Genome-wide association studies (GWAS) is an efficient method to detect quantitative trait locus (QTL), and has dissected many complex traits in soybean [Glycine max (L.) Merr.]. Although these results have undoubtedly played a far-reaching role in the study of soybean biology, environmental interactions for complex traits in traditional GWAS models are frequently overlooked. Recently, a new GWAS model, 3VmrMLM, was established to identify QTLs and QTL-by-environment interactions (QEIs) for complex traits. In this study, the GLM, MLM, CMLM, FarmCPU, BLINK, and 3VmrMLM models were used to identify QTLs and QEIs for tocopherol (Toc) content in soybean seed, including δ‐Tocotrienol (δ‐Toc) content, γ‐Tocotrienol (γ‐Toc) content, α‐Tocopherol (α‐Toc) content, and total Tocopherol (T-Toc) content. As a result, 101 QTLs were detected by the above methods in single-environment analysis, and 57 QTLs and 13 QEIs were detected by 3VmrMLM in multi-environment analysis. Among these QTLs, some QTLs (Group I) were repeatedly detected three times or by at least two models, and some QTLs (Group II) were repeatedly detected only by 3VmrMLM. In the two Groups, 3VmrMLM was able to correctly detect all known QTLs in group I, while good results were achieved in Group II, for example, 8 novel QTLs were detected in Group II. In addition, comparative genomic analysis revealed that the proportion of Glyma_max specific genes near QEIs was higher, in other words, these QEIs nearby genes are more susceptible to environmental influences. Finally, around the 8 novel QTLs, 11 important candidate genes were identified using haplotype, and validated by RNA-Seq data and qRT-PCR analysis. In summary, we used phenotypic data of Toc content in soybean, and tested the accuracy and reliability of 3VmrMLM, and then revealed novel QTLs, QEIs and candidate genes for these traits. Hence, the 3VmrMLM model has broad prospects and potential for analyzing the genetic structure of complex quantitative traits in soybean.

Frogeye leaf spot (FLS) causes severe yield loss in soybean and has been found in several countries worldwide. Therefore, it is necessary to select and utilize FLS-resistant varieties for the management of FLS. In the present study, 335 representative soybean materials were assessed for partial resistance to FLS race 7. Quantitative trait nucleotide (QTN) and FLS race 7 candidate genes were identified using genome-wide association analysis (GWAS) based on a site-specific amplified fragment sequencing (SLAF-seq) approach. A total of 23,156 single nucleotide polymorphisms (SNPs) were used to evaluate the level of linkage disequilibrium with a minor allele frequency ≥ 5 and deletion data < 3%. These SNPs covered about 947.01 MBP, nearly 86.09% of the entire soybean genome. In addition, a compressed mixed linear model was utilized to identify association signals for partial resistance to FLS race 7. A total of 15 QTNs associated with resistance were found to be novel for FLS race 7 resistance. A total of 217 candidate genes located in the 200 kb genomic region of these peak SNPs were identified. Based on gene association analysis, qRT-PCR, haplotype analysis and virus-induced gene silencing (VIGS) systems were used to further verify candidate genes Glyma.16G176800, Glyma.16G177300, Glyma.16G177400 and Glyma.16G182300. This indicates that these four candidate genes may participate in FLS race 7 resistance responses.

Frogeye leaf spot (FLS) causes severe yield loss in soybean and has been found in several countries worldwide. Therefore, it is necessary to select and utilize FLS-resistant varieties for the management of FLS. In the present study, 335 representative soybean materials were assessed for partial resistance to FLS race 7. Quantitative trait nucleotide (QTN) and FLS race 7 candidate genes were identi ed using genome-wide association analysis (GWAS) based on a site-speci c ampli ed fragment sequencing (SLAF-seq) approach. A total of 23,156 single nucleotide polymorphisms (SNPs) were used to evaluate the level of linkage disequilibrium with a minor allele frequency ≥ 5 and deletion data < 3%. These SNPs covered about 947.01 MBP, nearly 86.09% of the entire soybean genome. In addition, a compressed mixed linear model was utilized to identify association signals for partial resistance to FLS race 7. A total of 15 QTNs associated with resistance were found to be novel for FLS race 7 resistance. A total of 217 candidate genes located in the 200 kb genomic region of these peak SNPs were identi ed. Based on gene association analysis, qRT-PCR, haplotype analysis and virusinduced gene silencing (VIGS) systems were used to further verify candidate genes Glyma.16G176800, Glyma.16G177300, Glyma.16G177400 and Glyma.16G182300. This indicates that these four candidate genes may participate in FLS race 7 resistance responses. Key messageFLS is a disease that causes severe yield reduction in soybean. In this study, four genes (Glyma.16G176800, Glyma.16G177300, Glyma.16G177400 and Glyma.16G182300) were tentatively con rmed to play an important role in the resistance of soybean to FLS race 7.