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<p>Vitamin C (VC), also known as ascorbic acid and ascorbate, is a water-soluble antioxidant in plants that promotes skin health and immune function in humans. Spinach (<i>Spinacia oleracea</i> L.) is a leafy green widely consumed for its health benefits. Recent reports have shown that nutritional content, including VC, can be improved in spinach. However, due to its complex inheritance, new selection methods are needed to improve selection for cultivar development. In this study, single nucleotide polymorphism (SNP) markers identified by genome-wide association (GWAS) were used for genomic prediction (GP) to improve prediction accuracy (PA) for VC content in spinach. A set of 147,977 SNPs generated from whole genome resequencing was used for GWAS in a panel of 347 spinach genotypes by six GWAS models. Sixty-two SNP markers distributed on all six spinach chromosomes were associated with VC content. PA for the selection of VC content was estimated with fourteen random SNP sets across seven GP models. The results indicated that the PA can be &gt; 40% after using 1,000 or more SNPs in six of the seven models tested; using GWAS-derived significant SNP markers PA increases to a high <i>r</i>-value up to 0.7 when using 62 associated SNP markers in Bayes ridge regression (BRR) model. Upon validation, identified accessions with high VC and high PA genomic selection model can be used in spinach breeding programs to develop high VC content cultivars.</p>

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Improving genomic prediction of vitamin C content in spinach using GWAS-derived markers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月21日

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  • Jana Jeevan Rameneni,
  • A S M Faridul Islam,
  • Carlos A. Avila,
  • Ainong Shi

摘要

Vitamin C (VC), also known as ascorbic acid and ascorbate, is a water-soluble antioxidant in plants that promotes skin health and immune function in humans. Spinach (Spinacia oleracea L.) is a leafy green widely consumed for its health benefits. Recent reports have shown that nutritional content, including VC, can be improved in spinach. However, due to its complex inheritance, new selection methods are needed to improve selection for cultivar development. In this study, single nucleotide polymorphism (SNP) markers identified by genome-wide association (GWAS) were used for genomic prediction (GP) to improve prediction accuracy (PA) for VC content in spinach. A set of 147,977 SNPs generated from whole genome resequencing was used for GWAS in a panel of 347 spinach genotypes by six GWAS models. Sixty-two SNP markers distributed on all six spinach chromosomes were associated with VC content. PA for the selection of VC content was estimated with fourteen random SNP sets across seven GP models. The results indicated that the PA can be > 40% after using 1,000 or more SNPs in six of the seven models tested; using GWAS-derived significant SNP markers PA increases to a high r-value up to 0.7 when using 62 associated SNP markers in Bayes ridge regression (BRR) model. Upon validation, identified accessions with high VC and high PA genomic selection model can be used in spinach breeding programs to develop high VC content cultivars.