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.

Genetic architecture of protein and oil content in soybean seed and meal.

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.

Interactions Among Heterodera glycines, Macrophomina phaseolina, and Soybean Genotype.

Heterodera glycines, the soybean cyst nematode (SCN), and fungal pathogen Macrophomina phaseolina are economically important soybean pathogens that may coinfest fields. Resistance remains the most effective management tactic for SCN, and the rhg1-b resistance allele derived from plant introduction 88788 is most commonly deployed in the northern United States. The concomitant effects of SCN and M. phaseolina on soybean performance, as well as the effect of the rhg1-b allele in two different genetic backgrounds, were evaluated in three environments (during 2013 to 2015) and a greenhouse bioassay. Within two soybean populations, half of the lines had the rhg1-b allele, and the other half had the susceptible allele in the backgrounds of the cultivars IA3023 and LD00-3309. Significant interactions between soybean rhg1-b allele and M. phaseolina-infested plots were observed in 2014. In all experiments, initial SCN populations (Pi) and M. phaseolina in roots were associated with reduced soybean yield. SCN reproduction factor (RF = final population/Pi) was affected by SCN Pi, rhg1-b, and genetic background. A background-by-genotype interaction on yield was observed only in 2015, with a stronger rhg1-b effect in the LD00-3309 background, which suggested that the susceptible parent 'IA3023' is tolerant to SCN. SCN female index from greenhouse experiments was compared with field RF, and Lin's concordance and Pearson's correlation coefficients decreased with increasing field SCN Pi in soil. In this study, both SCN and M. phaseolina reduced soybean yield asymptomatically, and the impact of SCN rhg1-b resistance was dependent on SCN virulence but also population density.

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.

Candidate Genes Modulating Reproductive Timing in Elite US Soybean Lines Identified in Soybean Alleles of Arabidopsis Flowering Orthologs With Divergent Latitude Distribution.

Adaptation of soybean cultivars to the photoperiod in which they are grown is critical for optimizing plant yield. However, despite its importance, only the major loci conferring variation in flowering time and maturity of US soybean have been isolated. By contrast, over 200 genes contributing to floral induction in the model organism Arabidopsis thaliana have been described. In this work, putative alleles of a library of soybean orthologs of these Arabidopsis flowering genes were tested for their latitudinal distribution among elite US soybean lines developed in the United States. Furthermore, variants comprising the alleles of genes with significant differences in latitudinal distribution were assessed for amino acid conservation across disparate genera to infer their impact on gene function. From these efforts, several candidate genes from various biological pathways were identified that are likely being exploited toward adaptation of US soybean to various maturity groups.

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population.

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

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

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

Genotype imputation for soybean nested association mapping population to improve precision of QTL detection.

KEY MESSAGE: Software for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification. Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.

Detection of rare nematode resistance Rhg1 haplotypes in Glycine soja and a novel Rhg1 α-SNAP.

This study pursued the hypothesis that wild plant germplasm accessions carrying alleles of interest can be identified using available single nucleotide polymorphism (SNP) genotypes for particular alleles of other (unlinked) genes that contribute to the trait of interest. The soybean cyst nematode (SCN, Heterodera glycines [HG]) resistance locus Rhg1 is widely used in farmed soybean [Glycine max (L.) Merr.]. The two known resistance-conferring haplotypes, rhg1-a and rhg1-b, typically contain three or seven to 10 tandemly duplicated Rhg1 segments, respectively. Each Rhg1 repeat carries four genes, including Glyma.18G022500, which encodes unusual isoforms of the vesicle-trafficking chaperone α-SNAP. Using SoySNP50K data for NSFRAN07 allele presence, we discovered a new Rhg1 haplotype, rhg1-ds, in six accessions of wild soybean, Glycine soja Siebold & Zucc. (0.5% of the ∼1,100 G. soja accessions in the USDA collection). The α-SNAP encoded by rhg1-ds is unique at an important site of amino acid variation and shares with the rhg1-a and rhg1-b α-SNAP proteins the traits of cytotoxicity and altered N-ethylmaleimide sensitive factor (NSF) protein interaction. Copy number assays indicate three repeats of rhg1-ds. G. soja PI 507613 and PI 507623 exhibit resistance to HG type 2.5.7 SCN populations, in part because of contributions from other loci. In a segregating F2 population, rhg1-b and rhg1-ds made statistically indistinguishable contributions to resistance to a partially virulent HG type 2.5.7 SCN population. Hence, the unusual multigene copy number variation Rhg1 haplotype was present but rare in ancestral G. soja and was present in accessions that offer multiple traits for SCN resistance breeding. The accessions were initially identified for study based on an unlinked SNP.

Soybean Cyst Nematode Resistance Quantitative Trait Locus cqSCN-006 Alters the Expression of a γ-SNAP Protein.

Soybean cyst nematode (SCN) is the most economically damaging pathogen of soybean and host resistance is a core management strategy. The SCN resistance quantitative trait locus cqSCN-006, introgressed from the wild relative Glycine soja, provides intermediate resistance against nematode populations, including those with increased virulence on the heavily used rhg1-b resistance locus. cqSCN-006 was previously fine-mapped to a genome interval on chromosome 15. The present study determined that Glyma.15G191200 at cqSCN-006, encoding a γ-SNAP, contributes to SCN resistance. CRISPR/Cas9-mediated disruption of the cqSCN-006 allele reduced SCN resistance in transgenic roots. There are no encoded amino acid polymorphisms between resistant and susceptible alleles. However, other cqSCN-006-specific DNA polymorphisms in the Glyma.15G191200 promoter and gene body were identified, and we observed differing induction of γ-SNAP protein abundance at SCN infection sites between resistant and susceptible roots. We identified alternative RNA splice forms transcribed from the Glyma.15G191200 γ-SNAP gene and observed differential expression of the splice forms 2 days after SCN infection. Heterologous overexpression of γ-SNAPs in plant leaves caused moderate necrosis, suggesting that careful regulation of this protein is required for cellular homeostasis. Apparently, certain G. soja evolved quantitative SCN resistance through altered regulation of γ-SNAP. Previous work has demonstrated SCN resistance impacts of the soybean α-SNAP proteins encoded by Glyma.18G022500 (Rhg1) and Glyma.11G234500. The present study shows that a different type of SNAP protein can also impact SCN resistance. Little is known about γ-SNAPs in any system, but the present work suggests a role for γ-SNAPs during susceptible responses to cyst nematodes.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Meta-GWAS for quantitative trait loci identification in soybean.

We report a meta-Genome Wide Association Study involving 73 published studies in soybean [Glycine max L. (Merr.)] covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 peaks at 393 unique loci. Using stringent criteria to detect significant marker-trait associations, 59 candidate genes were identified, including 17 agronomic traits loci, 19 for seed-related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.

Resistance Gene Pyramiding and Rotation to Combat Widespread Soybean Cyst Nematode Virulence.

Soybean cyst nematode (SCN) is an important pathogen of soybean causing >$1 billion in yield losses annually in the United States. Planting SCN-resistant soybean cultivars is the primary management strategy. Resistance genes derived from the plant introduction (PI) 88788 (rhg1-b) and PI 548402 (Peking; rhg1-a and Rhg4) are the main types of resistance available in commercial cultivars. The PI 88788 rhg1-b resistance allele is found in the majority of SCN-resistant cultivars in the north central United States. The widespread use of PI 88788 rhg1-b has led to limited options for farmers to rotate resistance sources to manage SCN. Consequently, overreliance on a single type of resistance has resulted in the selection of SCN populations that have adapted to reproduce on these resistant cultivars. Here we evaluated the effectiveness of rotating soybean lines with different combinations of resistance genes to determine the best strategy for combating the widespread increase in virulent SCN and limit future nematode adaptation to resistant cultivars. Eight SCN populations were developed by continuous selection of a virulent SCN field population (Heterodera glycines [HG] type 1.2.5.7) on a single resistance source or in rotation with soybean pyramiding different resistance gene alleles derived from PI 88788 (rhg1-b), PI 437654 (rhg1-a and Rhg4), PI 468916 (cqSCN-006 and cqSCN-007), and PI 567516C (Chr10). SCN population densities were determined for eight generations. HG type tests were conducted after the eighth generation to evaluate population shifts. The continued use of rhg1-b or 006/007 had limited effectiveness for reducing SCN type 1.2.5.7 population density, whereas rotation to the use of rhg1-a/Rhg4 resistance significantly reduced SCN population density but selected for broader SCN virulence (HG type 1.2.3.5.6.7). A rotation of rhg1-a/Rhg4 with a pyramid of rhg1-b/006/007/Chr10 was the most effective combination at both reducing population density and minimizing selection pressure. Our results provide guidance for implementation of a strategic SCN resistance rotation plan to manage the widespread virulence on PI 88788 and sustain the future durability of SCN resistance genes.

Evaluation of Foliar Diseases for Soybean Entries in the Pan-African Trials in Malawi and Zambia.

Soybean production has expanded worldwide including countries in sub-Saharan Africa. Several national and international agencies and research groups have partnered to improve overall performance of soybean breeding stocks and have introduced new germplasm from Brazil and the United States with the goal of developing new high-yielding cultivars. Part of this effort has been to test improved soybean lines/cultivars accumulated from private and public sources in multilocational trials in sub-Saharan Africa. These trials are known as the Pan-African Soybean Variety Trials, and the entries come from both private and public breeding programs. The objective of this research was to evaluate entries in the trials that include commercial cultivars or advanced experimental lines for the incidence and severity of foliar diseases. All trials were planted in December 2018 with six located in Zambia and one in Malawi. Plants were evaluated during the reproductive growth stages using a visual pretransformed severity rating scale. Foliar disease ratings were recorded for three bacterial diseases, six fungal diseases, one oomycete, and viruses. The overall occurrence of most of the diseases was high except for soybean rust and target spot, which were only found at two and one location, respectively. However, disease severity was generally low, although there were differences in disease severity ratings among the entries at some of the locations for brown spot, downy mildew, frogeye leaf spot, red leaf blotch, and soybean rust.

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.

QTL mapping and epistatic interaction analysis of field resistance to sudden death syndrome (Fusarium virguliforme) in soybean.

KEY MESSAGE: Two interactive quantitative trait loci (QTLs) controlled the field resistance to sudden death syndrome (SDS) in soybean. The interaction between them was confirmed. Sudden death syndrome (SDS), caused by Fusarium virguliforme, is a major disease of soybean [Glycine max (L.) Merr.] in the United States. Breeding for soybean resistance to SDS is the most cost-effective method to manage the disease. The objective of this study was to identify and characterize quantitative trait loci (QTLs) underlying field resistance to SDS in a recombinant inbred line population from the cross GD2422 × LD01-5907. This population was genotyped with 1786 polymorphic single nucleotide polymorphisms (SNPs) using SoySNP6 K iSelect BeadChip and evaluated for SDS resistance in a naturally infested field. Four SDS resistance QTLs were mapped on Chromosomes 4, 8, 12 and 18. The resistant parent, LD01-5907, contributed the resistance alleles for the QTLs on Chromosomes 8 and 18 (qSDS-8 and qSDS-18), while the other parent, GD2422, provided the resistance alleles for the QTLs on Chromosomes 4 and 12 (qSDS-4 and qSDS-12). The minor QTL on Chromosome 12 (qSDS-12) is novel. The QTL on Chromosomes 8 and 18 (qSDS-8 and qSDS-18) overlapped with two soybean cyst nematode resistance-related loci, Rhg4 and Rhg1, respectively. A significant interaction between qSDS-8 and qSDS-18 was detected by disease incidence. Individual effects together with the interaction effect explained around 70% of the phenotypic variance. The epistatic interaction of qSDS-8 and qSDS-18 was confirmed by the field performance across multiple years. Furthermore, the resistance alleles at qSDS-8 and qSDS-18 were demonstrated to be recessive. The SNP markers linked to these QTLs will be useful for marker-assisted breeding to enhance the SDS resistance.

An atypical N-ethylmaleimide sensitive factor enables the viability of nematode-resistant Rhg1 soybeans.

N-ethylmaleimide sensitive factor (NSF) and α-soluble NSF attachment protein (α-SNAP) are essential eukaryotic housekeeping proteins that cooperatively function to sustain vesicular trafficking. The "resistance to Heterodera glycines 1" (Rhg1) locus of soybean (Glycine max) confers resistance to soybean cyst nematode, a highly damaging soybean pest. Rhg1 loci encode repeat copies of atypical α-SNAP proteins that are defective in promoting NSF function and are cytotoxic in certain contexts. Here, we discovered an unusual NSF allele (Rhg1-associated NSF on chromosome 07; NSFRAN07 ) in Rhg1+ germplasm. NSFRAN07 protein modeling to mammalian NSF/α-SNAP complex structures indicated that at least three of the five NSFRAN07 polymorphisms reside adjacent to the α-SNAP binding interface. NSFRAN07 exhibited stronger in vitro binding with Rhg1 resistance-type α-SNAPs. NSFRAN07 coexpression in planta was more protective against Rhg1 α-SNAP cytotoxicity, relative to WT NSFCh07 Investigation of a previously reported segregation distortion between chromosome 18 Rhg1 and a chromosome 07 interval now known to contain the Glyma.07G195900 NSF gene revealed 100% coinheritance of the NSFRAN07 allele with disease resistance Rhg1 alleles, across 855 soybean accessions and in all examined Rhg1+ progeny from biparental crosses. Additionally, we show that some Rhg1-mediated resistance is associated with depletion of WT α-SNAP abundance via selective loss of WT α-SNAP loci. Hence atypical coevolution of the soybean SNARE-recycling machinery has balanced the acquisition of an otherwise disruptive housekeeping protein, enabling a valuable disease resistance trait. Our findings further indicate that successful engineering of Rhg1-related resistance in plants will require a compatible NSF partner for the resistance-conferring α-SNAP.

Field evaluation of three sources of genetic resistance to sudden death syndrome of soybean.

KEY MESSAGE: Despite numerous challenges, field testing of three sources of genetic resistance to sudden death syndrome of soybean provides information to more effectively improve resistance to this disease in cultivars. Sudden death syndrome (SDS) of soybean [Glycine max (L.) Merrill] is a disease that causes yield loss in soybean growing regions across the USA and worldwide. While several quantitative trait loci (QTL) for SDS resistance have been mapped, studies to further evaluate these QTL are limited. The objective of our research was to map SDS resistance QTL and to test the effect of mapped resistance QTL on foliar symptoms when incorporated into elite soybean backgrounds. We mapped a QTL from Ripley to chromosome 10 (CHR10) and a QTL from PI507531 to chromosomes 1 and 18 (CHR1 and 18). Six populations were then developed to test the following QTL: cqSDS-001, with resistance originating from PI567374, CHR10, CHR1, and CHR18. The populations which segregated for resistant and susceptible QTL alleles were field tested in multiple environments and evaluated for SDS foliar symptoms. While foliar disease development was variable across environments and populations, a significant effect of each QTL on disease was detected within at least one environment. This includes the detection of cqSDS-001 in three genetic backgrounds. The QTL allele from the resistant parents was associated with greater resistance than the susceptible alleles for all QTL and backgrounds with the exception of the allele for CHR18, where the opposite occurred. This study highlights the importance and difficulties of evaluating QTL and the need for multi-year SDS field testing. The information presented in this study can aid breeders in making decisions to improve resistance to SDS.

Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population.

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.

A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow, GB-eaSy.

BACKGROUND: Genotyping-by-sequencing (GBS), a method to identify genetic variants and quickly genotype samples, reduces genome complexity by using restriction enzymes to divide the genome into fragments whose ends are sequenced on short-read sequencing platforms. While cost-effective, this method produces extensive missing data and requires complex bioinformatics analysis. GBS is most commonly used on crop plant genomes, and because crop plants have highly variable ploidy and repeat content, the performance of GBS analysis software can vary by target organism. Here we focus our analysis on soybean, a polyploid crop with a highly duplicated genome, relatively little public GBS data and few dedicated tools. RESULTS: We compared the performance of five GBS pipelines using low-coverage Illumina sequence data from three soybean populations. To address issues identified with existing methods, we developed GB-eaSy, a GBS bioinformatics workflow that incorporates widely used genomics tools, parallelization and automation to increase the accuracy and accessibility of GBS data analysis. Compared to other GBS pipelines, GB-eaSy rapidly and accurately identified the greatest number of SNPs, with SNP calls closely concordant with whole-genome sequencing of selected lines. Across all five GBS analysis platforms, SNP calls showed unexpectedly low convergence but generally high accuracy, indicating that the workflows arrived at largely complementary sets of valid SNP calls on the low-coverage data analyzed. CONCLUSIONS: We show that GB-eaSy is approximately as good as, or better than, other leading software solutions in the accuracy, yield and missing data fraction of variant calling, as tested on low-coverage genomic data from soybean. It also performs well relative to other solutions in terms of the run time and disk space required. In addition, GB-eaSy is built from existing open-source, modular software packages that are regularly updated and commonly used, making it straightforward to install and maintain. While GB-eaSy outperformed other individual methods on the datasets analyzed, our findings suggest that a comprehensive approach integrating the results from multiple GBS bioinformatics pipelines may be the optimal strategy to obtain the largest, most highly accurate SNP yield possible from low-coverage polyploid sequence data.