Analysis of population structure in natural populations using genetic data is a common practice in ecological and evolutionary studies. With large genomic data sets of populations now appearing more frequently across the taxonomic spectrum, it is becoming increasingly possible to reveal many hierarchical levels of structure, including fine-scale genetic clusters. To analyze these data sets, methods need to be appropriately suited to the challenges of extracting multilevel structure from whole-genome data. Here, we present a network-based approach for constructing population structure representations from genetic data. The use of community-detection algorithms from network theory generates a natural hierarchical perspective on the representation that the method produces. The method is computationally efficient, and it requires relatively few assumptions regarding the biological processes that underlie the data. We show the approach by analyzing population structure in the model plant species Arabidopsis thaliana and in human populations. These examples illustrate how network-based approaches for population structure analysis are well-suited to extracting valuable ecological and evolutionary information in the era of large genomic data sets.

中文翻译：

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大型基因组数据集的基于网络的分层种群结构分析。

使用遗传数据分析自然种群的种群结构是生态和进化研究中的常见做法。随着种群的大型基因组数据集现在在分类学范围内出现得越来越频繁，揭示许多结构层次的可能性越来越大，包括精细的遗传簇。为了分析这些数据集，方法需要适当地适应从全基因组数据中提取多级结构的挑战。在这里，我们提出了一种基于网络的方法，用于从遗传数据构建种群结构表示。使用网络理论中的社区检测算法可以在该方法产生的表示上生成自然的层次结构视角。该方法计算效率高，并且它需要相对较少的关于数据背后的生物过程的假设。我们通过分析模型植物拟南芥和人类种群中的种群结构来展示该方法。这些示例说明了基于网络的种群结构分析方法如何非常适合在大型基因组数据集时代提取有价值的生态和进化信息。