Detecting Signatures of Positive Selection against a Backdrop of Compensatory Processes
Detecting Signatures of Positive Selection against a Backdrop of Compensatory Processes
复制标题
在补偿过程的背景下检测正选择的特征
DOI：
10.1093/molbev/msaa161
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发表时间：
2020
影响因子：
10.7
通讯作者：
Liberles, David A
中科院分区：
文献类型：
--
作者：
Chi, Peter B;Kosater, Westin M;Liberles, David A
已知检测阳性选择的方法存在局限性。常见的方法不能区分正选择和补偿性共变，这是一个主要的限制。此外，计算非同义与同义取代比（dN/dS）的传统方法没有考虑生物大分子的三维结构，也没有考虑氨基酸之间的差异。它也没有考虑到同义突变（dS）在长进化时间内的饱和，这使得基于密码子的方法对较早的分化无效。这项工作旨在通过开发一种统计模型来解决检测积极选择的这些缺点，该模型可以检查可变半径集群中的替代集群。此外，它使用参数自举方法来区分正选择和补偿过程。先前报道的灵长类动物瘦素蛋白阳性选择病例使用这种方法进行了重新检查。
There are known limitations in methods of detecting positive selection. Common methods do not enable differentiation between positive selection and compensatory covariation, a major limitation. Further, the traditional method of calculating the ratio of nonsynonymous to synonymous substitutions (dN/dS) does not take into account the 3D structure of biomacromolecules nor differences between amino acids. It also does not account for saturation of synonymous mutations (dS) over long evolutionary time that renders codon-based methods ineffective for older divergences. This work aims to address these shortcomings for detecting positive selection through the development of a statistical model that examines clusters of substitutions in clusters of variable radii. Additionally, it uses a parametric bootstrapping approach to differentiate positive selection from compensatory processes. A previously reported case of positive selection in the leptin protein of primates was reexamined using this methodology.