Indiana University Bloomington, Bloomington, IN, USA.
Mol Ecol Resour. 2018 May;18(3):448-460. doi: 10.1111/1755-0998.12747. Epub 2018 Jan 18.
With advances in sequencing technology, research in the field of landscape genetics can now be conducted at unprecedented spatial and genomic scales. This has been especially evident when using sequence data to visualize patterns of genetic differentiation across a landscape due to demographic history, including changes in migration. Two recent model-based visualization methods that can highlight unusual patterns of genetic differentiation across a landscape, SpaceMix and EEMS, are increasingly used. While SpaceMix's model can infer long-distance migration, EEMS' model is more sensitive to short-distance changes in genetic differentiation, and it is unclear how these differences may affect their results in various situations. Here, we compare SpaceMix and EEMS side by side using landscape genetics simulations representing different migration scenarios. While both methods excel when patterns of simulated migration closely match their underlying models, they can produce either un-intuitive or misleading results when the simulated migration patterns match their models less well, and this may be difficult to assess in empirical data sets. We also introduce unbundled principal components (un-PC), a fast, model-free method to visualize patterns of genetic differentiation by combining principal components analysis (PCA), which is already used in many landscape genetics studies, with the locations of sampled individuals. Un-PC has characteristics of both SpaceMix and EEMS and works well with simulated and empirical data. Finally, we introduce msLandscape, a collection of tools that streamline the creation of customizable landscape-scale simulations using the popular coalescent simulator ms and conversion of the simulated data for use with un-PC, SpaceMix and EEMS.
随着测序技术的进步，景观遗传学领域的研究现在可以以前所未有的空间和基因组尺度进行。当使用序列数据可视化由于人口历史（包括迁移变化）而导致的景观遗传分化模式时，这一点尤为明显。最近有两种基于模型的可视化方法，即 SpaceMix 和 EEMS，越来越多地被用于突出景观遗传分化的异常模式。虽然 SpaceMix 的模型可以推断远距离迁移，但 EEMS 的模型对遗传分化的短距离变化更敏感，并且不清楚这些差异在各种情况下如何影响它们的结果。在这里，我们使用代表不同迁移情景的景观遗传学模拟来并排比较 SpaceMix 和 EEMS。虽然这两种方法在模拟迁移模式与它们的基础模型非常匹配时表现出色，但当模拟迁移模式与它们的模型不太匹配时，它们可能会产生不直观或误导性的结果，并且在实际数据集中可能难以评估。我们还引入了未捆绑主成分（un-PC），这是一种快速、无模型的方法，通过将主成分分析（PCA）与采样个体的位置相结合，可视化遗传分化模式，PCA 已经在许多景观遗传学研究中使用。un-PC 具有 SpaceMix 和 EEMS 的特点，并且与模拟和实际数据都能很好地配合使用。最后，我们介绍了 msLandscape，这是一组工具，可使用流行的合并模拟器 ms 简化自定义景观尺度模拟的创建，并转换为 un-PC、SpaceMix 和 EEMS 使用的模拟数据。
