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面来检测柬埔寨恶性疟原虫寄生虫迁移的地理空间格局。

背景技术了解自然种群的遗传结构提供了对影响这些种群的人口统计和适应过程的见识。此类信息（尤其是与地理空间数据集成在一起的信息）可以在包括公共卫生在内的多个领域中进行翻译应用。估计有效移民面积（EEMS）是一种允许可视化基因组数据中的空间模式以了解种群结构和移民的方法。在这项研究中，我们开发了一个工作流程以优化用于生成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恶性疟原虫在柬埔寨以及泰国和越南边境地区的迁移。方法基于新的工作流程确定EEMS网格的最佳密度，该工作流程使用密度聚类来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，基因组和空间聚类均被用来指导EEMS网格的优化。测试了使用优化工作流程进行的迁移估算的模型准确性，并将其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，将优化的工作流程应用于从柬埔寨和周边地区采样的恶性疟原虫产生的基因组数据，并将迁移图与疟疾流行率估计值以及研究区域的地理特征进行了比较，作为验证观察到的迁移模式的一种手段。结果与以非引导方式选择的网格密度相比，优化的网格既显示了较高的模型精度，又减少了计算时间。此外，使用优化的网格为恶性疟原虫生成的EEMS迁移图对应于研究区域的疟疾流行性和地理特性的估计值，这些估计值可能会影响疟疾寄生虫的迁移，从而支持所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，该不确定性可能由用户定义的参数（例如模型中使用的空间网格的分辨率）引起。该工作流程可用于广泛的EEMS用户，因为它可用于涉及其他感兴趣的生物和地理区域的分析。