Development of a Medium-Density Genotyping Platform to Accelerate Genetic Gain in Fresh Edible Maize

Genotyping is a key step in molecular breeding. Due to its cost-effectiveness, accuracy, and flexibility, genotyping by target sequencing (GBTS) has become a preferred technology for medium-density genotyping. In this study, a new GBTS array for fresh edible maize was developed using resequencing data from 477 lines. The array contains 5759 SNPs evenly distributed across the maize genome, with average minor allele frequency (MAF) and polymorphism information content (PIC) values of 0.40 and 0.36, respectively. These SNPs are closely associated with 1566 functional genes. Cluster analysis of 198 maize lines based on the GBTS array was consistent with their pedigree relationships. Furthermore, 277 fresh waxy maize lines were genotyped and used for genomic selection analyses of hundred-kernel weight, kernel length, and kernel width. Comparative evaluation of different models indicated that Ridge Regression Best Linear Unbiased Prediction (rrBLUP) was the optimal model, with prediction accuracies of 0.33, 0.64, and 0.36, respectively. Additional analyses using different marker densities based on the rrBLUP model showed that prediction accuracy did not increase when the number of markers exceeded 2000, indicating that this array provides sufficient marker density for genetic analysis and genomic selection. Overall, this array provides a useful tool for genetic studies of fresh edible maize and facilitates the application of genomic selection in breeding programs.