生物多样性 ›› 2002, Vol. 10 ›› Issue (4): 438-444.  DOI: 10.17520/biods.2002060

• 论文 • 上一篇    

群体遗传学研究中的数据处理方法Ⅰ.RAPD数据的AMOVA分析

张富民,葛颂   

  1. 中国科学院植物研究所系统与进化植物学重点实验室,北京 100090
  • 收稿日期:2002-05-20 修回日期:2002-09-29 出版日期:2002-11-20 发布日期:2002-11-20

Data analysis in population genetics.I. analysis of RAPD data with AMOVA

ZHANG Fu Min, GE Song*   

  1. Laboratory of systematic and Evolutionary Botany,Institute of Botany,Chinese Academy of Sciences,Beijing 100093
  • Received:2002-05-20 Revised:2002-09-29 Online:2002-11-20 Published:2002-11-20

摘要:

近年来,RAPD数据和AMOVA分析广泛地应用于群体遗传学和保护遗传学研究。然而,由于RAPD标记具显性特点,加上目前进行AMOVA分析所依赖的RAPDistance软件不完善,使得对RAPD数据进行AMOVA分析时存在许多不足。本文介绍了AMOVA分析的基本过程,同时引入一个新的程序DCFA用以替代RAPDistance,并详述了将DCFA与WINAMOVA联用,对RAPD数据进行AMOVA分析的具体步骤与注意事项。最后,以产自中国和巴西8个普通野生稻(Oryza rufipogon)天然群体为例,演示了对RAPD表型数据进行AMOVA分析的过程,讨论了AMOVA分析结果在群体遗传结构上的意义。通过对AMOVA算法的分析,同时比较4种距离系数所得AMOVA结果,我们认为在进行AMOVA分析时选择NEILI距离和欧氏距离平方较为合适,而目前国内使用较多的JACCARD系数不适合AMOVA分析。

关键词: 群体遗传结构, RAPDistance;DCFA;进化距离

Abstract

Random Amplified Polymorphic DNA (RAPD) data have been increasingly used in studies on population and conservation genetics. Analysis of molecular variance (AMOVA) has become one of the important methods utilized in analysis of population genetic structure. Currently, WINAMOVA is the most popular software that is used for AMOVA analysis, and often runs together with RAPDistance, a software to calculate genetic distances. However,cautions should be taken when AMOVA analysis is used to process the RAPD data because of the dominant characteristic of RAPD marker and the limitation of RAPDistance. In this paper, we briefly introduce the principle and algorithm of AMOVA analysis and describe a new program, DCFA, that substitutes for RAPDistance. We also illustrate the processes of running the programs DCFA and WINAMOVA. In addition, we analyze eight Oryza rufipogon populations as an example to demonstrate how to process RAPD data with AMOVA, and discuss the results in light of population genetic structure. Finally, based on the comparison of four commonly used distance coefficients, we suggest that the coefficients of Euclidean squared distance and NEI-LI rather than JACCARD are suitable for analysis of molecular variance.