摘要
利用25对籼粳特异性SSR标记分析81份江淮稻区不同生态型粳稻品种籼粳分化程度,品种TDj值变异幅度为0.773~0.962,以中熟中粳类型品种最高,晚粳类型品种最低。49对多态性SSR引物共检测到131个等位基因,每个位点为2~5个,平均为2.67个;多态性信息量(PIC)为0.068~0.657,平均为0.362,第11染色体平均位点等位基因数和平均PIC值最高。在三个生态型粳稻品种群体中,中熟中粳品种检测出的等位基因数最多,而迟熟中粳品种的平均位点PIC值和Nei基因多样性指数(He)均高于中熟中粳和晚粳品种,表明在DNA水平上迟熟中粳品种的遗传多样性高于中熟中粳和晚粳品种。中熟中粳与迟熟中粳群体之间的遗传相似性最高,遗传距离最近,而中熟中粳与晚粳群体之间的遗传相似性最低,遗传距离最远。UPGMA聚类结果表明,同一育种单位育成品种的品种间遗传距离较小,亲缘关系较近。
Indica-japonica differentiation degree of 81 japonica cultivars belonging to different ecotypes from Jiang-Huai Region were investigated by 25 specific SSR markers of indica-japonica differentiation.TDj values of the cultivars ranged from 0.773 to 0.962,indicating most of the cultivars had a high japonica component degree with the medium maturing mid-season japonica(MMMj) being the highest and late japonica being the lowest.In the 81 japonica cultivars,a total of 131 alleles were detected at 49 SSR loci,and allele number per marker ranged from 2 to 5,with an average of 2.67.Polymorphism information content(PIC) value of 49 SSR primer ranged from 0.068 to 0.657,with the average value 0.362.Among the 12 chromosomes,chromosome 11 showed the highest average allele number and PIC value.Of the three different ecological type japonica cultivars,genetic diversity of the late maturing mid-season japonica(LMMj) was the highest according to PIC and mean heterozygosity per locus(He).The coefficient of genetic similarity was the highest while the value of genetic distance was the lowest between MMMj and LMMj,and the coefficient of genetic similarity was the lowest while the value of genetic distance was the highest between MMMj and late japonica.Clustering analysis with UPGMA method showed cultivars bred in the same breeding organization had a small average genetic distance among cultivars.
出处
《中国水稻科学》
CAS
CSCD
北大核心
2012年第4期431-437,共7页
Chinese Journal of Rice Science
基金
现代农业产业技术体系建设专项
江苏省农业三项工程项目(SX2011002)
江苏省科技成果转化专项(BA2011098)
关键词
粳稻
微卫星标记
籼粳分化
遗传多样性
聚类分析
japonica rice
simple sequence repeats
indica-japonica differentiation
genetic diversity~ cluster analysis