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.

Context Soybean cyst nematode is the most important pest of soybean (Glycine max (L.) Merr.) worldwide, causing serious yield losses. Lignin is a vital component of the cell wall that can provide resistance to cyst nematode. O-Methyltransferase (OMT) is a key enzyme involved in lignin metabolism in the phenylalanine pathway. Aims In this study, the soybean OMT gene family was systematically identified, and the expression response of GmOMT to abiotic and cyst nematode stresses was investigated. Methods In total, 67 OMT genes were obtained from the soybean genome through conserved structural domain alignment. GmOMT expression under abiotic stress of soybean was examined based on next-generation RNA sequencing (RNA-Seq). Comprehensive analysis of the genes was conducted, including gene structure, conserved structure, affinity, chromosomal localisation, functional prediction, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, promoter analysis, and expression pattern analysis. Key results The 67 GmOMT genes were identified and distributed among the 19 chromosomes. The GmOMT genes were classified into two categories: CCOMT subfamily and COMT subfamily. GmOMT genes from the same family shared similar gene structures and conserved structural domains, which have undergone strong purifying selection during evolution. The presence of multiple cis-responsive elements in the promoters of GmOMT genes suggested that members of the soybean OMT family may be involved in growth and developmental activities and resistance to stress in soybean. Conclusions GmOMT expression under abiotic stress showed that some of the genes may play a role in abiotic stress. Of them, GmCCOMT3 and GmCCOMT7 were closely associated with lignin synthesis based on both RNA-Seq and quantitative real-time PCR analysis. Implications These findings are valuable for elucidating the function of GmOMT in lignin metabolism and the relationship with SCN resistance.

Polyamine oxidases (PAOs) are flavin adenine dinucleotide-dependent enzymes that are involved in polyamine catabolism and play an essential role in growth and developmental processes as well as the response to abiotic stresses. Although the PAO gene families have been intensively studied in many plants, the soybean ( Glycine max (L.) Merr.) PAO gene family has not been systematically identified. Here, we identified six PAO genes in the soybean genome and named them GmPAO1– GmPAO6. The phylogenetic analysis revealed that plant PAO proteins are divided into four classes. GmPAO1 and GmPAO4 belong to class I; GmPAO2, GmPAO5, and GmPAO6 belong to class IV. Similar to most dicotyledonous plants, soybeans do not contain class II. Interestingly, we identified an additional SWIRM-domain PAO gene GmPAO3, which exists between classes III and IV . GmPAO3 had a different gene structure and expression. To determine the individual roles of GmPAOs, we analyzed their expression levels in various tissues and under abiotic stress. Each GmPAO gene can respond in a specific tissue under specific abiotic stress. The data can help to clarify the role of GmPAOs in abiotic stress responses in soybean and provide a breeding basis for enhancing soybean tolerance to abiotic stresses.

Lodging is an important agronomic trait that affects soybean seed yield. In this study, a recombinant inbred line (RIL) population derived from ‘Zhongdou 27’ × ‘Jiunong 20’ (including 112 lines) was used to identify quantitative trait loci (QTL) associated with lodging of soybean. A genetic map of 2050.27 cM was previously constructed using 4412 single nucleotide polymorphism (SNP) bins in this population. Three major QTL were identified in the single environment for 3 years, accounting for 12.38–16.5% of the phenotypic variation. Among these QTL, qldg-1 was stable for 3 years and qldg-2 was stable for 2 years. QTL by environment interactions (QEI) mapping was also used to detect QTL. A total of 14 QTL were detected, which could explain 2.62–11.28% of the phenotypic variation. The constructed residual heterozygous lines (RHL) were used for the verification of qldg-1 and qldg-2, and the results showed that these two QTL could significantly improve lodging resistance. In addition, genes in the confidence interval of qldg-1 and qldg-2 were designed to predict the candidates. The results of quantitative real-time PCR (qRT-PCR) verification of five genes revealed that two genes (Glyma.17G048100 and Glyma.09G239000) were expressed differentially during the dynamic stages between the parents, demonstrating that these two were the candidates associated with soybean lodging. The QTL and candidate genes related to soybean lodging identified in this study will be of great significance to the future soybean molecular-assisted breeding for lodging resistance.