Legume Genomics and Genetics 2025, Vol.16, No.1, 44-53 http://cropscipublisher.com/index.php/lgg 47 discover many interesting genetic variations. However, sometimes in order to verify the results, it is necessary to use the parent population for assistance - that is, to specifically hybridize two parents with particularly large differences (Copley et al., 2018), so that the gene localization is more precise. When it comes to genetic testing technology, the two main types currently used are GBS and SNP chips. The GBS technology is quite powerful. It can scan the entire genome (Kim et al., 2022), and is particularly suitable for finding genes of those complex traits. Sure, if cost is taken into account, the SoySNP50K chip (Zhang et al., 2015) is also an affordable option, but the detection sites are not as comprehensive. Finally, it is necessary to mention the statistical methods. The commonly used MLM model nowadays (Zhang et al., 2015) can indeed solve the interference caused by the group structure. However, researchers are still constantly improving. New methods such as FarmCPU (Priyanatha et al., 2022) and BLINK (Ravelombola et al., 2021) are faster in calculation and more accurate in finding gene loci. To put it bluntly, GWAS research requires mastering the three key techniques of sample size, technology and statistics. Figure 2 SNP distributions across the soybean genome (v2) and SNP effects within the population of plant introduction genotypes (Adopted from Copley et al., 2018) Image caption: a Gene and SNP distributions used for genotyping across the soybean chromosomes. From the outer to inner circle: Soybean chromosomes 1 to 20; gene locations on the positive and negative chromosome strands; and GBS, SoySNP50K microarray and the merged data set SNP locations. b Distribution of SNPs based on genomic region within the merged data set. c Predicted SNP effects based on degree of impact within the merged data set. d Predicted SNP effects based on function class for SNPs located within coding regions within the merged data set (Adopted from Copley et al., 2018) 3.3 Challenges in GWAS for soybean (population structure, false positives) GWAS is indeed useful in soybean research, but it is not omnipotent. The most headache-inducing issue is the population structure problem - simply put, different subgroups may be mixed in the sample, which can lead to
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