Maize Genomics and Genetics 2025, Vol.16, No.2, 70-79 http://cropscipublisher.com/index.php/mgg 72 2.4 Statistical and analytical tools The data analysis of this study mainly relies on tools such as R software and Meta-Analyst. You may not know that there is a meta package in R that is particularly useful. We use it to calculate effect sizes and evaluate data differences. When it comes to drawing pictures to show the genetic relationship between varieties, we have to mention the two R packages adegenet and factoextra (Bedoya et al., 2017)-they can intuitively present the results of cluster analysis and PCA. However, the most troublesome thing is to analyze linkage disequilibrium. Thanks to the LDheatmap package (Chiu et al., 2022), we can figure out the genetic recombination of different corn varieties. To be honest, although these tools are very professional, they are not as complicated to operate as imagined. 3 Overview of Genetic Diversity in Global Sweet Corn Germplasm 3.1 Distribution and classification of global germplasm resources When it comes to fresh corn germplasm resources, they are actually distributed all over the world, but the United States, Brazil and China have made the greatest contributions. You may not know that many varieties can be traced back to a few old varieties, such as 'Stowell's Evergreen' and 'Country Gentleman' (Chhabra et al., 2022)-they all belong to the northern hard corn population. Interestingly, the National Plant Germplasm System (NPGS) of the United States has a collection of many inbred line resources from all over the world, which is a very comprehensive resource library. How do researchers generally classify these germplasms? It mainly depends on three points: genetic markers, appearance characteristics, and their "genealogical" relationship (Figure 1) (Stansluos et al., 2023). Through these methods, we can figure out what genetic connections there are between different populations and how diverse they are. Figure 1 The relationship between genetic diversity (i. e., heterozygosity) and gene flow or genetic drift based on individual conditional expectation (ICE) (Adopted from Chiu et al., 2022) 3.2 Diversity assessment based on morphological, molecular, and genomic data To understand the genetic diversity of fresh corn, researchers have put a lot of effort. They not only look at what corn looks like (morphological traits), but also use high-tech means such as molecular markers and genomic data. For example, they first classified different inbred lines in an "appearance association" style, and found that the genetic differences between them are really not small. When it comes to molecular markers, SSR and SNP are the most used technologies (Mahato et al., 2018). SSR markers can generate clear bands, and the degree of genetic
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