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甜高粱种质资源主要农艺性状的主成分及聚类分析 被引量:16

Main Agronomic Characters of Sweet Sorghum Germplasm:Principal Component Analysis and Cluster Analysis
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摘要 为了解甜高粱种质资源的多样性,充分挖掘利用其有益基因,提高甜高粱育种效率。本试验对15份甜高粱种质资源的7个农艺性状进行遗传变异、相关性、主成分及聚类分析。结果表明:7个农艺性状中锤度变异系数最大,为31.22%;茎粗变异系数最小,为7.73%。相关性分析结果显示,株高与茎粗呈极显著负相关,与穗粒重、千粒重呈弱负相关;锤度与其他6个性状相关性较弱。主成分分析结果表明,株型因子、籽粒产量因子、锤度因子累计贡献率达83.115%,可基本反映7个农艺性状的遗传信息。通过聚类分析将15份甜高粱种质资源分为4大类,各类群间农艺性状差异明显,其中第Ⅲ、第Ⅳ类群性状优良,可作为核心种质资源进一步挖掘利用。 To understand the diversity of germplasm, explore beneficial genes and improve breeding efficiency of sweet sorghum, we carried out the genetic variation, correlation, principal component and cluster analysis of 7 agronomic characters of 15 sweet sorghum germplasm resources. The results showed that: the variation coefficient of brix was the largest(31.22%) and that of stem diameter was the smallest(7.73%) among the 7 agronomic characters;the correlation analysis showed that plant height was significantly and negatively correlated with stem diameter, and was negatively correlated with 1000-grain weight and weight of grains per panicle;the correlation between brix and the other 6 characters was weak;the principal component analysis showed that the cumulative contribution rate of the plant type factor, the grain yield factor and the bix factor was 83.115%, reflecting the genetic information of the 7 agronomic characters;the 15 sweet sorghum germplasm resources could be divided into 4 types with cluster analysis, the difference of agronomic characters among the 4 types was obvious;the Ⅲ and Ⅳ type with superior characters could be further excavated and utilized as a core germplasm resource.
作者 王官 赵威军 张阳 邵荣峰 张伟 薛丁丁 范国华 王磊 Wang Guan;Zhao Weijun;Zhang Yang;Shao Rongfeng;Zhang Wei;Xue Dingding;Fan Guohua;Wang Lei(Sorghum Institute, Shanxi Academy of Agricultural Sciences, Jinzhong 030600, Shanxi, China)
出处 《农学学报》 2019年第5期5-9,共5页 Journal of Agriculture
基金 山西省农业科学院生物育种工程项目"甜高粱光敏品系创制及新品种选育"(17yzgc033) 高粱遗传与种质创新山西省重点实验室青年基金"秋水仙素诱导甜高粱染色体加倍效应研究"(2018Q-4)
关键词 甜高粱 农艺性状 主成分分析 聚类分析 遗传变异 Sweet Sorghum Agronomic Character Principal Component Analysis Cluster Analysis Genetic Variation
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