Field Crop 2025, Vol.8, No.4, 166-175 http://cropscipublisher.com/index.php/fc 168 (AMOVA) to observe the changes between and within populations (Labate, 2000). As for software, names like Arlequin, GDA, GENEPOP, and POPGENE may not be frequently heard, but their interfaces are quite intuitive and they have many functions, capable of handling various marker types and large amounts of data. With these tools, it is possible to identify which individuals belong to the same subgroup, calculate the genetic distance between them, and also compare the differences between groups. Figure 1 Schematic representation of the steps involved in GBS (genotype-by-sequencing) technology for plant research (Adopted from Bidyananda et al., 2024) 3.3 Integration of genomic, transcriptomic, and phenotypic datasets In recent years, analyzing genetic data alone is no longer a novelty. Researchers tend to view genomic, transcriptomic and phenotypic data together (Grover and Sharma, 2016). The advantage of doing so is that it enables us to understand the genetic structure and trait differences of corn from multiple perspectives. For instance, by integrating high-density labeling data with transcriptome information and phenotypic data measured in the field, association analysis, genomic prediction, and genes that may affect important agronomic traits can be conducted (Bunjkar et al., 2024). This approach will make the analysis more accurate and be more conducive to breeding new varieties through marker-assisted selection or genomic selection. 4 Patterns of Genetic Structure in Global Maize Populations 4.1 Regional clustering and differentiation Genetic analysis has found that corn populations often cluster together by region, which reflects their adaptation to the local environment and the different breeding histories of each area. Studies using microsatellites and SNPS have shown that there are several major population types, such as those in the Mexican Highlands, northern United States, tropical lowlands and the Andes. Among them, the genetic diversity of the highland population in Mexico was the highest, while that of the Andes and the northern United States was the lowest (Vigouroux et al., 2008). In China, germplasm used for breeding can be classified into two major categories: tropical and temperate types. Further classification not only reflects local adaptability but also is influenced by foreign germplasm (Shu et al., 2021). Geographical distance isolation and historical migration routes have played a significant role in the formation of these patterns.
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