Genome-wide association analyses reveal the genetic basis of biomass accumulation under symbiotic nitrogen fixation in African soybean

Genome-wide association studies reveal novel QTLs for agronomic traits in soybean

Introduction

Soybean, as a globally significant crop, has garnered substantial attention due to its agricultural importance. The utilization of molecular approaches to enhance grain yield in soybean has gained popularity.

Methods

In this study, we conducted a genome-wide association study (GWAS) using 156 Chinese soybean accessions over a two-year period. We employed the general linear model (GLM) and the mixed linear model (MLM) to analyze three agronomic traits: pod number, grain number, and grain weight.

Results

Our findings revealed significant associations between qgPNpP-98, qgGNpP-89 and qgHGW-85 QTLs and pod number, grain number, and grain weight, respectively. These QTLs were identified on chromosome 16, a region spanning 413171bp exhibited associations with all three traits.

Discussion

These QTL markers identified in this study hold potential for improving yield and agronomic traits through marker-assisted selection and genomic selection in breeding programs.

Genetic architecture of the response of Arabidopsis thaliana to a native plant-growth-promoting bacterial strain

By improving plant nutrition and alleviating abiotic and biotic stresses, plant growth-promoting bacteria (PGPB) can help to develop eco-friendly and sustainable agricultural practices. Besides climatic conditions, soil conditions, and microbe-microbe interactions, the host genotype influences the effectiveness of PGPB. Yet, most GWAS conducted to characterize the genetic architecture of response to PGPB are based on non-native interactions between a host plant and PGPB strains isolated from the belowground compartment of other plants. In this study, a GWAS was set up under in vitro conditions to describe the genetic architecture of the response of Arabidopsis thaliana to the PGPB Pseudomonas siliginis, by inoculating seeds of 162 natural accessions from the southwest of France with one strain isolated from the leaf compartment in the same geographical region. Strong genetic variation of plant growth response to this native PGPB was observed at a regional scale, with the strain having a positive effect on the vegetative growth of small plants and a negative effect on the vegetative growth of large plants. The polygenic genetic architecture underlying this negative trade-off showed suggestive signatures of local adaptation. The main eco-evolutionary relevant candidate genes are involved in seed and root development.

Optimization of in vitro and ex vitro Agrobacterium rhizogenes-mediated hairy root transformation of soybean for visual screening of transformants using RUBY

In vitro and ex vitro Agrobacterium rhizogenes-mediated hairy root transformation (HRT) assays are key components of the plant biotechnology and functional genomics toolkit. In this report, both in vitro and ex vitro HRT were optimized in soybean using the RUBY reporter. Different parameters including A. rhizogenes strain, optical density of the bacterial cell culture (OD₆₀₀), co-cultivation media, soybean genotype, explant age, and acetosyringone addition and concentration were evaluated. Overall, the in vitro assay was more efficient than the ex vitro assay in terms of the percentage of induction of hairy roots and transformed roots (expressing RUBY). Nonetheless, the ex vitro technique was deemed faster and a less complicated approach. The highest transformation of RUBY was observed on 7-d-old cotyledons of cv. Bert inoculated for 30 minutes with the R1000 resuspended in ¼ B5 medium to OD₆₀₀ (0.3) and 150 µM of acetosyringone. The parameters of this assay also led to the highest percentage of RUBY through two-step ex vitro hairy root transformation. Finally, using machine learning-based modeling, optimal protocols for both assays were further defined. This study establishes efficient and reliable hairy root transformation protocols applicable for functional studies in soybean.

Schizotrophic Sclerotinia sclerotiorum-Mediated Root and Rhizosphere Microbiome Alterations Activate Growth and Disease Resistance in Wheat

Sclerotinia sclerotiorum, a widespread pathogen of dicotyledons, can grow endophytically in wheat, providing protection against Fusarium head blight and stripe rust and enhancing wheat yield. In this study, we found that wheat seed treatment with strain DT-8, infected with S. sclerotiorum hypovirulence-associated DNA virus 1 (SsHADV-1) and used as a "plant vaccine" for brassica protection, could significantly increase the diversity of the fungal and bacterial community in rhizosphere soil, while the diversity of the fungal community was obviously decreased in the wheat root. Interestingly, the relative abundance of potential plant growth-promoting rhizobacteria (PGPR) and biocontrol agents increased significantly in the DT-8-treated wheat rhizosphere soil. These data might be responsible for wheat growth promotion and disease resistance. These results may provide novel insights for understanding the interaction between the schizotrophic microorganism and the microbiota of plant roots and rhizosphere, screening and utilizing beneficial microorganisms, and further reducing chemical pesticide utilization and increasing crop productivity. IMPORTANCE Fungal pathogens are seriously threatening food security and natural ecosystems; efficient and environmentally friendly control methods are essential to increase world crop production. S. sclerotiorum, a widespread pathogen of dicotyledons, can grow endophytically in wheat, providing protection against Fusarium head blight and stripe rust and enhancing wheat yield. In this study, we discovered that S. sclerotiorum treatment increased the diversity of the soil fungal and bacterial community in rhizosphere soil, while the diversity of the fungal community was obviously decreased in the wheat root. More importantly, the relative abundance of potential PGPR and bio-control agents increased significantly in the S. sclerotiorum-treated wheat rhizosphere soil. The importance of this work is that schizotrophic S. sclerotiorum promotes wheat growth and enhances resistance against fungal diseases via changes in the structure of the root and rhizosphere microbiome.

Identification of candidate genes for soybean seed coat-related traits using QTL mapping and GWAS

Seed coat color is a typical morphological trait that can be used to reveal the evolution of soybean. The study of seed coat color-related traits in soybeans is of great significance for both evolutionary theory and breeding practices. In this study, 180 F₁₀ recombinant inbred lines (RILs) derived from the cross between the yellow-seed coat cultivar Jidou12 (ZDD23040, JD12) and the wild black-seed coat accession Y9 (ZYD02739) were used as materials. Three methods, single-marker analysis (SMA), interval mapping (IM), and inclusive composite interval mapping (ICIM), were used to identify quantitative trait loci (QTLs) controlling seed coat color and seed hilum color. Simultaneously, two genome-wide association study (GWAS) models, the generalized linear model (GLM) and mixed linear model (MLM), were used to jointly identify seed coat color and seed hilum color QTLs in 250 natural populations. By integrating the results from QTL mapping and GWAS analysis, we identified two stable QTLs (qSCC02 and qSCC08) associated with seed coat color and one stable QTL (qSHC08) related to seed hilum color. By combining the results of linkage analysis and association analysis, two stable QTLs (qSCC02, qSCC08) for seed coat color and one stable QTL (qSHC08) for seed hilum color were identified. Upon further investigation using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we validated the previous findings that two candidate genes (CHS3C and CHS4A) reside within the qSCC08 region and identified a new QTL, qSCC02. There were a total of 28 candidate genes in the interval, among which Glyma.02G024600, Glyma.02G024700, and Glyma.02G024800 were mapped to the glutathione metabolic pathway, which is related to the transport or accumulation of anthocyanin. We considered the three genes as potential candidate genes for soybean seed coat-related traits. The QTLs and candidate genes detected in this study provide a foundation for further understanding the genetic mechanisms underlying soybean seed coat color and seed hilum color and are of significant value in marker-assisted breeding.

Genome-wide association study for biomass accumulation traits in soybean

Soybean is one of the most versatile crops for oil production, human diets, and feedstocks. The vegetative biomass of soybean is an important determinant of seed yield and is crucial for the forage usages. However, the genetic control of soybean biomass is not well explained. In this work, we used a soybean germplasm population, including 231 improved cultivars, 207 landraces, and 121 wild soybeans, to investigate the genetic basis of biomass accumulation of soybean plants at the V6 stage. We found that biomass-related traits, including NDW (nodule dry weight), RDW (root dry weight), SDW (shoot dry weight), and TDW (total dry weight), were domesticated during soybean evolution. In total, 10 loci, encompassing 47 putative candidate genes, were detected for all biomass-related traits by a genome-wide association study. Among these loci, seven domestication sweeps and six improvement sweeps were identified. Glyma.05G047900, a purple acid phosphatase, was a strong candidate gene to improve biomass for future soybean breeding. This study provided new insights into the genetic basis of biomass accumulation during soybean evolution.

Supplementary information

The online version contains supplementary material available at 10.1007/s11032-023-01380-6.

The Genotypic Variability among Short-Season Soybean Cultivars for Nitrogen Fixation under Drought Stress

Soybean fixes atmospheric nitrogen through the symbiotic rhizobia bacteria that inhabit root nodules. Drought stress negatively affect symbiotic nitrogen fixation (SNF) in soybean. The main objective of this study was to identify allelic variations associated with SNF in short-season Canadian soybean varieties under drought stress. A diversity panel of 103 early-maturity Canadian soybean varieties was evaluated under greenhouse conditions to determine SNF-related traits under drought stress. Drought was imposed after three weeks of plant growth, where plants were maintained at 30% field capacity (FC) (drought) and 80% FC (well-watered) until seed maturity. Under drought stress, soybean plants had lower seed yield, yield components, seed nitrogen content, % nitrogen derived from the atmosphere (%Ndfa), and total seed nitrogen fixed compared to those under well-watered conditions. Significant genotypic variability among soybean varieties was found for yield, yield parameters, and nitrogen fixation traits. A genome-wide association study (GWAS) was conducted using 2.16 M single nucleotide single nucleotide polymorphisms (SNPs) for different yield and nitrogen fixation related parameters for 30% FC and their relative performance (30% FC/80% FC). In total, five quantitative trait locus (QTL) regions, including candidate genes, were detected as significantly associated with %Ndfa under drought stress and relative performance. These genes can potentially aid in future breeding efforts to develop drought-resistant soybean varieties.

3D-GBS: a universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes

Despite the increased efficiency of sequencing technologies and the development of reduced-representation sequencing (RRS) approaches allowing high-throughput sequencing (HTS) of multiplexed samples, the per-sample genotyping cost remains the most limiting factor in the context of large-scale studies. For example, in the context of genomic selection (GS), breeders need genome-wide markers to predict the breeding value of large cohorts of progenies, requiring the genotyping of thousands candidates. Here, we introduce 3D-GBS, an optimized GBS procedure, to provide an ultra-high-throughput and ultra-low-cost genotyping solution for species with small to medium-sized genome and illustrate its use in soybean. Using a combination of three restriction enzymes (PstI/NsiI/MspI), the portion of the genome that is captured was reduced fourfold (compared to a "standard" ApeKI-based protocol) while reducing the number of markers by only 40%. By better focusing the sequencing effort on limited set of restriction fragments, fourfold more samples can be genotyped at the same minimal depth of coverage. This GBS protocol also resulted in a lower proportion of missing data and provided a more uniform distribution of SNPs across the genome. Moreover, we investigated the optimal number of reads per sample needed to obtain an adequate number of markers for GS and QTL mapping (500-1000 markers per biparental cross). This optimization allows sequencing costs to be decreased by ~ 92% and ~ 86% for GS and QTL mapping studies, respectively, compared to previously published work. Overall, 3D-GBS represents a unique and affordable solution for applications requiring extremely high-throughput genotyping where cost remains the most limiting factor.

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

Genome-wide Association Study for Yield and Yield-Related Traits in Diverse Blackgram Panel (Vigna mungo L. Hepper) Reveals Novel Putative Alleles for Future Breeding Programs

Blackgram (Vigna mungo L. Hepper) is an important tropical and sub-tropical short-duration legume that is rich in dietary protein and micronutrients. Producing high-yielding blackgram varieties is hampered by insufficient genetic variability, absence of suitable ideotypes, low harvest index and susceptibility to biotic-abiotic stresses. Seed yield, a complex trait resulting from the expression and interaction of multiple genes, necessitates the evaluation of diverse germplasm for the identification of novel yield contributing traits. Henceforth, a panel of 100 blackgram genotypes was evaluated at two locations (Ludhiana and Gurdaspur) across two seasons (Spring 2019 and Spring 2020) for 14 different yield related traits. A wide range of variability, high broad-sense heritability and a high correlation of grain yield were observed for 12 out of 14 traits studied among all environments. Investigation of population structure in the panel using a set of 4,623 filtered SNPs led to identification of four sub-populations based on ad-hoc delta K and Cross entropy value. Using Farm CPU model and Mixed Linear Model algorithms, a total of 49 significant SNP associations representing 42 QTLs were identified. Allelic effects were found to be statistically significant at 37 out of 42 QTLs and 50 known candidate genes were identified in 24 of QTLs.

Negotiating mutualism: A locus for exploitation by rhizobia has a broad effect size distribution and context-dependent effects on legume hosts

In mutualisms, variation at genes determining partner fitness provides the raw material upon which coevolutionary selection acts, setting the dynamics and pace of coevolution. However, we know little about variation in the effects of genes that underlie symbiotic fitness in natural mutualist populations. In some species of legumes that form root nodule symbioses with nitrogen‐fixing rhizobial bacteria, hosts secrete nodule‐specific cysteine‐rich (NCR) peptides that cause rhizobia to differentiate in the nodule environment. However, rhizobia can cleave NCR peptides through the expression of genes like the plasmid‐borne Host range restriction peptidase (hrrP), whose product degrades specific NCR peptides. Although hrrP activity can confer host exploitation by depressing host fitness and enhancing symbiont fitness, the effects of hrrP on symbiosis phenotypes depend strongly on the genotypes of the interacting partners. However, the effects of hrrP have yet to be characterised in a natural population context, so its contribution to variation in wild mutualist populations is unknown. To understand the distribution of effects of hrrP in wild rhizobia, we measured mutualism phenotypes conferred by hrrP in 12 wild Ensifer medicae strains. To evaluate context dependency of hrrP effects, we compared hrrP effects across two Medicago polymorpha host genotypes and across two experimental years for five E. medicae strains. We show for the first time in a natural population context that hrrP has a wide distribution of effect sizes for many mutualism traits, ranging from strongly positive to strongly negative. Furthermore, we show that hrrP effect size varies across host genotypes and experiment years, suggesting that researchers should be cautious about extrapolating the role of genes in natural populations from controlled laboratory studies of single genetic variants.

Genome-Wide Association Study of Soybean Germplasm Derived From Canadian × Chinese Crosses to Mine for Novel Alleles to Improve Seed Yield and Seed Quality Traits

Genome-wide association study (GWAS) has emerged in the past decade as a viable tool for identifying beneficial alleles from a genomic diversity panel. In an ongoing effort to improve soybean [Glycine max (L.) Merr.], which is the third largest field crop in Canada, a GWAS was conducted to identify novel alleles underlying seed yield and seed quality and agronomic traits. The genomic panel consisted of 200 genotypes including lines derived from several generations of bi-parental crosses between modern Canadian × Chinese cultivars (CD-CH). The genomic diversity panel was field evaluated at two field locations in Ontario in 2019 and 2020. Genotyping-by-sequencing (GBS) was conducted and yielded almost 32 K high-quality SNPs. GWAS was conducted using Fixed and random model Circulating Probability Unification (FarmCPU) model on the following traits: seed yield, seed protein concentration, seed oil concentration, plant height, 100 seed weight, days to maturity, and lodging score that allowed to identify five QTL regions controlling seed yield and seed oil and protein content. A candidate gene search identified a putative gene for each of the three traits. The results of this GWAS study provide insight into potentially valuable genetic resources residing in Chinese modern cultivars that breeders may use to further improve soybean seed yield and seed quality traits.

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 Study Statistical Models: A Review

Statistical models are at the core of the genome-wide association study (GWAS). In this chapter, we provide an overview of single- and multilocus statistical models, Bayesian, and machine learning approaches for association studies in plants. These models are discussed based on their basic methodology, cofactors adjustment accounted for, statistical power and computational efficiency. New statistical models and machine learning algorithms are both showing improved performance in detecting missed signals, rare mutations and prioritizing causal genetic variants; nevertheless, further optimization and validation studies are required to maximize the power of GWAS.

Comprehensive Genome-Wide Association Analysis Reveals the Genetic Basis of Root System Architecture in Soybean

Increasing the understanding genetic basis of the variability in root system architecture (RSA) is essential to improve resource-use efficiency in agriculture systems and to develop climate-resilient crop cultivars. Roots being underground, their direct observation and detailed characterization are challenging. Here, were characterized twelve RSA-related traits in a panel of 137 early maturing soybean lines (Canadian soybean core collection) using rhizoboxes and two-dimensional imaging. Significant phenotypic variation (P < 0.001) was observed among these lines for different RSA-related traits. This panel was genotyped with 2.18 million genome-wide single-nucleotide polymorphisms (SNPs) using a combination of genotyping-by-sequencing and whole-genome sequencing. A total of 10 quantitative trait locus (QTL) regions were detected for root total length and primary root diameter through a comprehensive genome-wide association study. These QTL regions explained from 15 to 25% of the phenotypic variation and contained two putative candidate genes with homology to genes previously reported to play a role in RSA in other species. These genes can serve to accelerate future efforts aimed to dissect genetic architecture of RSA and breed more resilient varieties.