Detecting isolation by distance using phylogenies of genes.
Detecting isolation by distance using phylogenies of genes.
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发表时间：
1990-09
期刊：
影响因子：
3.3
通讯作者：
M. Slatkin;Wayne P. Maddison
中科院分区：
文献类型：
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作者：
M. Slatkin;Wayne P. Maddison
我们介绍了一种分析从地理结构群体中采样的基因的系统发育的方法。简约法可用于计算 s，即采样的种群对之间的最小迁移事件数，并且 s 的值可用于估计有效迁移率 M、当地种群大小为 N 的岛屿模型中的 Nm 值以及将产生相同 s 值的迁移率 m。大量模拟表明，在一维和二维距离隔离模型中，M 与样本对之间的地理距离之间存在简单的关系。垫脚石模型和晶格模型都进行了模拟。如果对相距 k 步的两个 deme 进行采样，则 s（s 的平均值）在一维模型中仅是 k/(Nm) 的函数，在二维模型中仅是 k/(Nm)2 的函数。此外，log(M)近似为log(k)的线性函数。在一维模型中，回归系数大约为-1，在二维模型中，回归系数大约为-0.5。使用来自多个位置的数据，log(M) 对 log(距离) 的回归可以指示处于平衡状态的种群中是否存在距离隔离，并且可以允许估计相邻采样位置之间的有效迁移率。讨论了分析来自地理结构群体的 DNA 序列数据的替代方法。介绍了我们的方法在 R. L. Cann、M. Stoneking 和 A. C. Wilson 的人类线粒体 DNA 数据中的应用。
We introduce a method for analyzing phylogenies of genes sampled from a geographically structured population. A parsimony method can be used to compute s, the minimum number of migration events between pairs of populations sampled, and the value of s can be used to estimate the effective migration rate M, the value of Nm in an island model with local populations of size N and a migration rate m that would yield the same value of s. Extensive simulations show that there is a simple relationship between M and the geographic distance between pairs of samples in one- and two-dimensional models of isolation by distance. Both stepping-stone and lattice models were simulated. If two demes k steps apart are sampled, then, s, the average value of s, is a function only of k/(Nm) in a one-dimensional model and is a function only of k/(Nm)2 in a two-dimensional model. Furthermore, log(M) is approximately a linear function of log(k). In a one-dimensional model, the regression coefficient is approximately -1 and in a two-dimensional model the regression coefficient is approximately -0.5. Using data from several locations, the regression of log(M) on log(distance) may indicate whether there is isolation by distance in a population at equilibrium and may allow an estimate of the effective migration rate between adjacent sampling locations. Alternative methods for analyzing DNA sequence data from a geographically structured population are discussed. An application of our method to the data of R. L. Cann, M. Stoneking and A. C. Wilson on human mitochondrial DNA is presented.