Recent research shows that introgression between closely-related species is an important source of adaptive alleles for a wide range of taxa. Typically, detection of adaptive introgression from genomic data relies on comparative analyses that require sequence data from both the recipient and the donor species. However, in many cases, the donor is unknown or the data is not currently available. Here, we introduce a genome-scan method—VolcanoFinder—to detect recent events of adaptive introgression using polymorphism data from the recipient species only. VolcanoFinder detects adaptive introgression sweeps from the pattern of excess intermediate-frequency polymorphism they produce in the flanking region of the genome, a pattern which appears as a volcano-shape in pairwise genetic diversity. Using coalescent theory, we derive analytical predictions for these patterns. Based on these results, we develop a composite-likelihood test to detect signatures of adaptive introgression relative to the genomic background. Simulation results show that VolcanoFinder has high statistical power to detect these signatures, even for older sweeps and for soft sweeps initiated by multiple migrant haplotypes. Finally, we implement VolcanoFinder to detect archaic introgression in European and sub-Saharan African human populations, and uncovered interesting candidates in both populations, such as TSHR in Europeans and TCHH-RPTN in Africans. We discuss their biological implications and provide guidelines for identifying and circumventing artifactual signals during empirical applications of VolcanoFinder.

最近的研究表明，密切相关物种之间的基因渗入是广泛分类群适应性等位基因的重要来源。通常，从基因组数据中检测适应性基因渗入依赖于需要来自受体和供体物种的序列数据的比较分析。然而，在许多情况下，捐赠者是未知的，或者目前无法获得数据。在这里，我们介绍了一种基因组扫描方法——VolcanoFinder——仅使用来自受体物种的多态性数据来检测最近的适应性渐渗事件。火山探测器从它们在基因组侧翼区域产生的过量中频多态性模式中检测自适应基因渗入扫描，这种模式在成对遗传多样性中表现为火山形状。使用聚结理论，我们得出这些模式的分析预测。基于这些结果，我们开发了一种复合似然检验来检测相对于基因组背景的适应性基因渗入的特征。模拟结果表明，VolcanoFinder具有很高的统计能力来检测这些特征，即使对于较旧的扫描和由多个迁移单倍型发起的软扫描也是如此。最后，我们实现VolcanoFinder检测欧洲和撒哈拉以南非洲人群的古老基因渗入，并在这两个人群中发现了有趣的候选者，例如欧洲人的TSHR和非洲人的TCHH - RPTN。我们讨论了它们的生物学意义，并为在VolcanoFinder的经验应用过程中识别和规避伪信号提供了指导。