Associational Analysis of Yield and Quality Traits with Simple Repeat Sequence (SSR) Markers in Maize(Zea mays) | 玉米自交系产量和品质相关性状与简单重复序列(SSR)的关联分析

chinois. 为寻找并确定控制玉米产量、品质的基因组区域，本研究利用64对核心SSR(simple sequence repeats)标记对257份玉米(Zea mays L.)自交系构成的群体进行基因分型，分析群体连锁不平衡位点、群体结构，在此基础上，采用TASSEL软件的GLM(general linear mode)程序对株高、穗位、生育期、穗长、穗行数、百粒重、脂肪含量、蛋白质含量和淀粉含量共9个性状的表型数据与标记进行回归分析，确定标记对表型的解释率。结果表明：(1)在公共图谱上的SSR位点组合都有一定程度连锁不平衡(linkage disequilibrium，LD)，P0.01时，LD成对位点占总位点组合20.29%，D'0.5成对位点比例为12.65%。(2)SSR数据遗传结构分析表明，群体可分为5个亚群。(3)共鉴定出26个标记位点与9个性状相关联，大多集中在4、6、7和10染色体上，其中第7染色体上标记最多，为5个。8个位点的变异分别与株高、穗位、生育期、穗长、穗行数和蛋白质含量等6个表型性状极显著相关(P0.01)，分别为umc1294作用于株高，对表型的解释率为7.2%；umc1741及phi116作用于穗位，对表型的解释率为11.37%和8.57%；phi328175及phi260485作用于生育期，对表型的解释率为4.74%和5.6%；umc1741作用于穗长，对表型的解释率为5.77%；umc1309作用于穗行数，对表型的解释率为6.68%；bnlg1450及bnlg1185作用于蛋白质含量，对表型的解释率为9.41%和9.81%；其他18个标记与9个性状显著相关(P0.05)。与单个性状关联的标记数目为1~7个，解释率为4.74%~14.31%。与产量相关性状关联的位点(次)累计为30个，与品质相关性状关联的位点(次)累计为7个，位点数目上品质性状远少于产量性状。部分标记与多个性状关联，可能是性状相关或一因多效的遗传基础，一些标记同时与某性状关联，多数标记与定位于遗传图谱的QTL(quantitative trait loci)一致，也有互补性。研究结果表明，这些位点及其区域内存在很多与产量、品质等性状相关的QTL，对提高玉米产量、改善玉米品质可能起到重要作用。

anglais. 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 P0.01, 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 (P0.01).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(P0.05). 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.