPR), which comprised virulence to 14 Rps genes. Over 2,300 single-nucleotide polymorphism markers were used to construct genetic maps in each population to identify chromosomal regions associated with resistance to P. sojae. Resistance segregated as one or two genes to the individual isolates and one gene toward PPR in each population and mapped to chromosomes 3, 13, or 18 in one or more of the four RIL populations. Resistance to five isolates mapped to the same chromosome 3 region are as follows: OH7 (PI 424477 and PI408029), OH12168, OH7/8, PPR (PI 407985), and 1.S.1.1 (PI408029). The resistance regions on chromosome 13 also overlapped for OH1, OH25, OH-MIA (PI424477), PPR (PI 424477, PI 407985, and PI 408097), PPR and OH0217 (PI 408097), and OH4 (PI 408029), but were distinct for each population suggesting multiple genes confer resistance. Two regions were identified on chromosome 18 but all appear to map to known loci; notably, resistance to the combined inoculum (PPR) did not map at this locus. However, there are putative new alleles in three of four populations, three on chromosome 3 and two on chromosome 13 based on mapping location but also known virulence in the isolate used. This characterization of all the Rps genes segregating in these populations to these isolates will be informative for breeding, but the combined inoculum was able to map a novel loci. Furthermore, within each of these P. sojae isolates, there was virulence to more than the described Rps genes, and the effectiveness of the novel genes requires testing in larger populations.

Abstract 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.

Abstract Background Symbiotic nitrogen fixation differs among Bradyrhizobium japonicum strains. Soybean inoculated with USDA123 has a lower yield than strains known to have high nitrogen fixation efficiency, such as USDA110. In the main soybean-producing area in the Midwest of the United States, USDA123 has a high nodule incidence in field-grown soybean and is competitive but inefficient in nitrogen fixation. In this study, a high-throughput system was developed to characterize nodule number among 1,321 Glycine max and 69 Glycine soja accessions single inoculated with USDA110 and USDA123. Results Seventy-three G. max accessions with significantly different nodule number of USDA110 and USDA123 were identified. After double inoculating 35 of the 73 accessions, it was observed that PI189939, PI317335, PI324187B, PI548461, PI562373, and PI628961 were occupied by USDA110 and double-strain nodules but not by USDA123 nodules alone. PI567624 was only occupied by USDA110 nodules, and PI507429 restricted all strains. Analysis showed that 35 loci were associated with nodule number in G. max when inoculated with strain USDA110 and 35 loci with USDA123. Twenty-three loci were identified in G. soja when inoculated with strain USDA110 and 34 with USDA123. Only four loci were common across two treatments, and each locus could only explain 0.8 to 1.5% of phenotypic variation. Conclusions High-throughput phenotyping systems to characterize nodule number and occupancy were developed, and soybean germplasm restricting rhizobium strain USDA123 but preferring USDA110 was identified. The larger number of minor effects and a small few common loci controlling the nodule number indicated trait genetic complexity and strain-dependent nodulation restriction. The information from the present study will add to the development of cultivars that limit USDA123, thereby increasing nitrogen fixation efficiency and productivity.

Diaporthe longicolla (syn. Phomopsis longicolla) is an important seed-borne fungal pathogen and the primary cause of Phomopsis seed decay (PSD) in soybean. PSD is one of the most devastating seed diseases, reducing soybean seed quality and yield worldwide. As part of a genome sequencing project on the fungal Diaporthe–Phomopsis complex, draft genomes of eight D. longicolla isolates were sequenced and assembled. Sequences of mitochondrial genomes were extracted and analyzed. The circular mitochondrial genomes ranged from 52,534 bp to 58,280 bp long, with a mean GC content of 34%. A total of 14 core protein-coding genes, 23 tRNA, and 2 rRNA genes were identified. Introns were detected in the genes of atp6, cob, cox1, cox2, cox3, nad1, nad2, nad5, and rnl. Three isolates (PL7, PL10, and PL185E) had more introns than other isolates. Approximately 6.4% of the mitochondrial genomes consist of repetitive elements. Moreover, 48 single-nucleotide polymorphisms (SNPs) and were identified. The mitochondrial genome sequences of D. longicolla will be useful to further study the molecular basis of seed-borne pathogens causing seed diseases, investigate genetic variation among isolates, and develop improved control strategies for Phomopsis seed decay of soybean.

Abstract Genomic selection has been utilized for genetic improvement in both plant and animal breeding and is a favorable technique for quantitative trait development. Within this study, genomic selection was evaluated within a breeding program, using novel validation methods in addition to plant materials and data from a commercial soybean (Glycine max) breeding program. A total of 1501 inbred lines were used to test multiple genomic selection models for multiple traits. Validation included cross‐validation, inter‐environment, and empirical validation. The results indicated that the extended genomic best linear unbiased prediction (EGBLUP) model was the most effective model tested for yield, protein, and oil in cross‐validation with accuracies of 0.50, 0.68, and 0.64, respectively. Increasing marker number from 1000 to 3000 to 6000 single nucleotide polymorphism markers leads to statistically significant increases in accuracy. Cross‐environment predictions were statistically lower than cross‐validation with accuracies of 0.24, 0.54, and 0.42 for yield, protein, and oil, respectively, using the extended genomic BLUP model. Empirical validation, predicting the yield of 510 soybean lines, had a prediction accuracy of 0.34, with the inclusion of a maturity covariate leading to a notable increase in accuracy. Genomic selection identified high‐performance lines in inter‐environment predictions: 34% of lines within the upper quartile of yield, and 51% and 48% of the highest quartile protein and oil lines, respectively. Statistically similar results occurred comparing rankings in empirical validation and selection for advancements in yield trials. These results indicate that genomic selection is a useful tool for selection decisions.

Abstract Breeding for increased protein without a reduction in oil content in soybeans [Glycine max (L.) Merr.] is a challenge for soybean breeders but an expected goal. Many efforts have been made to develop new soybean varieties with high yield in combination with desirable protein and/or oil traits. An elite line, R05‐1415, was reported to be high yielding, high protein, and low oil. Several significant quantitative trait loci (QTL) for protein and oil were reported in this line, but many of them were unstable across environments or genetic backgrounds. Thus, a new study under multiple field environments using the Infinium BARCSoySNP6K BeadChips was conducted to detect and confirm stable genomic loci for these traits. Genetic analyses consistently detected a single major genomic locus conveying these two traits with remarkably high phenotypic variation explained (R2), varying between 24.2% and 43.5%. This new genomic locus is located between 25.0 and 26.7 Mb, distant from the previously reported QTL and did not overlap with other commonly reported QTL and the recently cloned gene Glyma.20G085100. Homolog analysis indicated that this QTL did not result from the paracentric chromosome inversion with an adjacent genomic fragment that harbors the reported QTL. The pleiotropic effect of this QTL could be a challenge for improving protein and oil simultaneously; however, a further study of four candidate genes with significant expressions in the seed developmental stages coupled with haplotype analysis may be able to pinpoint causative genes. The functionality and roles of these genes can be determined and characterized, which lay a solid foundation for the improvement of protein and oil content in soybeans.

Soybean cyst nematode (SCN) is a destructive pathogen of soybeans responsible for annual yield loss exceeding $1.5 billion in the United States. Here, we conducted a series of genome-wide association studies (GWASs) to understand the genetic landscape of SCN resistance in the University of Missouri soybean breeding programs (Missouri panel), as well as germplasm and cultivars within the United States Department of Agriculture (USDA) Uniform Soybean Tests—Northern Region (NUST). For the Missouri panel, we evaluated the resistance of breeding lines to SCN populations HG 2.5.7 (Race 1), HG 1.2.5.7 (Race 2), HG 0 (Race 3), HG 2.5.7 (Race 5), and HG 1.3.6.7 (Race 14) and identified seven quantitative trait nucleotides (QTNs) associated with SCN resistance on chromosomes 2, 8, 11, 14, 17, and 18. Additionally, we evaluated breeding lines in the NUST panel for resistance to SCN populations HG 2.5.7 (Race 1) and HG 0 (Race 3), and we found three SCN resistance-associated QTNs on chromosomes 7 and 18. Through these analyses, we were able to decipher the impact of seven major genetic loci, including three novel loci, on resistance to several SCN populations and identified candidate genes within each locus. Further, we identified favorable allelic combinations for resistance to individual SCN HG types and provided a list of available germplasm for integration of these unique alleles into soybean breeding programs. Overall, this study offers valuable insight into the landscape of SCN resistance loci in U.S. public soybean breeding programs and provides a framework to develop new and improved soybean cultivars with diverse plant genetic modes of SCN resistance.

Beet curly top virus (BCTV), which is synonymous with curly top virus (CTV), causes significant yield loss in common bean (snap and dry beans) cultivars and several other important crops. Common bean cultivars have been found to be resistant to CTV, but screening for resistance is challenging due to the cyclical nature of epidemics and spotty feeding by the leafhopper that vectors the virus. We used an SNP dataset for the Snap Bean Association Panel (SnAP) agro-inoculated with CTV-Logan (CA/Logan) strain to locate the Bct gene region to a 1.7-Mb interval on chromosome Pv07 using genome-wide association study (GWAS) analysis. Recombinant lines from the SnAP were used to further narrow the Bct region to a 58.0-kb interval. A missense SNP (S07_2970381) in candidate gene Phvul.007G036300 Exonuclease V (EXO5) was identified as the most likely causal mutation, and it was the most significant SNP detected by GWAS in a dry bean population (DBP) naturally infected by the CTV-Worland (Wor) strain. Tm-shift assay markers developed for SNP S07_2970381 and two linked SNPs, S07_2970276 and S07_2966197, were useful for tracking different origins of the Bct EXO5 candidate gene resistance to CTV in common bean. The three SNPs identified four haplotypes, with haplotype 3-1 (Haplo3-1) of Middle American origin associated with the highest levels of CTV resistance. This SNP-haplotype assay will enable breeders to track resistance sources and to develop cultivars with better CTV resistance.

In soybean production, level of seed protein frequently shows a negative correlation with seed oil content and yield. Here, the authors report a CCT gene pleiotropically regulates these traits and the selection of larger seeds determining allele leads to higher oil content but lower protein content in soybean cultivars.

Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross’s offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.

Abstract Background With advances in next-generation sequencing technologies, an unprecedented amount of soybean accessions has been sequenced by many individual studies and made available as raw sequencing reads for post-genomic research. Results To develop a consolidated and user-friendly genomic resource for post-genomic research, we consolidated the raw resequencing data of 1465 soybean genomes available in the public and 91 highly diverse wild soybean genomes newly sequenced. These altogether provided a collection of 1556 sequenced genomes of 1501 diverse accessions (1.5 K). The collection comprises of wild, landraces and elite cultivars of soybean that were grown in East Asia or major soybean cultivating areas around the world. Our extensive sequence analysis discovered 32 million single nucleotide polymorphisms (32mSNPs) and revealed a SNP density of 30 SNPs/kb and 12 non-synonymous SNPs/gene reflecting a high structural and functional genomic diversity of the new collection. Each SNP was annotated with 30 categories of structural and/or functional information. We further identified paired accessions between the 1.5 K and 20,087 (20 K) accessions in US collection as genomic “equivalent” accessions sharing the highest genomic identity for minimizing the barriers in soybean germplasm exchange between countries. We also exemplified the utility of 32mSNPs in enhancing post-genomics research through in-silico genotyping, high-resolution GWAS, discovering and/or characterizing genes and alleles/mutations, identifying germplasms containing beneficial alleles that are potentially experiencing artificial selection. Conclusion The comprehensive analysis of publicly available large-scale genome sequencing data of diverse cultivated accessions and the newly in-house sequenced wild accessions greatly increased the soybean genome-wide variation resolution. This could facilitate a variety of genetic and molecular-level analyses in soybean. The 32mSNPs and 1.5 K accessions with their comprehensive annotation have been made available at the SoyBase and Ag Data Commons. The dataset could further serve as a versatile and expandable core resource for exploring the exponentially increasing genome sequencing data for a variety of post-genomic research.

Abstract 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.

Abstract Increasing rate of genetic gain for key agronomic traits through genomic selection requires the development of new molecular methods to run genome‐wide single‐nucleotide polymorphisms (SNPs). The main limitation of current methods is the cost is too high to screen breeding populations. Molecular inversion probes (MIPs) are a targeted genotyping‐by‐sequencing (GBS) method that could be used for soybean [Glycine max (L.) Merr.] that is both cost‐effective, high‐throughput, and provides high data quality to screen breeder's germplasm for genomic selection. A 1K MIP SNP set was developed for soybean with uniformly distributed markers across the genome. The SNPs were selected to maximize the number of informative markers in germplasm being tested in soybean breeding programs located in the northern‐central and middle‐southern regions of the United States. The 1K SNP MIP set was tested on diverse germplasm and a recombinant inbred line (RIL) population. Targeted sequencing with MIPs obtained an 85% enrichment for the targeted SNPs. The MIP genotyping accuracy was 93% overall, whereas homozygous call accuracy was 98% with

AbstractRecombination allows for the exchange of genetic material between two parents, which plant breeders exploit to make improved cultivars. This recombination is not distributed evenly across the chromosome. Recombination mostly occurs in euchromatic regions of the genome and even then, recombination is focused into clusters of crossovers termed recombination hotspots. Understanding the distribution of these hotspots along with the sequence motifs associated with them may lead to methods that enable breeders to better exploit recombination in breeding. To map recombination hotspots and identify sequence motifs associated with hotspots in soybean [Glycine max

Soybean is a major source of seed protein and oil globally with an average composition of 40% protein and 20% oil in the seed. The goal of this study was to identify quantitative trait loci (QTL) conferring seed protein and oil content utilizing a population constructed by crossing an above average protein content line, PI 399084 to another line that had a low protein content value, PI 507429, both from the USDA soybean germplasm collection. The recombinant inbred line (RIL) population, PI 507429 x PI 399084, was evaluated in two replications over four years (2018-2021); the seeds were analyzed for seed protein and oil content using near-infrared reflectance spectroscopy. The recombinant inbred lines and the two parents were re-sequenced using genotyping by sequencing. A total of 12,761 molecular markers, which came from genotyping by sequencing, the SoySNP6k BeadChip and selected simple sequence repeat (SSR) markers from known protein QTL chromosomal regions were used for mapping. One QTL was identified on chromosome 2 explaining up to 56.8% of the variation for seed protein content and up to 43% for seed oil content. Another QTL identified on chromosome 15 explained up to 27.2% of the variation for seed protein and up to 41% of the variation for seed oil content. The protein and oil QTLs of this study and their associated molecular markers will be useful in breeding to improve nutritional quality in soybean.

Understanding the genetic basis of seed Ni and Mo is essential. Since soybean is a major crop in the world and a major source for nutrients, including Ni and Mo, the objective of the current research was to map genetic regions (quantitative trait loci, QTL) linked to Ni and Mo concentrations in soybean seed. A recombinant inbred line (RIL) population was derived from a cross between ‘Forrest’ and ‘Williams 82’ (F × W82). A total of 306 lines was used for genotyping using 5405 single nucleotides polymorphism (SNP) markers using Infinium SNP6K BeadChips. A two-year experiment was conducted and included the parents and the RIL population. One experiment was conducted in 2018 in North Carolina (NC), and the second experiment was conducted in Illinois in 2020 (IL). Logarithm of the odds (LOD) of ≥2.5 was set as a threshold to report identified QTL using the composite interval mapping (CIM) method. A wide range of Ni and Mo concentrations among RILs was observed. A total of four QTL (qNi-01, qNi-02, and qNi-03 on Chr 2, 8, and 9, respectively, in 2018, and qNi-01 on Chr 20 in 2020) was identified for seed Ni. All these QTL were significantly (LOD threshold > 2.5) associated with seed Ni, with LOD scores ranging between 2.71–3.44, and with phenotypic variance ranging from 4.48–6.97%. A total of three QTL for Mo (qMo-01, qMo-02, and qMo-03 on Chr 1, 3, 17, respectively) was identified in 2018, and four QTL (qMo-01, qMo-02, qMo-03, and qMo-04, on Chr 5, 11, 14, and 16, respectively) were identified in 2020. Some of the current QTL had high LOD and significantly contributed to the phenotypic variance for the trait. For example, in 2018, Mo QTL qMo-01 on Chr 1 had LOD of 7.8, explaining a phenotypic variance of 41.17%, and qMo-03 on Chr 17 had LOD of 5.33, with phenotypic variance explained of 41.49%. In addition, one Mo QTL (qMo-03 on Chr 14) had LOD of 9.77, explaining 51.57% of phenotypic variance related to the trait, and another Mo QTL (qMo-04 on Chr 16) had LOD of 7.62 and explained 49.95% of phenotypic variance. None of the QTL identified here were identified twice across locations/years. Based on a search of the available literature and of SoyBase, the four QTL for Ni, identified on Chr 2, 8, 9, and 20, and the five QTL associated with Mo, identified on Chr 1, 17, 11, 14, and 16, are novel and not previously reported. This research contributes new insights into the genetic mapping of Ni and Mo, and provides valuable QTL and molecular markers that can potentially assist in selecting Ni and Mo levels in soybean seeds.

We studied the effect of freeze–thaw on isoflavone composition in germinated soybeans, particularly the conversion of aglycones, the isoflavone monomers with high biological activity. Germinated soybeans were frozen at −20 °C, −80 °C, and −196 °C respectively, and then the frozen samples (-20 °C) were thawed at 4 °C, 10 °C, and 25 °C respectively. Results showed total isoflavone increased after germination. Aglycones content increased most at −20 °C, which increased about 24 times. The effect of thaw temperature and time indicated there were approximately 89 % glucosides forms converted to aglycones during freeze–thaw. Isoflavone conjugate-hydrolyzing β-glucosidase (ICHG) activity increased by 65.78 % (25 °C) and 59.14 % (48 h) with freeze–thaw. The cells of germinated soybeans were broken, promoting ICHG contact with glucosides and malonyl-glucosides. These results indicated that freeze–thaw greatly changed the content and profile of isoflavones, resulting in a sharp increase in the content of aglycones.

IntroductionGenomic selection (GS) is a potential breeding approach for soybean improvement.MethodsIn this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN.ResultsOur results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log10(P) greater than 1 were included.DiscussionThis information will help improve GS efficiency and facilitate the application of GS.

Abstract Background Common bean (Phaseolus vulgaris L.) is a legume whose grain can be stored for months, a common practice among Brazilian growers. Over time, seed coats become darker and harder to cook, traits that are undesirable to consumers, who associate darker-colored beans with greater age. Like commercial pinto and cranberry bean varieties, carioca beans that have darker seeds at harvest time and after storage are subject to decreased market values. Results The goal of our study was to identify the genetic control associated with lightness of seed coat color at harvest (HL) and with tolerance to post-harvest seed coat darkening (PHD) by a genome-wide association study. For that purpose, a carioca diversity panel previously validated for association mapping studies was used with 138 genotypes and 1,516 high-quality SNPs. The panel was evaluated in two environments using a colorimeter and the CIELAB scale. Shelf storage for 30 days had the most expressive results and the L* (luminosity) parameter led to the greatest discrimination of genotypes. Three QTL were identified for HL, two on chromosome Pv04 and one on Pv10. Regarding PHD, results showed that genetic control differs for L* after 30 days and for the ΔL* (final L*—initial L*); only ΔL* was able to properly express the PHD trait. Four phenotypic classes were proposed, and five QTL were identified through six significant SNPs. Conclusions Lightness of seed coat color at harvest showed an oligogenic inheritance corroborated by moderate broad-sense heritability and high genotypic correlation among the experiments. Only three QTL were significant for this trait – two were mapped on Pv04 and one on Pv10. Considering the ΔL, six QTL were mapped on four different chromosomes for PHD. The same HL QTL at the beginning of Pv10 was also associated with ΔL* and could be used as a tool in marker-assisted selection. Several candidate genes were identified and may be useful to accelerate the genetic breeding process.

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.

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.

Measurement(s) genetic variation • SNP • Linkage Disequilibrium Technology Type(s) DNA sequencing • bioinformatics analysis • computational phylogenetic analysis Sample Characteristic - Organism Glycine soja • Glycine max Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13568552

Abstract The nuclear fertility restorer gene Rf5 in HA-R9, originating from the wild sunflower species Helianthus annuus, is able to restore the widely used PET1 cytoplasmic male sterility in sunflowers. Previous mapping placed Rf5 at an interval of 5.8 cM on sunflower chromosome 13, distal to a rust resistance gene R 11 at a 1.6 cM genetic distance in an SSR map. In the present study, publicly available SNP markers were further mapped around Rf5 and R 11 using 192 F2 individuals, reducing the Rf5 interval from 5.8 to 0.8 cM. Additional SNP markers were developed in the target region of the two genes from the whole-genome resequencing of HA-R9, a donor line carrying Rf5 and R 11 . Fine mapping using 3517 F3 individuals placed Rf5 at a 0.00071 cM interval and the gene co-segregated with SNP marker S13_216392091. Similarly, fine mapping performed using 8795 F3 individuals mapped R 11 at an interval of 0.00210 cM, co-segregating with two SNP markers, S13_225290789 and C13_181790141. Sequence analysis identified Rf5 as a pentatricopeptide repeat-encoding gene. The high-density map and diagnostic SNP markers developed in this study will accelerate the use of Rf5 and R 11 in sunflower breeding.

Soybean is a primary meal protein for human consumption, poultry, and livestock feed. In this study, quantitative trait locus (QTL) controlling protein content was explored via genome-wide association studies (GWAS) and linkage mapping approaches based on 284 soybean accessions and 180 recombinant inbred lines (RILs), respectively, which were evaluated for protein content for 4 years. A total of 22 single nucleotide polymorphisms (SNPs) associated with protein content were detected using mixed linear model (MLM) and general linear model (GLM) methods in Tassel and 5 QTLs using Bayesian interval mapping (IM), single-trait multiple interval mapping (SMIM), single-trait composite interval mapping maximum likelihood estimation (SMLE), and single marker regression (SMR) models in Q-Gene and IciMapping. Major QTLs were detected on chromosomes 6 and 20 in both populations. The new QTL genomic region on chromosome 6 (Chr6_18844283–19315351) included 7 candidate genes and the Hap.XAA at the Chr6_19172961 position was associated with high protein content. Genomic selection (GS) of protein content was performed using Bayesian Lasso (BL) and ridge regression best linear unbiased prediction (rrBULP) based on all the SNPs and the SNPs significantly associated with protein content resulted from GWAS. The results showed that BL and rrBLUP performed similarly; GS accuracy was dependent on the SNP set and training population size. GS efficiency was higher for the SNPs derived from GWAS than random SNPs and reached a plateau when the number of markers was >2,000. The SNP markers identified in this study and other information were essential in establishing an efficient marker-assisted selection (MAS) and GS pipelines for improving soybean protein content.

Soybean cyst nematode (SCN; Heterodera glycines Ichinohe) is a highly destructive pathogen for soybean production worldwide. The use of resistant varieties is the most effective way of preventing yield loss. Handou 10 is a commercial soybean variety with desirable agronomic traits and SCN resistance, however genes underlying the SCN resistance in the variety are unknown. An F2:8 recombinant inbred line (RIL) population derived from a cross between Zheng 9525 (susceptible) and Handou 10 was developed and its resistance to SCN HG type 2.5.7 (race 1) and 1.2.5.7 (race 2) was identified. We identified seven quantitative trait loci (QTLs) with additive effects. Among these, three QTLs on Chromosomes 7, 8, and 18 were resistant to both races. These QTLs could explain 1.91–7.73% of the phenotypic variation of SCN’s female index. The QTLs on chromosomes 8 and 18 have already been reported and were most likely overlapped with rhg1 and Rhg4 loci, respectively. However, the QTL on chromosome 7 was novel. Candidate genes for the three QTLs were predicted through genes functional analysis and transcriptome analysis of infected roots of Handou 10 vs. Zheng 9525. Transcriptome analysis performed also indicated that the plant–pathogen interaction played an important role in the SCN resistance for Handou 10. The information will facilitate SCN–resistant gene cloning, and the novel resistant gene will be a source for improving soybeans’ resistance to SCN.

Abstract The gene content of plants varies between individuals of the same species due to gene presence/absence variation, and selection can alter the frequency of specific genes in a population. Selection during domestication and breeding will modify the genomic landscape, though the nature of these modifications is only understood for specific genes or on a more general level (e.g., by a loss of genetic diversity). Here we have assembled and analyzed a soybean (Glycine spp.) pangenome representing more than 1,000 soybean accessions derived from the USDA Soybean Germplasm Collection, including both wild and cultivated lineages, to assess genomewide changes in gene and allele frequency during domestication and breeding. We identified 3,765 genes that are absent from the Lee reference genome assembly and assessed the presence/absence of all genes across this population. In addition to a loss of genetic diversity, we found a significant reduction in the average number of protein‐coding genes per individual during domestication and subsequent breeding, though with some genes and allelic variants increasing in frequency associated with selection for agronomic traits. This analysis provides a genomic perspective of domestication and breeding in this important oilseed crop.

For soybean, novel single dominant Resistance to Phytophthora sojae (Rps) genes are sought to manage Phytophthora root and stem rot. In this study, resistance to P. sojae was mapped individually in four recombinant inbred line (RIL) populations derived from crosses of the susceptible cultivar Williams with PI 407985, PI 408029, PI 408097, and PI424477 previously identified as putative novel sources of disease resistance. Each population was screened for resistance with five to seven isolates of P. sojae separately over multiple F7-F10 generations. Additionally, three of the populations were screened with inoculum from the combination of three P. sojae isolates (PPR), which comprised virulence to 14 Rps genes. Over 2,300 single-nucleotide polymorphism markers were used to construct genetic maps in each population to identify chromosomal regions associated with resistance to P. sojae. Resistance segregated as one or two genes to the individual isolates and one gene toward PPR in each population and mapped to chromosomes 3, 13, or 18 in one or more of the four RIL populations. Resistance to five isolates mapped to the same chromosome 3 region are as follows: OH7 (PI 424477 and PI408029), OH12168, OH7/8, PPR (PI 407985), and 1.S.1.1 (PI408029). The resistance regions on chromosome 13 also overlapped for OH1, OH25, OH-MIA (PI424477), PPR (PI 424477, PI 407985, and PI 408097), PPR and OH0217 (PI 408097), and OH4 (PI 408029), but were distinct for each population suggesting multiple genes confer resistance. Two regions were identified on chromosome 18 but all appear to map to known loci; notably, resistance to the combined inoculum (PPR) did not map at this locus. However, there are putative new alleles in three of four populations, three on chromosome 3 and two on chromosome 13 based on mapping location but also known virulence in the isolate used. This characterization of all the Rps genes segregating in these populations to these isolates will be informative for breeding, but the combined inoculum was able to map a novel loci. Furthermore, within each of these P. sojae isolates, there was virulence to more than the described Rps genes, and the effectiveness of the novel genes requires testing in larger populations. [ABSTRACT FROM AUTHOR]

We have estimated the average genetic diversity of two Glycine annual and six perennial species based upon 76 orthologous gene sets and performed phylogenetic analysis, divergence analysis and tests for departure from neutrality of the eight species using 52 orthologous gene sets. In addition, 367 orthologous gene sets were used to estimate the relationships of 11 G. canescens accessions. Among the perennials, G. canescens showed the highest nucleotide diversity. The other perennials, except for G. tomentella, had higher nucleotide diversity than the two annuals. Phylogenetic analysis of the Glycine showed a similar genome grouping with the previous report except for G. cyrtoloba and G. stenophita which formed a sister clade in the study. Divergence analysis supported the phylogenetic relationships that G. falcata was the most divergent from G. max, followed by G. cyrtoloba, G. syndetika, G. tomentella D3, G. stenophita and G. canescens. Most genic sequences were homogeneous in the levels of polymorphism and divergence between G. max and other Glycine species based on the HKA test, thus, Glycine perennials may have experienced a very similar evolution as inferred by trans-specific mutation analysis. The greater genetic diversity of most perennial Glycine species and their origins from the warmer and drier climates of Australia suggests the perennials maybe a potential source of heat and drought resistance that will be of value in the face of climate change.

Genomic selection (GS) has become viable for selection of quantitative traits for which marker-assisted selection has often proven less effective. The potential of GS for soybean was characterized using 483 elite breeding lines, genotyped with BARCSoySNP6K iSelect BeadChips. Cross validation was performed using RR-BLUP and predictive abilities (rMP) of 0.81, 0.71, and 0.26 for protein, oil, and yield, were achieved at the largest tested training set size. Minimal differences were observed when comparing different marker densities and there appeared to be inflation in rMP due to population structure. For comparison purposes, two additional methods to predict breeding values for lines of four bi-parental populations within the GS dataset were tested. The first method predicted within each bi-parental population (WP method) and utilized a training set of full-sibs of the validation set. The second method utilized a training set of all remaining breeding lines except for full-sibs of the validation set to predict across populations (AP method). The AP method is more practical as the WP method would likely delay the breeding cycle and leverage smaller training sets. Averaging across populations for protein and oil content, rMP for the AP method (0.55, 0.30) approached rMP for the WP method (0.60, 0.52). Though comparable, rMP for yield was low for both AP and WP methods (0.12, 0.13). Based on increases in rMP as training sets increased and the effectiveness of WP vs. AP method, the AP method could potentially improve with larger training sets and increased relatedness between training and validation sets.

Abstract In common bean (Phaseolus vulgaris L.), seed storage protein deficiency is associated with increased total cysteine and methionine concentration. The goal of this study was to generate germplasm lines that combine this characteristic with adaptation to short season conditions in Manitoba, Canada. A recombinant inbred line population was developed by crossing the storage protein deficient genotype, SMARC1N‐PN1 with the cultivar Morden003. Two lines, 2‐37 and 3‐84, with a stable protein profile over 2 years at two locations were identified. Like SMARC1N‐PN1, both lines had a significantly higher cysteine concentration than Morden003, by approximately 35%. Methionine levels were elevated by approximately 15%, while tryptophan levels were also increased by approximately 30%. Line 2‐37 had a significant increase in protein quality, as measured by in vitro protein digestibility corrected amino acid score, by approximately 40%, as compared with Morden003. The increased protein quality for this line is attributable to higher levels of total cysteine and methionine, while having an overall reduction in crude protein concentration. Line 2‐37 had a similar seed yield as SMARC1N‐PN1, with a maturity comparable to Morden003. The results of high‐density single nucleotide polymorphism (SNP) genotyping and quantitative trait locus analysis of recombinant inbred lines indicated that variation in cysteine concentration was determined by the phaseolin locus, while variation in methionine concentration was determined by both the phaseolin and lectin loci. SNP markers that track the introgression of phaseolin and lectin deficiency into the Morden003 background were identified and validated.

Manteca yellow dry beans (Phaseolus vulgaris L.) have many quality traits that appeal to consumers, including fast cooking times, creamy texture, and sweet, buttery flavor. They are native to Chile and consumed in regions of South America and Africa but are largely unfamiliar to United States consumers. While cooking time, flavor, and texture have not been prioritized in United States dry bean breeding programs, genetic variability exists such that these traits could be addressed through breeding. In this study, a recombinant inbred line (RIL) population was developed from a cross between Ervilha (Manteca) and PI527538 (Njano), yellow dry beans with contrasting cooking time and sensory attributes. The population and parents were grown for 2 years in Michigan and evaluated for cooking time and sensory attribute intensities, including total flavor, beany, vegetative, earthy, starchy, sweet, bitter, seed-coat perception, and cotyledon texture. Cooking time ranged 19–34 min and exhibited high broad-sense heritability (0.68). Sensory attribute intensities also exhibited variation among RILs, although broad-sense heritability was low, with beany and total flavor exhibiting the highest (0.33 and 0.27). A linkage map of 870 single nucleotide polymorphisms markers across 11 chromosomes was developed for quantitative trait loci (QTL) mapping, which revealed QTL for water uptake (3), cooking time (6), sensory attribute intensities (28), color (13), seed-coat postharvest non-darkening (1), seed weight (5), and seed yield (2) identified from data across 2 years. Co-localization was identified for starchy, sweet, and seed-coat perception on Pv01; for total flavor, beany, earthy, starchy, sweet, bitter, seed-coat perception, cotyledon texture, and color on Pv03; water uptake and color on Pv04; total flavor, vegetative, sweet, and cotyledon texture on Pv07; cooking time, starchy, sweet, and color on Pv08; and water uptake, cooking time, total flavor, beany, starchy, bitter, seed-coat perception, cotyledon texture, color, and seed-coat postharvest non-darkening on Pv10. The QTL identified in this work, in particular CT8.2 and CT10.2, can be used to develop molecular markers to improve seed quality traits in future dry bean varieties. Considering yellow dry beans already excel in quality and convenience, they might be an ideal market class to signal a new focus on consumer-valued traits in the United States.

Soybean cyst nematode (SCN, Heterodera glycines) has become the major yield-limiting biological factor in soybean production. Common bean is also a good host of SCN, and its production is challenged by this emerging pest in many regions such as the upper Midwest USA. The use of host genetic resistance has been the most effective and environmentally friendly method to manage SCN. The objectives of this study were to evaluate the SCN resistance in the USDA common bean core collection and conduct a genome-wide association study (GWAS) of single nucleotide polymorphism (SNP) markers with SCN resistance. A total of 315 accessions of the USDA common bean core collection were evaluated for resistance to SCN HG Type 0 (race 6). The common bean core set was genotyped with the BARCBean6K_3 Infinium BeadChips, consisting of 4,654 SNPs. Results showed that 15 accessions were resistant to SCN with a Female Index (FI) at 4.8 to 9.4, and 62 accessions were moderately resistant (10 < FI < 30) to HG Type 0. The association study showed that 11 SNP markers, located on chromosomes Pv04, 07, 09, and 11, were strongly associated with resistance to HG Type 0. GWAS was also conducted for resistance to HG Type 2.5.7 and HG Type 1.2.3.5.6.7 based on the public dataset (N = 276), consisting of a diverse set of common bean accessions genotyped with the BARCBean6K_3 chip. Six SNPs associated with HG Type 2.5.7 resistance on Pv 01, 02, 03, and 07, and 12 SNPs with HG Type 1.2.3.5.6.7 resistance on Pv 01, 03, 06, 07, 09, 10, and 11 were detected. The accuracy of genomic prediction (GP) was 0.36 to 0.49 for resistance to the three SCN HG types, indicating that genomic selection (GS) of SCN resistance is feasible. This study provides basic information for developing SCN-resistant common bean cultivars, using the USDA core germ plasm accessions. The SNP markers can be used in molecular breeding in common beans through marker-assisted selection (MAS) and GS.

Angular leaf spot (ALS) is a disease that causes major yield losses in the common bean crop. Studies based on different isolates and populations have already been carried out to elucidate the genetic mechanisms of resistance to ALS. However, understanding of the interaction of this resistance with the reproductive stages of common bean is lacking. The aim of the present study was to identify ALS resistance loci at different plant growth stages (PGS) by association and linkage mapping approaches. An BC2F3 inter-gene pool cross population (AND 277 × IAC-Milênio – AM population) profiled with 1,091 SNPs from genotyping by sequencing (GBS) was used for linkage mapping, and a carioca diversity panel (CDP) genotyped by 5,398 SNPs from BeadChip assay technology was used for association mapping. Both populations were evaluated for ALS resistance at the V2 and V3 PGSs (controlled conditions) and R8 PGS (field conditions). Different QTL (quantitative trait loci) were detected for the three PGSs and both populations, showing a different quantitative profile of the disease at different plant growth stages. For the three PGS, multiple interval mapping (MIM) identified seven significant QTL, and the Genome-wide association study (GWAS) identified fourteen associate SNPs. Several loci validated regions of previous studies, and Phg-1, Phg-2, Phg-4, and Phg-5, among the 5 loci of greatest effects reported in the literature, were detected in the CDP. The AND 277 cultivar contained both the Phg-1 and the Phg-5 QTL, which is reported for the first time in the descendant cultivar CAL143 as ALS10.1UC. The novel QTL named ALS11.1AM was located at the beginning of chromosome Pv11. Gene annotation revealed several putative resistance genes involved in the ALS response at the three PGSs, and with the markers and loci identified, new specific molecular markers can be developed, representing a powerful tool for common bean crop improvement and for gain in ALS resistance.

Genetic resistance is the primary means for control of Bean golden yellow mosaic virus (BGYMV) in common bean (Phaseolus vulgaris L.). Breeding for resistance is difficult because of sporadic and uneven infection across field nurseries. We sought to facilitate breeding for BGYMV resistance by improving marker-assisted selection (MAS) for the recessive bgm-1 gene and identifying and developing MAS for quantitative trait loci (QTL) conditioning resistance. Genetic linkage mapping in two recombinant inbred line populations and genome-wide association study (GWAS) in a large breeding population and two diversity panels revealed a candidate gene for bgm-1 and three QTL BGY4.1, BGY7.1, and BGY8.1 on independent chromosomes. A mutation (5 bp deletion) in a NAC (No Apical Meristem) domain transcriptional regulator superfamily protein gene Phvul.003G027100 on chromosome Pv03 corresponded with the recessive bgm-1 resistance allele. The five bp deletion in exon 2 starting at 20 bp (Pv03: 2,601,582) is expected to cause a stop codon at codon 23 (Pv03: 2,601,625), disrupting further translation of the gene. A Tm-shift assay marker named PvNAC1 was developed to track bgm-1. PvNAC1 corresponded with bgm-1 across ∼1,000 lines which trace bgm-1 back to a single landrace “Garrapato” from Mexico. BGY8.1 has no effect on its own but exhibited a major effect when combined with bgm-1. BGY4.1 and BGY7.1 acted additively, and they enhanced the level of resistance when combined with bgm-1. Tm-shift assay markers were generated for MAS of the QTL, but their effectiveness requires further validation.

In this study, four recombinant inbred line (RIL) soybean populations were screened for their response to infection by Pythium sylvaticum, Pythium irregulare, Pythium oopapillum, and Pythium torulosum. The parents, PI 424237A, PI 424237B, PI 408097, and PI 408029, had higher levels of resistance to these species in a preliminary screening and were crossed with “Williams,” a susceptible cultivar. A modified seed rot assay was used to evaluate RIL populations for their response to specific Pythium species selected for a particular population based on preliminary screenings. Over 2500 single-nucleotide polymorphism (SNP) markers were used to construct chromosomal maps to identify regions associated with resistance to Pythium species. Several minor and large effect quantitative disease resistance loci (QDRL) were identified including one large effect QDRL on chromosome 8 in the population of PI 408097 × Williams. It was identified by two different disease reaction traits in P. sylvaticum, P. irregulare, and P. torulosum. Another large effect QDRL was identified on chromosome 6 in the population of PI 408029 × Williams, and conferred resistance to P. sylvaticum and P. irregulare. These large effect QDRL will contribute toward the development of improved soybean cultivars with higher levels of resistance to these common soil-borne pathogens.

Soybean [Glycine max (L.) Merr.] is a crop of great interest worldwide. Exploring molecular approaches to increase yield genetic gain has been one of the main challenges for soybean breeders and geneticists. Agronomic traits such as maturity, plant height, and seed weight have been found to contribute to yield. In this study, a total of 250 soybean accessions were genotyped with 10,259 high-quality SNPs postulated from genotyping by sequencing (GBS) and evaluated for grain yield, maturity, plant height, and seed weight over three years. A genome-wide association study (GWAS) was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model. Genomic selection (GS) was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that 20, 31, 37, and 23 SNPs were significantly associated with maturity, plant height, seed weight, and yield, respectively; Many SNPs were mapped to previously described maturity and plant height loci (E2, E4, and Dt1) and a new plant height locus was mapped to chromosome 20. Candidate genes were found in the vicinity of the two SNPs with the highest significant levels associated with yield, maturity, plant height, seed weight, respectively. A 11.5-Mb region of chromosome 10 was associated with both yield and seed weight. Overall, the accuracy of GS was dependent on the trait, year, and population structure, and high accuracy indicates that these agronomic traits can be selected in molecular breeding through GS. The SNP markers identified in this study can be used to improve yield and agronomic traits through the marker-assisted selection and GS in breeding programs.

Soybean [Glycine max (L.) Merr.] was domesticated from wild soybean (G. soja Sieb. and Zucc.) and has been further improved as a dual-use seed crop to provide highly valuable oil and protein for food, feed, and industrial applications. However, the underlying genetic and molecular basis remains less understood. Having combined high-confidence bi-parental linkage mapping with high-resolution association analysis based on 631 whole sequenced genomes, we mapped major soybean protein and oil QTLs on chromosome15 to a sugar transporter gene (GmSWEET39). A two-nucleotide CC deletion truncating C-terminus of GmSWEET39 was strongly associated with high seed oil and low seed protein, suggesting its pleiotropic effect on protein and oil content. GmSWEET39 was predominantly expressed in parenchyma and integument of the seed coat, and likely regulates oil and protein accumulation by affecting sugar delivery from maternal seed coat to the filial embryo. We demonstrated that GmSWEET39 has a dual function for both oil and protein improvement and undergoes two different paths of artificial selection. A CC deletion (CC-) haplotype H1 has been intensively selected during domestication and extensively used in soybean improvement worldwide. H1 is fixed in North American soybean cultivars. The protein-favored (CC+) haplotype H3 still undergoes ongoing selection, reflecting its sustainable role for soybean protein improvement. The comprehensive knowledge on the molecular basis underlying the major QTL and GmSWEET39 haplotypes associated with soybean improvement would be valuable to design new strategies for soybean seed quality improvement using molecular breeding and biotechnological approaches.

Soybean is the world’s leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding.

Abstract Background Cytoplasmic male sterility (CMS) is a natural phenomenon of pollen abortion caused by the interaction between cytoplasmic genes and nuclear genes. CMS is a simple and effective pollination control system, and plays an important role in crop heterosis utilization. Circular RNAs (circRNAs) are a vital type of non-coding RNAs, which play crucial roles in microRNAs (miRNAs) function and post-transcription control. To explore the expression profile and possible functions of circRNAs in the soybean CMS line NJCMS1A and its maintainer NJCMS1B, high-throughput deep sequencing coupled with RNase R enrichment strategy was conducted. Results CircRNA libraries were constructed from flower buds of NJCMS1A and its maintainer NJCMS1B with three biological replicates. A total of 2867 circRNAs were identified, with 1009 circRNAs differentially expressed between NJCMS1A and NJCMS1B based on analysis of high-throughput sequencing. Of the 12 randomly selected circRNAs with different expression levels, 10 showed consistent expression patterns based on high-throughput sequencing and quantitative real-time PCR analyses. Tissue specific expression patterns were also verified with two circRNAs by quantitative real-time PCR. Most parental genes of differentially expressed circRNAs were mainly involved in biological processes such as metabolic process, biological regulation, and reproductive process. Moreover, 83 miRNAs were predicted from the differentially expressed circRNAs, some of which were strongly related to pollen development and male fertility; The functions of miRNA targets were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and the target mRNAs were significantly enriched in signal transduction and programmed cell death. Furthermore, a total of 165 soybean circRNAs were predicted to contain at least one internal ribosome entry site (IRES) element and an open reading frame, indicating their potential to encode polypeptides or proteins. Conclusions Our study indicated that the circRNAs might participate in the regulation of flower and pollen development, which could provide a new insight into the molecular mechanisms of CMS in soybean.

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.

Abstract Background Soybean seed weight is not only a yield component, but also a critical trait for various soybean food products such as sprouts, edamame, soy nuts, natto and miso. Linkage analysis and genome-wide association study (GWAS) are two complementary and powerful tools to connect phenotypic differences to the underlying contributing loci. Linkage analysis is based on progeny derived from two parents, given sufficient sample size and biological replication, it usually has high statistical power to map alleles with relatively small effect on phenotype, however, linkage analysis of the bi-parental population can’t detect quantitative trait loci (QTL) that are fixed in the two parents. Because of the small seed weight difference between the two parents in most families of previous studies, these populations are not suitable to detect QTL that have considerable effects on seed weight. GWAS is based on unrelated individuals to detect alleles associated with the trait under investigation. The ability of GWAS to capture major seed weight QTL depends on the frequency of the accessions with small and large seed weight in the population being investigated. Our objective was to identify QTL that had a pronounced effect on seed weight using a selective population of soybean germplasm accessions and the approach of GWAS and fixation index analysis. Results We selected 166 accessions from the USDA Soybean Germplasm Collection with either large or small seed weight and could typically grow in the same location. The accessions were evaluated for seed weight in the field for two years and genotyped with the SoySNP50K BeadChip containing >42,000 SNPs. Of the 17 SNPs on six chromosomes that were significantly associated with seed weight in two years based on a GWAS of the selective population, eight on chromosome 4 or chromosome 17 had significant Fst values between the large and small seed weight sub-populations. The seed weight difference of the two alleles of these eight significant SNPs varied from 8.1 g to 11.7 g/100 seeds in two years. We also identified haplotypes in three haplotype blocks with significant effects on seed weight. These findings were validated in a panel with 3753 accessions from the USDA Soybean Germplasm Collection. Conclusion This study highlighted the usefulness of selective genotyping populations coupled with GWAS and fixation index analysis for the identification of QTL with substantial effects on seed weight in soybean. This approach may help geneticists and breeders to more efficiently identify major QTL controlling other traits. The major regions and haplotypes we have identified that control seed weight differences in soybean will facilitate the identification of genes regulating this important trait.

Abstract Background Ozone is an air pollutant widely known to cause a decrease in productivity in many plant species, including soybean (Glycine max (L.) Merr). While the response of cultivated soybean to ozone has been studied, very little information is available regarding the ozone response of its wild relatives. Results Ozone-resistant wild soybean accessions were identified by measuring the response of a genetically diverse group of 66 wild soybean (Glycine soja Zucc. and Sieb.) accessions to elevated ozone levels. RNA-Seq analyses were performed on leaves of different ages from selected ozone-sensitive and ozone-resistant accessions that were subjected to treatment with an environmentally relevant level of ozone. Many more genes responded to elevated ozone in the two ozone-sensitive accessions than in the ozone-resistant accessions. Analyses of the ozone response genes indicated that leaves of different ages responded differently to ozone. Older leaves displayed a consistent reduction in expression of genes involved in photosynthesis in response to ozone, while changes in expression of defense genes dominated younger leaf tissue in response to ozone. As expected, there is a substantial difference between the response of ozone-sensitive and ozone-resistant accessions. Genes associated with photosystem 2 were substantially reduced in expression in response to ozone in the ozone-resistant accessions. A decrease in peptidase inhibitors was one of several responses specific to one of the ozone resistant accessions. Conclusion The decrease in expression in genes associated with photosynthesis confirms that the photosynthetic apparatus may be an early casualty in response to moderate levels of ozone. A compromise of photosynthesis would substantially impact plant growth and seed production. However, the resistant accessions may preserve their photosynthetic apparatus in response to the ozone levels used in this study. Older leaf tissue of the ozone-resistant accessions showed a unique down-regulation of genes associated with endopeptidase inhibitor activity. This study demonstrates the existence of significant diversity in wild soybean for ozone response. Wild soybean accessions characterized in this study can be used by soybean breeders to enhance ozone tolerance of this important food crop.

Abstract Background Soybean (Glycine max (L.) Merr.) is a short day plant. Its flowering and maturity time are controlled by genetic and environmental factors, as well the interaction between the two factors. Previous studies have shown that both genetic and environmental factors, mainly photoperiod and temperature, control flowering time of soybean. Additionally, these studies have reported gene × gene and gene × environment interactions on flowering time. However, the effects of quantitative trait loci (QTL) in response to photoperiod and temperature have not been well evaluated. The objectives of the current study were to identify the effects of loci associated with flowering time under different photo-thermal conditions and to understand the effects of interaction between loci and environment on soybean flowering. Methods Different photoperiod and temperature combinations were obtained by adjusting sowing dates (spring sowing and summer sowing) or day-length (12 h, 16 h). Association mapping was performed on 91 soybean cultivars from different maturity groups (MG000-VIII) using 172 SSR markers and 5107 SNPs from the Illumina SoySNP6K iSelectBeadChip. The effects of the interaction between QTL and environments on flowering time were also analysed using the QTXNetwork. Results Large-effect loci were detected on Gm 11, Gm 16 and Gm 20 as in previous reports. Most loci associated with flowering time are sensitive to photo-thermal conditions. Number of loci associated with flowering time was more under the long day (LD) than under the short day (SD) condition. The variation of flowering time among the soybean cultivars mostly resulted from the epistasis × environment and additive × environment interactions. Among the three candidate loci, i.e. Gm04_4497001 (near GmCOL3a), Gm16_30766209 (near GmFT2a and GmFT2b) and Gm19_47514601 (E3 or GmPhyA3), the Gm04_4497001 may be the key locus interacting with other loci for controlling soybean flowering time. Conclusion The effects of loci associated with the flowering time of soybean were dependent upon the photo-thermal conditions. This study facilitates the understanding of the genetic mechanism of soybean flowering and molecular breeding for the improvement of soybean adaptability to specific and/or broad regions.

Abstract Background The Andean cultivar Paloma is resistant to Mesoamerican and Andean races of Colletotrichum lindemuthianum, the fungal pathogen that causes the destructive anthracnose disease in common bean. Remarkably, Paloma is resistant to Mesoamerican races 2047 and 3481, which are among the most virulent races of the anthracnose pathogen. Most genes conferring anthracnose resistance in common bean are overcome by these races. The genetic mapping and the relationship between the resistant Co-Pa gene of Paloma and previously characterized anthracnose resistance genes can be a great contribution for breeding programs. Results The inheritance of resistance studies for Paloma was performed in F2 population from the cross Paloma (resistant) × Cornell 49–242 (susceptible) inoculated with race 2047, and in F2 and F2:3 generations from the cross Paloma (resistant) × PI 207262 (susceptible) inoculated with race 3481. The results of these studies demonstrated that a single dominant gene confers the resistance in Paloma. Allelism tests performed with multiple races of C. lindemuthianum showed that the resistance gene in Paloma, provisionally named Co-Pa, is independent from the anthracnose resistance genes Co-1, Co-2, Co-3, Co-4, Co-5, Co-6, Co-12, Co-13, Co-14, Co-15 and Co-16. Bulk segregant analysis using the SNP chip BARCBean6K_3 positioned the approximate location of Co-Pa in the lower arm of chromosome Pv01. Further mapping analysis located the Co-Pa gene at a 390 kb region of Pv01 flanked by SNP markers SS82 and SS83 at a distance of 1.3 and 2.1 cM, respectively. Conclusions The results presented here showed that Paloma cultivar has a new dominant gene conferring resistance to anthracnose, which is independent from those genes previously described. The linkage between the Co-Pa gene and the SS82 and SS83 SNP markers will be extremely important for marker-assisted introgression of the gene into elite cultivars in order to enhance resistance.

Anthracnose, caused by the fungal pathogen Colletotrichum lindemuthianum, is one of the world's most destructive diseases of common bean. The use of resistant cultivars is the most cost-effective strategy to manage this disease; however, durable resistance is difficult to achieve due to the vast virulence diversity of the anthracnose pathogen. Finding new genes with broad-spectrum resistance increases the prospect of designing an effective anthracnose-management strategy. Genetic analysis confirmed the presence of a single, dominant anthracnose-resistance locus in AC, which we provisionally named Co-AC. Bulk segregant analysis and genetic mapping of two F2 populations from the crosses AC × PI207262 and AC × G 2333 were used to determine the position of the Co-AC locus in a 631 Kbp genomic region flanked by the SNP markers SS56 and SS92 on the lower arm of chromosome Pv01. By genotyping 77 F3 plants from the AC × PI207262 cross using nine additional markers, we fine-mapped the Co-AC locus to a significantly smaller genomic region (9.4 Kbp) flanked by the SNP markers SS102 and SS165. This 9.4 Kbp region harbors three predicted genes based on the common bean reference genome, notably including the gene model Phvul.001G244300, which encodes Clathrin heavy chain 1, a protein that supports specific stomatal regulation functions and might play a role in plant defense signaling. Because the Co-AC resistance locus is linked in cis, it can be selected with great efficiency using molecular markers. These results will be very useful for breeding programs aimed at developing bean cultivars with anthracnose resistance using marker-assisted selection. This study revealed the broad-spectrum resistance of AC to C. lindemuthianum and the existence of the Co-AC anthracnose-resistance locus. Fine mapping positioned this locus in a small genomic region on the lower end of chromosome Pv01 that contained three candidate genes for the Co-AC locus.