Department of Medicine, University of California-San Diego, CA, USA.
Mol Biol Evol. 2011 Nov;28(11):3033-43. doi: 10.1093/molbev/msr125. Epub 2011 Jun 13.
Adaptive evolution frequently occurs in episodic bursts, localized to a few sites in a gene, and to a small number of lineages in a phylogenetic tree. A popular class of "branch-site" evolutionary models provides a statistical framework to search for evidence of such episodic selection. For computational tractability, current branch-site models unrealistically assume that all branches in the tree can be partitioned a priori into two rigid classes--"foreground" branches that are allowed to undergo diversifying selective bursts and "background" branches that are negatively selected or neutral. We demonstrate that this assumption leads to unacceptably high rates of false positives or false negatives when the evolutionary process along background branches strongly deviates from modeling assumptions. To address this problem, we extend Felsenstein's pruning algorithm to allow efficient likelihood computations for models in which variation over branches (and not just sites) is described in the random effects likelihood framework. This enables us to model the process at every branch-site combination as a mixture of three Markov substitution models--our model treats the selective class of every branch at a particular site as an unobserved state that is chosen independently of that at any other branch. When benchmarked on a previously published set of simulated sequences, our method consistently matched or outperformed existing branch-site tests in terms of power and error rates. Using three empirical data sets, previously analyzed for episodic selection, we discuss how modeling assumptions can influence inference in practical situations.
适应性进化常常以爆发的形式发生，局限于基因中的少数几个位点和系统发育树中的少数几个谱系。一类流行的“分支位点”进化模型提供了一个统计框架，用于搜索此类爆发性选择的证据。为了计算的可行性，当前的分支位点模型不切实际地假设树中的所有分支都可以先验地划分为两个严格的类别——“前景”分支，允许经历多样化的选择爆发，以及“背景”分支，受到负选择或中性选择。我们证明，当背景分支上的进化过程强烈偏离建模假设时，这种假设会导致不可接受的高假阳性或假阴性率。为了解决这个问题，我们扩展了 Felsenstein 的修剪算法，以允许在随机效应似然框架中对分支上的变异（而不仅仅是位点）进行描述的模型进行有效的似然计算。这使我们能够将每个分支位点组合的过程建模为三个马尔可夫替换模型的混合体——我们的模型将特定位点上每个分支的选择类别视为一个未被观察到的状态，该状态独立于任何其他分支的状态进行选择。在对以前发布的一组模拟序列进行基准测试时，我们的方法在功效和错误率方面始终与现有分支位点测试相匹配或超过。使用三个以前为爆发性选择分析过的经验数据集，我们讨论了建模假设如何在实际情况下影响推断。
