Nichols R A, Freeman K L M
School of Biological Sciences, Queen Mary, University of London, London, E1 4NS, UK.
Mol Ecol. 2004 Apr;13(4):775-87. doi: 10.1111/j.1365-294x.2004.02112.x.
We propose a method of analysing genetic data to obtain separate estimates of the size (N(p)) and migration rate (m(p)) for the sampled populations, without precise prior knowledge of mutation rates at each locus ( micro(L)). The effects of migration and mutation can be distinguished because high migration has the effect of reducing genetic differentiation across all loci, whereas a high mutation rate will only affect the locus in question. The method also takes account of any differences between the spectra of immigrant alleles and of new mutant alleles. If the genetic data come from a range of population sizes, and the loci have a range of mutation rates, it is possible to estimate the relative sizes of the different N(p) values, and likewise the m(p) and the micro(L). Microsatellite loci may also be particularly appropriate because loci with a high mutation rate can reach mutation-drift-migration equilibrium more quickly, and because the spectra of mutants arriving in a population can be particularly distinct from the immigrants. We demonstrate this principle using a microsatellite data set from Mauritian skinks. The method identifies low gene flow between a putative new species and populations of its sister species, whereas the differentiation of two other populations is attributed to small population size. These distinct interpretations were not readily apparent from conventional measures of genetic differentiation and gene diversity. When the method is evaluated using simulated data sets, it correctly distinguishes low gene flow from small population size. Loci that are not at mutation-migration-drift equilibrium can distort the parameter estimates slightly. We discuss strategies for detecting and overcoming this effect.
我们提出了一种分析遗传数据的方法,可在无需精确了解每个基因座(μ(L))突变率的先验知识的情况下,获得抽样群体大小(N(p))和迁移率(m(p))的单独估计值。迁移和突变的影响可以区分,因为高迁移率会降低所有基因座间的遗传分化,而高突变率只会影响相关基因座。该方法还考虑了移民等位基因谱和新突变等位基因谱之间的任何差异。如果遗传数据来自一系列群体大小,且基因座具有一系列突变率,则有可能估计不同N(p)值的相对大小,同样也能估计m(p)和μ(L)。微卫星基因座可能特别合适,因为突变率高的基因座能更快达到突变 - 漂变 - 迁移平衡,且因为进入群体的突变体谱可能与移民的谱特别不同。我们使用来自毛里求斯石龙子的微卫星数据集证明了这一原理。该方法识别出一个假定新物种与其姐妹物种群体之间的低基因流,而另外两个群体的分化则归因于群体规模小。这些不同的解释从传统的遗传分化和基因多样性测量中并不容易看出。当使用模拟数据集评估该方法时,它能正确区分低基因流和小群体规模。未处于突变 - 迁移 - 漂变平衡的基因座会使参数估计略有偏差。我们讨论了检测和克服这种影响的策略。