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Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of <i>s</i> and <i>T</i> for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease-risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. 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引用次数: 0
摘要
事实证明,利用过去的等位基因频率是识别跨时间自然选择影响的关键。然而,这种方法存在对选择强度(s)和时间(T)估计不精确的问题,尤其是在特定时代的古样本稀缺的情况下。在这里,我们的目标是通过实施卷积神经网络(CNN)算法,直接使用跨时间采样的古代基因型,绕过计算任意定义的过去时代的等位基因频率,并完善选择参数的估计。通过计算机模拟,我们首先证明了基于基因型的 CNN 始终优于基于过去等位基因频率轨迹的近似贝叶斯计算(ABC)方法,而与假设的选择模型和可用的古代基因型数量无关。将这种方法应用于现代和古代欧洲人的经验数据时,我们复制了新石器时代后欧洲选择事件数量增加的报道,与所研究的大陆亚区无关。此外,我们还大大改进了对一系列正选择和负选择变异的基于 ABC 的 s 和 T 估计,其中包括标志性的正选择案例和经实验验证的疾病风险变异。我们的 CNN 预测支持近期针对与宿主防御病原体有关的变体的正选择和负选择的历史,这与以前的工作相一致,以前的工作强调了传染性疾病(如结核病)在欧洲的重大影响。这些发现共同表明,检测古代基因组上自然选择的足迹对于揭示人类严重疾病的历史至关重要。
Deep estimation of the intensity and timing of natural selection from ancient genomes
Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease-risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases.
期刊介绍:
Molecular Ecology Resources promotes the creation of comprehensive resources for the scientific community, encompassing computer programs, statistical and molecular advancements, and a diverse array of molecular tools. Serving as a conduit for disseminating these resources, the journal targets a broad audience of researchers in the fields of evolution, ecology, and conservation. Articles in Molecular Ecology Resources are crafted to support investigations tackling significant questions within these disciplines.
In addition to original resource articles, Molecular Ecology Resources features Reviews, Opinions, and Comments relevant to the field. The journal also periodically releases Special Issues focusing on resource development within specific areas.