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Updated: May 31, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
Published on: June 17, 2012
Identifying Candidate Genes Related to Soybean (Glycine max) Seed Coat Color via RNA-Seq and Coexpression Network
Cheng Wang1, Pingchun Fu2, Tingting Sun1
1College of Plant Science and Technology, Beijing Key Laboratory of New Agricultural Technology in Agriculture Application, National Demonstration Center for Experimental Plant Production Education, Beijing University of Agriculture, Beijing 102206, China.
Background:
The quality of soybeans is reflected in the seed coat color, which indicates soybean quality and commercial value. Researchers have identified genes related to seed coat color in various plants. However, research on the regulation of genes related to seed coat color in soybeans is rare.
Methods:
In this study, four lines of seed coats with different colors (medium yellow 14, black, green, and brown) were selected from the F2:5 population, with Beinong 108 as the female parent and green bean as the male parent, and the dynamic changes in the anthocyanins in the seed coat were stained with 4-dimethylaminocinnamaldehyde (DMACA) during the grain maturation process (20 days from grain drum to seed harvest). Through RNA-seq of soybean lines with four different colored seed coats at 30 and 50 days after seeding, we can further understand the key pathways and gene regulation modules between soybean seed coats of different colors.
Results:
DMACA revealed that black seed coat soybeans produce anthocyanins first and have the deepest staining. Clustering and principal component analysis (PCA) of the RNA-seq data divided the eight samples into two groups, resulting in 16,456 DEGs, including 5359 TFs. GO and KEGG enrichment analyses revealed that the flavonoid biosynthesis, starch and sucrose metabolism, carotenoid biosynthesis, and circadian rhythm pathways were significantly enriched. We also conducted statistical and expression pattern analyses on the differentially expressed transcription factors. Based on weighted gene coexpression network analysis (WGCNA), we identified seven specific modules that were significantly related to the four soybean lines with different seed coat colors. The connectivity and functional annotation of genes within the modules were calculated, and 21 candidate genes related to soybean seed coat color were identified, including six transcription factor (TF) genes and three flavonoid pathway genes.
Conclusions:
These findings provide a theoretical basis for an in-depth understanding of the molecular mechanisms underlying differences in soybean seed coat color and provide new genetic resources.
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