Maize Genomics and Genetics 2025, Vol.16, No.1, 45-59 http://cropscipublisher.com/index.php/mgg 49 When it comes to genetic variation in corn, there is an interesting phenomenon - some important genetic information is not in the conventional reference genome at all. Scientists have discovered a lot of "hidden" new transcripts using RNA sequencing technology, which has added new information to the genetic diversity of corn (Hansey et al., 2012). Take one of the studies as an example, they found 1 321 reliable new transcripts. Some are common in all corn varieties, but some are very "selective" and only appear in specific lines (Hansey et al., 2012). This kind of transcript variation (what experts call ePAV) is actually very important in explaining the hybrid advantage of corn (Jin et al., 2015). Simply put, it may be these genetic fragments that appear from time to time that make hybrid corn grow better than its parents. 4.2 Common tools and techniques for studying genotypic diversity in maize There are many methods to study the genetic differences of corn now, and scientists have several "brushes" in their hands. Old technologies such as SSR markers are still very easy to use - they can clearly tell us how big the genetic differences are between different inbred lines (Patto et al., 2004). It's interesting to say that although there are new methods such as SNP and RAD-seq, SSR markers are still particularly useful in distinguishing corn varieties (Patto et al., 2004). After all, to figure out "who is who" among these inbred lines and their genetic relationships, the answers given by SSR are both intuitive and reliable. However, each technology has its strengths, and researchers usually use them in combination according to specific needs. When it comes to studying genetic differences in corn, SNP markers are now very popular. This type of marker is very precise and can find those tiny variation points in the genome. A study used RNA-seq technology to discover more than 350 000 polymorphic sites (Hansey et al., 2012). Relying on these sites, scientists successfully classified different corn inbred lines. But SNP is more useful than that. Genotyping methods such as GBS also like to use it, after all, it can clearly show the genetic differences between corn varieties (Shu et al., 2020). Interestingly, through SNP analysis, it was found that those seemingly similar inbred lines can actually be divided into several subgroups. This shows that the genetic diversity of corn may be much richer than we imagined. There is another trick called RAD-seq for studying corn genes, which is quite interesting. It is like using scissors to cut a specific position of DNA, and then studying the cut gene fragment (Hufford et al., 2021). Although it sounds a bit violent, the effect is unexpectedly good. Using this method, we can not only find the location of genes that control important characteristics, but also draw high-quality gene maps for different corn varieties (Hufford et al., 2021). In fact, whether it is RAD-seq or other technologies, the ultimate goal is to understand the genetic code of corn. With these tools, breeders will have more direction in improving varieties, and they don't have to rely on luck as before. 4.3 Application of genomic data in genetic research on maize Corn genomic data is now a treasure, and breeders are scrambling to use it. What are its uses? The most practical one is that it can help us find those gene loci (QTLs) that control important traits. But interestingly, some genes have very small effects, which cannot be found by previous technologies. Now with the tool of association population, the situation is different (Flint-Garcia et al., 2005). It can cover most of the genetic variation in corn varieties, and even those QTLs with subtle effects can be found. Although the effect of a single small effect gene is limited, the sum of them is very significant. This is particularly helpful for analyzing complex traits, and it also allows breeding work to better utilize the genetic diversity of corn. Speaking of corn breeding, scientists recently discovered an interesting phenomenon. By comparing the genomes of temperate and tropical corn, they found more than 1 100 artificially selected regions (He et al., 2017). These places are not simple. Some control key genes, while others affect gene expression. Interestingly, these selected regions are often related to important characteristics. For example, sucrose transport and oil storage, which directly affect the quality of corn (He et al., 2017). Although traditional breeding is also effective, this method starting from the genetic level makes the improvement work more targeted. After all, knowing where the "switch" is, it is much easier to adjust.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==