Soybean being a major cash crop provides half of the vegetable oil and a quarter of the plant proteins to the global population. Seed size traits are the most important agronomic traits determining the soybean yield. These are complex traits governed by polygenes with low heritability as well as are highly influenced by the environment as well as by genotype x environment interactions. Although, extensive efforts have been made to unravel the genetic basis and molecular mechanism of seed size in soybean. But most of these efforts were majorly limited to QTL identification, and only a few genes for seed size were isolated and their molecular mechanism was elucidated. Hence, elucidating the detailed molecular regulatory networks controlling seed size in soybeans has been an important area of research in soybeans from the past decades. This paper describes the current progress of genetic architecture, molecular mechanisms, and regulatory networks for seed sizes of soybeans. Additionally, the main problems and bottlenecks/challenges soybean researchers currently face in seed size research are also discussed. This review summarizes the comprehensive and systematic information to the soybean researchers regarding the molecular understanding of seed size in soybeans and will help future research work on seed size in soybeans.

Although CRISPR-Cas9 technology has been rapidly applied in soybean genetic improvement, it is difficult to achieve the targeted editing of the specific loci in the soybean complex genome due to the limitations of the classical protospacer adjacent motif (PAM). Here, we developed a PAM-less genome editing system mediated by SpRY in soybean. By performing targeted editing of representative agronomic trait targets in soybean and evaluating the results, we demonstrate that the SpRY protein can achieve efficient targeted mutagenesis at relaxed PAM sites in soybean. Furthermore, the SpRY-based cytosine base editor SpRY-hA3A and the adenine base editor SpRY-ABE8e both can accurately induce C-to-T and A-to-G conversion in soybean, respectively. Thus, our data illustrate that the SpRY toolbox can edit the soybean genomic sequence in a PAM-free manner, breaking restrictive PAM barriers in the soybean genome editing technology system. More importantly, our research enriches soybean genome editing tools, which has important practical application value for precise editing and molecular design in soybean breeding.

Alkaline stress is one of the major abiotic constraints that limits plant growth and development. However, the genetic basis underlying alkaline tolerance in soybean [Glycine max (L.) Merr.] remains largely unexplored. In this study, an integrated genomic analysis approach was employed to elucidate the genetic architecture of alkaline tolerance in a diverse panel of 326 soybean cultivars. Through association mapping, we detected 28 single nucleotide polymorphisms (SNPs) significantly associated with alkaline tolerance. By examining the genomic distances around these significant SNPs, five genomic regions were characterized as stable quantitative trait loci (QTLs), which were designated as qAT1, qAT4, qAT14, qAT18, and qAT20. These QTLs are reported here for the first time in soybean. Seventeen putative candidate genes were identified within the physical intervals of these QTLs. Haplotype analysis indicated that four of these candidate genes exhibited significant allele variation associated with alkaline tolerance-related traits, and the haplotype alleles for these four genes varied in number from two to four. The findings of this study may have important implications for soybean breeding programs aimed at enhancing alkaline tolerance.

Alkaline stress is one of the major abiotic constraints limiting the plant growth and development. The alkaline tolerance in crop plants is a complex trait regulated by many genes. The genetic basis of alkaline tolerance is largely unknown in soybean. In this study, an integrated strategy of genomic analysis was utilized to elucidate the genetic architecture of alkaline tolerance across the panel of 326 diverse soybean cultivars. Our study revealed 28 SNPs was significantly associated with alkaline tolerance through association mapping using seven GWAS models. By considering the detection of SNPs in multiple environments and GWAS models, the genomic regions of five consistent SNPs were depicted as stable QTLs viz., qAT1, qAT4, qAT14, qAT18 and qAT20. All these five alkaline tolerance QTLs are reported for the first time. By using the in silico analysis, we detected 17 genes within physical intervals of five QTLs as putative candidates. Haplotype alleles of four candidate genes showed significant differences in the regulation of alkaline tolerance-related traits varying from two to four regulating alkaline tolerance from lowest to highest through moderate levels. The outcome of the current investigation can be used in soybean breeding programs for producing cultivars with enhanced alkaline tolerance.