Measurement of genetic structure within populations using Moran's spatial autocorrelation statistics
Measurement of genetic structure within populations using Moran's spatial autocorrelation statistics
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DOI：
10.1073/pnas.93.19.10528
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发表时间：
1996-09-17
影响因子：
11.1
通讯作者：
Li, TQ
中科院分区：
文献类型：
--
作者：
Epperson, BK;Li, TQ
种群内遗传变异的空间结构是影响进化和生态过程的重要因素，利用空间自相关统计可以对种群内遗传变异的空间结构进行详细分析。本文描述了空间自相关统计的统计性质，并基于遗传变异的固定模式数据，提出了基因扩散的估计方法。大量的蒙特卡罗模拟和各种各样的采样策略被利用。结果表明，空间自相关统计是高度可预测性和信息。因此，中性理论的强假设检验可以公式化。最引人注目的是，鲁棒估计的基因扩散，可以得到实际的样本量。还描述了关于最佳采样策略的细节。
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.