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在隔离-迁移模型中识别针对基因流的选择位点。

Identifying loci under selection against gene flow in isolation-with-migration models.

机构信息

Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA.

出版信息

Genetics. 2013 May;194(1):211-33. doi: 10.1534/genetics.113.149211. Epub 2013 Mar 2.

Abstract

When divergence occurs in the presence of gene flow, there can arise an interesting dynamic in which selection against gene flow, at sites associated with population-specific adaptations or genetic incompatibilities, can cause net gene flow to vary across the genome. Loci linked to sites under selection may experience reduced gene flow and may experience genetic bottlenecks by the action of nearby selective sweeps. Data from histories such as these may be poorly fitted by conventional neutral model approaches to demographic inference, which treat all loci as equally subject to forces of genetic drift and gene flow. To allow for demographic inference in the face of such histories, as well as the identification of loci affected by selection, we developed an isolation-with-migration model that explicitly provides for variation among genomic regions in migration rates and/or rates of genetic drift. The method allows for loci to fall into any of multiple groups, each characterized by a different set of parameters, thus relaxing the assumption that all loci share the same demography. By grouping loci, the method can be applied to data with multiple loci and still have tractable dimensionality and statistical power. We studied the performance of the method using simulated data, and we applied the method to study the divergence of two subspecies of European rabbits (Oryctolagus cuniculus).

摘要

当基因流存在时出现分歧时,可能会出现一种有趣的动态,即针对与种群特定适应性或遗传不相容性相关的位点的基因流选择,可能导致净基因流在整个基因组中发生变化。与受选择位点相关的基因座可能会经历基因流减少,并可能因附近选择清扫的作用而经历遗传瓶颈。来自这些历史的数据可能与传统的中性模型方法对人口推断的拟合不佳,因为这些方法将所有基因座都视为同样受到遗传漂变和基因流的影响。为了在面对这种历史和选择影响的基因座的情况下进行人口推断,我们开发了一种隔离与迁移模型,该模型明确规定了迁移率和/或遗传漂变率在基因组区域之间的变化。该方法允许基因座落入多个组中的任何一个,每个组都有一组不同的参数,从而放宽了所有基因座都具有相同人口统计学的假设。通过对基因座进行分组,该方法可以应用于具有多个基因座的数据,并且仍然具有可处理的维度和统计能力。我们使用模拟数据研究了该方法的性能,并应用该方法研究了欧洲兔(Oryctolagus cuniculus)两个亚种的分化。

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