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Maize is the most important crop of food and fodder, how to increase the output of maize was a major target of corn breeding. In order to achieve the target, breeders took breeding density-tolerance maize varieties as primary breeding direction and emphasize the importance of plant type breeding to improve light energy utilization and coordinate contradiction between group and individual. And the flowering related traits were as important as plant type related traits. Therefore, the objective of this study was to mapping and analysis genetic effect for plant architectures and flowering components based on a genome-wide simple sequence repeat (SSR) genotyping and on the plant and growth period related traits at two different environment, using a four-way cross population which constructed deriving from the inbreed line 276,72,A188 and Jiao 51. It was the first time that four-way cross populatio was used for constructing genetic linkage map and QTL mapping.The major results were as follows.1. A genetic linkage map containing 213 SSR markers was constructed based on an F2 population with 277 individuals.The map spanned a total of 1626.3 cM with an average interval of 7.64 cM.For the study of 213 markers, there are 187 sites alleles frequency in accordance with the rate of 1:1 or 1:1:1:1 or 1:2:1 and a total of 26 markers (11.8%) showed a distorted segregation. The result of goodness test indicated that the four-way cross population used in this study was a random population and it could be used for genetic mapping and analyzing QTLs. The genetic lingkage map of this study showed that it’s feasible and practical and it can improve the efficiency and accuracy of mapping.2. Transgressive segregation were observed for six plant type related traits and three growth period related traits in the four-way cross population. Normal distribution was observed for all traits. The heritability of six plant type related traits and three flowering related traits were all high, ranging from 0.81 to 0.90, 0.75 to 0.87, respectively.3. Thirty-one and forty-three QTL were detected for six plant type related traits under Zhengzhou and Jiyuan environment, respectively, which in combined analysis is thirty-six. Phenotypic variation explained by a single QTL varied from 4.9% to 25.4%, and forty-eight QTL explained up to 10% phenotypic variation. Of these, qPH1a, qPH8a, qPH8b, qPH9, qEH1, qEH3b, qEH3c, qEH8a, qEH9, qLN1a, qLN8a, qLN9, qLNAE3, qLNAE4, qLNAE8a, qLNAE8b, qTTL1b, qTTL2a, qTTL4b, qTTL5a, qTTL5b, qTTL5d, qTTL6b, qTTL9, qLA1b, qLA1c, qLA2b, qLA4a, qLA5, qLA7, qLA8a, qLA8b were common under two different environments and in combined analysis, which showed stability across environments.4. Thirty and twenty-seven QTL were detected for three flowering related traits under Zhengzhou and Jiyuan environment, respectively, which in combined analysis is thirty-four. Phenotypic variation explained by a single QTL varied from 4.9% to 14.1%, and thirteen QTL explained up to 10% phenotypic variation. Of these, qTE1a, qTE2, qTE3a, qTE3b, qTE4, qTE6, qTE8, qTE9b, qTE9c, qTE10b, qSE1a, qSE1c, qSE2, qSE3, qSE4a, qSE7b, qSE8a, qSE9a, qSE9b, qAN1, qAN2, qAN3a, qAN3b, qAN3c, qAN3d, qAN4a, qAN4b, qAN7, qAN8, qAN9a, qAN9b, qAN10a, qAN10c were common under two different environments and in combined analysis, which showed stability across environments.5. QTLs detected for plant-type and flowering related traits in this study tended to be clustered together. For example, five QTLs (qPH1b, qLN1b, qLNAE1b, qTTL1c, qLA1a ) related to plant height, leaf number, leaf number above ear, total tassel length, leaf area, respectively, were all in bin 1.06-1.07 and five QTLs (qEH3b, qLNAE3, qLA3a, qTE3b, qAN3b) related to ear height, leaf number above ear, leaf area, tassel emergence, anthesis, respectively, were all in bin 3.05-3.06. The reason of QTLs clustered together might be related to the correlations among different characters. These QTLs which were in the same marker interval of the same chromosome and related to different traits of plant type or flowering can greatly improve the efficiency of MAS as we could select one but in favour of multi-effect. At the same time it also proved that using a four-way cross population was not only practical but also could greatly improve the efficiency of QTL mapping in maize.
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