At present, the application of CRISPR/Cas9 in soybean (Glycine max (L.) Merr.) has been mainly focused on knocking out target genes, and most site-directed mutagenesis has occurred at single cleavage sites and resulted in short deletions and/or insertions. However, the use of multiple guide RNAs for complex genome editing, especially the deletion of large DNA fragments in soybean, has not been systematically explored. In this study, we employed CRISPR/Cas9 technology to specifically induce targeted deletions of DNA fragments in GmFT2a (Glyma16g26660) and GmFT5a (Glyma16g04830) in soybean using a dual-sgRNA/Cas9 design. We achieved a deletion frequency of 15.6% for target fragments ranging from 599 to 1618 bp in GmFT2a. We also achieved deletion frequencies of 12.1% for target fragments exceeding 4.5 kb in GmFT2a and 15.8% for target fragments ranging from 1069 to 1161 bp in GmFT5a. In addition, we demonstrated that these CRISPR/Cas9-induced large fragment deletions can be inherited. The T2 ‘transgene-free’ homozygous ft2a mutants with a 1618 bp deletion exhibited the late-flowering phenotype. In this study, we developed an efficient system for deleting large fragments in soybean using CRISPR/Cas9; this system could benefit future research on gene function and improve agriculture via chromosome engineering or customized genetic breeding in soybean.

Soybean is an excellent source of vegetable protein and edible oil. Understanding the genetic basis of protein and oil content will improve the breeding programs for soybean. Linkage analysis and genome-wide association study (GWAS) tools were combined to detect quantitative trait loci (QTL) that are associated with protein and oil content in soybean. Three hundred and eight recombinant inbred lines (RILs) containing 3454 single nucleotide polymorphism (SNP) markers and 200 soybean accessions, including 94,462 SNPs and indels, were applied to identify QTL intervals and significant SNP loci. Intervals on chromosomes 1, 15, and 20 were correlated with both traits, and QTL qPro15-1, qPro20-1, and qOil5-1 reproducibly correlated with large phenotypic variations. SNP loci on chromosome 20 that overlapped with qPro20-1 were reproducibly connected to both traits by GWAS (p < 10⁻⁴). Twenty-five candidate genes with putative roles in protein and/or oil metabolisms within two regions (qPro15-1, qPro20-1) were identified, and eight of these genes showed differential expressions in parent lines during late reproductive growth stages, consistent with a role in controlling protein and oil content. The new well-defined QTL should significantly improve molecular breeding programs, and the identified candidate genes may help elucidate the mechanisms of protein and oil biosynthesis.

Soybean (Glycine max (L.) Merr.) is a short day plant. Its flowering and maturity time are controlled by genetic and environmental factors, as well the interaction between the two factors. Previous studies have shown that both genetic and environmental factors, mainly photoperiod and temperature, control flowering time of soybean. Additionally, these studies have reported gene × gene and gene × environment interactions on flowering time. However, the effects of quantitative trait loci (QTL) in response to photoperiod and temperature have not been well evaluated. The objectives of the current study were to identify the effects of loci associated with flowering time under different photo-thermal conditions and to understand the effects of interaction between loci and environment on soybean flowering. Different photoperiod and temperature combinations were obtained by adjusting sowing dates (spring sowing and summer sowing) or day-length (12 h, 16 h). Association mapping was performed on 91 soybean cultivars from different maturity groups (MG000-VIII) using 172 SSR markers and 5107 SNPs from the Illumina SoySNP6K iSelectBeadChip. The effects of the interaction between QTL and environments on flowering time were also analysed using the QTXNetwork. Large-effect loci were detected on Gm 11, Gm 16 and Gm 20 as in previous reports. Most loci associated with flowering time are sensitive to photo-thermal conditions. Number of loci associated with flowering time was more under the long day (LD) than under the short day (SD) condition. The variation of flowering time among the soybean cultivars mostly resulted from the epistasis × environment and additive × environment interactions. Among the three candidate loci, i.e. Gm04_4497001 (near GmCOL3a), Gm16_30766209 (near GmFT2a and GmFT2b) and Gm19_47514601 (E3 or GmPhyA3), the Gm04_4497001 may be the key locus interacting with other loci for controlling soybean flowering time. The effects of loci associated with the flowering time of soybean were dependent upon the photo-thermal conditions. This study facilitates the understanding of the genetic mechanism of soybean flowering and molecular breeding for the improvement of soybean adaptability to specific and/or broad regions.

As a genetically modified crop, transgenic soybean occupies the largest global scale with its food, nutritional, industrial, and pharmaceutical uses.Efficient transformation is a key factor for the improvement of genetically modified soybean. At present, the Agrobacterium-mediated method is primarily used for soybean transformation, but the efficiency of this method is still relatively low (below 5%) compared with rice (above 90%). In this study, we examined the influence of l-glutamine and/or l-asparagine on Agrobacterium-mediated transformation in soybean and explored the probable role in the process of Agrobacterium-mediated transformation. The results showed that when the amino acids l-glutamine and l-asparagine were added separately or together to the culture medium, the shoot induction frequency, elongation rate, and transformation frequency were improved. The combined effects of l-glutamine and l-asparagine were better than those of l-glutamine and l-asparagine alone. The 50 mg/L l-glutamine and 50 mg/L l-asparagine together can enhance the transformation frequency of soybean by attenuating the expression level of GmPRs (GmPR1, GmPR4, GmPR5, and GmPR10) and suppression of the plant defense response. The transgene was successfully transmitted to the T1 generation. This study will be useful in genetic engineering of soybean.

Optimizing the spatial distribution of plants under normal conditions of water and fertilizer is widely used by farmers to improve soybean yield. However, the relationship between soybean yield and spatial plant distribution in the field has not been well studied. This study examined the effect of planting density and plant distribution pattern on soybean plant growth, yield components, canopy light interception, and dry matter accumulation. We also analyzed the relationship between photosynthetic rate, dry matter accumulation, and yield under different planting densities and plant distribution. A two year field experiment was conducted during the 2018 and 2019 soybean planting seasons. Two planting densities (1.8 × 10⁵ and 2.7 × 10⁵ plants ha⁻¹) and two plant distribution patterns (uniform and non-uniform plant spacing) were tested. Higher planting density significantly increased the canopy light interception and dry matter accumulation during soybean growth, leading to increased soybean productivity. The seed yield of soybean under higher planting density was 22.8% higher than under normal planting density. Soybean planted under uniform spacing significantly reduced the differences plant-to-plant. Uniform plant spacing significantly increased the canopy light interception and dry matter accumulation of the soybean population. In addition, the coefficient of variation of seed weight per plant between individual plants under uniform plant distribution decreased by 71.5% compared with non-uniform plant distribution. Furthermore, uniform plant distribution increased soybean seed yield by 9.5% over non-uniform plant distribution. This study demonstrates that increasing planting density under uniform plant distribution can be useful to obtain higher seed yield without increasing other farm inputs.

Increasing planting density is one of the key management practices to enhance soybean yield. A 2-yr field experiment was conducted in 2018 and 2019 including six planting densities and two soybean cultivars to determine the effects of planting density on branch number and yield, and analyze the contribution of branches to yield. The yield of ZZXA12938 was 4389 kg ha⁻¹, which was significantly higher than that of ZH13 (+22.4%). In combination with planting year and cultivar, the soybean yield increased significantly by 16.2%, 31.4%, 41.4%, and 46.7% for every increase in density of 45,000 plants ha⁻¹. Yield will not increase when planting density exceeds 315,000 plants ha⁻¹. A correlation analysis showed that pod number per plant increased with the increased branch number, while pod number per unit area decreased; thus, soybean yield decreased. With the increase of branch number, the branch contribution to yield increased first, and then plateaued. ZH13 could produce a high yield under a lower planting density due to more branches, while ZZXA12938 had a higher yield potential under a higher planting density due to the smaller branch number and higher tolerance to close planting. Therefore, seed yield can be increased by selecting cultivars with a little branching capacity under moderately close planting.