BACKGROUND Understanding the genetic structure of natural populations provides insight into the demographic and adaptive processes that have affected those populations. Such information, particularly when integrated with geospatial data, can have translational applications for a variety of fields, including public health. Estimated effective migration surfaces (EEMS) is an approach that allows visualization of the spatial patterns in genomic data to understand population structure and migration. In this study, we developed a workflow to optimize the resolution of spatial grids used to generate EEMS migration maps and applied this optimized workflow to estimate migration of Plasmodium falciparum in Cambodia and bordering regions of Thailand and Vietnam. METHODS The optimal density of EEMS grids was determined based on a new workflow created using density clustering to define genomic clusters and the spatial distance between genomic clusters. Topological skeletons were used to capture the spatial distribution for each genomic cluster and to determine the EEMS grid density; i.e., both genomic and spatial clustering were used to guide the optimization of EEMS grids. Model accuracy for migration estimates using the optimized workflow was tested and compared to grid resolutions selected without the optimized workflow. As a test case, the optimized workflow was applied to genomic data generated from P. falciparum sampled in Cambodia and bordering regions, and migration maps were compared to estimates of malaria endemicity, as well as geographic properties of the study area, as a means of validating observed migration patterns. RESULTS Optimized grids displayed both high model accuracy and reduced computing time compared to grid densities selected in an unguided manner. In addition, EEMS migration maps generated for P. falciparum using the optimized grid corresponded to estimates of malaria endemicity and geographic properties of the study region that might be expected to impact malaria parasite migration, supporting the validity of the observed migration patterns. CONCLUSIONS Optimized grids reduce spatial uncertainty in the EEMS contours that can result from user-defined parameters, such as the resolution of the spatial grid used in the model. This workflow will be useful to a broad range of EEMS users as it can be applied to analyses involving other organisms of interest and geographic areas.

中文翻译：

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使用优化的估计有效迁移表面检测柬埔寨恶性疟原虫寄生虫迁移的地理空间模式。


背景技术了解自然种群的遗传结构可以深入了解影响这些种群的人口统计和适应过程。此类信息，特别是与地理空间数据集成时，可以在包括公共卫生在内的各个领域具有转化应用。估计有效迁移表面 (EEMS) 是一种允许基因组数据中的空间模式可视化的方法，以了解种群结构和迁移。在本研究中，我们开发了一个工作流程来优化用于生成 EEMS 迁移图的空间网格的分辨率，并应用此优化的工作流程来估计柬埔寨以及泰国和越南边境地区的恶性疟原虫的迁移。方法 EEMS 网格的最佳密度是根据使用密度聚类创建的新工作流程来确定的，以定义基因组簇和基因组簇之间的空间距离。拓扑骨架用于捕获每个基因组簇的空间分布并确定 EEMS 网格密度；即，基因组和空间聚类都被用来指导EEMS网格的优化。使用优化工作流程进行迁移估计的模型准确性进行了测试，并与未使用优化工作流程选择的网格分辨率进行了比较。作为测试案例，优化的工作流程应用于从柬埔寨和边境地区采样的恶性疟原虫生成的基因组数据，并将迁移图与疟疾流行的估计值以及研究区域的地理特性进行比较，作为一种手段验证观察到的迁移模式。结果与以非引导方式选择的网格密度相比，优化的网格显示出较高的模型精度并减少了计算时间。 此外，使用优化网格为恶性疟原虫生成的 EEMS 迁移地图与对可能影响疟疾寄生虫迁移的研究区域的疟疾流行和地理特性的估计相对应，支持了观察到的迁移模式的有效性。结论 优化的网格减少了 EEMS 轮廓中的空间不确定性，这些不确定性可能是由用户定义的参数（例如模型中使用的空间网格的分辨率）引起的。该工作流程将对广泛的 EEMS 用户有用，因为它可以应用于涉及其他感兴趣的生物体和地理区域的分析。