| China is the second largest maize production country after the US; although in China the maize has surpassed other crops including rice and wheat in production development, planting area and total output, the maize production technology level in China is still far behind when compared with the US. With the acceleration of globalization, our agricultural economy was shocked to some extent, therefore achieving high quality, high yield, broad-resistance, broad adaption in maize is the major goal in our current breeding work. The yield trait of maize is the result of the comprehensive function of multi-agronomic traits, which has a relatively complex genetic basis. Currently, maize scientific researchers has identified a number of maize-yield related traits via different mapping methods, while the mapping results of QTLs controlling the same traits differ greatly due to the different test back-ground in different researches. In addition, selecting population is essential for QTL mapping; employing advanced mapping population can enable repeated identification to ensure the mapping accuracy, only in this way can better combine the identified QTLs and molecular markers then applied in the maize breeding. Thus, integrating maize traits-related QTLs in different researches the analyzing integrated QTL loci using bioinformatics tools to gain candidate regions will be instructional for MAS.In this study, maize yield-related traits QTLs were meta-analyzed to detect consensus QTLs, and stringent quality control was employed for the mapping quality in the integrating process, then these consensus QTLs were analyzed again to identify Hot MQTL via overview analysis and heatmap mapping, in the meantime combining maize yield traits related GEO was combined to identify the maize yield related candidate genes then protein structure prediction and function analysis were conducted for key candidate genes, and the results are as follows1. Stringent quality control was employed for the mapping quality in the QTL meta-analysis process so as to ensure the accuracy of experiment. QTLs were categorized into 5 parent groups and 43 sub-groups, including maize yield-related traitsgrain yield, flowering stage, plant height, kernel quality, resistance, and 2812 QTLs related to high-quality and high-yield were successfully mapped into the consensus map. The results indicated that each chromosome contains different numbers of QTLs and QTLs of different traits are tended to be cluster in each chromosome.2. Single trait overview analysis and multi-traits overview association analysis were conducted for integrated QTLs respectively, and both of the two results showed multi-peak values in the RPL curves of each chromosome. Meanwhile, Heatmap mapping was conducted and the results showed that QTLs of different traits has "hot spots" in different chromosomes.3. Tow strategies were employed when conducting meta-analysis for current QTLs. First,264 MQTLs were gained from the meta-analysis for QTLs of the same trait and 99 Hot MQTLs including 48 HotMQTLs related to yield and 16,11,16,8 HotMQTLs for flowering stage, resistance, plant height, kernel quality, respectively, were screened out. The confidence interval of HotMQTL ranged from 0.22cM-28.93cM with an average value of 10.87cM which narrowed down 32.32%, among them there were 37 HotMQTLs less than 5cM. Meanwhile, the results of conducting QTL meta-analysis for parents of mapping population were agree with the QTL meta-analysis for the same traits.4. By means of homologous alignment for maize candidate genes identified in the HotMQTL with rice and sorghum,18 cloned rice yield-related homologous genes and 4 sorghum yield related genes were identified, including genes controlling traits such as grain yield, flowering stage and plant height. |
