Background: Seed weight is a complex yield-related trait with a lot of quantitative trait loci (QTL) reported through linkage mapping studies. Integration of QTL from linkage mapping into breeding program is challenging due to numerous limitations, therefore, Genome-wide association study (GWAS) provides more precise location of QTL due to higher resolution and diverse genetic diversity in un-related individuals.Results: The present study utilized 573 breeding lines population with 61,166 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and candidate genes for seed weight in Chinese summer-sowing soybean. GWAS was conducted with two single-locus models (SLMs) and six multi-locus models (MLMs). Thirty-nine SNPs were detected by the two SLMs while 209 SNPs were detected by the six MLMs. In all, two hundred and thirty-one QTNs were found to be associated with seed weight in YHSBLP with various effects. Out of these, seventy SNPs were concurrently detected by both SLMs and MLMs on 8 chromosomes. Ninety-four QTNs co-localized with previously reported QTL/QTN by linkage/association mapping studies. A total of 36 candidate genes were predicted. Out of these candidate genes, four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280andGlyma19g28070) were identified by the integration of co-expression network. Among them, three were relatively expressed higher in the high HSW genotypes at R5 stage compared with low HSW genotypes exceptGlyma12g33280. Our results show that using more models especially MLMs are effective to find important QTNs, and the identified HSW QTNs/genes could be utilized in molecular breeding work for soybean seed weight and yield.Conclusion: Application of two single-locus plus six multi-locus models of GWAS identified 231 QTNs. Four hub genes (Glyma06g44510,Glyma08g06420,Glyma12g33280&Glyma19g28070) detected via integration of co-expression network among the predicted candidate genes.

Red crown rot (RCR) disease caused by Calonectria ilicicola negatively impacts soybean yield and quality. Unfortunately, the knowledge of the genetic architecture of RCR resistance in soybeans is limited. In this study, 299 diverse soybean accessions were used to explore their genetic diversity and resistance to RCR, and to mine for candidate genes via emergence rate (ER), survival rate (SR), and disease severity (DS) by a multi-locus random-SNP-effect mixed linear model of GWAS. All accessions had brown necrotic lesions on the primary root, with five genotypes identified as resistant. Nine single-nucleotide polymorphism (SNP) markers were detected to underlie RCR response (ER, SR, and DS). Two SNPs colocalized with at least two traits to form a haplotype block which possessed nine genes. Based on their annotation and the qRT-PCR, three genes, namely Glyma.08G074600, Glyma.08G074700, and Glyma.12G043600, are suggested to modulate soybean resistance to RCR. The findings from this study could serve as the foundation for breeding RCR-tolerant soybean varieties, and the candidate genes could be validated to deepen our understanding of soybean response to RCR.

With the rapid emergence and distribution of red crown rot (RCR) across countries, durable sources of resistance against Calonectria ilicicola in soybean [Glycine max (L.) Merrill] is required to control the disease. We employed two RIL populations for the experiment. We identified 15 and 14 QTLs associated with RCR resistance in ZM6 and MN populations, respectively, totaling 29 QTLs. Six and eight QTLs had phenotypic variation above 10% in ZM6 and MN populations, respectively. We identified six (6) “QTL hotspots” for resistance to RCR from the ZM6 and MN RIL populations on chromosomes 1, 7, 10, 11, 13, and 18. Gene annotations, gene ontology enhancement, and RNA sequencing assessment detected 23 genes located within six “QTL Hotspots” as potential candidate genes that could govern RCR resistance in soybeans. Our data will generally assist breeders in rapidly and effectively incorporating RCR resistance into high-yielding accession through marker-assisted selection.

Introduction: Ordinal traits are important complex traits in crops, while genome-wide association study (GWAS) is a widely-used method in their gene mining. Presently, GWAS of continuous quantitative traits (C-GWAS) and single-locus association analysis method of ordinal traits are the main methods used for ordinal traits. However, the detection power of these two methods is low.Methods: To address this issue, we proposed a new method, named MTOTC, in which hierarchical data of ordinal traits are transformed into continuous phenotypic data (CPData).Results: Then, FASTmrMLM, one C-GWAS method, was used to conduct GWAS for CPData. The results from the simulation studies showed that, MTOTC+FASTmrMLM for ordinal traits was better than the classical methods when there were four and fewer hierarchical levels. In addition, when MTOTC was combined with FASTmrEMMA, mrMLM, ISIS EM-BLASSO, pLARmEB, and pKWmEB, relatively high power and low false positive rate in QTN detection were observed as well. Subsequently, MTOTC was applied to analyze the hierarchical data of soybean salt-alkali tolerance. It was revealed that more significant QTNs were detected when MTOTC was combined with any of the above six C-GWAs.Discussion: Accordingly, the new method increases the choices of the GWAS methods for ordinal traits and helps to mine the genes for ordinal traits in resource populations.

Soybean hundred seed weight (HSW) is a complex quantitative trait affected by multiple genes and environmental factors. To date, a large number of quantitative trait nucleotides (QTNs) have been reported, but less information on QTN-by-environment interactions (QEIs) and QTN-QTN interaction (QQIs) for soybean HSW is available. Mapping without QEIs and QQIs result in missing some important QTNs that are significantly related to HSW. Therefore, the present study conducted genome-wide association analysis to map main QTNs, QEIs and QQIs for HSW in a panel with 573 diverse soybean lines tested in three independent environments (E1, E2 and E3) with Mean- and best linear unbiased value (BLUP)- phenotype. In all, 147 main effect QTNs, 11 QEIs, and 24 pairs of QQIs were detected in the Mean-phenotype, and 138 main effect QTNs, 13 QEIs, and 27 pairs of QQIs in the BLUP-phenotype. The total phenotypic variation explained by the main effect QTNs, QEIs, and QQIs were 35.31–39.71, 8.52–8.89 and 34.77–35.09%, respectively, indicating an important role of non-additive effects on HSW. Out of these, 33 QTNs were considered as stable with 23 colocalized with previously known loci, while 10 were novel QTNs. In addition, 10 pairs stable QQIs were simultaneously detected in the two phenotypes. Based on homolog search in Arabidopsis thaliana and in silico transcriptome data, seven genes (Glyma13g42310, Glyma13g42320, Glyma08g19580, Glyma13g44020, Glyma13g43800, Glyma17g16620 and Glyma07g08950) from some main-QTNs and two genes (Glyma06g19000 and Glyma17g09110) of QQIs were identified as potential candidate genes, however their functional role warrant further screening and functional validation. Our results shed light on the involvement of QEIs and QQIs in regulating HSW in soybean, and these together with candidate genes identified would be valuable genomic resources in developing soybean cultivars with desirable seed weight.

Although the mixed linear model (MLM) such as efficient mixed model association (EMMA), has been widely used in genome-wide association studies (GWAS), relatively little is known about fast and efficient algorithms to implement multi-locus GWAS. To address this issue, we report a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA). In this method, a new matrix transformation was constructed to obtain a new genetic model that includes only quantitative trait nucleotide (QTN) variation and normal residual error; letting the number of nonzero eigenvalues be one and fixing the polygenic-to-residual variance ratio was used to increase computing speed. All the putative QTNs with the ≤0.005 P-values in the first step of the new method were included in one multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection, model fit and robustness, has less bias in QTN effect estimation, and requires less running time than the current single- and multi-locus methodologies for GWAS, such as E-BAYES, SUPER, EMMA, CMLM and ECMLM. Therefore, FASTmrEMMA provides an alternative for multi-locus GWAS.