Department of Biology, Stanford University, Stanford, California 94305, USA.
Department of Computer Science, Ben-Gurion University of the Negev, Be'er-Sheva, 8410501, Israel.
Genome Res. 2019 Dec;29(12):2020-2033. doi: 10.1101/gr.250092.119. Epub 2019 Nov 6.
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 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.
利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。随着越来越多的种群基因组大数据集出现在分类学范围内，人们越来越有可能揭示出许多层次的结构，包括精细的遗传聚类。为了分析这些数据集，需要采用适当的方法来从全基因组数据中提取多层次结构。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。网络理论中的社区检测算法的使用为该方法生成的表示产生了自然的层次视角。该方法计算效率高，并且对构成数据的生物学过程的假设相对较少。我们通过分析模式植物物种和人类群体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在大型基因组数据集时代非常适合提取有价值的生态和进化信息。
