An efficient and robust ABC approach to infer the rate and strength of adaptation.
An efficient and robust ABC approach to infer the rate and strength of adaptation.
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一种高效且稳健的 ABC 方法，用于推断适应率和强度。
DOI：
10.1101/2023.08.29.555322
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Enard,David
中科院分区：
文献类型：
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作者：
Murga-Moreno,Jesús;Casillas,Sònia;Barbadilla,Antonio;Uricchio,Lawrence;Enard,David
推断正选择对基因组的影响仍然是表征物种间适应的最终和近似原因的关键步骤，由于许多其他进化过程的混淆效应，量化正选择仍然是一个挑战。适应性推断的稳健和有效的方法可以帮助描述非模式物种的适应速度和强度，这些物种的人口历史、突变过程和重组模式目前还没有很好地描述。在这里，我们介绍了一种高效且用户友好的麦当劳-克雷特曼测试（ABC-MK）的扩展，用于量化感兴趣的特定谱系的长期蛋白质适应。我们用正演模拟表征了我们的方法的性能，并发现它对许多人口扰动和正选择配置具有鲁棒性，证明了它适用于非模型基因组的应用。我们将ABC-MK应用于人类蛋白质组和一组已知的病毒相互作用蛋白（VIPs），以测试与病毒相互作用的基因的长期适应性。我们在RNA- vip上发现了比dna - vip更强的正选择特征，这表明RNA病毒可能是人类在深度进化时间尺度上适应的重要驱动因素。
Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in nonmodel species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald–Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to nonmodel genomes. We apply ABC-MK to the human proteome and a set of known virus interacting proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.