Identification of Loci and Candidate Genes Associated with Arginine Content in Soybean

文献类型： 外文期刊

第一作者： Ma, Jiahao

作者： Ma, Jiahao;Yang, Qing;Yu, Cuihong;Liu, Zhi;Shi, Xiaolei;Wu, Xintong;Xu, Rongqing;Shen, Pengshuo;Yan, Long;Ma, Jiahao;Xu, Rongqing;Shen, Pengshuo;Zhang, Yuechen;Yu, Cuihong;Shi, Ainong

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关键词： Glycine max; soybean; arginine; GWAS; genomic prediction; SNP

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

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年卷期： 2025 年 15 卷 6 期

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收录情况： SCI

摘要： Soybean (Glycine max) seeds are rich in amino acids, offering key nutritional and physiological benefits. In this study, 290 soybean accessions from the USDA Germplasm Collection based in Urbana, IL Information Network (GRIN) were analyzed. Four Genome-Wide Association Study (GWAS) models-Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), Mixed Linear Model (MLM), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Multi-Locus Mixed Model (MLMM)-identified two significant Single Nucleotide Polymorphisms (SNPs) associated with arginine content: Gm06_19014194_ss715593808 (LOD = 9.91, 3.91% variation) at 19,014,194 bp on chromosome 6 and Gm11_2054710_ss715609614 (LOD = 9.05, 19% variation) at 2,054,710 bp on chromosome 11. Two candidate genes, Glyma.06g203200 and Glyma.11G028600, were found in the two SNP marker regions, respectively. Genomic Prediction (GP) was performed for arginine content using several models: Bayes A (BA), Bayes B (BB), Bayesian LASSO (BL), Bayesian Ridge Regression (BRR), Ridge Regression Best Linear Unbiased Prediction (rrBLUP), Random Forest (RF), and Support Vector Machine (SVM). A high GP accuracy was observed in both across- and cross-populations, supporting Genomic Selection (GS) for breeding high-arginine soybean cultivars. This study holds significant commercial potential by providing valuable genetic resources and molecular tools for improving the nutritional quality and market value of soybean cultivars. Through the identification of SNP markers associated with high arginine content and the demonstration of high prediction accuracy using genomic selection, this research supports the development of soybean accessions with enhanced protein profiles. These advancements can better meet the demands of health-conscious consumers and serve high-value food and feed markets.

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