Legume Genomics and Genetics 2025, Vol.16, No.1, 23-32 http://cropscipublisher.com/index.php/lgg 27 Figure 2 Common agronomic traits selected in soybean breeding include seed color and yield, plant architecture, nodulation efficiency, and the tolerance to abiotic and biotic stresses (Adopted from Ku et al., 2022) Image caption: These traits have been reported to be regulated by post-transcriptional regulatory mechanisms such as transcript regulation by ncRNAs, proteins modification, and translational regulation. The interaction between ncRNAs further increases the versatility of post-transcriptional regulations. This figure is created with BioRender.com (Adopted from Ku et al., 2022) 4.3 Integrative bioinformatics platforms for soybean trait analysis Processing soybean genetic data now relies heavily on powerful analytical tools. Take SoyMAGIC as an example, this platform is quite interesting as it uses a special hybrid population design (MAGIC) that can precisely locate the gene regions that affect agronomic traits (Hashemi et al., 2022). However, on the other hand, having genetic data alone is not enough. The most powerful feature of these platforms is their ability to integrate the actual performance data of plants, so as to find the truly important genetic markers. It is interesting that the same genotype may perform significantly differently in different environments, and current analytical tools can help us identify which varieties perform better under specific conditions (Rani et al., 2023b). Speaking of technological progress, from gene sequencing to data analysis, this whole set of methods has indeed made breeding work different. Although it is not yet possible to fully predict all traits, at least now we can screen out potential new varieties faster. 5 Case Study: Gene-Gene Interaction in Soybean Yield Improvement 5.1 Identifying key yield-related genes and their interactions To increase soybean yield, the key is to identify the genes that play a decisive role. The recent research is quite interesting, such as the discovery of some important SNP markers using GWAS method, which are not only related to yield, but also affect characteristics such as maturity time and plant height (Ravelombola et al., 2021). Speaking of which, some wild soybean varieties contain good genes, and introducing these excellent genes into cultivated varieties can indeed increase yield (Diers et al., 2018). However, the most surprising thing is that genes like Glyma.01g199200 and Glyma.10g065700, although not previously studied, are closely related to yield traits in their location (Rani et al., 2023a). Of course, these findings are just the beginning, as soybean yield is influenced by too many factors and the interactions between genes are much more complex than imagined. 5.2 Experimental validation of synergistic gene functions It is not enough to rely solely on calculations to understand how these genes affect soybean yield. Experimental verification is needed. For example, in the study of the NAM population containing 5 600 inbred lines, many genetic markers related to yield were indeed found (Figure 3) (Diers et al., 2018). Interestingly, even machine learning is now being used to predict yield by analyzing combinations of various traits, and the results are surprisingly good (Yoosefzadeh Najafabadi et al., 2021). However, what is most surprising is that some QTLs act like "transportation hubs" and can simultaneously affect several important agronomic traits (Fu et al., 2022). Of course, to truly apply these findings to breeding, they still need to be tested through field experiments, as there is always a gap between laboratory data and actual yields.
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