ASHS 2025 Conference

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.), a leafy green vegetable widely valued for its health benefits, has been identified as a target for nutritional enhancement, including increased VC content. However, the complex inheritance of VC necessitates advanced selection methods to accelerate cultivar development. In this study, VC- associated single nucleotide polymorphism (SNP) markers identified through genome-wide association (GWAS) were employed for genomic prediction (GP) to estimate prediction accuracy (PA) for VC content in spinach. A dataset of 147,977 SNPs generated from whole genome resequencing was analyzed in a panel of 347 spinach genotypes using six GWAS models. Sixty-two SNP markers, distributed across six spinach chromosomes, were significantly associated with VC content. PA was assessed using randomly selected SNP sets and GWAS-derived SNP marker sets across various GP models. Results demonstrated that PA exceeded 40% when using 1,000 or more SNPs. Furthermore, incorporating GWAS-derived SNP markers improved PA, achieving a r-value greater 0.70 through Bayes ridge regression (BRR) model. This study highlights the potential of GWAS-derived SNP markers for marker-assisted selection (MAS) and genomic selection (GS) in spinach breeding programs aimed at enhancing VC content. Keywords: Genomic Selection, Genome-Wide Association Study, Spinach, SNP, Prediction Accuracy