Field Crop 2025, Vol.8, No.4, 166-175 http://cropscipublisher.com/index.php/fc 167 the process of subpopulation formation, and their possible impact on breeding strategies. Our focus is not limited to superior breeding strains, but also includes local varieties and some wild relatives. Ultimately, what is desired is not a simple conclusion, but an understanding of how these resources can support the continuous improvement of corn and maintain its ability to withstand risks when the agricultural environment is constantly changing. 2 Overview of Global Maize Germplasm 2.1 Geographic origins and historical spread of maize across continents About 9 000 years ago, corn was domesticated in central and southern Mexico from its wild ancestor, the large ruminant (Matsuoka et al., 2002). From here, corn spread to various parts of the Americas along multiple human migration routes and reached South America approximately 6 500 years ago, where different local varieties were formed (Van Heerwaarden et al., 2010). After Europeans arrived in America, they brought corn to Europe, Africa and Asia. After multiple introductions, local selections and adaptations, corn has gradually been able to grow in various environments (Byerlee, 2020). Studies in genetics, archaeology and linguistics all indicate that the spread history of corn is very complex. Some regions, such as the southwestern part of the Amazon, have become important improvement centers for it, which has also led to the rich diversity of corn worldwide (Kistler et al., 2018). 2.2 Classification of germplasm pools Corn germplasms around the world can be classified into several types based on their adaptability to different agricultural ecological regions: tropical type, temperate type, subtropical type and highland type. For instance, in Latin America, there are Andean populations, Central American lowland populations and highland populations. The germplasm in Europe includes varieties introduced from the Caribbean region and North America in different periods (Rebourg et al., 2003). In temperate regions such as the United States and China, the horse tooth type germplasm of the maize belt holds a dominant position. Tropical and subtropical germplasms are more common in Africa, Asia and Latin America (Smith et al., 2022). These gene pools not only reflect the propagation process of corn, but also the natural selection pressure it experiences when entering new environments (Mir et al., 2013). 2.3 Diversity in phenotypic traits and adaptation to local agro-ecological conditions The appearance and characteristics of corn are not determined in one place. It took a long time for it to spread around the world. During this period, it constantly adapted to new environments and gradually changed. The color of the grains varies from light to dark, the height of the plants is different, there are early and late flowering times, and the strength of their stress resistance also varies greatly (Bedoya et al., 2017). In some places, the environment is very unique, which has also led to the formation of distinctive populations and gene pools there. For instance, the Guarani communities in South America, as well as the highlands of Mexico and the Andes Mountains (Bracco et al., 2016). These differences are not just for show; they come in handy in breeding work. These diversities are all very useful resources for increasing yield, enhancing stress resistance, or improving nutritional quality. 3 Methodologies for Assessing Genetic Structure 3.1 Use of molecular markers for genetic profiling When conducting genetic analysis on corn, many people's first reaction is to use some molecular markers. The names might sound a bit awkward-simple sequence repeats (SSR), single nucleotide polymorphisms (SNP), and DArTseq-but their function is simple: to help us identify the genetic differences among various corns. It is not surprising that SSR and SNP occur more frequently. Firstly, they have high polymorphism. Secondly, the results are stable. Moreover, they can handle many samples at once, making them convenient to use (Bidyananda et al., 2024). Later, with the development of next-generation sequencing (NGS), the discovery and utilization of these markers became even faster (Figure 1). This not only enables a genome-wide assessment but also helps identify significant variations related to breeding or germplasm conservation. 3.2 Population genetics analytical tools Data alone is not enough; analysis is also necessary. There are many statistical and computational methods used in the research. Some people prefer to use model-based clustering algorithms (such as STRUCTURE), some use principal component analysis (PCA) to identify major differences, and others use molecular variance analysis
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