Legume Genomics and Genetics 2025, Vol.16, No.1, 23-32 http://cropscipublisher.com/index.php/lgg 28 5.3 Outcomes and implications for breeding high-yield soybean varieties Now, those engaged in soybean breeding can be said to have a new weapon. The key genes discovered and their interactions have made breeding efforts more targeted. Take SNP markers for example. Combined with genomic selection technology, the breeding of high-yield varieties is now much more accurate than before (Yoosefzadeh-Najafabadi et al., 2021). Interestingly, some newly discovered gene loci recently, such as those controlling adaptability genes, have provided new ideas for breeding (Copley et al., 2018; Lodhi et al., 2023). However, to be honest, although the laboratory data is impressive, to truly cultivate new varieties that are both high-yielding and stress-resistant, repeated field verifications are still necessary. After all, the global demand for soybeans has been on the rise, and relying solely on old methods will definitely not keep up. Now that these genetic discoveries have been made, at least breeders have seen new hope and know which direction to focus their efforts on. Figure 3 Marker trait associations for yield (Adopted from Diers et al., 2018) Image caption: The estimated magnitude of each allelic effect (in kg/ha) is depicted by circle symbol diameter, with negative and positive effects rel- ative to the common parent (IA3023) respectively depicted by red and green. The observed-log10(p) values for the each of the 23 marker-trait associa- tions across the 39 founder families are column color-coded by magnitude in the row labeled JMp (acronym for Joint Mapping p value) (Adopted from Diers et al., 2018) 6 Challenges in Deciphering Genetic Interactions 6.1 Limitations of current experimental approaches Although the current methods for studying soybean genes are advanced, they are not omnipotent. Take the commonly used GWAS and GS techniques for example. Although many important genes can be identified, it is quite difficult to operate in practice-thousands of samples are often required, and a large number of genotypes have to be done (Ravelombola et al., 2021). For instance, there was a previous study that conducted tens of thousands of SNP analyses on 250 soybean materials and indeed found significant clues. However, if the year or batch of materials were changed, the results might be quite different. What is more troublesome is that for complex traits like yield controlled by multiple genes, it is particularly difficult to accurately locate the relevant QTL (Fu et al., 2022). Sometimes, even if a promising gene is found in the laboratory, when it is tested in the field, the result is just passable. These restrictions have forced researchers to consider whether they should develop some more precise and stable new methods.
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