中国水稻科学

中国水稻科学

• 研究报告 • 上一篇    下一篇

建立在基因型值和分子标记信息上的水稻核心种质评价参数

王建成;胡晋*;张彩芳;徐海明;张胜   

  1. 浙江大学 农业与生物技术学院 农学系， 浙江 杭州 310029；* 通讯联系人， E-mail： jhu@dial.zju.edu.cn
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-01-10 发布日期:2007-01-10

Evaluating Parameters of Rice Core Collections Based on Genotypic Values and Molecular Marker Information

WANG Jian-cheng, HU Jin*, ZHANG Cai-fang, XU Hai-ming, ZHANG Sheng   

  • Received:1900-01-01 Revised:1900-01-01 Online:2007-01-10 Published:2007-01-10

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被引次数

32

摘要： 采用蒙特卡洛模拟结合混合线性模型的方法，直接从基因型值和分子标记水平上研究了水稻核心种质的11个评价参数，排除了环境因素的干扰，对各个评价参数做出了准确的评价。研究表明，极差符合率（CR）可以作为评价核心种质代表性的首选参数。平均Simpson指数（MD）、平均ShannonWeaver多样性指数（MI）和平均多态信息含量（MPIC）是评价核心种质代表性的重要参数。变异系数变化率（VR）可以作为评价核心种质变异程度的重要参考参数。多态位点百分率（p）可以作为判断核心种质取样规模的判定参数。均值差异百分率（MD）可作为判断核心种质是否具有代表性的判定参数。本研究筛选出的核心种质评价参数，适用于不同的种质资源群体，可以用作确定核心种质取样比例的判定依据，进而解决了确定核心种质合理取样比例的问题。

关键词: 核心种质, 基因型值, 分子标记信息, 蒙特卡洛模拟, 混合线性模型, 评价参数, 水稻

Abstract: Monte Carlo simulation combining with mixed linear model were used in the research of evaluating parameters for rice core collection based on genotypic values and molecular marker information, which eliminated the interference of environment and obtained more reliable results. The coincidence rate of range (CR) was the optimal evaluating parameter. Mean Simpson index (MD), mean Shannon-Weaver index of genetic diversity (MI) and mean polymorphism information content (MPIC) were important evaluating parameters. The variable rate of coefficient of variation (VR) could act as an important referential parameter for evaluating the variation degree of core collection. Percentage of polymorphic loci (p) could act as a determination parameter for the size of core collection. Mean difference percentage (MD) was a determination parameter for the reliability judgement of core collection. The effective evaluating parameters for core collection selected by present research could be used in different plant germplasm population as criteria for sampling percentage.

Key words: core collection, genotypic value, molecular marker information, Monte Carlo simulation, mixed linear model, evaluating parameter, rice