Robust method for detecting convergent shifts in evolutionary rates.
Robust method for detecting convergent shifts in evolutionary rates.
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DOI：
10.1093/molbev/msz107
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发表时间：
2019-05
影响因子：
10.7
通讯作者：
Raghavendran Partha;Amanda Kowalczyk;N. Clark;M. Chikina
中科院分区：
文献类型：
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作者：
Raghavendran Partha;Amanda Kowalczyk;N. Clark;M. Chikina
识别表型适应的基因组元件是进化生物学中的一个重要问题。比较分析从性状的趋同进化中学习，在准确检测这些因素方面正在获得动力。我们以前开发了一种方法，通过对比显示表型的物种和不显示表型的物种的序列进化模式来预测遗传元素的表型关联。使用这种方法，我们成功地证明了与两种表型适应相关的遗传元件的收敛进化速率变化，即陆地哺乳动物谱系的独立的地下和海洋过渡。我们最初的方法是使用线性回归来计算系统发育树分支上的特定基因进化率。这些速率表示在去除由于背景因素导致的分支上的预期分歧之后，分支上的序列分歧的程度。使用这种回归分析计算的比率显示出一个重要的统计限制，即异方差。我们观察到，平均而言，较长分支的利率表现出较高的方差，并描述了这个问题如何对我们可以用来推断利率变化的置信度产生不利影响。利用数据转换和加权回归的组合，我们开发了一种更新的方法来纠正比率中的异方差。此外，我们还说明了更新的方法在稳健地检测哺乳动物蛋白质编码基因系统发育树中收敛的速率变化以及使用模拟树数据集方面所提供的改进的性能。总体而言，我们提出了我们的基于进化率的方法的一个重要扩展，它在检测进化率的收敛变化方面执行得更稳健和一致。
Identifying genomic elements underlying phenotypic adaptations is an important problem in evolutionary biology. Comparative analyses learning from convergent evolution of traits are gaining momentum in accurately detecting such elements. We previously developed a method for predicting phenotypic associations of genetic elements by contrasting patterns of sequence evolution in species showing a phenotype with those that do not. Using this method, we successfully demonstrated convergent evolutionary rate shifts in genetic elements associated with two phenotypic adaptations, namely the independent subterranean and marine transitions of terrestrial mammalian lineages. Our original method calculates gene-specific rates of evolution on branches of phylogenetic trees using linear regression. These rates represent the extent of sequence divergence on a branch after removing the expected divergence on the branch due to background factors. The rates calculated using this regression analysis exhibit an important statistical limitation, namely heteroscedasticity. We observe that the rates on branches that are longer on average show higher variance, and describe how this problem adversely affects the confidence with which we can make inferences about rate shifts. Using a combination of data transformation and weighted regression, we have developed an updated method that corrects this heteroscedasticity in the rates. We additionally illustrate the improved performance offered by the updated method at robust detection of convergent rate shifts in phylogenetic trees of protein-coding genes across mammals, as well as using simulated tree datasets. Overall, we present an important extension to our evolutionary-rates-based method that performs more robustly and consistently at detecting convergent shifts in evolutionary rates.