Developing high yielding cultivars with outstanding quality traits are perpetual objectives throughout crop breeding operations. Confoundingly, both of these breeding objectives typically involve working with complex quantitative traits that can be affected by genetic and environmental factors. Establishing correlations of these complex traits with more easily identifiable and highly heritable traits can simplify breeding processes. In this study, two parental soybean genotypes contrasting in seed hilum size, yield, and seed quality, as well as 175 F₉ recombinant inbred lines (RILs) derived from these parents, were grown in 3 years. The h²_b of four hilum size, two quality and two yield traits, ranged from 0.72 to 0.87. The four observed hilum size traits exhibited significant correlation (P < 0.05) with most of seed yield and quality traits, as indicated by correlation coefficients varying from -0.35 to 0.42, which suggests that hilum size could be considered as a proxy trait for soybean yield and quality. Interestingly, among 53 significant quantitative trait loci (QTLs) with logarithm of odds (LOD) values ranging from 2.51 to 6.69 and accounting for 6.40–16.10% of genetic variation, three loci encoding hilum size, qSH6.2, qSH8, and qSH10, colocated with QTLs for seed yield and quality traits, demonstrating that genes impacting seed hilum size colocalize in part with genes acting on soybean yield and quality. As a result of the breeding efforts and field observations described in this work, it is reasonable to conclude that optimizing hilum size through selection focused on a few QTLs may be useful for breeding new high yielding soybean varieties with favorable quality characteristics.

MYB-CC transcription factors (TFs) are essential for plant growth and development. Members of the MYB-CC subfamily with long N terminal domains, such as phosphate starvation response 1 (PHR1) or PHR1-like TFs, have well documented functions, while those with short N terminal domains remain less understood. In this study, we identified a nodule specific MYB-CC transcription factor 1 (GmPHR1) in soybean that is different from other canonical PHR family genes in that GmPHR1 harbors a short N terminal ahead of its MYB-CC domain and was highly induced by rhizobium infection. The overexpression of GmPHR1 dramatically increased the ratio of deformed root hairs, enhanced subsequent soybean nodulation, and promoted soybean growth in pot experiments. The growth promotion effects of GmPHR1 overexpression were further demonstrated in field trails in which two GmPHR1-OE lines yielded 10.78% and 8.19% more than the wild type line. Transcriptome analysis suggested that GmPHR1 overexpression led to global reprogramming, with 749 genes upregulated and 279 genes downregulated, especially for genes involved in MYB transcription factor activities, root growth, and nutrient acquisition. Taken together, we conclude that GmPHR1 is a key gene involved in the global regulation of nodulation, root growth, and nutrient acquisition in soybeans, and is thus a promising candidate gene to target for soybean yield enhancement.

Soybean (Glycine max (L.) Merr.) is an important nutritional crop, not only as a rich source of protein and oil, but also for the production of isoflavones. There is a demand to breed soybean cultivars bearing consistently high protein, oil and isoflavone yields, yet this requires a clear heritable genetic relationship among isoflavone, protein and oil production. Here, two soybean genotypes contrasting in terms of protein, oil and isoflavone contents and their 185 F_8:10 recombinant inbred lines (RILs) were employed to characterize seed protein, oil and isoflavone contents over two years of field trials. In this population, protein, but not oil, was significantly correlated with isoflavone content. A high-density genetic linkage map containing 3943 SNP markers identified through genotyping-by-sequencing (GBS) technology was constructed for further genetic analysis, whereby a total of 25 integrated isoflavone loci were identified, including qISO1, qISO 6.1, qISO 6.3 and qISO 6.4, which are newly identified QTLs. Two major QTLs identified in this study, qISO5 and qISO6.2, were fine-mapped for production of daidzein and genistein derivatives, as well as for glycitein derivatives, in the sequences between nucleotide positions 41042159 and 42098680 on chromosome 5 and between 18449510 and 19395795 on chromosome 6, which, respectively, explain 9.3–20.4% and 7.8–24.8% of the phenotypic variation in these traits. Further combination of qISO5 and qISO6.2 resulted in additive impacts on isoflavone production. Among the 13 QTLs linked with seed protein content in this study, three also colocated with QTLs for isoflavone content, indicating that seed isoflavone and protein content may be coordinately inherited. These results contribute to understanding the relationships between isoflavone and protein or oil content in soybean seeds. This knowledge could be valuable for soybean breeding programs aiming to combine consistently high isoflavone production with high protein or oil content.

Soybean (Glycine max (L.) Merr.) is an important nutritional crop with high seed protein content. Production of high protein concentrations relies on sufficient nutrient supplies, especially of nitrogen (N) and phosphorus (P). Although the genetic basis for seed quality traits has been well studied, little information exists on any genetic connections between seed quality and nutrient supplies in soybean. Here, a recombinant inbred line (RIL) population of 179 progeny was generated using HC6 and JD17 as parents contrasting in seed quality and N and P translocation efficiencies. Seed protein and N and P translocation efficiencies were higher in HC6 than in JD17. Meanwhile, positive correlations were observed between seed protein content and translocation efficiency of N and P in RILs, implying that high N and P translocation efficiencies might facilitate seed protein accumulation. A genetic map was constructed using 5250 SNP markers covering a genetic distance of 3154.83 cM. A total of 6 loci for quality and 13 loci for N and P translocation efficiency were detected. Among them, two fragments on chromosome 6 and chromosome 20 contained multiple significant markers for both quality and N and P translocation efficiencies, with the respective observed LOD values ranging from 2.98 to 5.61, and 3.01 to 11.91, while the respective PVE values ranged from 8.2% to 13.9%, and 8.3% to 28.0%. Interestingly, one significant locus on chromosome 20 appears to be the product of a transposable element (TE) InDel in Glyma.20G085100, with progeny lacking the TE also exhibiting higher N and P translocation efficiencies, along with higher seed protein contents. Taken together, these results provide genetic evidence that increasing N and P translocation efficiencies may lead to increasing protein contents in soybean seeds. Furthermore, a TE InDel may be used as a genetic marker for breeding elite soybean cultivars with high protein content and N and P translocation efficiencies.

With the construction of new power system and electricity market, the adjustable resources and market entities in the power system are constantly enriched, which puts forward higher requirements for system flexibility and market mechanism. Energy storage is an important means to improve the system regulation capacity, and low-cost, low-constraint electric heat storage technology is the best way to solve the problem of surplus renewable energy consumption and winter heating pollution. Therefore, this paper analyzes the operation characteristics of electric heat storage, proposes a method and mechanism to participate in the electricity spot market, and establishes an optimized clearing model including renewable energy consumption, electric heat storage load, cross-sectional constraint and node electricity price, on this basis, optimizes the operation mode of electric heat storage, improves the renewable energy absorption capacity, and reduces the overall operating cost and energy efficiency of the system. An example verifies the effectiveness of the proposed model and algorithm.