Increasing the yield of soybean (Glycine max L. Merrill) is a main aim of soybean breeding. The 100-seed weight is a critical factor for soybean yield. To facilitate genetic analysis of quantitative traits and to improve the accuracy of marker-assisted breeding in soybean, a valuable mapping population consisting of 194 chromosome segment substitution lines (CSSLs) was developed. In these lines, different chromosomal segments of the Chinese cultivar Suinong 14 were substituted into the genetic background of wild soybean (Glycine soja Sieb. & Zucc.) ZYD00006. Based on these CSSLs, a genetic map covering the full genome was generated using 121 simple sequence repeat (SSR) markers. In the quantitative trait loci (QTL) analysis, twelve main effect QTLs (qSW-B1-1/2/3, qSW-D1b-1/2, qSW-D2-1/2, qSW-G-1/2/3, qSW-M-2 and qSW-N-2) underlying 100-seed weight were identified in 2011 and 2012. The epistatic effects of pairwise interactions between markers were analyzed in 2011 and 2012. The results clearly demonstrated that these CSSLs could be used to identify QTLs, and that an epistatic analysis was able to detect several sites with important epistatic effects on 100-seed weight. Thus, we identified loci that will be valuable for improving soybean 100-seed weight. These results provide a valuable foundation for identifying the precise location of genes of interest, and for designing cloning and marker-assisted selection breeding strategies targeting the 100-seed weight of soybean.
Soybean is an important crop providing edible oil and protein source. Soybean oil and protein contents are quantitatively inherited and significantly affected by environmental factors. In this study, meta-analysis was conducted based on soybean physical maps to integrate quantitative trait loci (QTLs) from multiple experiments in different environments. Meta-QTLs for seed oil, fatty acid composition, and protein were identified. Of them, 11 meta-QTLs were located on hot regions for both seed oil and protein. Next, we selected 4 chromosome segment substitution lines with different seed oil and protein contents to characterize their 3 years of phenotype selection in the field. Using strand-specific RNA-sequencing analysis, we profile the time-course transcriptome patterns of soybean seeds at early maturity, middle maturity, and dry seed stages. Pairwise comparison and K-means clustering analysis revealed 7,482 differentially expressed genes and 45 expression patterns clusters. Weighted gene coexpression network analysis uncovered 46 modules of gene expression patterns. The 2 most significant coexpression networks were visualized, and 7 hub genes were identified that were involved in soybean oil and seed storage protein accumulation processes. Our results provided a transcriptome dataset for soybean seed development, and the candidate hub genes represent a foundation for further research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.