| Soybean [Glycine max (L.) Merr.] is an important food and cash crop. Seed yield of soybean is directly or indirectly determined by many agronomic and economical traits, such as plant height, flowering time, maturity, yield components and photosynthetic performance. These traits are controlled in a complex manner by quantitative trait loci (QTLs), and environmental variations can trigger and modify the actions of related genes. In soybean, it is becoming more difficult to improve yield using traditional breeding methods. The development of genomics has provided alternative tools to improve breeding efficiency in plant breeding programs. Use of molecular breeding techniques to breed high-yielding soybean varieties has become one of the main objectives in soybean modern breeding programs. Using the functional markers located in or closely linked to the gene or QTL underlying the target trait in marker-assisted selection (MAS), or use of functional genes in transgenic breeding, can greatly increase breeding efficiency and directionally improve soybean yield.In present study, in order to identify the causal polymorphisms underlying the soybean yield-related traits, association analysis between SSR markers and soybean yield-related traits was conducted in candidate regions of major QTLs of soybean yield-related traits. Then genome-wide association analysis was performed in different environments to identify SNPs and haplotypes underlying soybean yield, yield components, and photosynthesis-related traits,. On this basis, combined soybean genomic information with bioinformatics, yield-related candidate gene was screened, and thus, candidate-gene association analysis was conducted to identified the polymorphisms and haplotypes in the yield-related candidate gene. The main results of this study are as follows:Using80SSR/EST-SSR/DT1markers, we conducted fine mapping of soybean yield-related traits based on the association analysis across five different environments in the candidate regions of major QTLs of soybean yield-related traits. The results showed that there was extensive phenotypic variation in196soybean landraces. Except for seed weight, days to flowering and plant height, the other traits were significantly related. The analysis indicated that there was extensive linkage disequilibrium intra-chromosome or inter-chromosome among chromosome6,7and19, and that the LD decayed rapidly in our studied population. Based MLM model, we detected40markers significantly associated with yield-related traits across five environments. Of which,26loci can be detected in all environments.7loci were co-associated with two or more different traits in all environments. The locus of Satt150on chromosome7was detected in all environments associated with soybean yield and other related traits. Moreover, this locus was detected repeatedly associated with soybean yield and related traits in previous reports. Based on the principle of linkage disequilibrium, the QTL underlying the soybean yield traits was fine mapped in the interval of518kb between the flanking markers on both sides of Satt150(GMES2109-Satt316).In order to identify the causal polymorphisms underlying soybean yield and yield components, in this study, we further evaluated a group of191soybean landraces in five different environments using1536single-nucleotide polymorphisms and209haplotypes. The analysis revealed that there was abundant phenotypic and genetic diversity in the studied population. The191soybean landraces could be divided into two subpopulations using1142SNPs with minor allele frequency of≥10%. No or weak relatedness was detected between pair-wise accessions within this population. The average decay distance of linkage disequilibrium intra-chromosome was estimated at approximately500kb. Genome-wide association analysis based on a unified mixed model approach identified19SNPs and5haplotypes associated with number of pods per plant, number of seeds per plant,100-seed weight, and seed yield in three or more different environments. Nine markers were co-associated with two or more different traits. Many markers were located in or close to quantitative trait loci mapped by linkage analysis in previous reports. The SNPs and haplotypes identified in this study will help to further understand the genetic basis of soybean yield and its components. This information lays the foundation for high-yield molecular breeding of soybean.According to the results of fine mapping for QTLs underlying soybean yield-related traits in the candidate regions of major QTLs of soybean yield-related traits and the genome-wide association analysis, using the soybean genomic sequence information and bioinformatics tools, we screened a candidate gene GmGA3ox on chromosome7, which may be associated with soybean yield-related traits. The sequence polymorphism analysis of GmGA3ox in191soybean landraces identified a total of16SNPs and4Indels (with minor allele frequency of≥10%), and these loci consisted five distinct haplotype blocks. Candidate-gene association analysis showed that5SNP/Indels were associated with seed yield in three or more environments. Of which,2loci were associated with seed yield in all environments. In present study, one and two haplotypes were significantly associated with100-seed weight and seed yield in three or more environments, respectively. Of which, GmGA3ox_H4was identified in all environments significantly associated with100-seed weight, with contribution rate of9.67%, the difference of seed weight between the optimal haplotype and worst haplotype was about4g. GmGA3ox_H5was significantly associated with the seed yield in all environments with contribution rate of20.19%. The yields of the optimal haplotype of GmGA3ox_H5were more than1fold to the worst haplotype. Sequence analysis suggested that GmGA3ox_H4and GmGA3ox_H5were located in the the3’non-coding region, and might be involved in mRNA post-transcriptional regulation. This will lead to a series of changes in metabolic pathways of GA, and results in differences in seed weight and seed yield within the studied population.Based on MLM, genome-wide association analysis was conducted to detect the genetic polymorphisms for chlorophyll content (CCI), chlorophyll fluorescence parameters (JIP-test parameter) and the photosynthetic rate (PN).In present study,123SNPs and22haplotypes were detected significantly associated with photosynthesis-related traits. Of which,47SNPs and11haplotypes were associated with CCI,30SNPs and8haplotypes were associated with Fv/Fm,33SNPs and5haplotypes were associated with ABS/RC,25SNPs and5haplotypes were associated with ETo/TRo,20SNPs and7haplotypes were associated with PIABS.15SNPs and2haplotypes were associated with PN in2008. Among all detected loci,26SNPs were co-associated with two or more traits or parameters. Of which,4loci associated with the JIP-test parameters were identified co-associated with PN,12SNPs and2haplotypes were co-associated with soybean yield or yield components.8 SNPs and2haplotype were detected stably associated with two or more different traits or parameters in two years. Use of these stablely associated loci in soybean molecule breeding, will improve photosynthetic performance and increase soybean yield synchronously. |
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