| Corn as an important forage, industrial materials and food crops, plays a significant role in the development on China’s grain production. Yield traits is a quantitative character, which are controlled by polygenes with a complex genetic mechanism, with lower heritability and can be easily affected by environment. Grain yield was improved slowly due to there is no breakthrough for the complex genetic basis of yield trait in the lat ten years. In this research, based on our previous study on IBM Syn10 DH population Binmap construction, a new genetic linkage Binmap was got for the grain yield and related traits. QTL/gene fine mapping for phenotypic traits under different environments were done to find candidate gene(s) controlling grain yield and reveal the genetic basis of grain yield, which are of great importance to improve grain yield. The main results are as follows:1. Phenotypic data of IBM Syn10 DH population, collected from in Haerbin (Heilongjang) and Mianyang (Sichuan) in 2013, were statistical analysed. The results showed that there is no significant difference between the groups, while the difference among families, environments, and families×environments are significant, indicating the grain yield could be easily affected by environment.2. In this research, the phenotypic results of Haerbin, Mianyang and the mean value of the two locations were analysed for six yield-related traits of IBMSyn10 DH population. The results showed all the six traits variation tend to be continuous in line with the normal distribution, confirm that this population could be used for research on yield trait QTL analysis. The correlation analysis showed that yield-related traits similar correlation exists in two locations, that is both the phenotype correlation and genetic correlation are high.3. The Binmap genetic linkage map of IBM Syn10 DH population containing 125 lines was re-constructed by using a "onemap" package of R software based on our previous research on the same population.6277 binmarkers were integrated in this map covering a 4558.4 cM maize genome and 277.37-722.64 cM chromosome region, the genetic distance of the binmarkers ranged from 0.01 cM to 14.90 cM with an mean value of 0.73 cM.4. The yield phenotypic data from Haerbin, Mianyang and the mean value were used for QTL analysis conducted by using QTL Cartographer Unix version 1.17f using composite interval mapping QTL mapping methods. Totally,137 corn yield-related traits QTLs were detected under the two environments. The number of QTL number for grain weight is 28, ear diameter QTL is 10, ear rows QTL is 25, ear length QTL is 24, ear weight QTL is 24, and cob diameter QTL is 26. Among them,7 QTLs including qre-3-1 for grain weight, qre-1-1, qre-4-1, and qre-5-1 for ear rows, qel-9-1 for ear length, qew-3-2 for ear weight, qcd-4-1 and qcd-5-1 for cob diameter were detected twice in the two environments, located in the same loci with other report within a smaller region. Meanwile, there were also some QTLs which havn’t reported before, like qre-7-2, qre-9-1, and qre-9-2 for ear rows, qew-3-2, qew-3-3, qew-4-2, and qew-9-3 for grain weight, qel-4-1 for ear length, qew-5-1, qew-6-1, and qew-9-2 for ear weight, qcd-3-2, qcd-4-1, qcd-5-1, qcd-6-3 and qcd-6-5 for cob diameter. In this study, all the QTL detected could be stable expressed and provided support for gene cloning and research on grain yield. |
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