Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA.
Genetics. 2022 Mar 3;220(3). doi: 10.1093/genetics/iyac009.
Neutrality tests such as Tajima's D and Fay and Wu's H are standard implements in the population genetics toolbox. One of their most common uses is to scan the genome for signals of natural selection. However, it is well understood that D and H are confounded by other evolutionary forces-in particular, population expansion-that may be unrelated to selection. Because they are not model-based, it is not clear how to deconfound these tests in a principled way. In this article, we derive new likelihood-based methods for detecting natural selection, which are robust to fluctuations in effective population size. At the core of our method is a novel probabilistic model of tree imbalance, which generalizes Kingman's coalescent to allow certain aberrant tree topologies to arise more frequently than is expected under neutrality. We derive a frequency spectrum-based estimator that can be used in place of D, and also extend to the case where genealogies are first estimated. We benchmark our methods on real and simulated data, and provide an open source software implementation.
中性检验，如 Tajima 的 D 检验和 Fay 和 Wu 的 H 检验，是群体遗传学工具包中的标准工具。它们最常见的用途之一是扫描基因组以寻找自然选择的信号。然而，人们清楚地知道，D 和 H 受到其他进化力量的混淆，特别是与选择无关的种群扩张。由于它们不是基于模型的，因此不清楚如何以有原则的方式对这些检验进行去混淆。在本文中，我们推导出了新的基于似然的方法来检测自然选择，这些方法对有效种群大小的波动具有鲁棒性。我们方法的核心是一种新的树不平衡概率模型，它将 Kingman 的合并扩展到允许某些异常的树拓扑结构比中性情况下更频繁地出现。我们推导出了一种基于频谱的估计器，可以替代 D 检验使用，并且还扩展到了首先估计系统发育的情况。我们在真实和模拟数据上对我们的方法进行了基准测试，并提供了一个开源软件实现。
