Abstract：
Detecting introgression between closely related populations or species is a fundamental objective in evolutionary biology. Existing methods for detecting migration and inferring migration rates from population genetic data often assume a neutral model of evolution. Growing evidence of the pervasive impact of selection on large portions of the genome across diverse taxa suggests that this assumption is unrealistic in most empirical systems. Further, ignoring selection has previously been shown to negatively impact demographic inferences (e.g. of population size histories). However, the impacts of biologically realistic selection on inferences of migration remain poorly explored. Here, we simulate data under models of background selection, selective sweeps, balancing selection, and adaptive introgression. We show that ignoring selection sometimes leads to false inferences of migration in popularly used methods that rely on the site frequency spectrum. Specifically, balancing selection and some models of background selection result in the rejection of isolation-only models in favor of isolation-with-migration models and lead to elevated estimates of migration rates. BPP, a method that analyzes sequence data directly, showed false positives for all conditions at recent divergence times, but balancing selection also led to false positives at medium-divergence times. Our results suggest that such methods may be unreliable in some empirical systems, such that new methods that are robust to selection need to be developed.
摘要：
检测密切相关的种群或物种之间的渗入是进化生物学的基本目标。从种群遗传数据中检测迁移和推断迁移率的现有方法通常采用中性进化模型。越来越多的证据表明，选择对不同分类单元中大部分基因组的普遍影响表明，这种假设在大多数经验系统中都是不现实的。Further,忽略选择以前已经被证明会对人口推断产生负面影响(例如，人口规模历史)。然而,生物现实选择对迁移推断的影响仍未得到充分探讨。这里,我们在背景选择模型下模拟数据，选择性扫描，平衡选择，和适应性渗入。我们证明，忽略选择有时会导致依赖站点频谱（SFS）的常用方法中的错误迁移推断。具体来说,平衡选择和一些背景选择模型导致拒绝仅隔离模型，而支持隔离与迁移模型，并导致迁移率估计升高。BPP,一种直接分析序列数据的方法，在最近的分歧时间显示所有条件的假阳性，但是平衡选择也会导致中等发散时间的假阳性。我们的结果表明，这种方法在一些经验系统中可能是不可靠的，这样就需要开发新的选择方法。
