As an essential crop that provides vegetable oil and protein, soybean (Glycine max (L.) Merr.) is widely planted all over the world. However, the scarcity of water resources worldwide has seriously impacted on the quality and yield of soybean. To address this, exploring excellent genes for improving drought resistance in soybean is crucial. In this study, we identified natural variations of GmFNSII-2 (flavone synthase Ⅱ) significantly affect the drought resistance of soybeans. Through sequence analysis of GmFNSII-2 in 632 cultivated and 44 wild soybeans nine haplotypes were identified. The full-length allele GmFNSII-2^C, but not the truncated allele GmFNSII-2^A possessing a nonsense nucleotide variation, increased enzyme activity. Further research found that GmDREB3, known to increase soybean drought resistance, bound to the promoter region of GmFNSII-2^C. GmDREB3 positively regulated the expression of GmFNSII-2^C, increased flavone synthase abundance and improved the drought resistance. Furthermore, a single-base mutation in the GmFNSII-2^C promoter generated an additional drought response element (CCCCT), which had stronger interaction strength with GmDREB3 and increased its transcriptional activity under drought conditions. The frequency of drought-resistant soybean varieties with Hap 1 (Pro:GmFNSII-2^C) has increased, suggesting that this haplotype may be selected during soybean breeding. In summary, GmFNSII-2^C could be used for molecular breeding of drought-tolerant soybean.

Automatic collecting of phenotypic information from plants has become a trend in breeding and smart agriculture. Targeting mature soybean plants at the harvesting stage, which are dense and overlapping, we have proposed the SPP-extractor (soybean plant phenotype extractor) algorithm to acquire phenotypic traits. First, to address the mutual occultation of pods, we augmented the standard YOLOv5s model for target detection with an additional attention mechanism. The resulting model could accurately identify pods and stems and could count the entire pod set of a plant in a single scan. Second, considering that mature branches are usually bent and covered with pods, we designed a branch recognition and measurement module combining image processing, target detection, semantic segmentation, and heuristic search. Experimental results on real plants showed that SPP-extractor achieved respective R² scores of 0.93–0.99 for four phenotypic traits, based on regression on manual measurements.

Plant architecture is a target of crop improvement. The soybean mutant ideal type 1 (it1) displays a pleiotropic phenotype characterized by compact plant architecture, reduced plant height, shortened petioles, wrinkled leaves, and indented seeds. Genetic analysis revealed that the pleiotropic phenotype was controlled by an incomplete dominant gene. We characterized the cellular phenotypes of it1 and positionally cloned the it1 locus. Detailed morphogenetic analysis of the it1 mutant revealed an excess of xylem cells and expanded phloem, and polygonal pavement cells. Positional cloning showed that the phenotype was caused by a G-to-A mutation in the second exon of the α-tubulin gene (Glyma.05G157300). The mutation altered microtubule arrangement in pavement cells, changing their morphology. Overexpression of Gmit1 resulted in an it1-like phenotype and polygonal pavement cells and microtubules of overexpressors were parallel or slightly inclined. Five suppressor mutants able to suppress the phenotype of it1 were obtained by EMS mutagenesis in the it1 background. All these mutants carried an additional mutation in the it1 gene. These results suggest that the pleiotropic phenotype of it1 is caused by the mutation in the α-tubulin gene.

A biparental soybean population of 364 recombinant inbred lines (RILs) derived from Zhongdou 41 × ZYD 02.878 was used to identify quantitative trait loci (QTL) associated with hundred-seed weight (100-SW), pod length (PL), and pod width (PW). 100-SW, PL, and PW showed moderate correlations among one another, and 100-SW was correlated most strongly with PW (0.64–0.74). Respectively 74, 70, 75 and 19 QTL accounting for 38.7%–78.8% of total phenotypic variance were identified by inclusive composite interval mapping, restricted two-stage multi-locus genome-wide association analysis, 3 variance-component multi-locus random-SNP-effect mixed linear model analysis, and conditional genome-wide association analysis. Of these QTL, 189 were novel, and 24 were detected by multiple methods. Six loci were associated with 100-SW, PL, and PW and may be pleiotropic loci. A total of 284 candidate genes were identified in colocalizing QTL regions, including the verified gene Seed thickness 1 (ST1). Eleven genes with functions involved in pectin biosynthesis, phytohormone, ubiquitin-protein, and photosynthesis pathways were prioritized by examining single nucleotide polymorphism (SNP) variation, calculating genetic differentiation index, and inquiring gene expression. The prediction accuracies of genomic selection (GS) for 100-SW, PL, and PW based on single trait-associated markers reached 0.82, 0.76, and 0.86 respectively, but selection index (SI)-assisted GS strategy did not increase GS efficiency and inclusion of trait-associated markers as fixed effects reduced prediction accuracy. These results shed light on the genetic basis of 100-SW, PL, and PW and provide GS models for these traits with potential application in breeding programs.

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.

Soluble sugar is a key quality trait of soybean seeds. We developed rapid and economic extraction and quantification methods for seed soluble sugars using an ultra-high performance liquid chromatography system with a refractive index detector. We evaluated the soluble sugar compositions of 1164 soybean accessions collected from diverse ecoregions and grown in multiple locations and years. Total soluble sugar (TSS) content was influenced by accession type, year of cultivation, and ecoregion. The mean contents of fructose, glucose, sucrose, raffinose, stachyose and TSS were 3.31, 5.21, 55.60, 6.60, 35.47, and 106.19 mg g⁻¹, respectively. The highest mean TSS content (108.71 mg g⁻¹) was observed in accessions from Northern Region of China. Cultivars contained higher contents of sucrose, raffinose, and TSS, whereas landraces had a higher content of stachyose. Fourteen accessions with mean TSS contents > 130 mg g⁻¹ were identified as elite soybean resources. TSS was correlated with sucrose, raffinose, stachyose, protein, oil and total tocopherol. The main soluble sugar components were correlated with latitude and longitude, indicating that the geographical origin of the accessions affected their seed soluble sugar compositions. The developed methods and elite identified accessions can be used in the food and feed industry and in soybean breeding programs aimed at improving soybean seed nutrition.

Drought is one of the primary abiotic stress factors affecting the yield, growth, and development of soybeans. In extreme cases, drought can reduce yield by more than 50%. The seedling stage is an important determinant of soybean growth: the number and vigor of seedlings will affect growth and yield at harvest. Therefore, it is important to study the drought resistance of soybean seedlings. In this study, a recombinant inbred line (RIL) population comprising 234 F_6:10 lines (derived from Zhonghuang 35 × Jindou 21) and a panel of 259 soybean accessions was subjected to drought conditions to identify the effects on phenotypic traits under these conditions. Using a genetic map constructed by single nucleotide polymorphism (SNPs) markers, 18 quantitative trait loci (QTL) on 7 soybean chromosomes were identified in two environments. This included 9 QTL clusters identified in the RIL population. Fifty-three QTL were identified in 19 soybean chromosomes by genome-wide association analysis (GWAS) in the panel of accessions, including 69 significant SNPs (−log₁₀ (P) ≥ 3.97). A combination of the two populations revealed that two SNPs (−log₁₀ (P) ≥ 3.0) fell within two of the QTL (qPH7-4 and qPH7-6) confidence intervals. We not only re-located several previously reported drought-resistance genes in soybean and other crops but also identified several non-synonymous stress-related mutation site differences between the two parents, involving Glyma.07g093000, Glyma.07g093200, Glyma.07g094100 and Glyma.07g094200. One previously unreported new gene related to drought stress, Glyma.07g094200, was found by regional association analysis. The significant SNP CHR7-17619 (G/T) was within an exon of the Glyma.07g094200 gene. In the RIL population, the DSP value of the “T” allele of CHR7-17619 was significantly (P < 0.05) larger than the “G” allele in different environments. The results of our study lay the groundwork for cloning and molecular marker-assisted selection of drought-resistance genes in soybeans at the seedling stage.

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is one of the most economically destructive pathogens. The soybean line Zhongpin03-5373 (ZP), which combines resistance genes from several donors, is highly resistant to SCN race 3 (SCN3). In our previous study, two QTL (rhg1 and GmSNAP11) were identified in a population of recombinant inbred lines derived from a cross between ZP and the susceptible parent Zhonghuang 13. The two QTL explained around one-third of the resistance, suggesting the presence of further QTL contributing to SCN resistance. In the present study, we used an improved version of the genetic map comprising the previously applied 1062 molecular markers and 47 newly developed InDel (insertion-deletion) markers. The improved map revealed a novel locus contributing to SCN3 resistance: qSCN3-1, flanked by InDel marker InDel1-7 and SNP marker Map-0047, explained 4.55% of the phenotypic variance for resistance to SCN3 and was not involved in digenic epistatic interaction with rhg1 and GmSNAP11. Haplotypes of Map-0047_CAPS (a CAPS marker developed for Map-0047) and InDel1-7 were significantly associated with SCN3 resistance in a panel of 209 resistant and susceptible accessions. Using further allele-combination analysis for three functional markers representing three cloned resistance genes (rhg1, Rhg4, and GmSNAP11) and two markers flanking qSCN3-1, we found that adding the resistance allele of qSCN3-1 greatly increased soybean resistance to SCN, even in diverse genetic backgrounds. The qSCN3-1 locus will be useful for marker-assisted polygene pyramid breeding and should be targeted for the future identification of candidate genes.

Drought stress is an important factor affecting soybean yield. Improving drought tolerance of soybean varieties can increase yield and yield stability when the stress occurs. Identifying QTL related to drought tolerance using molecular marker-assisted selection is able to facilitate the development of drought-tolerant soybean varieties. In this study, we used a high-yielding and drought-sensitive cultivar ‘Zhonghuang 35’ and a drought-tolerant cultivar ‘Jindou 21’ to establish F_6:9 recombinant inbred lines. We constructed a high-density genetic map using specific locus amplified fragment sequencing (SLAF-Seq) technology. The genetic map contained 8078 SLAF markers distributing across 20 soybean chromosomes with a total genetic distance of 3780.98 cM and an average genetic distance of 0.59 cM between adjacent markers. Two treatments (irrigation and drought) were used in the field tests, the Additive-Inclusive Composite Interval Mapping (ICIM-ADD) was used to call QTL, and plant height and seed weight per plant were used as the indicators of drought tolerance. We identified a total of 23 QTL related to drought tolerance. Among them, seven QTL (qPH2, qPH6, qPH7, qPH17, qPH19-1, qPH19-2, and qPH19-3) on chromosomes 2, 6, 7, 17, and 19 were related to plant height, and five QTL (qSWPP2, qSWPP6, qSWPP13, qSWPP17, and qSWPP19) on chromosomes 2, 6, 13, 17, and 19 were related to seed weight and could be considered as the major QTL. In addition, three common QTL (qPH6/qSWPP6, qPH17/qSWPP17, and qPH19-3/qSWPP19) for both plant height and seed weight per plant were located in the same genomic regions on the same chromosomes. Three (qPH2, qPH17, and qPH19-2) and four novel QTL (qSWPP2, qSWPP13, qSWPP17, and qSWPP19) were identified for plant height and seed weight per plant, respectively. Two pairs of QTL (qPH2/qSWPP2 and qPH17/qSWPP17) were also common for both plant height and seed weight per plant. These QTL and closely linked SLAF markers could be used to accelerate breeding for drought tolerant cultivars via MAS.

Branch number (BN) is an important agronomic attribute related to the plant architecture, adaptability, and yield of soybean. To date, few studies of BN have been conducted to elucidate its genetic background. We aimed to localize genetic factors affecting BN using segregating populations derived from the high-branching cultivar ‘Kennong24’ (KN24) and the low-branching cultivar ‘Kenfeng19’ (KF19). Composite interval mapping analysis detected a QTL (qBN-1) on chromosome 6 between the SSR markers BARCSOYSSR_06_0993 and BARCSOYSSR_06_1070 using an F₂ population. To fine-map qBN-1, a RIL population was developed and genotyped with 14 SSR markers located in the QTL region. qBN-1 was localized to a 115.67-kb interval flanked by markers BARCSOYSSR_06_1048 and BARCSOYSSR_06_1053. The QTL was further confirmed using backcross populations of size 1305 (BC₂F₂ with KN24 as a recurrent parent) and 1712 (BC₃F₂ with KF19 as a recurrent parent). The fine-mapping region of qBN-1 contained only two candidate genes, Glyma.06G208800 and Glyma.06G208900, whose expression patterns were investigated by qRT-PCR. Compared to Glyma.06G208800 gene expression, Glyma.06G208900 showed the highest expression of the two genes and showed a significant difference in expression between high- and low-branching genotypes in either axillary meristem or shoot apical meristem, and showed opposite expression patterns in the two tissues at V4 and R1 stages. These results identify Glyma.06G208900 as a novel candidate gene controlling BN. Taken together, the results of this study provide a foundation for cloning and functional analysis of the qBN-1 gene and for the improvement of BN by marker-assisted selection in soybean breeding.