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.

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, Glyma12g33280 and Glyma19g28070) 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 except Glyma12g33280. 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.

Seed-flooding stress is one of the major abiotic constraints severely affecting soybean yield and quality. Understanding the molecular mechanism and genetic basis underlying seed-flooding tolerance will be of greatly importance in soybean breeding. However, very limited information is available about the genetic basis of seed-flooding tolerance in soybean. The present study performed Genome-Wide Association Study (GWAS) to identify the quantitative trait nucleotides (QTNs) associated with three seed-flooding tolerance related traits, viz., germination rate (GR), normal seedling rate (NSR) and electric conductivity (EC), using a panel of 347 soybean lines and the genotypic data of 60,109 SNPs with MAF > 0.05. A total of 25 and 21 QTNs associated with all three traits were identified via mixed linear model (MLM) and multi-locus random-SNP-effect mixed linear model (mrMLM) in three different environments (JP14, HY15, and Combined). Among these QTNs, three major QTNs, viz., QTN13, qNSR-10 and qEC-7-2, were identified through both methods MLM and mrMLM. Interestingly, QTN13 located on Chr.13 has been consistently identified to be associated with all three studied traits in both methods and multiple environments. Within the 1.0 Mb physical interval surrounding the QTN13, nine candidate genes were screened for their involvement in seed-flooding tolerance based on gene annotation information and available literature. Based on the qRT-PCR and sequence analysis, only one gene designated as GmSFT (Glyma.13g248000) displayed significantly higher expression level in all tolerant genotypes compared to sensitive ones under flooding treatment, as well as revealed nonsynonymous mutation in tolerant genotypes, leading to amino acid change in the protein. Additionally, subcellular localization showed that GmSFT was localized in the nucleus and cell membrane. Hence, GmSFT was considered as the most likely candidate gene for seed-flooding tolerance in soybean. In conclusion, the findings of the present study not only increase our knowledge of the genetic control of seed-flooding tolerance in soybean, but will also be of great utility in marker-assisted selection and gene cloning to elucidate the mechanisms of seed-flooding tolerance.

Plant height (PH) and the number of nodes on the main stem (NN) serve as major plant architecture traits affecting soybean seed yield. Although many quantitative trait loci for the two traits have been reported, their genetic controls at different developmental stages in soybeans remain unclear. Here, 368 soybean breeding lines were genotyped using 62,423 single nucleotide polymorphism (SNP) markers and phenotyped for the two traits at three different developmental stages over two locations in order to identify their quantitative trait nucleotides (QTNs) using compressed mixed linear model (CMLM) and multi-locus random-SNP-effect mixed linear model (mrMLM) approaches. As a result, 11 and 13 QTNs were found by CMLM to be associated with PH and NN, respectively. Among these QTNs, 8, 3, and 4 for PH and 6, 6, and 8 for NN were found at the three stages, and 3 and 6 were repeatedly detected for PH and NN. In addition, 34 and 30 QTNs were found by mrMLM to be associated with PH and NN, respectively. Among these QTNs, 11, 13, and 16 for PH and 11, 15, and 8 for NN were found at the three stages. A majority of these QTNs overlapped with the previously reported loci. Moreover, one QTN within the known E2 locus for flowering time was detected for the two traits at all three stages, and another that overlapped with the Dt1 locus for stem growth habit was also identified for the two traits at the mature stage. This may explain the highly significant correlation between the two traits. Our findings provide evidence for mixed major plus polygenes inheritance for dynamic traits and an extended understanding of their genetic architecture for molecular dissection and breeding utilization in soybeans.

Phytophthora root rot (PRR) is among the most important soybean (Glycine max (L.) Merr.) diseases worldwide, and the host displays complex genetic resistance. A genome-wide association study was performed on 337 accessions from the Yangtze-Huai soybean breeding germplasm to identify resistance regions associated with PRR resistance using 60,862 high-quality single nucleotide polymorphisms markers. Twenty-six significant SNP-trait associations were detected on chromosomes 01 using a mixed linear model with the Q matrix and K matrix as covariates. In addition, twenty-six SNPs belonged to three adjacent haplotype blocks according to a linkage disequilibrium blocks analysis, and no previous studies have reported resistance loci in this 441kb region. The real-time RT-PCR analysis of the possible candidate genes showed that two genes (Glyma01g32800 and Glyma01g32855) are likely involved in PRR resistance. Markers associated with resistance can contribute to marker-assisted selection in breeding programs. Analyses of candidate genes can lay a foundation for exploring the mechanism of P. sojae resistance.

Soybean oil is the most widely produced vegetable oil in the world and its content in soybean seed is an important quality trait in breeding programs. More than 100 quantitative trait loci (QTLs) for soybean oil content have been identified. However, most of them are genotype specific and/or environment sensitive. Here, we used both a linkage and association mapping methodology to dissect the genetic basis of seed oil content of Chinese soybean cultivars in various environments in the Jiang-Huai River Valley. One recombinant inbred line (RIL) population (NJMN-RIL), with 104 lines developed from a cross between M8108 and NN1138-2, was planted in five environments to investigate phenotypic data, and a new genetic map with 2,062 specific-locus amplified fragment markers was constructed to map oil content QTLs. A derived F2 population between MN-5 (a line of NJMN-RIL) and NN1138-2 was also developed to confirm one major QTL. A soybean breeding germplasm population (279 lines) was established to perform a genome-wide association study (GWAS) using 59,845 high-quality single nucleotide polymorphism markers. In the NJMN-RIL population, 8 QTLs were found that explained a range of phenotypic variance from 6.3 to 26.3% in certain planting environments. Among them, qOil-5-1, qOil-10-1, and qOil-14-1 were detected in different environments, and qOil-5-1 was further confirmed using the secondary F2 population. Three loci located on chromosomes 5 and 20 were detected in a 2-year long GWAS, and one locus that overlapped with qOil-5-1 was found repeatedly and treated as the same locus. qOil-5-1 was further localized to a linkage disequilibrium block region of approximately 440 kb. These results will not only increase our understanding of the genetic control of seed oil content in soybean, but will also be helpful in marker-assisted selection for breeding high seed oil content soybean and gene cloning to elucidate the mechanisms of seed oil content.

Phytophthora sojae is an oomycete soil-borne plant pathogen that causes the serious disease Phytophthora root rot in soybean, leading to great loss of soybean production every year. Understanding the genetic basis of this plant–pathogen interaction is important to improve soybean disease resistance. To discover genes or QTLs underlying naturally occurring variations in soybean P.sojae resistance, we performed a genome-wide association study using 59,845 single-nucleotide polymorphisms identified from re-sequencing of 279 accessions from Yangtze-Huai soybean breeding germplasm. We used two models for association analysis. The same strong peak was detected by both two models on chromosome 13. Within the 500-kb flanking regions, three candidate genes (Glyma13g32980, Glyma13g33900, Glyma13g33512) had SNPs in their exon regions. Four other genes were located in this region, two of which contained a leucine-rich repeat domain, which is an important characteristic of R genes in plants. These candidate genes could be potentially useful for improving the resistance of cultivated soybean to P.sojae in future soybean breeding.

大豆细菌性斑点病是江淮地区大豆生产中常见病害,但大豆种质资源抗性水平及抗源鉴定工作较少.本研究采用对大豆叶片正反面高压喷雾的接种方法鉴定了江淮地区309份育成品种(系)及亲本材料对大豆细菌性斑点病生理小种S1的抗感反应.结果表明:供试材料抗性差异明显,分别鉴定出高抗和中抗材料61和68份,占总数的19.74％和22.1％,表现为感病和高感的材料共有180份,占总数的58.25％.适合淮北和淮南地区种植的140和169份品种(系)中,抗病材料(高抗+中抗)分别有68和61份,感病材料(感病+高感)分别有72和108份,江淮淮北地区抗病品种(系)的比例高于淮南地区.供试材料抗性反应等级与成熟期等性状存在相关性.同时还发掘出徐豆18、南农99-6等高抗品种,及具有高蛋白、高油特性的优质抗性种质材料.

基因组大片段序列存在/缺失变异(presence/absence variation,PAV)作为一种基于PCR技术、方便快捷的新标记受到人们的关注,但在大豆遗传育种工作中的应用较少.大豆育种计划中一般会有一批核心亲本,揭示其作用特点有助于亲本选配.应用PAV标记对国家大豆改良中心种质创新计划中3个核心亲本(南农86-4、南农88-48和诱处4号)与国内外材料杂交所获得34个组合衍生品系及亲本共154份材料所构建的样本进行核心亲本对其衍生品系遗传贡献分析.结果表明:221个PAV标记的平均等位变异数为2.1,多态性信息含量指数(PIC)平均为0.239,位于基因内和基因间标记的丰富度和PIC平均值相当.基于PAV标记信息可将34个供试组合聚为6类,其中3个大类可分别与3个核心亲本的组合相对应,核心亲本与其衍生品系的遗传距离最小.154份材料可聚为9类,来自同一核心亲本、同一组合的材料多聚在一起,但也存在交叉现象.对23个单交和6个三交组合的亲本遗传贡献率分析表明,共有22个组合(占75.90％)的核心亲本对衍生品系的平均贡献率高于基于系谱的理论值,其中来自本地区的南农86-4、南农88-48对衍生品系的平均遗传贡献值总体上高于其它杂交亲本,而异生态区的诱处4号的贡献率相对较低.PAV标记能在系谱信息基础上进一步反映大豆亲本及衍生品系的亲缘关系.

本研究对国家大豆改良中心种质创新计划中296份稳定品系进行多环境田间试验获得蛋白质含量数据,并利用227对PAV标记和93对SSR标记基因型数据进行蛋白质QTL关联分析.结果表明,供试材料蛋白质含量变幅为34.50％ ～ 53.84％,平均遗传率达92.57％,品系与环境间存在极显著互作.6个环境下与蛋白质含量显著(P≤0.05)和极显著(P≤0.01)关联的位点分别有139、44个,分布于大豆基因组的20个染色体,其中在2个以上环境被重复检测到的有8个.以平均蛋白质含量＞45％为标准筛选出64个新品系,分别来自33个杂交组合.基于AMMI模型的品种稳定性分析发现高蛋白含量材料Di变幅达0.08 ～ 1.27,来自菜豆5号/泰兴黑豆、南农73-932/早熟18等组合的新品系在6个环境表现稳定;基于分子标记可将高蛋白品系聚为6类,具有共同亲本的材料多聚在一起,且高值材料(＞47％)和低值材料的杂交组合也被区分开.对5个在多环境都检测到的关联位点进行分析,发现64份高蛋白材料分别含0～4个优异等位变异,材料间优异等位变异的构成存在差异.聚合不同优异等位变异可能创造更高蛋白质含量的新品系.

Flooding stress after soybean planting can cause poor seedling stand and consequently yield reduction.Development of soybean cultivars with good waterlogging tolerance is one of the most effective ways to reduce production loss.In the present paper,two recombinant inbred line populations,NJRISP and NJRINP,derived from the crosses between two sensitive cultivars Nannong 493-1,Nannong 86-4 and one tolerant wild soybean PI342618B were used to reveal the variation and inheritance of the tolerance to seed-submergence at germination stage.It showed that there were significant differences and high genetic variation in the RIL populations.The heritability values of the germination rate at 3 and 5 d after treatment,root length and sprout length were higher than those of other indicators,and there were highly positive correlations among the four traits.The results from segregation analysis under major genes plus polygene mixed inheritance model showed that the inheritance of germination rate at 3 d and 5 d after treatment fitted four major genes model(H or I genetic model).The values of major gene heritability were more than 93%,and the polygene's were relatively low.All the tolerant alleles were only detected in wild soybean,indicating the existence of elite tolerant genes for soybean tolerance breeding.