Abstract：To search and identify the gene regions which control yield and quality in maize, 64 pairs of SSR (simple sequence repeats) markers for the core 257 groups composed of maize(Zea mays L.) inbred lines were genotyped, linkage disequilibrium(LD) of groups of loci and population structure were analysed on this basis, using the GLM(general linear mode) procedure in TASSEL software to make regression analysis for phenotypic data of plant height, ear height, growth duration, ear length, row number of ear, 100-seed weight, fat content, protein content, starch content and maker data, and determined the interpretation of phenotypic markers of the rate in this study. The results showed that: (1) SSR loci combinations had a certain degree of linkage disequilibrium in the public genetic map. LD pairs of loci of the total sites combined was 20.29% when P<0.01, and the proportion of sites was 12.65% when D'>0.5. (2)SSR analysis of genetic structure of the data showed that groups could be divided into five subgroups.(3) A total of 26 marker loci associated with the 9 traits was identified, mostly concentrated in the 4,6,7 and 10 chromosom. The most markers was chromosome 7, which was possessed of five markers. Eight markers were significantly related with six traits such as plant height, ear height, growth duration, ear length, ear number of rows, protein content at the level of 0.01 (P<0.01).Umc194 was associated with plant height and the phenotype variation explained was 7.2%; umc1741 and phi116 acted on the ear and the phenotype variation explained were11.37% and 8.57% respectively; phi328175 and phi260485 played a vital role in reproductive age and the phenotype variation explained were 4.74% and 5.6% respectively ; umc1741 has effected on ear length and the phenotype variation explained was 5.77%; umc1309 has effected on rows per ear and the phenotype variation explained was 6.68%; bnlg1450 and bnlg1185 have effected on protein content and the phenotype variation explained were 9.41% and 9.81% respectively. The other 18 markers were associated with 9 traits at the level of 0.05(P<0.05). The number of markers which associated with a single trait ranged from one to seven, and the phenotype variation explained varied from 4.74% to 14.31%. There were 30 and 7 markers associated with yield and quality traits respectively. The former number was far more than the later number. One of those markers was simultaneously associated with multiple traits, which may be explained by the traits relationship and the gene pleiotropic effect. Some traits were associated with a marker at the same time, and much of which were according with the result of QTL mapping. The study suggests that those makers and many QTLs associating with yield and quality traits may play important roles to improve those traits in maize.
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