Main and epistatic loci studies in soybean for Sclerotinia sclerotiorum resistance reveal multiple modes of resistance in multi-environments

Genetic dissection of resistance to Phytophthora sojae using genome-wide association and linkage analysis in soybean [Glycine max (L.) Merr.]

KEY MESSAGE

Two novel and one known genomic regions associated with R-gene resistance to Phytophthora sojae were identified by genome-wide association analysis and linkage analysis in soybean. Phytophthora root and stem rot (PRR) caused by Phytophthora sojae is a severe disease that causes substantial economic losses in soybean [Glycine max (L.) Merr.]. The primary approach for successful disease management of PRR is using R-gene-mediated resistance. Based on the phenotypic evaluation of 376 cultivated soybean accessions for the R-gene type resistance to P. sojae (isolate 2457), a genome-wide association analysis identified two regions on chromosomes 3 and 8. The most significant genomic region (20.7-21.3 Mbp) on chromosome 8 was a novel resistance locus where no Rps gene was previously reported. Instead, multiple copies of the UDP-glycosyltransferase superfamily protein-coding gene, associated with disease resistance, were annotated in this new locus. Another genomic region on chromosome 3 was a well-known Rps cluster. Using the Daepung × Ilpumgeomjeong RIL population, a linkage analysis confirmed these two resistance loci and identified a resistance locus on chromosome 2. A unique feature of the resistance in Ilpumgeomjeong was discovered when phenotypic distribution was projected upon eight groups of RILs carrying different combinations of resistance alleles for the three loci. Interestingly, the seven groups carrying at least one resistance allele statistically differed from the other with none, regardless of the number of resistance alleles. This suggests that the respective three different resistance genes can confer resistance to P. sojae isolate 2457. Deployment of the three regions via marker-assisted selection will facilitate effectively improving resistance to particular P. sojae isolates in soybean breeding programs.

Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review

Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.

Genetic diversity and genome-wide association study of partial resistance to Sclerotinia stem rot in a Canadian soybean germplasm panel

KEY MESSAGE

Developing genetically resistant soybean cultivars is key in controlling the destructive Sclerotinia Stem Rot (SSR) disease. Here, a GWAS study in Canadian soybeans identified potential marker-trait associations and candidate genes, paving the way for more efficient breeding methods for SSR. Sclerotinia stem rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is one of the most important diseases leading to significant soybean yield losses in Canada and worldwide. Developing soybean cultivars that are genetically resistant to the disease is the most inexpensive and reliable method to control the disease. However, breeding for resistance is hampered by the highly complex nature of genetic resistance to SSR in soybean. This study sought to understand the genetic basis underlying SSR resistance particularly in soybean grown in Canada. Consequently, a panel of 193 genotypes was assembled based on maturity group and genetic diversity as representative of Canadian soybean cultivars. Plants were inoculated and screened for SSR resistance in controlled environments, where variation for SSR phenotypic response was observed. The panel was also genotyped via genotyping-by-sequencing and the resulting genotypic data were imputed using BEAGLE v5 leading to a catalogue of 417 K SNPs. Through genome-wide association analyses (GWAS) using FarmCPU method with threshold of FDR-adjusted p-values < 0.1, we identified significant SNPs on chromosomes 2 and 9 with allele effects of 16.1 and 14.3, respectively. Further analysis identified three potential candidate genes linked to SSR disease resistance within a 100 Kb window surrounding each of the peak SNPs. Our results will be important in developing molecular markers that can speed up the breeding for SSR resistance in Canadian grown soybean.

Deciphering genetic factors contributing to enhanced resistance against Cercospora leaf blight in soybean (Glycine max L.) using GWAS analysis

Cercospora leaf blight (CLB), caused by Cercospora cf. flagellaris, C. kikuchii, and C. cf. sigesbeckiae, is a significant soybean [Glycine max (L.) Merr.] disease in regions with hot and humid conditions causing yield loss in the United States and Canada. There is limited information regarding resistant soybean cultivars, and there have been marginal efforts to identify the genomic regions underlying resistance to CLB. A Genome-Wide Association Study was conducted using a diverse panel of 460 soybean accessions from maturity groups III to VII to identify the genomic regions associated to the CLB disease. These accessions were evaluated for CLB in different regions of the southeastern United States over 3 years. In total, the study identified 99 Single Nucleotide Polymorphism (SNPs) associated with the disease severity and 85 SNPs associated with disease incidence. Across multiple environments, 47 disease severity SNPs and 23 incidence SNPs were common. Candidate genes within 10 kb of these SNPs were involved in biotic and abiotic stress pathways. This information will contribute to the development of resistant soybean germplasm. Further research is warranted to study the effect of pyramiding desirable genomic regions and investigate the role of identified genes in soybean CLB resistance.

Identification of novel genes associated with herbicide tolerance in Lentil (Lens culinaris ssp. culinaris Medik.)

Weeds pose a major constraint in lentil cultivation, leading to decrease farmers' revenues by reducing the yield and increasing the management costs. The development of herbicide tolerant cultivars is essential to increase lentil yield. Even though herbicide tolerant lines have been identified in lentils, breeding efforts are still limited and lack proper validation. Marker assisted selection (MAS) can increase selection accuracy at early generations. Total 292 lentil accessions were evaluated under different dosages of two herbicides, metribuzin and imazethapyr, during two seasons at Marchouch, Morocco and Terbol, Lebanon. Highly significant differences among accessions were observed for days to flowering (DF) and maturity (DM), plant height (PH), biological yield (BY), seed yield (SY), number of pods per plant (NP), as well as the reduction indices (RI) for PH, BY, SY and NP. A total of 10,271 SNPs markers uniformly distributed along the lentil genome were assayed using Multispecies Pulse SNP chip developed at Agriculture Victoria, Melbourne. Meta-GWAS analysis was used to detect marker-trait associations, which detected 125 SNPs markers associated with different traits and clustered in 85 unique quantitative trait loci. These findings provide valuable insights for initiating MAS programs aiming to enhance herbicide tolerance in lentil crop.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.

Multi-omics assisted breeding for biotic stress resistance in soybean

Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

Digital gene expression profiling of the transcriptional response to Sclerotinia sclerotiorum and its antagonistic bacterium Bacillus amyloliquefaciens in soybean

Soybean Sclerotinia stem rot caused by Sclerotinia sclerotiorum is a common disease in soybean, and effective biological control is urgently needed. We have previously confirmed that Bacillus amyloliquefaciens can effectively antagonize S. sclerotiorum in a plate competition experiment and a soybean seedling inoculation experiment. In this study, the mechanisms underlying plant death caused by S. sclerotiorum and soybean resistance to S. sclerotiorum induced by B. amyloliquefaciens were evaluated. The stems of potted soybean seedlings were inoculated with S. sclerotiorum (Gm-Ss), B. amyloliquefaciens (Gm-Ba), and their combination (Gm-Ba-Ss), using scratch treatments as a control, followed by dual RNA sequencing and bioinformatics analyses. Global gene expression levels in the Gm-Ss treatment were much lower than those in the Gm-Ba, Gm-Ba-Ss, and Gm groups, suggesting that S. sclerotiorum strongly inhibited global gene expression in soybean. In a pairwise comparison of Gm-Ss vs. Gm, 19983 differentially expressed genes (DEGs) were identified. Down-regulated DEGs were involved in various KEGG pathways, including ko01110 (biosynthesis of secondary metabolites), ko01100 (metabolic pathways), ko01120 (microbial metabolism in diverse environments), ko00500 (starch and sucrose metabolism), and ko04075 (plant hormone signal transmission), suggesting that S. sclerotiorum inoculation had a serious negative effect on soybean metabolism. In Gm-Ba vs. Gm, 13091 DEGs were identified, and these DEGs were significantly enriched in ko03010 (ribosome) and ko03008 (ribosome biogenesis in eucaryotes). Our results suggest that B. amyloliquefaciens increases the expression of genes encoding the ribosomal subunit, promotes cell wall biogenesis, and induces systemic resistance. S. sclerotiorum strongly inhibited metabolism in soybean, inhibited the synthesis of the cytoskeleton, and induced the up-regulation of programmed death and senescence-related genes via an ethylene signal transduction pathway. These results improve our understanding of S. sclerotiorum-induced plant death and soybean resistance to S. sclerotiorum induced by B. amyloliquefaciens and may contribute to the improvement of strategies to avoid yield losses.

Breeding for disease resistance in soybean: a global perspective

KEY MESSAGE

This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.

Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean

Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms.

Identification and characterization of pleiotropic and epistatic QDRL conferring partial resistance to Pythium irregulare and P. sylvaticum in soybean

Pleiotropic and epistatic quantitative disease resistance loci (QDRL) were identified for soybean partial resistance to different isolates of Pythium irregulare and Pythium sylvaticum. Pythium root rot is an important seedling disease of soybean [Glycine max (L.) Merr.], a crop grown worldwide for protein and oil content. Pythium irregulare and P. sylvaticum are two of the most prevalent and aggressive Pythium species in soybean producing regions in the North Central U.S. Few studies have been conducted to identify soybean resistance for management against these two pathogens. In this study, a mapping population (derived from E13390 x E13901) with 228 F4:5 recombinant inbred lines were screened against P. irregulare isolate MISO 11-6 and P. sylvaticum isolate C-MISO2-2-30 for QDRL mapping. Correlation analysis indicated significant positive correlations between soybean responses to the two pathogens, and a pleiotropic QDRL (qPirr16.1) was identified. Further investigation found that the qPirr16.1 imparts dominant resistance against P. irregulare, but recessive resistance against P. sylvaticum. In addition, two QDRL, qPsyl15.1, and qPsyl18.1 were identified for partial resistance to P. sylvaticum. Further analysis revealed epistatic interactions between qPirr16.1 and qPsyl15.1 for RRW and DRX, whereas qPsyl18.1 contributed resistance to RSE. Marker-assisted resistance spectrum analysis using F6:7 progeny lines verified the resistance of qPirr16.1 against four additional P. irregulare isolates. Intriguingly, although the epistatic interaction of qPirr16.1 and qPsyl15.1 can be confirmed using two additional isolates of P. sylvaticum, the interaction appears to be suppressed for the other two P. sylvaticum isolates. An 'epistatic gene-for-gene' model was proposed to explain the isolate-specific epistatic interactions. The integration of the QDRL into elite soybean lines containing all the desirable alleles has been initiated.

Transcriptome Profiling Reveals Molecular Players in Early Soybean-Sclerotinia sclerotiorum Interaction

Sclerotinia sclerotiorum causes Sclerotinia stem rot on soybean. Using RNA sequencing, the transcriptomes of the soybean host and the S. sclerotiorum pathogen were simultaneously determined at 4 and 8 h postinoculation (hpi). Two soybean genotypes were involved: a resistant oxalate oxidase (OxO)-transgenic line and its susceptible parent, AC Colibri (AC). Of the 594 genes that were significantly induced by S. sclerotiorum, both hosts expressed genes related to jasmonic acid, ethylene, oxidative burst, and phenylpropanoids. In all, 36% of the differentially expressed genes encoded genes associated with transcription factors, ubiquitination, or general signaling transduction such as receptor-like kinases, mitogen-activated protein kinase kinases, and hormones. No significant differentially expressed genes were identified between genotypes, suggesting that oxalic acid (OA) did not play a differential role in early disease development or primary lesion formation under the conditions used. Looking at pathogen behavior through its gene expression during infection, thousands of genes in S. sclerotiorum were induced at 8 hpi, compared with expression in culture. Many plant cell-wall-degrading enzymes (PCWDEs), sugar transport genes, and genes involved in secondary metabolism were upregulated and could contribute to early pathogenesis. When infecting the OxO plants, there was a higher induction of genes encoding OA, botcinic acid, PCWDEs, proteases, and potential effectors, revealing the wealth of virulence factors available to this pathogen as it attempts to colonize a host. Data presented identify hundreds of genes associated with the very early stages of infection for both the host and pathogen.

The evolutionary and molecular features of the broad-host-range plant pathogen Sclerotinia sclerotiorum

Sclerotinia sclerotiorum is a pathogenic fungus that infects hundreds of plant species, including many of the world's most important crops. Key features of S. sclerotiorum include its extraordinary host range, preference for dicotyledonous plants, relatively slow evolution, and production of protein effectors that are active in multiple host species. Plant resistance to this pathogen is highly complex, typically involving numerous polymorphisms with infinitesimally small effects, which makes resistance breeding a major challenge. Due to its economic significance, S. sclerotiorum has been subjected to a large amount of molecular and evolutionary research. In this updated pathogen profile, we review the evolutionary and molecular features of S. sclerotiorum and discuss avenues for future research into this important species.

Molecular Breeding to Overcome Biotic Stresses in Soybean: Update

Soybean (Glycine max (L.) Merr.) is an important leguminous crop and biotic stresses are a global concern for soybean growers. In recent decades, significant development has been carried outtowards identification of the diseases caused by pathogens, sources of resistance and determination of loci conferring resistance to different diseases on linkage maps of soybean. Host-plant resistance is generally accepted as the bestsolution because of its role in the management of environmental and economic conditions of farmers owing to low input in terms of chemicals. The main objectives of soybean crop improvement are based on the identification of sources of resistance or tolerance against various biotic as well as abiotic stresses and utilization of these sources for further hybridization and transgenic processes for development of new cultivars for stress management. The focus of the present review is to summarize genetic aspects of various diseases caused by pathogens in soybean and molecular breeding research work conducted to date.

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

Sequencing the USDA core soybean collection reveals gene loss during domestication and breeding

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.

GWAS for main effects and epistatic interactions for grain morphology traits in wheat

In the present study in wheat, GWAS was conducted for identification of marker trait associations (MTAs) for the following six grain morphology traits: (1) grain cross-sectional area (GCSA), (2) grain perimeter (GP), (3) grain length (GL), (4) grain width (GWid), (5) grain length-width ratio (GLWR) and (6) grain form-density (GFD). The data were recorded on a subset of spring wheat reference set (SWRS) comprising 225 diverse genotypes, which were genotyped using 10,904 SNPs and phenotyped for two consecutive years (2017-2018, 2018-2019). GWAS was conducted using five different models including two single-locus models (CMLM, SUPER), one multi-locus model (FarmCPU), one multi-trait model (mvLMM) and a model for Q x Q epistatic interactions. False discovery rate (FDR) [P value -log10(p) ≥ 5] and Bonferroni correction [P value -log10(p) ≥ 6] (corrected p value < 0.05) were applied to eliminate false positives due to multiple testing. This exercise gave 88 main effect and 29 epistatic MTAs after FDR and 13 main effect and 6 epistatic MTAs after Bonferroni corrections. MTAs obtained after Bonferroni corrections were further utilized for identification of 55 candidate genes (CGs). In silico expression analysis of CGs in different tissues at different parts of the seed at different developmental stages was also carried out. MTAs and CGs identified during the present study are useful addition to available resources for MAS to supplement wheat breeding programmes after due validation and also for future strategic basic research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01164-w.

Genetic Analysis for Resistance to Sclerotinia Stem Rot, Yield and Its Component Traits in Indian Mustard [Brassica juncea (L.) Czern & Coss.]

Understanding the mode of gene action that controls seed yield and Sclerotinia stem rot resistance in Indian mustard is critical for boosting yield potential. In a line × tester mating design, ten susceptible lines and four resistant testers were used to conduct genetic analysis. The significance of general combining ability (GCA) and specific combining ability (SCA) variances revealed that both additive and non-additive gene actions were involved in the inheritance of Sclerotinia stem rot resistance and yield attributing traits. In addition to 1000-seed weight and number of primary and secondary branches/plant, the genotypes RH 1569 (line) and DRMR 2035 (tester) appeared to be the strongest general combiners for Sclerotinia stem rot resistance. RH 1657 × EC 597317 was the only cross among several that demonstrated a significant desired SCA value for Sclerotinia rot resistance. Regarding SCA effects for yield and component traits, the cross RH 1658 × EC 597328 performed best, with a non-significant but acceptable negative SCA effect for resistance. DRMR 2035, RH 1222-28, RH 1569, RH 1599-41, RH 1657, RH 1658, and EC 597328 are promising genotypes to use as parents in future heterosis breeding and for obtaining populations with high yield potential and greater resistance to Sclerotinia stem rot disease in Indian mustard, based on GCA effects of parents, per se performance, and SCA effects of hybrids. Days to 50% flowering, number of primary branches/plant, main shoot length, and 1000-seed weight all had a high genotypic coefficient of variability (GCV), broad-sense heritability (h²bs), and genetic advance as percent of the mean (GAM) values, as well as significant and desirable correlations and direct effects on seed yield. As a result, these traits have been recognized as the most critical selection criterion for Indian mustard breeding programs.

Mapping Major Disease Resistance Genes in Soybean by Genome-Wide Association Studies

Soybean is one of the most valuable agricultural crops in the world. Besides, this legume is constantly attacked by a wide range of pathogens (fungi, bacteria, viruses, and nematodes) compromising yield and increasing production costs. One of the major disease management strategies is the genetic resistance provided by single genes and quantitative trait loci (QTL). Identifying the genomic regions underlying the resistance against these pathogens on soybean is one of the first steps performed by molecular breeders. In the past, genetic mapping studies have been widely used to discover these genomic regions. However, over the last decade, advances in next-generation sequencing technologies and their subsequent cost decreasing led to the development of cost-effective approaches to high-throughput genotyping. Thus, genome-wide association studies applying thousands of SNPs in large sets composed of diverse soybean accessions have been successfully done. In this chapter, a comprehensive review of the majority of GWAS for soybean diseases published since this approach was developed is provided. Important diseases caused by Heterodera glycines, Phytophthora sojae, and Sclerotinia sclerotiorum have been the focus of the several GWAS. However, other bacterial and fungi diseases also have been targets of GWAS. As such, this GWAS summary can serve as a guide for future studies of these diseases. The protocol begins by describing several considerations about the pathogens and bringing different procedures of molecular characterization of them. Advice to choose the best isolate/race to maximize the discovery of multiple R genes or to directly map an effective R gene is provided. A summary of protocols, methods, and tools to phenotyping the soybean panel is given to several diseases. We also give details of options of DNA extraction protocols and genotyping methods, and we describe parameters of SNP quality to soybean data. Websites and their online tools to obtain genotypic and phenotypic data for thousands of soybean accessions are highlighted. Finally, we report several tricks and tips in Subheading 4, especially related to composing the soybean panel as well as generating and analyzing the phenotype data. We hope this protocol will be helpful to achieve GWAS success in identifying resistance genes on soybean.

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.

Genetic and Proteomic Basis of Sclerotinia Stem Rot Resistance in Indian Mustard [Brassica juncea (L.) Czern & Coss.]

Sclerotinia stem rot is one of the utmost important disease of mustard, causing considerable losses in seed yield and oil quality. The study of the genetic and proteomic basis of resistance to this disease is imperative for its effective utilization in developing resistant cultivars. Therefore, the genetic pattern of Sclerotinia stem rot resistance in Indian mustard was studied using six generations (P₁, P₂, F₁, F₂, BC₁P₁, and BC₁P₂) developed from the crossing of one resistant (RH 1222-28) and two susceptible (EC 766300 and EC 766123) genotypes. Genetic analysis revealed that resistance was governed by duplicate epistasis. Comparative proteome analysis of resistant and susceptible genotypes indicated that peptidyl-prolyl cis-trans isomerase (A0A078IDN6 PPIase) showed high expression in resistant genotype at the early infection stage while its expression was delayed in susceptible genotypes. This study provides important insight to mustard breeders for designing effective breeding programs to develop resistant cultivars against this devastating disease.

Patterns of Genetic Variation in a Soybean Germplasm Collection as Characterized with Genotyping-by-Sequencing

Genomic characterization is playing an increasing role in plant germplasm conservation and utilization, as it can provide higher resolution with genome-wide SNP markers than before to identify and analyze genetic variation. A genotyping-by-sequencing technique was applied to genotype 541 soybean accessions conserved at Plant Gene Resources of Canada and 30 soybean cultivars and breeding lines developed by the Ottawa soybean breeding program of Agriculture and Agri-Food Canada. The sequencing generated an average of 952,074 raw sequence reads per sample. SNP calling identified 43,891 SNPs across 20 soybean chromosomes and 69 scaffolds with variable levels of missing values. Based on 19,898 SNPs with up to 50% missing values, three distinct genetic groups were found in the assayed samples. These groups were a mixture of the samples that originated from different countries and the samples of known maturity groups. The samples that originated from Canada were clustered into all three distinct groups, but 30 Ottawa breeding lines fell into two groups only. Based on the average pairwise dissimilarity estimates, 40 samples with the most genetic distinctness were identified from three genetic groups with diverse sample origin and known maturity. Additionally, 40 samples with the highest genetic redundancy were detected and they consisted of different sample origins and maturity groups, largely from one genetic group. Moreover, some genetically duplicated samples were identified, but the overall level of genetic duplication was relatively low in the collection. These findings are useful for soybean germplasm management and utilization.

Identification of Soybean (Glycine max) Check Lines for Evaluating Genetic Resistance to Sclerotinia Stem Rot

Soybean production in the upper midwestern United States is affected by Sclerotinia stem rot (SSR) caused by the fungal pathogen Sclerotinia sclerotiorum. Genetic resistance is an important management strategy for this disease; however, assessing genetic resistance to S. sclerotiorum is challenging because a standardized method of examining resistance across genotypes is lacking. Using a panel of nine diverse S. sclerotiorum isolates, four soybean lines were assessed for reproducible responses to S. sclerotiorum infection. Significant differences in SSR severity were found across isolates (P < 0.01) and soybean lines (P < 0.01), including one susceptible, two moderately resistant, and one highly resistant line. These four validated lines were used to screen 11 other soybean genotypes to evaluate their resistance levels, and significant differences were found across genotypes (P < 0.01). Among these 11 genotypes, five commercial and public cultivars displayed high resistance and were assessed during field studies across the upper midwestern United States growing region to determine their response to SSR and yield. These five cultivars resulted in low disease levels (P < 0.01) in the field that were consistent with greenhouse experiment results. The yields were significantly different in fields with disease present (P < 0.01) and disease absent (P < 0.01), and the order of cultivar performance was consistent between environments where disease was present or absent, suggesting that resistance prevented yield loss to disease. This study suggests that the use of a soybean check panel can accurately assess SSR resistance in soybean germplasm and aid in breeding and commercial soybean development.

Identification of glutathione transferase gene associated with partial resistance to Sclerotinia stem rot of soybean using genome-wide association and linkage mapping

KEY MESSAGE

Association and linkage mapping techniques were used to identify and verify single nucleotide polymorphisms (SNPs) associated with Sclerotinia sclerotiorum resistance. A novel resistant gene, GmGST , was cloned and shown to be involved in soybean resistance to SSR. Sclerotinia stem rot (SSR), caused by the fungus Sclerotinia sclerotiorum, is one of the most devastating diseases in soybean (Glycine max (Linn.) Merr.) However, the genetic architecture underlying soybean resistance to SSR is poorly understood, despite several mapping and gene mining studies. In the present study, the identification of quantitative trait loci (QTLs) involved in the resistance to S. sclerotiorum was conducted in two segregating populations: an association population that consisted of 261 diverse soybean germplasms, and the MH population, derived from a cross between a partially resistant cultivar (Maple arrow) and a susceptible cultivar (Hefeng25). Three and five genomic regions affecting resistance were detected by genome-wide association study to control the lesion length of stems (LLS) and the death rate of seedling (DRS), respectively. Four QTLs were detected to underlie LLS, and one QTL controlled DRS after SSR infection. A major locus on chromosome (Chr.) 13 (qDRS13-1), which affected both DRS and LLS, was detected in both the natural population and the MH population. GmGST, encoding a glutathione S-transferase, was cloned as a candidate gene in qDRS13-1. GmGST was upregulated by the induction of the partially resistant cultivar Maple arrow. Transgenic experiments showed that the overexpression of GmGST in soybean increased resistance to S. sclerotiorum and the content of soluble pigment in stems of soybean. The results increase our understanding of the genetic architecture of soybean resistance to SSR and provide a framework for the future marker-assisted breeding of resistant soybean cultivars.

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.

Genetic dissection of soybean partial resistance to sclerotinia stem rot through genome wide association study and high throughout single nucleotide polymorphisms

Sclerotinia stem rot (SSR) is a disease of soybean [Glycine max (L.) Merr] that causes severe yield losses. We studied 185 representative soybean accessions to evaluate partial SSR resistance and sequenced these by the specific-locus amplified fragment sequencing method. In total, 22,048 single-nucleotide polymorphisms (SNPs), with minor allele frequencies (MAF) ≥5% and missing data <3%, were developed and applied to genome-wide association study of SSR responsiveness and assess linkage disequilibrium (LD) level for candidate gene selection. We identified 18 association signals related to SSR partial resistance. Among them, six overlapped the regions of previous quantitative trait loci, and twelve were novel. We identified 243 candidate genes located in the 200 kb genomic region of these peak SNPs. Based on quantitative real-time polymerase chain reaction and haplotype analysis, Glyma.03G196000 and Glyma.20G095100, encoding pentatricopeptide repeat proteins, might be important factors in the resistance response of soybean to SSR.

Genotype-Specific Antioxidant Responses and Assessment of Resistance Against Sclerotinia sclerotiorum Causing Sclerotinia Rot in Indian Mustard

Productivity of Indian mustard, an important oilseed crop of India, is affected by several pathogens. Among them, the hemibiotroph Sclerotinia sclerotiorum, which causes sclerotinia rot disease, is the most devastating fungal pathogen causing up to 90% yield losses. The availability of host resistance is the only efficient approach to control and understand the host-pathogen interaction. Therefore, the present investigation was carried out using six Indian mustard genotypes with contrasting behavior towards sclerotinia rot to study the antioxidant resistance mechanism against S. sclerotiorum. The plants at post-flowering stage were inoculated with five-day-old pure culture of S. sclerotiorum using artificial stem inoculation method. Disease evaluation revealed significant genotypic differences for mean lesion length among the tested genotypes, where genotype DRMR 2035 was found highly resistant, while genotypes RH 1569 and RH 1633 were found highly susceptible. The resistant genotypes had more phenolics and higher activities of peroxidase, catalase and polyphenol oxidase which provide them more efficient and strong antioxidant systems as compared with susceptible genotypes. Studies of antioxidative mechanisms validate the results of disease responses.

CaASR1 promotes salicylic acid- but represses jasmonic acid-dependent signaling to enhance the resistance of Capsicum annuum to bacterial wilt by modulating CabZIP63

CabZIP63 acts positively in the resistance of pepper (Capsicum annuum) to bacterial wilt caused by Ralstonia solanacearum or tolerance to high-temperature/high-humidity stress, but it is unclear how CabZIP63 achieves its functional specificity against R. solanacearum. Here, CaASR1, an abscisic acid-, stress-, and ripening-inducible protein of C. annuum, was functionally characterized in modulating the functional specificity of CabZIP63 during the defense response of pepper to R. solanacearum. In pepper plants inoculated with R. solanacearum, CaASR1 was up-regulated before 24 h post-inoculation but down-regulated thereafter, and was down-regulated by high-temperature/high-humidity stress. Data from gene silencing and transient overexpression experiments indicated that CaASR1 acts as a positive regulator in the immunity of pepper against R. solanacearum and a negative regulator of thermotolerance. Pull-down combined with mass spectrometry revealed that CaASR1 interacted with CabZIP63 upon R. solanacearum infection; the interaction was confirmed by microscale thermophoresis and bimolecular fluorescence complementation assays.CaASR1 silencing upon R. solanacearum inoculation repressed CabZIP63-mediated transcription from the promoters of the salicylic acid (SA)-dependent CaPR1 and CaNPR1, but derepressed transcription of CaHSP24 and the jasmonic acid (JA)-dependent CaDEF1. Our findings suggest that CaASR1 acts as a positive regulator of the defense response of pepper to R. solanacearum by interacting with CabZIP63, enabling it to promote SA-dependent but repress JA-dependent immunity and thermotolerance during the early stages of infection.

Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters

We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications = 14) were studied for RSA traits to decipher the genetic diversity. Based on literature search for root shape and morphology parameters, we used an ideotype-based approach to develop informative root (iRoot) categories using root traits. The RSA traits displayed genetic variability for root shape, length, number, mass, and angle. Soybean accessions clustered into eight genotype- and phenotype-based clusters and displayed similarity. Genotype-based clusters correlated with geographical origins. SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits, while diverse accession could infuse useful genetic variation for these traits. Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods, enabling trait cataloging for breeding and research applications. The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.

Genome-wide association study of partial resistance to sclerotinia stem rot of cultivated soybean based on the detached leaf method

Sclerotinia stem rot (SSR) is a devastating fungal disease that causes severe yield losses of soybean worldwide. In the present study, a representative population of 185 soybean accessions was selected and utilized to identify the quantitative trait nucleotide (QTN) of partial resistance to soybean SSR via a genome-wide association study (GWAS). A total of 22,048 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) > 5% and missing data < 3% were used to assess linkage disequilibrium (LD) levels. Association signals associated with SSR partial resistance were identified by two models, including compressed mixed linear model (CMLM) and multi-locus random-SNP-effect mixed linear model (mrMLM). Finally, seven QTNs with major effects (a known locus and six novel loci) via CMLM and nine novel QTNs with minor effects via mrMLM were detected in relation to partial resistance to SSR, respectively. One of all the novel loci (Gm05:14834789 on Chr.05), which was co-located by these two methods, might be a stable one that showed high significance in SSR partial resistance. Additionally, a total of 71 major and 85 minor candidate genes located in the 200-kb genomic region of each peak SNP detected by CMLM and mrMLM were found, respectively. By using a gene-based association, a total of six SNPs from three major effects genes and eight SNPs from four minor effects genes were identified. Of them, Glyma.18G012200 has been characterized as a significant element in controlling fungal disease in plants.

Genome-wide association mapping of Sclerotinia sclerotiorum resistance in soybean using whole-genome resequencing data

BACKGROUND

Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum (Lib.) de Bary, is an important cause of yield loss in soybean. Although many papers have reported different loci contributing to partial resistance, few of these were proved to reproduce the same phenotypic impact in different populations.

RESULTS

In this study, we identified a major quantitative trait loci (QTL) associated with resistance to SSR progression on the main stem by using a genome-wide association mapping (GWAM). A population of 127 soybean accessions was genotyped with 1.5 M SNPs derived from genotyping-by-sequencing (GBS) and whole-genome sequencing (WGS) ensuring an extensive genome coverage and phenotyped for SSR resistance. SNP-trait association led to discovery of a new QTL on chromosome 1 (Chr01) where resistant lines had shorter lesions on the stem by 29 mm. A single gene (Glyma.01 g048000) resided in the same LD block as the peak SNP, but it is of unknown function. The impact of this QTL was even more significant in the descendants of a cross between two lines carrying contrasted alleles for Chr01. Individuals carrying the resistance allele developed lesions almost 50% shorter than those bearing the sensitivity allele.

CONCLUSION

These results suggest that the new region on chromosome 1 harbors a promising resistance QTL to SSR that can be used in soybean breeding program.

Genome-wide association mapping of Sclerotinia sclerotiorum resistance in soybean using whole-genome resequencing data

BACKGROUND

Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum (Lib.) de Bary, is an important cause of yield loss in soybean. Although many papers have reported different loci contributing to partial resistance, few of these were proved to reproduce the same phenotypic impact in different populations.

RESULTS

In this study, we identified a major quantitative trait loci (QTL) associated with resistance to SSR progression on the main stem by using a genome-wide association mapping (GWAM). A population of 127 soybean accessions was genotyped with 1.5 M SNPs derived from genotyping-by-sequencing (GBS) and whole-genome sequencing (WGS) ensuring an extensive genome coverage and phenotyped for SSR resistance. SNP-trait association led to discovery of a new QTL on chromosome 1 (Chr01) where resistant lines had shorter lesions on the stem by 29 mm. A single gene (Glyma.01 g048000) resided in the same LD block as the peak SNP, but it is of unknown function. The impact of this QTL was even more significant in the descendants of a cross between two lines carrying contrasted alleles for Chr01. Individuals carrying the resistance allele developed lesions almost 50% shorter than those bearing the sensitivity allele.

CONCLUSION

These results suggest that the new region on chromosome 1 harbors a promising resistance QTL to SSR that can be used in soybean breeding program.

Genome-wide association analyses of quantitative disease resistance in diverse sets of soybean [Glycine max (L.) Merr.] plant introductions

Phytophthora sojae is one of the costliest soybean pathogens in the US. Quantitative disease resistance (QDR) is a vital part of Phytophthora disease management. In this study, QDR was measured in 478 and 495 plant introductions (PIs) towards P. sojae isolates OH.121 and C2.S1, respectively, in genome-wide association (GWA) analyses to identify genetic markers linked to QDR loci (QDRL). Populations were generated by sampling PIs from the US, the Republic of Korea, and the full collection of PIs maintained by the USDA. Additionally, a meta-analysis of QDRL reported from bi-parental studies was done to compare past and present findings. Twenty-four significant marker-trait associations were identified from the 478 PIs phenotyped with OH.121, and an additional 24 marker-trait associations were identified from the 495 PIs phenotyped with C2.S1. In total, 48 significant markers were distributed across 16 chromosomes and based on linkage analysis, represent a total of 44 QDRL. The majority of QDRL were identified with only one of the two isolates, and only a region on chromosome 13 was consistently identified. Regions on chromosomes 3, 13, and 17 were identified in previous GWA-analyses and were re-identified in this study. Five QDRL co-localized with P. sojae meta-QDRL identified from QDRL reported in previous biparental mapping studies. The remaining regions represent novel QDRL, in the soybean-P. sojae pathosystem and were primarily identified in germplasm from the Republic of Korea. Overall, the number of loci identified in this study highlights the complexity of QDR to P. sojae.

Genetic Control and Geo-Climate Adaptation of Pod Dehiscence Provide Novel Insights into Soybean Domestication

Loss of pod dehiscence was a key step in soybean [Glycine max (L.) Merr.] domestication. Genome-wide association analysis for soybean shattering identified loci harboring Pdh1, NST1A and SHAT1-5 Pairwise epistatic interactions were observed, and the dehiscent Pdh1 overcomes resistance conferred by NST1A or SHAT1-5 locus. Further candidate gene association analysis identified a nonsense mutation in NST1A associated with pod dehiscence. Geographic analysis showed that in Northeast China (NEC), indehiscence at both Pdh1 and NST1A were required in cultivated soybean, while indehiscent Pdh1 alone is capable of preventing shattering in Huang-Huai-Hai (HHH) valleys. Indehiscent Pdh1 allele was only identified in wild soybean (Glycine soja L.) accession from HHH valleys suggesting that it may have originated in this region. No specific indehiscence was required in Southern China. Geo-climatic investigation revealed strong correlation between relative humidity and frequency of indehiscent Pdh1 across China. This study demonstrates that epistatic interaction between Pdh1 and NST1A fulfills a pivotal role in determining the level of resistance against pod dehiscence, and humidity shapes the distribution of indehiscent alleles. Our results give further evidence to the hypothesis that HHH valleys was at least one of the origin centers of cultivated soybean.

Whole-genome resequencing identifies quantitative trait loci associated with mycorrhizal colonization of soybean

KEY MESSAGE

A whole-genome resequencing-derived SNP dataset identified six quantitative trait loci (QTL) significantly associated with colonization of soybean by an arbuscular mycorrhizal fungus (Rhizophagus intraradices). Candidate genes identified in these QTL regions include homologs to known nodulin protein families and other symbiosis-specific genes. Arbuscular mycorrhizal fungi (AMF) form associations with over 80% of all terrestrial plant species and assist their host plants by increasing their nutrient uptake, drought tolerance, and resilience against pathogens and pests. Genotypic variation of crop plants to AMF colonization has been identified in crops, including soybean; however, the genetics controlling levels of AMF colonization in soybean are unknown. The overall goal of our study was to identify genomic regions associated with mycorrhizal colonization in soybean using genome-wide association analysis. A diverse panel of 350 exotic soybean genotypes inoculated with Rhizophagus intraradices were microscopically evaluated for root colonization using a modified gridline intersect method. Root colonization differed significantly (P < 0.001) among genotypes and ranged from 11 to 70%. A whole-genome resequencing-derived SNP dataset identified six quantitative trait loci (QTL) significantly associated with R. intraradices colonization that explained 24% of the phenotypic variance. Candidate genes identified in these QTL regions include homologs to known nodulin protein families and other symbiosis-specific genes. The results showed there was a significant genetic component to the level of colonization by R. intraradices in soybean. This information may be useful in the development of AMF-sensitive soybean cultivars to enhance nutrient uptake, drought tolerance, and disease resistance in the crop.

BnaMPK6 is a determinant of quantitative disease resistance against Sclerotinia sclerotiorum in oilseed rape

Sclerotinia sclerotiorum causes a devastating disease in oilseed rape (Brassica napus), resulting in major economic losses. Resistance response of B. napus against S. sclerotiorum exhibits a typical quantitative disease resistance (QDR) characteristic, but the molecular determinants of this QDR are largely unknown. In this study, we isolated a B. napus mitogen-activated protein kinase gene, BnaMPK6, and found that BnaMPK6 expression is highly responsive to infection by S. sclerotiorum and treatment with salicylic acid (SA) or jasmonic acid (JA). Moreover, overexpression (OE) of BnaMPK6 significantly enhances resistance to S. sclerotiorum, whereas RNAi in BnaMPK6 significantly reduces this resistance. These results showed that BnaMPK6 plays an important role in defense to S. sclerotiorum. Furthermore, expression of defense genes associated with SA-, JA- and ethylene (ET)-mediated signaling was investigated in BnaMPK6-RNAi, WT and BnaMPK6-OE plants after S. sclerotiorum infection, and consequently, it was indicated that the activation of ET signaling by BnaMPK6 may play a role in the defense. Further, four BnaMPK6-encoding homologous loci were mapped in the B. napus genome. Using the allele analysis and expression analysis on the four loci, we demonstrated that the locus BnaA03.MPK6 makes an important contribution to QDR against S. sclerotiorum. Our data indicated that BnaMPK6 is a previously unknown determinant of QDR against S. sclerotiorum in B. napus.

Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches

BACKGROUND

Iron (Fe) is an essential micronutrient for plant growth and development. Iron deficiency chlorosis (IDC), caused by calcareous soils or high soil pH, can limit iron availability, negatively affecting soybean (Glycine max) yield. This study leverages genome-wide association study (GWAS) and a genome-wide epistatic study (GWES) with previous gene expression studies to identify regions of the soybean genome important in iron deficiency tolerance.

RESULTS

A GWAS and a GWES were performed using 460 diverse soybean PI lines from 27 countries, in field and hydroponic iron stress conditions, using more than 36,000 single nucleotide polymorphism (SNP) markers. Combining this approach with available RNA-sequencing data identified significant markers, genomic regions, and novel genes associated with or responding to iron deficiency. Sixty-nine genomic regions associated with IDC tolerance were identified across 19 chromosomes via the GWAS, including the major-effect quantitative trait locus (QTL) on chromosome Gm03. Cluster analysis of significant SNPs in this region deconstructed this historically prominent QTL into four distinct linkage blocks, enabling the identification of multiple candidate genes for iron chlorosis tolerance. The complementary GWES identified SNPs in this region interacting with nine other genomic regions, providing the first evidence of epistatic interactions impacting iron deficiency tolerance.

CONCLUSIONS

This study demonstrates that integrating cutting edge genome wide association (GWA), genome wide epistasis (GWE), and gene expression studies is a powerful strategy to identify novel iron tolerance QTL and candidate loci from diverse germplasm. Crops, unlike model species, have undergone selection for thousands of years, constraining and/or enhancing stress responses. Leveraging genomics-enabled approaches to study these adaptations is essential for future crop improvement.

Genome-wide association and epistatic interactions of flowering time in soybean cultivar

Genome-wide association studies (GWAS) have enabled the discovery of candidate markers that play significant roles in various complex traits in plants. Recently, with increased interest in the search for candidate markers, studies on epistatic interactions between single nucleotide polymorphism (SNP) markers have also increased, thus enabling the identification of more candidate markers along with GWAS on single-variant-additive-effect. Here, we focused on the identification of candidate markers associated with flowering time in soybean (Glycine max). A large population of 2,662 cultivated soybean accessions was genotyped using the 180k Axiom® SoyaSNP array, and the genomic architecture of these accessions was investigated to confirm the population structure. Then, GWAS was conducted to evaluate the association between SNP markers and flowering time. A total of 93 significant SNP markers were detected within 59 significant genes, including E1 and E3, which are the main determinants of flowering time. Based on the GWAS results, multilocus epistatic interactions were examined between the significant and non-significant SNP markers. Two significant and 16 non-significant SNP markers were discovered as candidate markers affecting flowering time via interactions with each other. These 18 candidate SNP markers mapped to 18 candidate genes including E1 and E3, and the 18 candidate genes were involved in six major flowering pathways. Although further biological validation is needed, our results provide additional information on the existing flowering time markers and present another option to marker-assisted breeding programs for regulating flowering time of soybean.

Molecular and genetic analysis of defensive responses of Brassica juncea - B. fruticulosa introgression lines to Sclerotinia infection

Sclerotinia stem rot caused by Sclerotinia sclerotiorum is a major disease of crop brassicas, with inadequate variation for resistance in primary gene pools. We utilized a wild Brassicaceae species with excellent resistance against stem rot to develop a set of B. juncea - B. fruticulosa introgression lines (ILs). These were assessed for resistance using a highly reproducible stem inoculation technique against a virulent pathogen isolate. Over 40% of ILs showed higher levels of resistance. IL-43, IL-175, IL-215, IL-223 and IL-277 were most resistant ILs over three crop seasons. Sequence reads (21x) from the three most diverse ILs were then used to create B. juncea pseudomolecules, by replacing SNPs of reference B. juncea with those of re-sequenced ILs. Genotyping by sequencing (GBS) was also carried out for 88 ILs. Resultant sequence tags were then mapped on to the B. juncea pseudomolecules, and SNP genotypes prepared for each IL. Genome wide association studies helped to map resistance responses to stem rot. A total of 13 significant loci were identified on seven B. juncea chromosomes (A01, A03, A04, A05, A08, A09 and B05). Annotation of the genomic region around identified SNPs allowed identification of 20 candidate genes belonging to major disease resistance protein families, including TIR-NBS-LRR class, Chitinase, Malectin/receptor-like protein kinase, defensin-like (DEFL), desulfoglucosinolate sulfotransferase protein and lipoxygenase. A majority of the significant SNPs could be validated using whole genome sequences (21x) from five advanced generation lines being bred for Sclerotinia resistance as compared to three susceptible B. juncea germplasm lines. Our findings not only provide critical new understanding of the defensive pathway of B. fruticulosa resistance, but will also enable development of marker candidates for assisted transfer of introgressed resistant loci in to agronomically superior cultivars of crop Brassica.

Plant disease identification using explainable 3D deep learning on hyperspectral images

BACKGROUND

Hyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economically important disease, charcoal rot, which is a soil borne fungal disease that affects the yield of soybean crops worldwide.

RESULTS

Based on hyperspectral imaging of inoculated and mock-inoculated stem images, our 3D DCNN has a classification accuracy of 95.73% and an infected class F1 score of 0.87. Using the concept of a saliency map, we visualize the most sensitive pixel locations, and show that the spatial regions with visible disease symptoms are overwhelmingly chosen by the model for classification. We also find that the most sensitive wavelengths used by the model for classification are in the near infrared region (NIR), which is also the commonly used spectral range for determining the vegetative health of a plant.

CONCLUSION

The use of an explainable deep learning model not only provides high accuracy, but also provides physiological insight into model predictions, thus generating confidence in model predictions. These explained predictions lend themselves for eventual use in precision agriculture and research application using automated phenotyping platforms.

Resistance against Sclerotinia sclerotiorum in soybean involves a reprogramming of the phenylpropanoid pathway and up-regulation of antifungal activity targeting ergosterol biosynthesis

Sclerotinia sclerotiorum, a predominately necrotrophic fungal pathogen with a broad host range, causes a significant yield-limiting disease of soybean called Sclerotinia stem rot. Resistance mechanisms against this pathogen in soybean are poorly understood, thus hindering the commercial deployment of resistant varieties. We used a multiomic approach utilizing RNA-sequencing, gas chromatography-mass spectrometry-based metabolomics and chemical genomics in yeast to decipher the molecular mechanisms governing resistance to S. sclerotiorum in soybean. Transcripts and metabolites of two soybean recombinant inbred lines, one resistant and one susceptible to S. sclerotiorum were analysed in a time course experiment. The combined results show that resistance to S. sclerotiorum in soybean is associated in part with an early accumulation of JA-Ile ((+)-7-iso-jasmonoyl-L-isoleucine), a bioactive jasmonate, increased ability to scavenge reactive oxygen species, and importantly, a reprogramming of the phenylpropanoid pathway leading to increased antifungal activities. Indeed, we noted that phenylpropanoid pathway intermediates, such as 4-hydroxybenzoate, cinnamic acid, ferulic acid and caffeic acid, were highly accumulated in the resistant line. In vitro assays show that these metabolites and total stem extracts from the resistant line clearly affect S. sclerotiorum growth and development. Using chemical genomics in yeast, we further show that this antifungal activity targets ergosterol biosynthesis in the fungus, by disrupting enzymes involved in lipid and sterol biosynthesis. Overall, our results are consistent with a model where resistance to S. sclerotiorum in soybean coincides with an early recognition of the pathogen, leading to the modulation of the redox capacity of the host and the production of antifungal metabolites.

Genome-wide associations and epistatic interactions for internode number, plant height, seed weight and seed yield in soybean

BACKGROUND

Breeding programs benefit from information about marker-trait associations for many traits, whether the goal is to place those traits under active selection or to maintain them through background selection. Association studies are also important for identifying accessions bearing potentially useful alleles by characterizing marker-trait associations and allelic states across germplasm collections. This study reports the results of a genome-wide association study and evaluation of epistatic interactions for four agronomic and seed-related traits in soybean.

RESULTS

Using 419 diverse soybean accessions, together with genotyping data from the SoySNP50K Illumina Infinium BeadChip, we identified marker-trait associations for internode number (IN), plant height (PH), seed weight (SW), and seed yield per plant (SYP). We conducted a genome-wide epistatic study (GWES), identifying candidate genes that show evidence of SNP-SNP interactions. Although these candidate genes will require further experimental validation, several appear to be involved in developmental processes related to the respective traits. For IN and PH, these include the Dt1 determinacy locus (a soybean meristematic transcription factor), as well as a pectinesterase gene and a squamosa promoter binding gene that in other plants are involved in cell elongation and the vegetative-to-reproductive transition, respectively. For SW, candidate genes include an ortholog of the AP2 gene, which in other species is involved in maintaining seed size, embryo size, seed weight and seed yield. Another SW candidate gene is a histidine phosphotransfer protein - orthologs of which are involved in cytokinin-mediated seed weight regulating pathways. The SYP association loci overlap with regions reported in previous QTL studies to be involved in seed yield.

CONCLUSIONS

This study further confirms the utility of GWAS and GWES approaches for identifying marker-trait associations and interactions within a diverse germplasm collection.

Maize responsiveness to Azospirillum brasilense: Insights into genetic control, heterosis and genomic prediction

Several studies have shown differences in the abilities of maize genotypes to facilitate or impede Azospirillum brasilense colonization and to receive benefits from this association. Hence, our aim was to study the genetic control, heterosis effect and the prediction accuracy of the shoot and root traits of maize in response to A. brasilense. For that, we evaluated 118 hybrids under two contrasting scenarios: i) N stress (control) and ii) N stress plus A. brasilense inoculation. The diallel analyses were performed using mixed model equations, and the genomic prediction models accounted for the general and specific combining ability (GCA and SCA, respectively) and the presence or not of G×E effects. In addition, the genomic models were fitted considering parametric (G-BLUP) and semi-parametric (RKHS) kernels. The genotypes showed significant inoculation effect for five root traits, and the GCA and SCA were significant for both. The GCA in the inoculated treatment presented a greater magnitude than the control, whereas the opposite was observed for SCA. Heterosis was weakly influenced by the inoculation, and the heterozygosity and N status in the plant can have a role in the benefits that can be obtained from this Plant Growth-Promoting Bacteria (PGPB). Prediction accuracies for N stress plus A. brasilense ranged from 0.42 to 0.78, depending on the scenario and trait, and were higher, in most cases, than the non-inoculated treatment. Finally, our findings provide an understanding of the quantitative variation of maize responsiveness to A. brasilense and important insights to be applied in maize breeding aiming the development of superior hybrids for this association.

Optimising the identification of causal variants across varying genetic architectures in crops

Association studies use statistical links between genetic markers and the phenotype variation across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome-wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures. In this study, we employ real-world genotype datasets from four crop species with distinct minor allele frequency distributions, population structures and linkage disequilibrium patterns. We demonstrate that different GWAS statistical approaches provide favourable trade-offs between power and accuracy for traits controlled by different types of genetic architectures. FarmCPU provides the most favourable outcomes for moderately complex traits while a Bayesian approach adopted from genomic prediction provides the most favourable outcomes for extremely complex traits. We assert that by estimating the complexity of genetic architectures for target traits and selecting an appropriate statistical approach for the degree of complexity detected, researchers can substantially improve the ability to dissect the genetic factors controlling complex traits such as flowering time, plant height and yield component.

Exploring the genetics of lesion and nodal resistance in pea (Pisum sativum L.) to Sclerotinia sclerotiorum using genome-wide association studies and RNA-Seq

The disease white mold caused by the fungus Sclerotinia sclerotiorum is a significant threat to pea production, and improved resistance to this disease is needed. Nodal resistance in plants is a phenomenon where a fungal infection is prevented from passing through a node, and the infection is limited to an internode region. Nodal resistance has been observed in some pathosystems such as the pea (Pisum sativum L.)-S. sclerotiorum pathosystem. In addition to nodal resistance, different pea lines display different levels of stem lesion size restriction, referred to as lesion resistance. It is unclear whether the genetics of lesion resistance and nodal resistance are identical or different. This study applied genome-wide association studies (GWAS) and RNA-Seq to understand the genetic makeup of these two types of resistance. The time series RNA-Seq experiment consisted of two pea lines (the susceptible 'Lifter' and the partially resistant PI 240515), two treatments (mock inoculated samples and S. sclerotiorum-inoculated samples), and three time points (12, 24, and 48 hr post inoculation). Integrated results from GWAS and RNA-Seq analyses identified different redox-related transcripts for lesion and nodal resistances. A transcript encoding a glutathione S-transferase was the only shared resistance variant for both phenotypes. There were more leucine rich-repeat containing transcripts found for lesion resistance, while different candidate resistance transcripts such as a VQ motif-containing protein and a myo-inositol oxygenase were found for nodal resistance. This study demonstrated the robustness of combining GWAS and RNA-Seq for identifying white mold resistance in pea, and results suggest different genetics underlying lesion and nodal resistance.

Soybean Resistance to White Mold: Evaluation of Soybean Germplasm Under Different Conditions and Validation of QTL

Soybean (Glycine max L. Merr.) white mold (SWM), caused by Sclerotinia sclerotiorum (Lib) de Barry), is a devastating fungal disease in the Upper Midwest of the United States and southern Canada. Various methods exist to evaluate for SWM resistance and many quantitative trait loci (QTL) with minor effect governing SWM resistance have been identified in prior studies. This study aimed to predict field resistance to SWM using low-cost and efficient greenhouse inoculation methods and to confirm the QTL reported in previous studies. Three related but independent studies were conducted in the field, greenhouse, and laboratory to evaluate for SWM resistance. The first study evaluated 66 soybean plant introductions (PIs) with known field resistance to SWM using the greenhouse drop-mycelium inoculation method. These 66 PIs were significantly (P < 0.043) different for resistance to SWM. However, year was highly significant (P < 0.00001), while PI x year interaction was not significant (P < 0.623). The second study compared plant mortality (PM) of 35 soybean breeding lines or varieties in greenhouse inoculation methods with disease severity index (DSI) in field evaluations. Moderate correlation (r) between PM under drop-mycelium method and DSI in field trials (r = 0.65, p < 0.0001) was obtained. The PM under spray-mycelium was also correlated significantly with DSI from field trials (r = 0.51, p < 0.0018). Likewise, significant correlation (r = 0.62, p < 0.0001) was obtained between PM across greenhouse inoculation methods and DSI across field trials. These findings suggest that greenhouse inoculation methods could predict the field resistance to SWM. The third study attempted to validate 33 QTL reported in prior studies using seven populations that comprised a total of 392 F4 : 6 lines derived from crosses involving a partially resistant cultivar "Skylla," five partially resistant PIs, and a known susceptible cultivar "E00290." The estimates of broad-sense heritability (h2) ranged from 0.39 to 0.66 in the populations. Of the seven populations, four had h2 estimates that were significantly different from zero (p < 0.05). Single marker analysis across populations and inoculation methods identified 11 significant SSRs (p < 0.05) corresponding to 10 QTL identified by prior studies. Thus, these five new PIs could be used as new sources of resistant alleles to develop SWM resistant commercial cultivars.

Development and Evaluation of Glycine max Germplasm Lines with Quantitative Resistance to Sclerotinia sclerotiorum

Sclerotinia sclerotiorum, the causal agent of Sclerotinia stem rot, is a devastating fungal pathogen of soybean that can cause significant yield losses to growers when environmental conditions are favorable for the disease. The development of resistant varieties has proven difficult. However, poor resistance in commercial cultivars can be improved through additional breeding efforts and understanding the genetic basis of resistance. The objective of this project was to develop soybean germplasm lines that have a high level of Sclerotinia stem rot resistance to be used directly as cultivars or in breeding programs as a source of improved Sclerotinia stem rot resistance. Sclerotinia stem rot-resistant soybean germplasm was developed by crossing two sources of resistance, W04-1002 and AxN-1-55, with lines exhibiting resistance to Heterodera glycines and Cadophora gregata in addition to favorable agronomic traits. Following greenhouse evaluations of 1,076 inbred lines derived from these crosses, 31 lines were evaluated for resistance in field tests during the 2014 field season. Subsequently, 11 Sclerotinia stem rot resistant breeding lines were moved forward for field evaluation in 2015, and seven elite breeding lines were selected and evaluated in the 2016 field season. To better understand resistance mechanisms, a marker analysis was conducted to identify quantitative trait loci linked to resistance. Thirteen markers associated with Sclerotinia stem rot resistance were identified on chromosomes 15, 16, 17, 18, and 19. Our markers confirm previously reported chromosomal regions associated with Sclerotinia stem rot resistance as well as a novel region of chromosome 16. The seven elite germplasm lines were also re-evaluated within a greenhouse setting using a cut petiole technique with multiple S. sclerotiorum isolates to test the durability of physiological resistance of the lines in a controlled environment. This work presents a novel and comprehensive classical breeding method for selecting lines with physiological resistance to Sclerotinia stem rot and a range of agronomic traits. In these studies, we identify four germplasm lines; 91-38, 51-23, SSR51-70, and 52-82B exhibiting a high level of Sclerotinia stem rot resistance combined with desirable agronomic traits, including high protein and oil contents. The germplasm identified in this study will serve as a valuable source of physiological resistance to Sclerotinia stem rot that could be improved through further breeding to generate high-yielding commercial soybean cultivars.