Legume Genomics and Genetics 2024, Vol.15, No.1, 13-22 http://cropscipublisher.com/index.php/lgg 16 Figure 1 Multiple genetic characteristics of the adzuki bean genome (Adopted from Aleena et al., 2022) In order to analyze the genetic network behind soybean agronomic traits using genome-wide association studies, the research team conducted GWAS on 84 traits based on more than 4 million markers (with minor allele frequency [MAF]≥0.05] SNPs), 809 accessions were genotyped by MLM implemented in Efficient Mixed-Model Association eXped ited (EMMAX). Kinship relationships were used to define the variance structure of random variables for the total genetic effects of 809 accessions. No inflated P values were found, and the majority of markers (99%) exhibited P values equal to those expected under the null hypothesis, indicating that MLM controls group structure and cryptic relationships well. To control for false positives and false negatives, we also performed permutation testing by randomly shuffling phenotypes to break their relationship with genotypes, thereby deriving genome-wide thresholds (Figure 2). By using empirical thresholds, we identified 150 SALs that were significantly associated with 57 of the 4 traits using all 809 accessions (Figure 2) (Fang et al., 2022). Kim et al. (2021) used the high-precision soybean genome reference sequence to conduct GWAS analysis on the protein and oil phenotypes of soybean seeds during the soybean domestication process. Many nearly identical materials were eliminated from the 116 soybean population. Then, using 36 of the SoySNP50K data 489 SNP pairs 8 844 non-redundant soybean materials were conducted 4 467 134 SNP genotype analysis, 3 082 234 SNPs were used for GWAS, and the median Beagle R2 after filtering was 0.95. As expected, main peaks appeared for both seed oil and protein. Interestingly, more than 10 new minor significant peaks appeared in both oil and protein GWAS, mainly located on chromosomes 2, 4, and 10. Deleterious mutation patterns during soybean domestication were used to construct a high-quality soybean SNP variation map, which can be used as a reference map to improve genotype assignment by GWAS for oil and protein traits. In addition to those unique genomic variation characteristics due to selfing, the author believes that the soybean variation map and method developed by him can be directly used for rapid and accurate mining of genetic variation in soybean (Figure 3).
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