Singer William M, Shea Zachary, Yu Dajun, Huang Haibo, Mian M A Rouf, Shang Chao, Rosso Maria L, Song Qijan J, Zhang Bo
School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States.
Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States.
Front Plant Sci. 2022 Apr 25;13:859109. doi: 10.3389/fpls.2022.859109. eCollection 2022.
Soybean [] seeds have an amino acid profile that provides excellent viability as a food and feed protein source. However, low concentrations of an essential amino acid, methionine, limit the nutritional utility of soybean protein. The objectives of this study were to identify genomic associations and evaluate the potential for genomic selection (GS) for methionine content in soybean seeds. We performed a genome-wide association study (GWAS) that utilized 311 soybean accessions from maturity groups IV and V grown in three locations in 2018 and 2019. A total of 35,570 single nucleotide polymorphisms (SNPs) were used to identify genomic associations with proteinogenic methionine content that was quantified by high-performance liquid chromatography (HPLC). Across four environments, 23 novel SNPs were identified as being associated with methionine content. The strongest associations were found on chromosomes 3 (ss715586112, ss715586120, ss715586126, ss715586203, and ss715586204), 8 (ss715599541 and ss715599547) and 16 (ss715625009). Several gene models were recognized within proximity to these SNPs, such as a leucine-rich repeat protein kinase and a serine/threonine protein kinase. Identification of these linked SNPs should help soybean breeders to improve protein quality in soybean seeds. GS was evaluated using k-fold cross validation within each environment with two SNP sets, the complete 35,570 set and a subset of 248 SNPs determined to be associated with methionine through GWAS. Average prediction accuracy ( ) was highest using the SNP subset ranging from 0.45 to 0.62, which was a significant improvement from the complete set accuracy that ranged from 0.03 to 0.27. This indicated that GS utilizing a significant subset of SNPs may be a viable tool for soybean breeders seeking to improve methionine content.
大豆种子具有氨基酸谱,作为食品和饲料蛋白质来源具有出色的适用性。然而,必需氨基酸甲硫氨酸的低浓度限制了大豆蛋白的营养效用。本研究的目的是确定基因组关联,并评估大豆种子中甲硫氨酸含量的基因组选择(GS)潜力。我们进行了一项全基因组关联研究(GWAS),该研究利用了2018年和2019年在三个地点种植的来自IV和V成熟组的311份大豆种质。总共35570个单核苷酸多态性(SNP)用于鉴定与通过高效液相色谱(HPLC)定量的蛋白质ogenic甲硫氨酸含量的基因组关联。在四个环境中,23个新的SNP被鉴定为与甲硫氨酸含量相关。最强的关联出现在3号染色体(ss715586112、ss715586120、ss715586126、ss715586203和ss715586204)、8号染色体(ss715599541和ss715599547)和16号染色体(ss715625009)上。在这些SNP附近识别出了几个基因模型,如富含亮氨酸的重复蛋白激酶和丝氨酸/苏氨酸蛋白激酶。这些连锁SNP的鉴定应有助于大豆育种者提高大豆种子的蛋白质质量。使用两组SNP在每个环境中通过k折交叉验证评估GS,一组是完整的35570个SNP,另一组是通过GWAS确定与甲硫氨酸相关的248个SNP的子集。使用SNP子集时平均预测准确性()最高,范围为0.45至0.62,这比完整集准确性(范围为0.03至0.27)有显著提高。这表明利用SNP的重要子集进行GS可能是寻求提高甲硫氨酸含量的大豆育种者的可行工具。