Dissecting Genomic Determinants of Positive Selection with an Evolution-Guided Regression Model.
Dissecting Genomic Determinants of Positive Selection with an Evolution-Guided Regression Model.
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用进化引导回归模型剖析正选择的基因组决定因素。
DOI：
10.1093/molbev/msab291
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发表时间：
2022-01-07
影响因子：
10.7
通讯作者：
Huang YF
中科院分区：
文献类型：
--
作者：
Huang YF
在进化基因组学中，理解基因组序列的特征（如基因表达水平）如何决定适应性进化的速率是至关重要的。虽然有许多统计方法，如麦克唐纳-克莱特曼（MK）测试，可用于检查基因组特征和适应率之间的关联，我们目前缺乏一种统计方法来解开一个基因组特征的独立影响，从其他相关的基因组特征的影响。为了解决这个问题，我提出了一种新的统计模型，MK回归，它增强了MK测试与广义线性模型。类似于经典的多元回归模型，MK回归可以同时分析多个基因组特征，以推断基因组特征的独立影响，保持所有其他基因组特征不变。使用MK回归，我确定了许多基因组特征驱动黑猩猩的积极选择。这些特征包括众所周知的特征，如局部突变率、残留暴露水平、组织特异性和免疫基因，以及以前未报道的新特征，如基因表达水平和代谢基因。特别是，我表明，高表达的基因可能有一个更高的适应率比他们的弱表达对应，即使更高的表达水平可能会施加更强的负选择。此外，我表明，代谢基因可能有一个更高的适应率比他们的非代谢对应物，可能是由于最近的变化，在灵长类动物的进化饮食。总的来说，MK回归是阐明适应的基因组基础的有力方法。
In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as gene expression level, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald–Kreitman (MK) test, are available to examine the association between genomic features and the rate of adaptation, we currently lack a statistical approach to disentangle the independent effect of a genomic feature from the effects of other correlated genomic features. To address this problem, I present a novel statistical model, the MK regression, which augments the MK test with a generalized linear model. Analogous to the classical multiple regression model, the MK regression can analyze multiple genomic features simultaneously to infer the independent effect of a genomic feature, holding constant all other genomic features. Using the MK regression, I identify numerous genomic features driving positive selection in chimpanzees. These features include well-known ones, such as local mutation rate, residue exposure level, tissue specificity, and immune genes, as well as new features not previously reported, such as gene expression level and metabolic genes. In particular, I show that highly expressed genes may have a higher adaptation rate than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection. Also, I show that metabolic genes may have a higher adaptation rate than their nonmetabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.
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