Excoffier L, Smouse P E, Quattro J M
Center for Theoretical and Applied Genetics (CTAG), Cook College, Rutgers University, New Brunswick, New Jersey 08903-0231.
Genetics. 1992 Jun;131(2):479-91. doi: 10.1093/genetics/131.2.479.
We present here a framework for the study of molecular variation within a single species. Information on DNA haplotype divergence is incorporated into an analysis of variance format, derived from a matrix of squared-distances among all pairs of haplotypes. This analysis of molecular variance (AMOVA) produces estimates of variance components and F-statistic analogs, designated here as phi-statistics, reflecting the correlation of haplotypic diversity at different levels of hierarchical subdivision. The method is flexible enough to accommodate several alternative input matrices, corresponding to different types of molecular data, as well as different types of evolutionary assumptions, without modifying the basic structure of the analysis. The significance of the variance components and phi-statistics is tested using a permutational approach, eliminating the normality assumption that is conventional for analysis of variance but inappropriate for molecular data. Application of AMOVA to human mitochondrial DNA haplotype data shows that population subdivisions are better resolved when some measure of molecular differences among haplotypes is introduced into the analysis. At the intraspecific level, however, the additional information provided by knowing the exact phylogenetic relations among haplotypes or by a nonlinear translation of restriction-site change into nucleotide diversity does not significantly modify the inferred population genetic structure. Monte Carlo studies show that site sampling does not fundamentally affect the significance of the molecular variance components. The AMOVA treatment is easily extended in several different directions and it constitutes a coherent and flexible framework for the statistical analysis of molecular data.
我们在此提出一个用于研究单一物种内分子变异的框架。关于DNA单倍型差异的信息被纳入方差分析格式，该格式源自所有成对单倍型之间平方距离的矩阵。这种分子方差分析（AMOVA）产生方差成分估计值和F统计量类似物，在此指定为φ统计量，反映了在不同层次细分水平上的单倍型多样性相关性。该方法足够灵活，能够适应几种替代输入矩阵，对应于不同类型的分子数据以及不同类型的进化假设，而无需修改分析的基本结构。使用置换方法对方差成分和φ统计量的显著性进行检验，消除了方差分析中常规但不适用于分子数据的正态性假设。将AMOVA应用于人类线粒体DNA单倍型数据表明，当将单倍型之间分子差异的某种度量引入分析时，群体细分能得到更好的解析。然而，在种内水平，通过了解单倍型之间的确切系统发育关系或通过将限制性位点变化非线性转化为核苷酸多样性所提供的额外信息，并不会显著改变推断出的群体遗传结构。蒙特卡罗研究表明，位点抽样从根本上不会影响分子方差成分的显著性。AMOVA处理很容易在几个不同方向上扩展，它构成了一个用于分子数据统计分析的连贯且灵活的框架。
