Abstract：
Characterizing the processes underlying reproductive isolation between diverging lineages is central to understanding speciation. Here, we present RIDGE-Reproductive Isolation Detection using Genomic polymorphisms-a tool tailored for quantifying gene flow barrier proportion and identifying the relevant genomic regions. RIDGE relies on an Approximate Bayesian Computation with a model-averaging approach to accommodate diverse scenarios of lineage divergence. It captures heterogeneity in effective migration rate along the genome while accounting for variation in linked selection and recombination. The barrier detection test relies on numerous summary statistics to compute a Bayes factor, offering a robust statistical framework that facilitates cross-species comparisons. Simulations revealed RIDGE\'s efficiency in capturing signals of ongoing migration. Model averaging proved particularly valuable in scenarios of high model uncertainty where no migration or migration homogeneity can be wrongly assumed, typically for recent divergence times <0.1 2Ne generations. Applying RIDGE to four published crow data sets, we first validated our tool by identifying a well-known large genomic region associated with mate choice patterns. Second, while we identified a significant overlap of outlier loci using RIDGE and traditional genomic scans, our results suggest that a substantial portion of previously identified outliers are likely false positives. Outlier detection relies on allele differentiation, relative measures of divergence and the count of shared polymorphisms and fixed differences. Our analyses also highlight the value of incorporating multiple summary statistics including our newly developed outlier ones that can be useful in challenging detection conditions.
摘要：
表征不同谱系之间生殖隔离的基础过程对于理解物种形成至关重要。这里,我们提出了使用基因组多态性的RIDGE-生殖分离检测-这是一种用于定量基因流屏障比例和鉴定相关基因组区域的工具。RIDGE依靠采用模型平均方法的近似贝叶斯计算来适应谱系差异的各种情况。它捕获了沿基因组的有效迁移率的异质性，同时考虑了连锁选择和重组的变化。屏障检测测试依赖于大量汇总统计来计算贝叶斯因子，提供一个强大的统计框架，促进跨物种比较。模拟显示RIDGE在捕获持续迁移信号方面的效率。模型平均被证明在模型不确定性高的情况下特别有价值，在这种情况下，不能错误地假设迁移或迁移同质性，通常适用于最近的发散时间<0.12Ne代。将RIDGE应用于四个已发布的乌鸦数据集，我们首先通过鉴定与配偶选择模式相关的一个众所周知的大基因组区域来验证我们的工具.第二,虽然我们使用RIDGE和传统的基因组扫描发现了异常位点的显著重叠，我们的研究结果表明,之前发现的异常值很可能是假阳性.异常值检测依赖于等位基因分化，差异的相对度量以及共享多态性和固定差异的计数。我们的分析还强调了合并多个汇总统计数据的价值，包括我们新开发的异常数据，这些数据在具有挑战性的检测条件下可能有用。
