Soybean Accessions Research Articles

Genome-wide association study reveals novel loci and acandidate gene for resistance to frogeye leaf spot (Cercospora sojina) in soybean.

Frogeye leaf spot, caused by the fungus Cercospora sojina, is a threat to soybeans in the southeastern and midwestern United States that can be controlled by crop genetic resistance. Limited genetic resistance to the disease has been reported, and only three sources of resistance have been used in modern soybean breeding. To discover novel sources and identify the genomic locations of resistance that could be used in soybean breeding, a GWAS was conducted using a panel of 329 soybean accessions selected to maximize genetic diversity. Accessions were phenotyped using a 1-5 visual rating and by using image analysis to count lesion number and measure the percent of leaf area diseased. Eight novel loci on eight chromosomes were identified for three traits utilizing the FarmCPU or BLINK models, of which a locus on chromosome 11 was highly significant across all model-trait combinations. KASP markers were designed using the SoySNP50K Beadchip and variant information from 65 of the accessions that have been sequenced to target SNPs in the gene model Glyma.11g230400, a LEUCINE-RICH REPEAT RECEPTOR-LIKE PROTEIN KINASE. The association of a KASP marker, GSM990, designed to detect a missense mutation in the gene was the most significant with all three traits in a genome-wide association, and the marker may be useful to select for resistance to frogeye leaf spot in soybean breeding.

Genetic dissection reveals the complex architecture of amino acid composition in soybean seeds.

Five loci related to soybean protein and amino acid contents were colocated by performing linkage mapping and GWAS. The haplotype analysis showed that Glyma.08G109100 may be useful to improve the soybean seed composition. Soybean (Glycine max (L.) Merr.) seeds are good protein sources. Although genetic variation is abundant, natural variation in seed amino acids and their derived traits is lacking across soybean accessions. Here, we determined the contents of protein and 17 amino acids, obtained 36 derived traits based on the protein and total amino acid contents, and derived 34 traits based on seven amino acid family groups. Furthermore, we performed a linkage analysis of the contents of 17 amino acids and 73 amino acid-derived traits based on the recombinant inbred line (RIL)-derived Kefeng No. 1 × Nannong 1138-2. Six hundred thirty-nine quantitative trait loci (QTLs) were identified, explaining 6.07-39.00% of the phenotypic variation. Among these loci, five were detected in diverse soybean accessions using a genome-wide association study. A network analysis revealed that some loci that were significantly associated with multiple amino acids were tightly linked on chromosome 8 based on linkage disequilibrium values, which also further confirmed the results of the correlation analysis among amino acid traits. Through a combination of a genome-wide association study, linkage analysis, qRT-PCR, and genomic polymorphism comparison, Glyma.08G109100 on chromosome 8, which may affect amino acid contents, was selected. The haplotype analysis showed that Hap-T of Glyma.08G109100 may be useful to improve the contents of protein and 16 amino acids in soybean. This study provides new insights into the genetic basis of the amino acid composition in soybean seeds and may facilitate marker-based breeding of soybean with improved nutritional value.

Using soybean starting material in breeding process

The paper evaluates new promising soybean varieties and their parental forms with high yield, seed quality, and resistance to diseases and the stress conditions of Primorsky kray for a number of traits. The soybean accessions were used in a breeding program for constructing genotypes. Based on the research results, we selected promising soybean varieties characterized by a high yield and a high content of protein and oil in seeds. The yield of varieties Primorskaya 1670 and Primorskaya 1672 exceeded the standard by more than 32.2%. Varieties Primorskaya 1551 and Primorskaya 1674 had a high oil content in seeds – 23.8% and 24.1%, respectively. Variety Primorskaya 1659 was characterized by a high content of protein (41.2%). The parental forms used in the breeding of the new varieties were evaluated for a number of economically important traits. The yield of the starting forms ranged from 0.149 to 0.405 kg/m2. It was determined that 69.5% of the soybean accessions belonged to the group with mid-season maturity. A high content of protein in seeds (> 40.0 %) was detected in varieties of various origin – Primorskaya 13, Hefeng 25, NIISKH 2, XN 4, Arisa, and Kioto; variety NIISKH had a high oil content (24.2%). Varieties NIISKH 6, D 402-HH51, Arisa, NIISKH 5, XN 8, and XN 4 demonstrated a high potential adaptability to stress conditions.

SoybeanGDB: A comprehensive genomic and bioinformatic platform for soybean genetics and genomics

Soybean (Glycine max (L.) Merr.) is a globally significant crop, widely cultivated for oilseed production and animal feeds. In recent years, the rapid growth of multi-omics data from thousands of soybean accessions has provided unprecedented opportunities for researchers to explore genomes, genetic variations, and gene functions. To facilitate the utilization of these abundant data for soybean breeding and genetic improvement, the SoybeanGDB database (https://venyao.xyz/SoybeanGDB/) was developed as a comprehensive platform. SoybeanGDB integrates high-quality de novo assemblies of 39 soybean genomes and genomic variations among thousands of soybean accessions. Genomic information and variations in user-specified genomic regions can be searched and downloaded from SoybeanGDB, in a user-friendly manner. To facilitate research on genetic resources and elucidate the biological significance of genes, SoybeanGDB also incorporates a variety of bioinformatics analysis modules with graphical interfaces, such as linkage disequilibrium analysis, nucleotide diversity analysis, allele frequency analysis, gene expression analysis, primer design, gene set enrichment analysis, etc. In summary, SoybeanGDB is an essential and valuable resource that provides an open and free platform to accelerate global soybean research.

Evaluation of soybean accessions for resistance to the Japanese beetle, 2023

Evaluation of soybean accessions for resistance to the Japanese beetle, 2023

Study the Effect of Shade Stress on Indonesian Soybeans to Anticipate the Need for Breeding Superior Variety

The need for superior soybeans which resistant against low light is a challenge for Indonesia that has large of soybean germplasms which potential for achieving national soybean self-sufficiency and sustainability. Unfortunately, research on this field still very lack. This study aims to investigate the responses of 28 soybean accessions to shade stress, and looking for potential candidates or basic material for the development of high-yielding varieties. The research was conducted in the Cikeumeuh-Bogor field using a randomized block factorial design with two treatments (genotype and percentage of shade), with three replications. Based on the results, all of the morph-agronomic characters significantly influenced by the treatments, except for the number of filled pods (p-value <0.001). There was interaction between genotypes and shade stress which influenced the plant height, number of branches, number of trifoliate leaves and flowering time. Based on this study, Kedelai Hijau (G-19) and Lokal Brebes (G-21) were potential to be candidate for breeding on shade-resistant varieties. G-19 had the best response to the number of branches and number of trifoliate leaves, while G-21 best on height and root length. This information becomes a novelty that contributes to breeding soybeans resistant to shade stress for food sustainability.

Identification of genes for seed isoflavones based on bulk segregant analysis sequencing in soybean natural population.

We identified four hub genes for isoflavone biosynthesis based on BSA-seq and WGCNA methods and validated that GmIE3-1 positively contribute to isoflavone accumulation in soybean. Soybean isoflavones are secondary metabolites of great interest owing to their beneficial impact on human health. Herein, we profiled the seed isoflavone content by HPLC in 1551 soybean accessions grown in two locations for two years and constructed two extreme pools with high (4065.1µgg-1) and low (1427.23µgg-1) isoflavone contents to identify candidate genes involved in isoflavone biosynthesis pathways using bulk segregant analysis sequencing (BSA-seq) approach. The results showed that the average sequencing depths were 50.3× and 65.7× in high and low pools, respectively. A total of 23,626 polymorphic SNPs and 5299 InDels were detected between both pools and 1492 genes with different variations were identified. Based on differential genes in BSA-seq and weighted gene co-expression network analysis (WGCNA), four hub genes, Glyma.06G290400 (designated as GmIE3-1), Glyma.01G239200, Glyma.01G241500, Glyma.13G256100 were identified, encoding E3 ubiquitin-protein ligase, arm repeat protein interacting with ABF2, zinc metallopeptidase EGY3, and dynamin-related protein 3A, respectively. The allelic variation in GmIE3-1 showed a significant influence on isoflavone accumulation. The virus-induced gene silencing (VIGS) and RNAi hairy root transformation of GmIE3-1 revealed partial suppression of this gene could cause a significant decrease (P < 0.0001) of total isoflavone content, suggesting GmIE3-1 is a positive regulator for isoflavones. The present study demonstrated that the BSA-seq approach combined with WGCNA, VIGS and hairy root transformation can efficiently identify isoflavone candidate genes in soybean natural population.

AccuCalc: A Python Package for Accuracy Calculation in GWAS.

The genome-wide association study (GWAS) is a popular genomic approach that identifies genomic regions associated with a phenotype and, thus, aims to discover causative mutations (CM) in the genes underlying the phenotype. However, GWAS discoveries are limited by many factors and typically identify associated genomic regions without the further ability to compare the viability of candidate genes and actual CMs. Therefore, the current methodology is limited to CM identification. In our recent work, we presented a novel approach to an empowered "GWAS to Genes" strategy that we named Synthetic phenotype to causative mutation (SP2CM). We established this strategy to identify CMs in soybean genes and developed a web-based tool for accuracy calculation (AccuTool) for a reference panel of soybean accessions. Here, we describe our further development of the tool that extends its utilization for other species and named it AccuCalc. We enhanced the tool for the analysis of datasets with a low-frequency distribution of a rare phenotype by automated formatting of a synthetic phenotype and added another accuracy-based GWAS evaluation criterion to the accuracy calculation. We designed AccuCalc as a Python package for GWAS data analysis for any user-defined species-independent variant calling format (vcf) or HapMap format (hmp) as input data. AccuCalc saves analysis outputs in user-friendly tab-delimited formats and also offers visualization of the GWAS results as Manhattan plots accentuated by accuracy. Under the hood of Python, AccuCalc is publicly available and, thus, can be used conveniently for the SP2CM strategy utilization for every species.

Improved Field-Based Soybean Seed Counting and Localization with Feature Level Considered.

Developing automated soybean seed counting tools will help automate yield prediction before harvesting and improving selection efficiency in breeding programs. An integrated approach for counting and localization is ideal for subsequent analysis. The traditional method of object counting is labor-intensive and error-prone and has low localization accuracy. To quantify soybean seed directly rather than sequentially, we propose a P2PNet-Soy method. Several strategies were considered to adjust the architecture and subsequent postprocessing to maximize model performance in seed counting and localization. First, unsupervised clustering was applied to merge closely located overcounts. Second, low-level features were included with high-level features to provide more information. Third, atrous convolution with different kernel sizes was applied to low- and high-level features to extract scale-invariant features to factor in soybean size variation. Fourth, channel and spatial attention effectively separated the foreground and background for easier soybean seed counting and localization. At last, the input image was added to these extracted features to improve model performance. Using 24 soybean accessions as experimental materials, we trained the model on field images of individual soybean plants obtained from one side and tested them on images obtained from the opposite side, with all the above strategies. The superiority of the proposed P2PNet-Soy in soybean seed counting and localization over the original P2PNet was confirmed by a reduction in the value of the mean absolute error, from 105.55 to 12.94. Furthermore, the trained model worked effectively on images obtained directly from the field without background interference.

The genetic basis of high-latitude adaptation in wild soybean

In many plants, flowering time is influenced by daylength as an adaptive response. In soybean (Glycine max) cultivars, however, photoperiodic flowering reduces crop yield and quality in high-latitude regions. Understanding the genetic basis of wild soybean (Glycine soja) adaptation to high latitudes could aid breeding of improved cultivars. Here, we identify the Tof4 (Time of flowering 4) locus, which encodes by an E1-like protein, E1La, that represses flowering and enhances adaptation to high latitudes in wild soybean. Moreover, we found that Tof4 physically associates with the promoters of two important FLOWERING LOCUS T (FT2a and FT5a) and with Tof5 to inhibit their transcription under long photoperiods. The effect of Tof4 on flowering and maturity is mediated by FT2a and FT5a proteins. Intriguingly, Tof4 and the key flowering repressor E1 independently but additively regulate flowering time, maturity, and grain yield in soybean. We determined that weak alleles of Tof4 have undergone natural selection, facilitating adaptation to high latitudes in wild soybean. Notably, over 71.5% of wild soybean accessions harbor the mutated alleles of Tof4 or a previously reported gain-of-function allele Tof5H2, suggesting that these two loci are the genetic basis of wild soybean adaptation to high latitudes. Almost no cultivated soybean carries the mutated tof4 allele. Introgression of the tof4-1 and Tof5H2 alleles into modern soybean or editing E1 family genes thus represents promising avenues to obtain early-maturity soybean, thereby improving productivity in high latitudes.

Results of a study of soybean source material for breeding purposes under the conditions of Primorsky Territory

Background. A study of the soybean gene pool adapted to the conditions of Primorsky Territory in search of useful agronomic traits is essential for further use in breeding programs.Materials and methods. Soybean accessions from the germplasm collection were tested in 2019–2021 at the Federal Scientific Center of Agricultural Biotechnology of the Far East named after A.K. Chaika. The study included 213 accessions of various origin. Cv. ‘Primorskaya 4’ served as the reference. An objective assessment of the potential of the said research material was made.Results. Three years of experiments resulted in selecting promising soybean genotypes with a set of important agronomic traits for breeding programs. Compared to the reference ‘Primorskaya 4’, an increase of more than 35% in productivity was observed in the cultivars ‘Mestnaya’ (Russia), ‘Jilin’ (China), ‘Montreal’ (France), and ‘XP 977-1.9’ (USA). Cvs. ‘No. 075-2’ (USA), ‘K0152’ (Ukraine), ‘Muzanze Stamm M 4789/74’, ‘SOJA 1065’ and ‘Adsoi’ (Germany) were characterized by earliness (100 days). Cvs. ‘Mestnaya’ and ‘HS Atlas’ may be interesting for breeders due to their highest oil content: 25.9% and 26.0%, respectively. The highest protein content was found in cvs. ‘Zhuravushka’ (39.2%), ‘XN 4’ (41.9%), ‘Torlitsa’ (41.9%) and ‘XP 977-1.9’ (39.5%). Cvs. ‘Pi 6D 4182’, ‘XN 4’, ‘Skelya’ and ‘HS Atlas’ manifested resistance to Septoria brown spot. The results of the assessment for adaptability potential showed that the following cultivars of different origin had the highest resistance to environmental stresses: ‘Primorskaya 4’ (–2.5), ‘Torlitsa’ (–2.0) and ‘Kassidi’ (–3.0).

Genetic mapping and confirmation of the Rdm3 locus underlying resistance to southern stem canker in soybean

AbstractSouthern stem canker (SSC) in soybean [Glycine max (L.) Merrill], caused by the fungal pathogen Diaporthe aspalathi, is a major disease in the southern United States. It can cause up to 80% yield loss in severely infected fields. Plant introduction (PI) 398469, a Maturity Group VI soybean accession from South Korea, is one of six known sources of resistance to SSC. To determine the genetics of the resistance provided by PI 398469, F2:3 and F5‐derived recombinant inbred line (RIL) populations were developed from a cross with the susceptible breeding line G81‐2057. Both populations were phenotyped in the greenhouse using a modified toothpick method. The SSC‐resistant and susceptible DNA bulks consisting of 20 resistant and 18 susceptible families were created from the F2:3 population and genotyped using the Infinium SoySNP50K BeadChips for a bulked segregant analysis (BSA). Based on BSA results, 22 Kompetitive allele specific polymerase chain reaction (KASP) markers were designed to genotype the RIL population. Quantitative trait locus (QTL) analysis identified qRdm_14, on chromosome (Chr) 14 with an R2 of 62%. Linkage mapping in the F2:3 population confirmed the presence of a QTL on Chr 14. F5‐derived RILs derived from G81‐2057 × Crockett were phenotyped for SSC resistance and genotyped with the KASP markers to examine the relationship between Rdm3 from Crockett and qRdm_14 from PI 398469. Both PI 398469 and Crockett carried realleles at the Rdm3 locus for resistance to SSC. The QTL and KASP marker information could be used to screen breeding lines for resistance to SSC.

Variability in Maturity, Oil and Protein Concentration, and Genetic Distinctness among Soybean Accessions Conserved at Plant Gene Resources of Canada.

Soybean (Glycine max (L.) Merr.) is one of the important crops in Canada and has the potential to expand its production further north into the Canadian Prairies. Such expansion, however, requires the search for adapted soybean germplasm useful for the development of productive cultivars with earlier maturity and increased protein concentration. We initiated several research activities to characterize 848 accessions of the soybean collection conserved at Plant Gene Resources of Canada (PGRC) for maturity, oil and protein concentration, and genetic distinctness. The characterization revealed a wide range of variations present in each assessed trait among the PGRC soybean accessions. The trait variabilities allowed for the identification of four core subsets of 35 PGRC soybean accessions, each specifically targeted for early maturity for growing in Saskatoon and Ottawa, and for high oil and protein concentration. The two early maturity core subsets for Saskatoon and Ottawa displayed days to maturity ranging from 103 to 126 days and 94 to 102 days, respectively. The two core subsets for high oil and protein concentration showed the highest oil and protein concentration from 25.0 to 22.7% and from 52.8 to 46.7%, respectively. However, these core subsets did not differ significantly in genetic distinctness (as measured with 19,898 SNP markers across 20 soybean chromosomes) from the whole PGRC soybean collection. These findings are useful, particularly for the management and utilization of the conserved soybean germplasm.

Genetic diversity and proteomic analysis of vegetable soybean (Glycine max (L.) Merrill) accessions grown in mineral and BRIS soils

Knowledge of the molecular mechanisms of response to environmental stress is fundamental for the development of genetically stress-tolerant crops. This study aims to find vegetable soybean accessions tolerant to cultivation in stressful tropical environments. Fourteen accessions of the vegetable soybean (Glycine max (L.) Merrill) were grown in mineral and beach ridges interspersed with swale (BRIS) soils. The genetic diversity, estimated using inter-simple sequence repeat (ISSR) markers, revealed 42.50% polymorphism and was regarded as moderate. The unweighted pair-group method arithmetic average (UPGMA) analysis allocated the tested accessions into five major clusters at a similarity coefficient level of 0.43. The lowest values of the genetic distance were between IIUMSOY11 and IIUMSOY13 &amp;amp; IIUMSOY13 and IIUMSOY14, indicating that these accessions were more genetically distant from the other accessions. Ten differentially expressed proteins were identified in the three selected accessions IIUMSOY1, IIUMSOY11 and IIUMSOY14 using mass spectrometry, revealing a unique expression of the proteins involved in the storage, flavonoid metabolism, protein modification, oxidative stress defence, carbohydrate metabolism and respiratory chain. The findings may be valuable for the selection of genetically diverse accessions, to enhance the breeding of vegetable soybean genotypes suitable for stressful tropical environments.

Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content.

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

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

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

Identification of loci governing soybean seed protein content via genome-wide association study and selective signature analyses.

Soybean [Glycine max (L.) Merr.] is an excellent source of protein. Understanding the genetic basis of protein content (PC) will accelerate breeding efforts to increase soybean quality. In the present study, a genome-wide association study (GWAS) was applied to detect quantitative trait loci (QTL) for PC in soybean using 264 re-sequenced soybean accessions and a high-quality single nucleotide polymorphism (SNP) map. Eleven QTL were identified as associated with PC. The QTL qPC-14 was detected by GWAS in both environments and was shown to have undergone strong selection during soybean improvement. Fifteen candidate genes were identified in qPC-14, and three candidate genes showed differential expression between a high-PC and a low-PC variety during the seed development stage. The QTL identified here will be of significant use in molecular breeding efforts, and the candidate genes will play essential roles in exploring the mechanisms of protein biosynthesis.

GmEIL4 enhances soybean (Glycine max) phosphorus efficiency by improving root system development.

Phosphorus (P) deficiency seriously affects plant growth and development and ultimately limits the quality and yield of crops. Here, a new P efficiency-related major quantitative trait locusgene, GmEIL4 (encoding an ethylene-insensitive 3-like 1 protein), was cloned at qP2, which was identified by linkage analysis and genome-wide association studyacross four environments. Overexpressing GmEIL4 significantly improved the P uptake efficiency by increasing the number, length and surface area of lateral roots of hairy roots in transgenic soybeans, while interfering with GmEIL4 resulted in poor root phenotypic characteristics compared with the control plants under low P conditions. Interestingly, we found that GmEIL4 interacted with EIN3-binding F box protein 1 (GmEBF1), which may regulate the root response to low P stress. We conclude that the expression of GmEIL4 was induced by low-P stress and that overexpressing GmEIL4 improved P accumulation by regulating root elongation and architecture. Analysis of allele variation of GmEIL4 in 894 soybean accessions suggested that GmEIL4 is undergoing artificial selection during soybean evolution, which will benefit soybean production. Together, this study further elucidates how plants respond to low P stress by modifying root structure and provides insight into the great potential of GmEIL4 in crop P-efficient breeding.

Identification of genomic regions associated with the plasticity of carbon 13 ratio in soybean.

Improving water use efficiency (WUE) for soybean [Glycine max (L.) Merr.] through selection for high carbon isotope (C13) ratio may increase drought tolerance, but increased WUE may limit growth in productive environments. An ideal genotype would be plastic for C13 ratio; that is, be able to alter C13 ratio in response to the environment. Our objective was to identify genomic regions associated with C13 ratio plasticity, C13 ratio stability, and overall C13 ratio in two panels of diverse Maturity Group IV soybean accessions. A second objective was to identify accessions that differed in their C13 ratio plasticity. Panel 1 (205 accessions) was evaluated in seven irrigated and four drought environments, and Panel 2 (373 accessions) was evaluated in four environments. Plasticity was quantified as the slope from regressing C13 ratio of individual genotypes against an environmental index calculated based on the mean within and across environments. The regression intercept was considered a measure of C13 ratio over all environments, and the root mean square error was considered a measure of stability. Combined over both panels, genome-wide association mapping (GWAM) identified 19 single nucleotide polymorphisms (SNPs) for plasticity, 39 SNPs for C13 ratio, and 16 SNPs for stability. Among these SNPs, 71 candidate genes had annotations associated with transpiration or water conservation and transport, root development, root hair elongation, and stomatal complex morphogenesis. The genomic regions associated with plasticity and stability identified in the current study will be a useful resource for implementing genomic selection for improving drought tolerance in soybean.

The prevalence of deleterious mutations during the domestication and improvement of soybean

Soybean (Glycine max L.) is a protein and oil crop grown worldwide. Its fitness may be reduced by deleterious mutations, whose identification and purging is desirable for crop breeding. In the published whole-genome re-sequenced data of 2214 soybean accessions, including 221 wild soybean, 1132 landrace cultivars and 861 improved soybean lines, we identified 115,275 deleterious single-nucleotide polymorphisms (SNPs). Numbers of deleterious alleles increased from wild soybeans to landraces and decreased from landraces to modern improved lines. Genes in selective-sweep regions showed fewer deleterious mutations than the remaining genes. Deleterious mutations explained 4.3%–48% more phenotypic variation than randomly selected SNPs for resistance to soybean cyst nematode race 2 (SCN2), soybean cyst nematode race 3 (SCN3) and soybean mosaic virus race 3 (SMV3). These findings illustrate how mutation load has shifted during soybean domestication, expansion and improvement and provide candidate sites for breeding out deleterious mutations in soybean by genome editing and/or conventional breeding focused on the selection of progeny with fewer deleterious alleles.