Data from: Population divergence with or without admixture: selecting models using an ABC approach

Genetic data have been widely used to reconstruct the demographic history of populations, including the estimation of migration rates, divergence times and relative admixture contribution from different populations. Recently, increasing interest has been given to the ability of genetic data to distinguish alternative models. One of the issues that has plagued this kind of inference is that ancestral shared polymorphism is often difficult to separate from admixture or gene flow. Here, we applied an Approximate Bayesian Computation (ABC) approach to select the model that best fits microsatellite data among alternative splitting and admixture models. We performed a simulation study and showed that with reasonably large data sets (20 loci) it is possible to identify with a high level of accuracy the model that generated the data. This suggests that it is possible to distinguish genetic patterns due to past admixture events from those due to shared polymorphism (population split without admixture). We then apply this approach to microsatellite data from an endangered and endemic Iberian freshwater fish species, in which a clustering analysis suggested that one of the populations could be admixed. In contrast, our results suggest that the observed genetic patterns are better explained by a population split model without admixture.

遗传数据已被广泛用于重建种群的群体历史，包括迁移率、分化时间以及不同种群间相对混合贡献的估算。近年来，学界对遗传数据区分备选演化模型的能力关注度日益提升。长期困扰此类推断的一大难题在于，祖先共享多态性往往难以与基因混合或基因流（gene flow）现象相区分。本研究采用近似贝叶斯计算（Approximate Bayesian Computation, ABC）方法，从候选的分化与混合模型中筛选出最适配微卫星（microsatellite）数据的最优模型。我们开展了模拟研究，结果表明，当数据集规模足够大（包含20个位点）时，可通过较高的准确率识别出生成该数据的模型。这一结果提示，我们能够区分由过往混合事件引发的遗传模式与祖先共享多态性（无混合的种群分化）所导致的遗传模式。随后，我们将该方法应用于伊比利亚半岛特有濒危淡水鱼类的微卫星数据——此前的聚类分析曾提示其中一个种群可能存在混合现象。与之相反，本研究结果显示，观测到的遗传模式更适合用无混合的种群分化模型来解释。