Maize Genomics and Genetics 2025, Vol.16, No.1, 45-59 http://cropscipublisher.com/index.php/mgg 50 Maize genome research has recently developed a new method-onstructing a pan-genome and a pan-transcriptome. This is not simply putting together several genomes, but it can discover many new sequences that cannot be found by traditional methods (Jin et al., 2015). Interestingly, some genes are regulated in a very special way and may be affected by distant eQTLs. By analyzing RNA-seq data from different inbred lines, researchers have found a large number of such new genes (Jin et al., 2015). Although their specific functions have yet to be studied, they are likely to affect various traits of maize. These findings provide new ideas for breeding work and may help us breed better maize varieties. After all, the more you know about a gene, the more possibilities for improvement. 5 Phenotypic and Genotypic Association Studies in Maize 5.1 Overview of phenotype-genotype association analysis To figure out why corn grows like this or that, scientists now love to play "matching" - matching the traits they see with the genes. It sounds simple, but there are many factors to consider in actual operation: the influence of the genes themselves, the role of the environment, and the complex interaction between them (Wallace et al., 2014). There is a particularly interesting study. They found more than 5 000 corn inbred lines and observed 41 different traits. Guess what? Nearly 4 800 gene variants related to these traits were found (Wallace et al., 2014). This number is really amazing, indicating that the genetic background of corn is much more complicated than we thought. However, this also brings new challenges to breeding work - after all, finding truly useful genes is like looking for a needle in a haystack. Breeding experts now attach great importance to genotype data, and there is a reason for this. Take one study, for example, they genotyped 2 815 maize inbred lines and found more than 680 000 SNP markers (Romay et al., 2013). This number may sound a bit abstract, but it is actually very useful. These markers can help us discover those rare genetic variants and clarify the relationship between different populations (Romay et al., 2013). Although it sounds very technical, to put it bluntly, it is to conduct GWAS analysis more accurately. After all, to improve maize varieties, you must first understand its genetic background. With this data, breeding work can avoid many detours. 5.2 Application of genome-wide association studies (GWAS) in fresh-eating maize GWAS has become a standard tool for corn genetic research now, thanks to the rapid development of sequencing technology. Corn is particularly suitable for this analysis method because it has many genetic variations and linkage disequilibrium decays quickly (Xiao et al., 2017). In fact, there have been quite a few achievements in this area in the past decade. Researchers have found that as long as the statistical model is used correctly, GWAS can match genetic variation with actual traits (Shikha et al., 2021). For example, the results shown in Figure 2 not only found the gene regions that control common traits, but also found many sites related to complex traits such as drought resistance and disease resistance. Although each discovery may be just a small piece of the puzzle, when accumulated, you can see the whole picture. Figure 2 Factors affecting GWAS accuracy and resolution at successive stages (Adopted from Shikha et al., 2021)
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