Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France.
Chair of Human Genomics and Evolution, Collège de France, Paris, France.
Mol Ecol Resour. 2024 Nov;24(8):e14015. doi: 10.1111/1755-0998.14015. Epub 2024 Aug 31.
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.
利用过去的等位基因频率已被证明是识别跨时间自然选择影响的关键。然而，这种方法在估计选择的强度 (s) 和时间 (T) 时存在不精确，尤其是在特定时期古代样本稀缺的情况下。在这里，我们旨在绕过在任意定义的过去时期计算等位基因频率，并通过实施卷积神经网络 (CNN) 算法来改进选择参数的估计，该算法直接使用跨时间采样的古代基因型。使用计算机模拟，我们首先表明，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，无论假设的选择模型如何以及可用的古代基因型数量如何。当将这种方法应用于现代和古代欧洲的经验数据时，我们复制了报告的新石器时代后欧洲选择事件的增加数量，而与所研究的大陆亚区无关。此外，我们大大改进了一组阳性和阴性选择变体的 ABC 估计的 s 和 T，包括阳性选择的标志性案例和经过实验验证的疾病风险变体。我们的 CNN 预测支持了针对与宿主防御病原体相关的变体的近期阳性和阴性选择的历史，与以前的工作一致，该工作强调了传染病（如结核病）对欧洲的重大影响。这些发现共同表明，检测古代基因组中自然选择的痕迹对于揭示严重人类疾病的历史至关重要。
