| Soybean is a significant oil and food crop in China,and achieving high yield has always been the main objective of soybean breeding.Seed weight,measured by the weight of 100 seeds,is a crucial factor in soybean yield,closely related to both yield and seed usage.Analyzing the genetic basis of soybean seed weight and identifying important QTLs(Quantitative trait loci)controlling the trait has significant theoretical and practical significance for soybean breeding for high yield.Although a large number of seed weight QTLs have been reported,the information provided for molecular-assisted breeding and candidate gene cloning is limited.Thus,this study utilized 281 soybean germplasms in field experiments across multiple environments and employed the genome-wide association analysis method to dissect the genetic basis of soybean seed weight.The results were compared under different environments and different analysis models to identify important loci.Furthermore,candidate genes controlling seed weight were identified by integrating transcriptome data from materials with significant phenotypic differences at different stages of grain development.The main research content and results are as follows:(1)The hundred-seed weight trait of soybean was evaluated in multiple environments in northern Shaanxi using 281 germplasms,and the genetic basis of the trait was analyzed using various genome-wide association analysis models.The soybean germplasms were evaluated for the hundred-seed weight trait in four different field environments.Results indicated that the average variation range of hundred-seed weight across the soybean germplasms was 6.41-38.11 g,demonstrating a wide range of genetic variation that well reflected the genetic basis of the trait.Genome-wide association analysis was performed on soybean hundred-seed weight by combining Single nucleotide polymorphism(SNP)markers that covered the entire genome.Using the single-locus analysis model,we identified a total of 172 significantly associated SNPs(P < 0.001)across four different environments and average conditions.Additionally,using six multi-locus models based on the average of multiple environments and a LOD threshold of 3,we identified a total of 30 Quantitative trait nucleotides(QTNs).The 3Vmr MLM model detected 43 QTNs and 5QEIs(QTN-by-environment interactions)in multiple environments.Integrating the results of single-locus,multi-locus,and 3Vmr MLM models,we identified a total of 215 SNPs significantly associated with hundred-seed weight and 5environment-interaction SNPs,which were distributed on all soybean chromosomes except chromosome 3.(2)The molecular regulatory mechanism of soybean seed weight/size was analyzed using time-series transcriptome data from different phenotype material groups,and six candidate genes for soybean seed weight were predicted by combining the results of genome-wide association analysis.In the single-locus analysis model,the results of genome-wide association analysis were compared across different environments(E1-E4 and the average).Among these comparisons,43 SNP markers were detected in four or more environments.Moreover,by comparing the results across different analysis models,10 SNPs were found to be identified by three or more models,and three SNPs were found to overlap with stable environmental SNPs.Combining these two strategies,we identified a total of 50 stable QTNs controlling soybean seed weight in this study.These markers were distributed within12 linkage disequilibrium blocks(genomic regions),indicating that these regions are important loci for controlling soybean seed weight,including two novel loci(locus 1and locus 3).Among the 12 important loci,multiple important QTNs were found at locus 2,7,9,and 10.Therefore,we designed InDel markers for these four loci.The marker InDel-L2-1(locus 2),InDel-L7-1(locus 7),InDel-L10-1(locus 10),and InDel-L10-2(locus 10)were found to significantly differentiate seed weight among different genotypes,making them useful as locus-specific molecular markers.(3)The molecular regulation mechanism of soybean seed weight/size was analyzed by using the time series transcriptome data of different phenotypic materials.Combined with the Genome-wide association study results,12 candidate genes for soybean seed weight were predicted.Two groups of materials,with significant differences in hundred-seed weight phenotype,were used in this study.Each group consisted of three materials,one with large seeds and the other with small seeds.Transcriptome sequencing was carried out at three developmental stages of soybean seeds,R4,R5,and R6.Comparing the differentially expressed genes(DEGs)between the two groups at each developmental stage,we found that the R5 stage had the greatest number of DEGs(1586).These DEGs were enriched in pathways related to hormone synthesis and metabolism,lipid transport,sugar transport,photosynthesis,and carbon fixation using GO and KEGG analysis.The greatest numbers of DEGs were found between R4 and R5 for both large and small seed groups(9614 and 7834,respectively)when comparing DEGs between adjacent seed developmental stages.These DEGs were enriched in biological processes such as metabolism,cell division,and photosynthesis by GO analysis,and in pathways related to plant hormone signal transduction,starch and sucrose metabolism,and lipid metabolism by KEGG analysis.Moreover,we selected 168 genes across the whole genome that were possible candidates involved in seed weight/size regulation based on the annotation information of DEGs.We identified 248 hub genes by constructing a weighted co-expression network based on gene expression levels.Through comprehensive genome-wide association analysis and transcriptome analysis,we identified 12 candidate genes within significant loci,which could be further targeted for gene cloning and functional validation.In summary,this study utilized 281 germplasm resources and the genome-wide association analysis approach to dissect the genetic architecture of soybean seed weight.By comparing the results of different analytical approaches and environmental conditions,important QTN and genomic intervals(loci)controlling the traits of interest were identified.InDel molecular markers were developed for four loci.Furthermore,by integrating temporal transcriptome data analysis,important candidate genes involved in regulating soybean seed weight were identified.The results of this study provide significant information for the genetic basis of soybean seed weight analysis,molecular-assisted soybean breeding,and cloning of candidate genes related to soybean seed weight. |
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