The rapid development of high-throughput sequencing, bioinformatics, and genotyping technologies has opened new avenues for the precise identification of crop varieties. In this study, based on 780,000 initially mined multi-allelic variant SNPs from the maize whole genome, a novel set of 40 tri-allelic SNP markers was evaluated and identified using bioinformatics approaches, statistical analysis, targeted sequencing validation, and optimal genetic algorithms. These 40 SNPs are evenly distributed across the 10 maize chromosome pairs, with four markers per chromosome. A comprehensive evaluation and comparison of the 40 SNPs were conducted using genotype data from 200 hybrids and 270 inbred lines. The results showed that: (1) For both hybrids and inbred lines, the PIC values of all loci exceeded the maximum value of 0.375 typical for bi-allelic SNP loci, with average values of 0.470 and 0.480, and average discrimination power (DP) values of 0.68 and 0.57, respectively. (2) Pairwise comparisons using the 40 tri-allelic and 96 bi-allelic SNP markers revealed that the percentage of differential loci among hybrids mainly ranged from 50% to 85% and 50% to 70%, respectively; for inbred lines, the ranges were 35% to 75% and 35% to 60%, respectively. The average percentage of differential loci among varieties increased by 10% for tri-allelic SNPs compared to bi-allelic SNPs. (3) For both hybrids and inbred lines, the average frequencies of the first, second, and third alleles at the 40 tri-allelic loci were 0.58 and 0.57, 0.31, and 0.11 and 0.12, respectively. (4) Clustering analysis showed that the 40 loci effectively classified the 270 inbred lines into 10 heterotic groups, consistent with classifications based on high-density SNP chip data. In summary, this study reports a high-quality, highly discriminative core set of tri-allelic SNP markers, providing a novel marker type for maize variety identification. With their enhanced ability to resolve genetic variation, these markers are expected to complement existing marker systems and contribute to the advancement of molecular identification technologies in crop breeding.
Tian et al. (Fri,) studied this question.