To strengthen the management of maize varieties and the protection of intellectual property rights to new varieties, we constructed a comprehensive single nucleotide polymorphism (SNP)-DNA standard fingerprint database of 20,075 materials covering nationally and provincially approved maize hybrid lines, hybridized combinations, and inbred lines. The database was based on 200 core SNPs selected from 60 K SNPs distributed in intragenic regions, including 106 (53.0%) located in exons. Average minor allele frequencies (MAF) of the 200 SNPs in 6755 maize hybrids, 7837 hybridized combinations, and 3478 inbred lines were 0.385, 0.350, and 0.378, respectively, with corresponding average polymorphism information content (PIC) values of 0.354, 0.335, and 0.351. Heterozygous genotype frequencies of maize hybrids, hybridized combinations, and inbred lines averaged 0.48, 0.47, and 0.012, respectively. The number of different loci in the three different maize groups ranged from one up to 164, 160, and 140, respectively. The percentage of different SNPs within 5% (the number of difference SNPs is less than 10) accounted for 0.013%, 0.011%, and 0.030% among pairwise comparisons of samples within hybrid lines, hybridized combinations and inbred lines, respectively. Genetic distances between varieties based on the 200 core SNPs were highly correlated with those obtained using 60 K SNPs, with a correlation coefficient of 0.82 and 0.87 in in inbred and hybrid lines, respectively. The maize SNP-DNA fingerprint database established in this study can play an important role in variety authentication, purity determination and the protection of variety rights, thereby providing reliable, comprehensive data support for use in the seed industry.

A DNA fingerprint database is an efficient, stable, and automated tool for plant molecular research that can provide comprehensive technical support for multiple fields of study, such as pan-genome analysis and crop breeding. However, constructing a DNA fingerprint database for plants requires significant resources for data output, storage, analysis, and quality control. Large amounts of heterogeneous data must be processed efficiently and accurately. Thus, we developed plant SNP database management system (PSNPdms) using an open-source web server and free software that is compatible with single nucleotide polymorphism (SNP), insertion–deletion (InDel) markers, Kompetitive Allele Specific PCR (KASP), SNP array platforms, and 23 species. It fully integrates with the KASP platform and allows for graphical presentation and modification of KASP data. The system has a simple, efficient, and versatile laboratory personnel management structure that adapts to complex and changing experimental needs with a simple workflow process. PSNPdms internally provides effective support for data quality control through multiple dimensions, such as the standardized experimental design, standard reference samples, fingerprint statistical selection algorithm, and raw data correlation queries. In addition, we developed a fingerprint-merging algorithm to solve the problem of merging fingerprints of mixed samples and single samples in plant detection, providing unique standard fingerprints of each plant species for construction of a standard DNA fingerprint database. Different laboratories can use the system to generate fingerprint packages for data interaction and sharing. In addition, we integrated genetic analysis into the system to enable drawing and downloading of dendrograms. PSNPdms has been widely used by 23 institutions and has proven to be a stable and effective system for sharing data and performing genetic analysis. Interested researchers are required to adapt and further develop the system.