摘要
以65份来自种质资源库的水稻品种为材料,根据品种的产量性状进行统计分析、多样性指数分析以及相关性分析。结果表明,供试材料在产量性状上存在丰富的遗传多样性,多样性指数介于1.607~2.000之间,单株产量与单株有效穗数、每穗实粒数及穗着粒数存在极显著的正相关,相关系数分别为0.346、0.586和0.508;聚类分析结果表明,65个水稻材料可聚为22个类群,说明供试材料的遗传距离较远,种质资源分化程度较高;通过主成分分析,获得3个主成分,累积贡献率达到85.212%,并通过对应的特征向量获得3个主成分因子,分别为粒数因子、结实率因子及有效穗数因子,可以作为种质资源综合评价指标。通过每个品种的各主成分排名及综合排名对各品种进行综合评价,排前5名的水稻材料分别为奥野占6号、R995、油粘8号、丝苗12和R998。在水稻育种中应注意利用具有丰富遗传多样性的种质资源,并在亲本选配时选择遗传距离较远且综合性状表现差异较大的种质材料。
In order to reveal the genetic diversity of 65 hybrid rice cultivars from germplasm bank, using statistical method, genetic diversity index and correlation index of yield related characters were obtained. According to yield related characters, there existed abundant genetic diversities, the genetic diversity indexes ranged from 1.607 to 2.000. The yield per plant was significantly positive correlated with panicle number per plant, grain number per panicle and total number grains, the correlation coefficients were 0.346, 0.586 and 0.508, respectively. Clustering analysis indicated that germplasm resources had a high degree of differentiation, 65 rice cultivars were clustered to 22 subpopulations. Three principal components was extracted by PCA, including spikelet number, seed setting rate and panicle number. Their accumulative contribution rate was 85.212%, which can be used as comprehensive evaluation for all cultivars. According to order each principal component score and comprehensive score, top five of 65 cuhivars included Aoyezhan6, R995, Youzhang, Simiaol2 and R998. As a result, germplasm resources of abundant genetic diversity should be concerned, and larger differences of genetic distance and comprehensive performance between parents should be selected in rice breeding.
出处
《广东农业科学》
CAS
2017年第3期8-16,共9页
Guangdong Agricultural Sciences
基金
国家科技支撑计划项目(2014BAD01B03-3)
杂交水稻国家重点实验室(湖南杂交水稻研究中心)开放课题基金(2014KF01)
四川省省属高校科研创新团队建设计划项目(14TD0011)
国家现代农业产业技术体系四川水稻创新团队岗位专家项目(川农业函[2014]91)
西南大学博士基金(15ZX7118)
国家转基因生物新品种培育重大专项(2016ZX08001-002)
关键词
水稻
育种
遗传多样性
主成分
聚类分析
rice
breeding
genetic diversity
principal component
cluster analysis