BACKGROUND The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum (AFS), the distribution of allele frequencies in populations. The joint AFS is commonly used to reconstruct the demographic history of multiple populations, and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. RESULTS Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA). CONCLUSIONS We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history.

中文翻译：

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GADMA：遗传算法，可根据等位基因频谱数据推断多个人群的人口统计历史。

背景技术任何人口的人口历史都被印在组成人口的个体的基因组中。基因信息的最流行，最方便的表示形式之一是等位基因频谱（AFS），即等位基因频率在人群中的分布。联合AFS通常用于重建多个人口的人口历史，并且已经开发了几种基于扩散近似（例如∂a∂i）和常微分方程（例如矩）的方法并将其应用于人口推断。这些方法提供了一个机会，可以在各种研究人员指定的人口统计模型下模拟AFS，并使用基于似然的局部优化来估计最佳模型和相关参数。然而，没有已知的算法可以使用给定的AFS对人口模型进行全局搜索。结果在这里，我们介绍了一种新方法，该方法使用遗传算法来实现全局搜索，以便根据联合AFS数据自动和无监督地推断人口历史。我们的方法是在软件GADMA（人口模型分析的遗传算法，https：//github.com/ctlab/GADMA）中实现的。结论我们通过将GADMA应用于人类和非模型生物的序列数据来证明GADMA的性能，并表明它能够自动推断出一种人口统计模型，该模型与之前手动获得的模型更为接近甚至更好。此外，GADMA可以在接近全球的不同局部最优条件下推断出多种人口模型，