Variety identification is vital for the security of agricultural production and integrity of seed markets. Current molecular approaches for identification of crop varieties predominantly require specialized laboratory equipment, resulting in relatively slow turnaround times and high costs. The Maize Point-of-Genuineness (M-POG) identification kit aims to extend variety identification from the laboratory to the field, address the lack of rapid on-site detection methods, and support the establishment of a rapid, efficient, and traceable molecular detection system for the modern seed industry.

Candidate InDel loci from the MaizeIDP50K chip were screened using a stepwise dichotomous partitioning method in combination with a dynamic optimization algorithm. Nine maize elite inbred lines and three closely related maize varieties were used to design dominant and co-dominant primers, which were evaluated by conducting quantitative real-time PCR and fluorescence capillary electrophoresis. An optimal combination algorithm was used to screen and determine the core set of loci. Based on the cycle threshold (Ct) value for the dominant primer for the core markers, a barcode conversion mechanism suitable for field detection was established. The detection thresholds were set using the core loci fingerprints of 2270 maize inbred lines. The consistency and single sample detection efficiency were evaluated by comparison with the traditional method for variety identification using simple sequence repeat (SSR) markers, thereby verifying the accuracy and practicability of the M-POG method.

Eighty InDel candidate loci from the chip were screened for which 76 pairs of dominant primers were designed. Among these loci, 31 high quality loci showed clear qPCR amplification curves, high repeatability, and uniform amplification efficiency. Ultimately, 16 highly discriminative core loci were identified, achieving a variety recognition rate of 95.83% across the 2270 inbred lines. The fingerprint detection threshold for each locus was calculated; loci with Ct<28 were classified as dominant type (coded as 1), and those with Ct>30 were classified as recessive type (coded as 0), enabling generation of a DNA fingerprinting for M-POG detection. Pairwise comparisons among the 2270 inbred lines revealed that 98.29% of the pairs of lines differed at four or more loci. A comparative analysis between the M-POG method and the SSR molecular marker method, conducted using elite inbred lines and closely related varieties, showed that the samples were consistently discriminated by both methods in 90.91% of the comparisons. This result demonstrated the reliability of the M-POG method. Thus, the M-POG approach enables efficient screening of genetically dissimilar inbred lines, thereby substantially reducing the number of samples requiring further laboratory analysis. The complete field detection process required only 42 min, approximately one-quarter of the time required by standard identification procedures.

Sixteen core InDel loci were selected for the M-POG method, achieving a variety recognition rate exceeding 95%. Based on the InDel on-site detection results, the minimum number of differential loci required for variety discrimination was determined. Development of the M-POG kit enables rapid and accurate identification of maize inbred lines in the field.

Crop germplasm resources hold a crucial strategic position. The Maize Germplasm Resources Bank in Jilin Province safeguards a collection of germplasm resources distinctively representative of the Northern Spring Maize Region. Traditional germplasm resource management faces challenges in ascertaining accurate identity information. To address this issue, molecular marker technology has been employed to establish a process for the construction and classification of molecular IDs for germplasm resources, thereby enabling precise identification and bolstering categorical management. Thorough exploration of the exceptional resources within Jilin Province's Maize Germplasm Resources Bank is intended to advance the shared utilization of these valuable germplasm resources.

A total of 2 918 maize germplasm resources were utilized from the Jilin Provincial Maize Germplasm Resources Bank as subjects of the study, the molecular IDs were constructed by using 40 pairs of SSR markers and 61 214 SNP markers recommended in maize variety identification standards. Based on the molecular ID information, the germplasm resources were categorized into core, closely related, heterogeneous, and population groups for management purposes. Furthermore, the core germplasms were analyzed on genetic diversity.

In this investigation, the SSR molecular IDs were constructed for 2 918 maize germplasm resources, while the SNP molecular IDs were constructed for 2 502 maize germplasm resources, excluding heterogeneous germplasm. The standards for the construction of SSR and SNP molecular IDs were established for maize germplasm resources. The SSR molecular ID is composed of a combination of three-digit numbers and one-letter code converted from 40 SSR loci fingerprints, stored in the form of a QR code. The SNP molecular ID converts the fingerprints of 61 214 SNP loci into visual barcodes. Based on the features of sample homozygosity and fingerprint specificity, the samples were categorized into 1 561 cores, 705 closely related, 416 heterogeneous, and 236 population types of germplasm resources. Genetic diversity analysis indicates that domestic germplasm resources, represented by Lüdahonggu and Huanggai groups, constituting the main germplasm resources in the Jilin Provincial Maize Germplasm Resources Bank, accounting for 64.38% of all core germplasm resources.

This research outlines a methodology for constructing molecular IDs for maize germplasm resources. The SSR molecular IDs were constructed for 2 918 accessions stored in the Jilin Provincial Maize Germplasm Resources Bank and the SNP molecular IDs were constructed for 2 502 among them. The germplasm resources were categorized into core, closely related, heterogeneous, and population types to achieve the classification management.