使用莫兰空间自相关统计量测量种群内的遗传结构。
Measurement of genetic structure within populations using Moran's spatial autocorrelation statistics.
作者信息
Epperson B K, Li T
机构信息
Department of Forestry, Michigan State University, East Lansing 48824, USA.
出版信息
Proc Natl Acad Sci U S A. 1996 Sep 17;93(19):10528-32. doi: 10.1073/pnas.93.19.10528.
Abstract
Spatial structure of genetic variation within populations, an important interacting influence on evolutionary and ecological processes, can be analyzed in detail by using spatial autocorrelation statistics. This paper characterizes the statistical properties of spatial autocorrelation statistics in this context and develops estimators of gene dispersal based on data on standing patterns of genetic variation. Large numbers of Monte Carlo simulations and a wide variety of sampling strategies are utilized. The results show that spatial autocorrelation statistics are highly predictable and informative. Thus, strong hypothesis tests for neutral theory can be formulated. Most strikingly, robust estimators of gene dispersal can be obtained with practical sample sizes. Details about optimal sampling strategies are also described.
摘要
种群内遗传变异的空间结构是对进化和生态过程的一种重要交互影响，可通过使用空间自相关统计进行详细分析。本文在此背景下描述了空间自相关统计的统计特性，并基于遗传变异现存模式的数据开发了基因扩散估计器。利用了大量的蒙特卡罗模拟和各种各样的抽样策略。结果表明，空间自相关统计具有高度可预测性和信息量。因此，可以制定针对中性理论的有力假设检验。最引人注目的是，在实际样本量下可以获得稳健的基因扩散估计器。还描述了关于最优抽样策略的细节。
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