The Leafminers, representing a diverse group of insects from various genera within the Agromyzidae family, pose a significant threat to spinach (Spinacia oleracea L.) production. This study aimed to identify single nucleotide polymorphism (SNP) markers associated with leafminer resistance through a genome-wide association study (GWAS) and to evaluate the prediction accuracy (PA) for selecting resistant spinach using genomic prediction (GP). Using a dataset of 84301 SNPs obtained from whole-genome resequencing, seven GWAS models, including BLINK, FarmCPU, MLM, and MLMM in GAPIT 3, as well as MLM, GLM, and SMR in TASSEL 5, were employed to perform GWAS on a panel of 286 USDA spinach germplasm accessions. Three SNP markers, namely 1_115279256_C_T, 3_157082529_C_T, and 4_168510908_T_G on chromosomes 1, 3, and 4, respectively, were identified as associated with leafminer resistance. In the 30 kb flanking regions of these markers, four candidate genes (SOV1g031330, SOV1g031340, SOV4g047270, and SOV4g047280), encoding LOB domain-containing protein, KH domain-containing protein, were discovered. Nodulin-like domain-containing protein, and SAM domain-containing protein, were discovered. The PA for leafminer resistance selection was estimated using ten different SNP sets, including two GWAS-derived marker sets (three and 51 SNPs) and eight random marker sets (ranging from 51 to 10 K SNPs) analyzed by seven GP models. The findings emphasized the superior performance of GWAS-derived SNP sets, reaching a PA of up to 0.79 using the cBLUP model. Notably, this research marks the pioneering application of GP in the context of insect resistance, providing a significant advancement in the understanding and management of leafminer resistance in spinach cultivation.

Oxalate content in spinach is a key trait of interest due to its relevance to human health. Understanding the genetic basis of it can facilitate the development of spinach varieties with reduced oxalate levels. In pursuit of understanding the genetic determinants, a diverse panel comprising 288 spinach accessions underwent thorough phenotyping of oxalate content and were subjected to whole-genome resequencing, resulting in a comprehensive dataset encompassing 14386 single-nucleotide polymorphisms (SNPs). Leveraging this dataset, we conducted a genome-wide association study (GWAS) to identify noteworthy SNPs associated with oxalate content. Furthermore, we employed genomic prediction (GP) via cross-prediction, utilizing five GP models, to assess genomic estimated breeding values (GEBVs) for oxalate content. The observed normal distribution and the wide range of oxalate content, exceeding 600.0 mg · 100 g⁻¹, underscore the complex and quantitative nature of this trait, likely influenced by multiple genes. Additionally, our analysis revealed distinct stratification, delineating the population into four discernible subpopulations. Furthermore, GWAS analysis employing five models in GAPIT 3 and TASSEL 5 unveiled nine significant SNPs (four SNPs on chromosome 1 and five on chromosome 5) associated with oxalate content. These loci exhibited associations with six candidate genes, which might have potential contribution to oxalate content regulation. Remarkably, our GP models exhibited notable predictive abilities, yielding average accuracies of up to 0.51 for GEBV estimation. The integration of GWAS and GP approaches offers a holistic comprehension of the genetic underpinnings of oxalate content in spinach. These findings offered a promising avenue for the development of spinach cultivars and hybrids optimized for oxalate levels, promoting consumer health.