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.

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.

Molecular mapping and genomics of soybean seed protein: a review and perspective for the future.

KEY MESSAGE: Genetic improvement of soybean protein meal is a complex process because of negative correlation with oil, yield, and temperature. This review describes the progress in mapping and genomics, identifies knowledge gaps, and highlights the need of integrated approaches. Meal protein derived from soybean [Glycine max (L) Merr.] seed is the primary source of protein in poultry and livestock feed. Protein is a key factor that determines the nutritional and economical value of soybean. Genetic improvement of soybean seed protein content is highly desirable, and major quantitative trait loci (QTL) for soybean protein have been detected and repeatedly mapped on chromosomes (Chr.) 20 (LG-I), and 15 (LG-E). However, practical breeding progress is challenging because of seed protein content's negative genetic correlation with seed yield, other seed components such as oil and sucrose, and interaction with environmental effects such as temperature during seed development. In this review, we discuss rate-limiting factors related to soybean protein content and nutritional quality, and potential control factors regulating seed storage protein. In addition, we describe advances in next-generation sequencing technologies for precise detection of natural variants and their integration with conventional and high-throughput genotyping technologies. A syntenic analysis of QTL on Chr. 15 and 20 was performed. Finally, we discuss comprehensive approaches for integrating protein and amino acid QTL, genome-wide association studies, whole-genome resequencing, and transcriptome data to accelerate identification of genomic hot spots for allele introgression and soybean meal protein improvement.

Evaluation of Commercial Soybean Cultivars for Reaction to Phomopsis Seed Decay.

Phomopsis seed decay (PSD), caused by Phomopsis longicolla (syn. Diaporthe longicolla), is an economically important soybean disease causing poor seed quality. Planting resistant cultivars is one of the most effective means to control PSD. In this study, 16 commercially available maturity groups IV and V soybean cultivars, including two previously identified PSD-resistant and two PSD-susceptible checks, were evaluated for seed infection by P. longicolla in inoculated and noninoculated plots, and harvested promptly or with a 2-week delay in harvest. The test was conducted at Stoneville, Mississippi, in 2012 and 2013. Seed infection by P. longicolla ranged from 0.5 to 76%, and seed germination ranged from 18 to 97%. One MG IV cultivar (Morsoy R2 491) and five MG V cultivars (Progeny 5650, Progeny 5706, Asgrow 5606, Asgrow 5831, and Dyna-Gro33C59) had significantly (P ≤ 0.05) lower percent seed infected by P. longicolla than their respective susceptible checks and other cultivars in the same tests. Information obtained from this study will be useful for soybean growers and breeders for selection of cultivars for planting or breeding and future genetic studies in the development of cultivars with improved resistance to PSD.

Evaluation of high yielding soybean germplasm under water limitation.

Limited information is available for soybean root traits and their plasticity under drought stress. To date, no studies have focused on examining diverse soybean germplasm for regulation of shoot and root response under water limited conditions across varying soil types. In this study, 17 genetically diverse soybean germplasm lines were selected to study root response to water limited conditions in clay (trial 1) and sandy soil (trial 2) in two target environments. Physiological data on shoot traits was measured at multiple crop stages ranging from early vegetative to pod filling. The phenotypic root traits, and biomass accumulation data are collected at pod filling stage. In trial 1, the number of lateral roots and forks were positively correlated with plot yield under water limitation and in trial 2, lateral root thickness was positively correlated with the hill plot yield. Plant Introduction (PI) 578477A and 088444 were found to have higher later root number and forks in clay soil with higher yield under water limitation. In sandy soil, PI458020 was found to have a thicker lateral root system and higher yield under water limitation. The genotypes identified in this study could be used to enhance drought tolerance of elite soybean cultivars through improved root traits specific to target environments.

Evaluation of Diverse Soybean Germplasm for Resistance to Phomopsis Seed Decay.

Phomopsis seed decay (PSD), caused primarily by the fungal pathogen Phomopsis longicolla, is one of the most important diseases reducing seed quality and yield of soybean. Few cultivars have been identified as resistant. To identify new sources of resistance to PSD, 135 soybean germplasm accessions, originating from 28 countries, were field screened in Arkansas, Mississippi, and Missouri in 2009. Based on seed assays of natural field infection by P. longicolla in 2009, 42 lines, including the most resistant and susceptible lines, were reevaluated in the field in 2010, 2011, and 2012 with P. longicolla-inoculated and noninoculated treatments. Six maturity group (MG) III (PI 189891, PI 398697, PI 417361, PI 504481, PI 504488, and PI 88490), four MG IV (PI 158765, PI 235335, PI 346308, and PI 416779), and five MG V (PI 381659, PI 381668, PI 407749, PI 417567, and PI 476920) lines had significantly lower percent seed infection by P. longicolla than the susceptible checks and other lines in the same test (P ≤ 0.05). They appeared to have some levels of resistance to PSD. These new sources of PSD resistance can be used in developing soybean breeding lines or cultivars with resistance to PSD, and for genetic mapping of PSD resistance genes.

Novel genetic resources associated with sucrose and stachyose content through genome-wide association study in soybean (Glycine max (L.) Merr.).

The nutritional value of soybean [Glycine max (L.) Merr.] for animals is influenced by soluble carbohydrates, such as sucrose and stachyose. Although sucrose is nutritionally desirable, stachyose is an antinutrient causing diarrhea and flatulence in non-ruminant animals. We conducted a genome-wide association study of 220 soybean accessions using 21,317 single nucleotide polymorphisms (SNPs) from the SoySNP50K iSelect Beadchip data to identify significant SNPs associated with sucrose and stachyose content. Seven significant SNPs were identified for sucrose content across chromosomes (Chrs.) 2, 8, 12, 17, and 20, while thirteen significant SNPs were identified for stachyose content across Chrs. 2, 5, 8, 9, 10, 13, 14, and 15. Among those significant SNPs, three sucrose-related SNPs on Chrs. 8 and 17 were novel, while twelve stachyose-related SNPs on Chrs. 2, 5, 8, 9, 10, 13, 14, and 15 were novel. Based on Phytozome, STRING, and GO annotation, 17 and 24 candidate genes for sucrose and stachyose content, respectively, were highly associated with the carbohydrate metabolic pathway. Among these, the publicly available RNA-seq Atlas database highlighted four candidate genes associated with sucrose (Glyma.08g361200 and Glyma.17g258100) and stachyose (Glyma.05g025300 and Glyma.13g077900) content, which had higher gene expression levels in developing seed and multiple parts of the soybean plant. The results of this study will extend knowledge of the molecular mechanism and genetic basis underlying sucrose and stachyose content in soybean seed. Furthermore, the novel candidate genes and SNPs can be valuable genetic resources that soybean breeders may utilize to modify carbohydrate profiles for animal and human usage.

Genetic architecture of soybean tolerance to off-target dicamba.

The adoption of dicamba-tolerant (DT) soybean in the United States resulted in extensive off-target dicamba damage to non-DT vegetation across soybean-producing states. Although soybeans are highly sensitive to dicamba, the intensity of observed symptoms and yield losses are affected by the genetic background of genotypes. Thus, the objective of this study was to detect novel marker-trait associations and expand on previously identified genomic regions related to soybean response to off-target dicamba. A total of 551 non-DT advanced breeding lines derived from 232 unique bi-parental populations were phenotyped for off-target dicamba across nine environments for three years. Breeding lines were genotyped using the Illumina Infinium BARCSoySNP6K BeadChip. Filtered SNPs were included as predictors in Random Forest (RF) and Support Vector Machine (SVM) models in a forward stepwise selection loop to identify the combination of SNPs yielding the highest classification accuracy. Both RF and SVM models yielded high classification accuracies (0.76 and 0.79, respectively) with minor extreme misclassifications (observed tolerant predicted as susceptible, and vice-versa). Eight genomic regions associated with off-target dicamba tolerance were identified on chromosomes 6 [Linkage Group (LG) C2], 8 (LG A2), 9 (LG K), 10 (LG O), and 19 (LG L). Although the genetic architecture of tolerance is complex, high classification accuracies were obtained when including the major effect SNP identified on chromosome 6 as the sole predictor. In addition, candidate genes with annotated functions associated with phases II (conjugation of hydroxylated herbicides to endogenous sugar molecules) and III (transportation of herbicide conjugates into the vacuole) of herbicide detoxification in plants were co-localized with significant markers within each genomic region. Genomic prediction models, as reported in this study, can greatly facilitate the identification of genotypes with superior tolerance to off-target dicamba.

Cataloging SCN resistance loci in North American public soybean breeding programs.

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.

Soybean genetics, genomics, and breeding for improving nutritional value and reducing antinutritional traits in food and feed.

Soybean [Glycine max (L.) Merr.] is a globally important crop due to its valuable seed composition, versatile feed, food, and industrial end-uses, and consistent genetic gain. Successful genetic gain in soybean has led to widespread adaptation and increased value for producers, processors, and consumers. Specific focus on the nutritional quality of soybean seed composition for food and feed has further elucidated genetic knowledge and bolstered breeding progress. Seed components are historical and current targets for soybean breeders seeking to improve nutritional quality of soybean. This article reviews genetic and genomic foundations for improvement of nutritionally important traits, such as protein and amino acids, oil and fatty acids, carbohydrates, and specific food-grade considerations; discusses the application of advanced breeding technology such as CRISPR/Cas9 in creating seed composition variations; and provides future directions and breeding recommendations regarding soybean seed composition traits.

Effect of Lactofen, Azoxystrobin, and Genotypes on Charcoal Rot, Phomopsis Seed Decay, and Pod and Stem Blight in Soybean.

Yield-limiting diseases such as charcoal rot and Phomopsis seed decay have a significant impact on the economic potential for soybean because there are few methods for management of these diseases. The objectives of this study were to determine the development of charcoal rot, infection of seed by Phomopsis spp., and severity of pod and stem blight on Asgrow 4403, Delta Pine 5806, United States Department of Agriculture-introduced DT 97-4290 and plant introduction (PI) number PI 567562A, and Asgrow 4403 treated and not treated with lactofen or azoxystrobin. This is the first report of high levels of resistance in PI 567562A to charcoal rot, and resistance in this PI was greater than for DT 97-4290. Application of lactofen at growth stage R1 and azoxystrobin at either planting, R3, or R6 had no significant impact on severity of charcoal rot, percentage of harvested seed infected by Phomopsis spp., or severity of pod and stem blight on genotype Asgrow 4403. Of four genotypes evaluated, none were resistant to infection by Phomopsis spp. The genotypes Asgrow 4403, DP 5806, and DT 97-4290 were susceptible to pod and stem blight and PI 567562A was resistant.

Identification of genomic regions associated with soybean responses to off-target dicamba exposure.

The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean.

Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans.

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

Regional analysis of planting date and cultivar maturity recommendations that improve soybean oil yield and meal protein concentration.

Planting date and cultivar maturity group (MG) are major management factors affecting soybean [Glycine max (L.) Merr.] yield, but their effect on seed oil and protein concentration, and in particular meal protein concentration, is less understood. We quantified changes in seed oil and protein, and estimated meal protein concentration, and total oil and protein yield in response to planting date and cultivar MG ranging from 3 to 6 and across locations comprising a 8.3° range in latitude in the U.S. Midsouth. Our results show that delayed planting date and later cultivar maturity reduced oil concentration, and this was partially associated with a decrease in temperature during the seed fill phase. Thus, optimum cultivar MG recommendations to maximize total oil yield (in kg ha-1) for planting dates in May and June required relatively earlier cultivar MGs than those recommended to maximize seed yield. For planting dates in April, short-season MG 3 cultivars did not increase oil yield compared to full-season MG 4 or 5 cultivars due to a quadratic yield response to planting date at most locations. Planting date and cultivar maturity effects on seed protein concentration were not always consistent with the effects on estimated meal protein concentration after oil extraction. Meal protein concentration decreased with lower temperatures during seed fill, and when the start of seed fill occurred after August 15, but relatively short-season cultivar MGs reduced the risk of low meal protein concentration. Meal protein concentration is a trait of interest for the feed industry that would be beneficial to report in future studies evaluating genetic, management, and environmental effects on seed protein concentration.

Major locus and other novel additive and epistatic loci involved in modulation of isoflavone concentration in soybean seeds.

Seeds of soybean [Glycine max (L.) Merr.] accumulate more isoflavones than any tissue of any plant species. In other plant parts, isoflavones are usually released to counteract the effects of various biotic and abiotic stresses. Because of the benefits to the plant and positive implications that consumption may have on human health, increasing isoflavones is a goal of many soybean breeding programs. However, altering isoflavone levels through marker-assisted selection (MAS) has been impractical due to the small and often environmentally variable contributions that each individual quantitative trait locus (QTL) has on total isoflavones. In this study, we developed a Magellan × PI 437654 F(7)-RIL population to construct a highly saturated non-redundant linkage map that encompassed 451 SNP and SSR molecular markers and used it to locate genomic regions that govern accumulation of isoflavones in the seeds of soybean. Five QTLs were found that contribute to the concentration of isoflavones, having single or multiple additive effects on isoflavone component traits. We also validated a major locus which alone accounted for up to 10% of the phenotypic variance for glycitein, and 35-37% for genistein, daidzein and the sum of all three soybean isoflavones. This QTL was consistently associated with increased concentration of isoflavones across different locations, years and crosses. It was the most important QTL in terms of net increased amounts of all isoflavone forms. Our results suggest that this locus would be an excellent candidate to target for MAS. Also, several minor QTLs were identified that interacted in an additive-by-additive epistatic manner, to increase isoflavone concentration.

Intricate environment-modulated genetic networks control isoflavone accumulation in soybean seeds.

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

Identification of Soybean Genotypes Resistant to Cercospora sojina by Field Screening and Molecular Markers.

Frogeye leaf spot (FLS) of soybean, caused by Cercospora sojina, has been a problem in the southern United States for many years and has recently become a greater problem in the northern United States. Cultivars resistant to FLS have been developed for planting in the southern United States and resistance in many of these cultivars is conditioned by the Rcs3 gene. This gene conditions immunity to all known races and isolates of the pathogen. Resistance to C. sojina in soybean genotypes (cultivars and breeding lines) adapted to north-central U.S. production region is unknown. The objectives of this study were to (i) identify maturity group (MG) III, IV, and V soybean genotypes resistant to C. sojina race 11 by field screening at multiple locations over years and (ii) determine whether FLS resistance in these genotypes is likely to be conditioned by the Rcs3 gene. In total, 1,350 genotypes were evaluated for resistance to race 11 in field trials, and 13 MG III, 45 MG IV, and 15 MG V genotypes did not develop symptoms of FLS. Of these, 54 were subsequently tested for the possible presence of Rcs3 using five molecular markers located within 2 centimorgans (cM) of the gene. None of the MG III genotypes tested had the Rcs3 haplotype of cv. Davis, the source of Rcs3; six of the MG IV genotypes and seven of the MG V genotypes had the Rcs3 haplotype. This is the first report of the presence of the Rcs3 haplotype in LN 97-15076 and S99-2281. The soybean genotypes predicted to have the Rcs3 gene and other genotypes with no FLS symptoms in field trials may be useful in developing soybean cultivars with broad resistance to FLS and adapted to the northern United States.

Evaluation of soybean genotypes for reaction to natural field infection by Cercospora species causing purple seed stain.

Purple seed stain (PSS) of soybean (Glycine max (L.) Merr.) is a prevalent seed disease. It results in poor seed quality and reduced seed lot market grade, and thus undermines value of soybean worldwide. The objectives of this research were to evaluate the reaction of selected soybean genotypes collected from 15 countries representing maturity groups (MGs) III, IV, and V to PSS, and to identify new sources of resistance to PSS based on three years of evaluation of natural field infection by Cercospora spp. in the Mississippi Delta of the U. S. In this study, 42 soybean genotypes were evaluated in 2010, 2011, and 2012. Seventeen lines including six MG III (PI 88490, PI 504488, PI 417361, PI 548298, PI 437482, and PI 578486), seven MG IV (PI 404173, PI 346308, PI 355070, PI 416779, PI 80479, PI 346307, and PI 264555), and four MG V (PI 417567, PI 417420, PI 381659, and PI 407749) genotypes had significantly lower percent seed infection by Cercospora spp. than the susceptible checks and other genotypes evaluated (P ≤ 0.05). These genotypes of soybean can be used in developing soybean cultivars or germplasm lines with resistance to PSS and for genetic mapping of PSS resistance genes. In addition, among these 17 lines with different levels of resistance to PSS, nine soybean genotypes (PI 417361, PI 504488, PI 88490, PI 346308, PI 416779, PI 417567, PI 381659, PI 417567, and PI 407749) were previously reported as resistant to Phomopsis seed decay. Therefore, they could be useful in breeding programs to develop soybean cultivars with improved resistance to both seed diseases.

Genetic Architecture of Soybean Yield and Agronomic Traits.

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.

Development of SNP Genotyping Assays for Seed Composition Traits in Soybean.

Seed composition is one of the most important determinants of the economic values in soybean. The quality and quantity of different seed components, such as oil, protein, and carbohydrates, are crucial ingredients in food, feed, and numerous industrial products. Soybean researchers have successfully developed and utilized a diverse set of molecular markers for seed trait improvement in soybean breeding programs. It is imperative to design and develop molecular assays that are accurate, robust, high-throughput, cost-effective, and available on a common genotyping platform. In the present study, we developed and validated KASP (Kompetitive allele-specific polymerase chain reaction) genotyping assays based on previously known functional mutant alleles for the seed composition traits, including fatty acids, oligosaccharides, trypsin inhibitor, and lipoxygenase. These assays were validated on mutant sources as well as mapping populations and precisely distinguish the homozygotes and heterozygotes of the mutant genes. With the obvious advantages, newly developed KASP assays in this study can substitute the genotyping assays that were previously developed for marker-assisted selection (MAS). The functional gene-based assay resource developed using common genotyping platform will be helpful to accelerate efforts to improve soybean seed composition traits.