Maize Genomics and Genetics 2025, Vol.16, No.1, 45-59 http://cropscipublisher.com/index.php/mgg 51 Recently, GWAS has come up with a new trick called XP-GWAS. This method is quite interesting. Instead of testing a large number of samples, it only selects those individuals with extreme performance for study (Yang et al., 2015). Take the number of rows of corn kernels as an example. This method can still find related gene variants and analyze complex QTLs. Although it sounds like lazy, it is actually particularly suitable for species with limited genetic testing resources (Yang et al., 2015). Think about it, traditional GWAS often tests hundreds or thousands of samples, while XP-GWAS only needs to focus on the extreme parts at both ends. It saves money and the effect is not discounted. It is particularly useful for studying quantitative trait genes. 5.3 Analysis of key traits and their associated control genes When it comes to corn breeding, what everyone is most concerned about is yield. But what's interesting is that although yield itself is important, traits that seem not directly related, such as plant height and ear length, can explain a lot of problems (Rahman et al., 2018). We did an experiment and selected 15 different genotypes of corn. The results showed that the differences between these traits were particularly obvious. Of course, not all traits are so useful, but the two indicators of thousand-grain weight and number of grains per ear are indeed very stable, with surprisingly high heritability, and breeders should pay special attention to them. Speaking of which, through path analysis, we found that some traits have a direct impact on yield, which is quite unexpected. But then again, these findings are nothing new, and similar studies have been done in previous years. Speaking of genomic analysis, there is now an interesting method called whole-genome mediation analysis. Simply put, it is to connect genotype and phenotype through the "middleman" of transcriptome data (Yang et al., 2022). Of course, this method has a premise assumption-gene expression plays a mediating role in this process. 736 such mediator genes have been found in corn research, but the most surprising thing is that some genes can actually affect multiple traits at the same time. Although this method still needs more verification, it has to be said that integrating different omics data can indeed help us better understand complex traits. You see, this provides a clear direction for subsequent functional verification. 6 Gene-Environment Interaction Effects on Maize Traits 6.1 Definition of gene-environment interaction and its effect on maize traits Anyone who is engaged in seed breeding knows that the same variety may perform very differently in different places. This is the so-called gene-environment interaction (GEI), which means that different genotypes respond differently to environmental changes (Boer et al., 2007; Li et al., 2022; Singamsetti et al., 2022). For example, in the case of corn, some varieties have a sharp drop in yield under drought conditions, but another variety may be quite resistant. However, the most troublesome thing is that this interaction not only affects yield, but also changes indicators such as grain moisture content. Once environmental factors such as temperature and soil moisture change, the performance of the variety will fluctuate, which brings a lot of headaches to breeding work. Of course, it is not completely unsolvable, but it does take more effort to breed varieties with wide adaptability. When it comes to QTL research in corn, there is a very interesting phenomenon. You may find that the same yield-related QTL performs very well under drought conditions, but it may be completely different in another environment (Boer et al., 2007; Wen et al., 2023). The same is true under high temperature stress, and genetic control seems to be particularly "dependent on the environment". This actually explains why the impact of GEI is so complicated - to put it bluntly, whether the gene can play a role depends on the environmental conditions at the time. Of course, to understand this complex interaction, traditional analysis methods alone are not enough. Now everyone is using more advanced statistical models. Although the process is a bit more complicated, it can indeed help us better understand the genetic structure of corn traits. 6.2 Regulation of key phenotypic traits in maize by environmental factors Anyone who has grown corn knows that the same variety may grow completely differently in different places. Nitrogen fertilizer is a typical example-some varieties have a high yield under high nitrogen conditions, but this may not be obvious for other varieties (Ljubičić et al., 2023). When it comes to flowering time, altitude has a particularly large impact, which is best understood by old farmers who grow corn in mountainous areas (Jin et al.,
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