Seed sugar composition, mainly including fructose, glucose, sucrose, raffinose, and stachyose, is an important indicator of soybean [Glycine max (L.) Merr.] seed quality. However, research on soybean sugar composition is limited. To better understand the genetic architecture underlying the sugar composition in soybean seeds, we conducted a genome-wide association study (GWAS) using a population of 323 soybean germplasm accessions which were grown and evaluated under three different environments. A total of 31,245 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected and used in the GWAS. The analysis identified 72 quantitative trait loci (QTLs) associated with individual sugars and 14 with total sugar. Ten candidate genes within the 100 Kb flanking regions of the lead SNPs across six chromosomes were significantly associated with sugar contents. According to GO and KEGG classification, eight genes were involved in the sugar metabolism in soybean and showed similar functions in Arabidopsis. The other two, located in known QTL regions associated with sugar composition, may play a role in sugar metabolism in soybean. This study advances our understanding of the genetic basis of soybean sugar composition and facilitates the identification of genes controlling this trait. The identified candidate genes will help improve seed sugar composition in soybean.

The breeding of high-yield varieties is a core objective of soybean breeding programs, and phenotypic trait-based selection offers an effective pathway to achieve this goal. The aim of this study was to identify the key phenotypic traits of high-yield soybean varieties and to utilize these traits for screening high-yield soybean varieties. In this study, the UAV (unmanned aerial vehicle)- and field-based phenotypic data were collected from 1923 and 1015 soybean breeding plots at the Xuzhou experimental site in 2022 and 2023, respectively. First, the soybean varieties were grouped by using a self-organizing map and K-means clustering to investigate the relationships between various traits and soybean yield and to identify the key ones for selecting high-yield soybean varieties. It was shown that the duration of canopy coverage remaining above 90% (Tcc90) was a critical phenotypic trait for selecting high-yield varieties. Moreover, high-yield soybean varieties typically exhibited several key phenotypic traits such as rapid development of canopy coverage (Tcc90r, the time when canopy coverage first reached 90%), prolonged duration of high canopy coverage (Tcc90), a delayed decline in canopy coverage (Tcc90d, the time when canopy coverage began to decline below 90%), and moderate-to-high plant height (PH) and hundred-grain weight (HGW). Based on these findings, a method for screening high-yield soybean varieties was proposed, through which 87% and 72% of high-yield varieties (top 5%) in 2022 and 2023, respectively, were successfully selected. Additionally, about 9% (in 2022) and 10% (in 2023) of the low-yielding (bottom 60%) were misclassified as high-yielding. This study demonstrates the benefit of high-throughput phenotyping for soybean yield-related traits and variety screening and provides helpful insights into identifying high-yield soybean varieties in breeding programs.

Shade tolerance is a key trait for cultivars in inter/relay-cropped soybeans in maize fields. Our previous genome-wide association study (GWAS) results on southern China soybean germplasm revealed that the shade tolerance was conferred by a complex of genes with multiple alleles. To complete our understanding of the shade tolerance gene system, GWAS with gene–allele sequences as markers (designated GASM-RTM-GWAS) was conducted in a recombinant inbred line (RIL) population between two extreme parents using the shade tolerance index (STI) and relative pith cell length (RCL) as indicators. Altogether, 211 genes, comprising 99 and 119 genes (seven shared) for STI and RCL, respectively, were identified and then annotated into a similar set of five biological categories. Furthermore, transcriptome analysis detected 7837 differentially expressed genes (DEGs), indicating plentiful DEGs involved in the expression of regulatory/causal GWAS genes. Protein–protein interaction (PPI) analysis and gene functional analysis for both GWAS genes and DEGs showed a group of interrelated causal genes and a group of interrelated DEGs; the former were included in the latter and their functions were interconnected as a gene network. For further understanding of the response of soybean to shade stress in a sequential connection, six chronological gene modules were grouped as signal activation and transport, signal-transduction, signal amplification, gene expression, regulated metabolites, and material transport. From the modules, 12 key genes were selected as entry points for further analysis. Our study provides an overview of the shade tolerance gene network as a new insight into a complex-trait genetic system, rather than the usual way of starting from a hand-picked single gene.