Extending Tools for Visualization of Geographic Structure in Population Genomic Data

项目总结 当代遗传学和基因组学研究的一大挑战是海量数据。视觉-- 自动化工具和定制的数据门户有助于克服这种复杂性，并极大地帮助研究人员 从数据到知识的路径。基因组数据中结构的一个重要来源是地理。理解 遗传变异的地理位置对于人类基因组学以及对其他物种的研究都是至关重要的 与人类健康密切相关。它在精准医学中尤为重要，精准医学旨在发展 对所有祖先的个体进行有效的治疗。目前，在全基因组范围内存在一种有据可查的偏见 对欧洲血统人口的协会研究(GWAS)，尽管这其中的相关性尚不清楚- 一些研究发现fi的结果在很大程度上是可以跨人群移植的，另一些研究则表明存在重大错误 将出现在跨种群应用GWAS结果的过程中，还有一些通过反式-- 种族fiNe映射。鉴于人口结构的广泛重要性，多种计算工具 是为了揭示种群结构而开发的，其中一些算法是 计算生物学。 然而，现有的计算基因组学方法很少明确地处理地理学问题。在这里，我们 建议开发和改进多种工具，使研究人员能够可视化和解释地理信息 基因组数据中的图形模式。在fiRst中，我们将构建我们的“遗传变异地理”浏览器，一个 基于Web的工具，用于访问和显示关于基因变异地理分布的信息 人类。在第二部分中，我们将扩展名为eEMS(用于评估有效)的软件的功能 迁徙表面)，它提供了可视化工具来构建揭示遗传连通性的图谱 在人群中。在第三章中，我们开发了一种新的以变量为中心的视图来显示人口模式-- 具有多个应用程序的TION结构。总体而言，我们希望生产出有效、重要的工具， 阐明遗传祖先和地理之间的关系。 在整个项目中，我们将特别注意构建用户友好的软件和交互数据 显示，例如由数据驱动文档(D3)JavaScript可视化库生成的那些。我们的目标是 要使用简单但fl可扩展的Python后端，并提供补充的R库以便于定制 以及与现有分析管道的集成。最后，虽然人口遗传应用激励我们的 工作，我们正在生成的工具将普遍适用于其他形式的结构化生物医学数据。

PROJECT SUMMARY A major challenge of contemporary research in genetics and genomics is the vast quantity of data. Visu- alization tools and customized data portals help conquer this complexity and greatly aid researchers on the path from data to knowledge. An important source of structure in genomic data is geography. Understanding the geography of genetic variation is crucial for human genomics as well as for the study of other species that are deeply relevant to human health. It is especially important in precision medicine, which aims to develop effective treatments for individuals of all ancestries. Currently there is a well-documented bias in genome-wide association studies (GWAS) towards European ancestry populations, though the relevance of this is unclear— some studies find that GWAS results are largely portable across populations, others suggest substantial errors will arise in applying GWAS results across populations, and yet others leverage population variation via trans- ethnic fine-mapping. Given the broad importance of population structure, multiple computational tools have been developed for revealing population structure, and some of them are among the most cited algorithms in computational biology. Nevertheless, few existing computational genomic methods grapple explicitly with geography. Here, we propose to develop and improve multiple tools that will empower researchers to visualize and interpret geo- graphic patterns in genomic data. In the first, we will build on our “Geography of Genetic Varaints” browser, a web-based tool for accessing and displaying information on the geographic distribution of genetic variants in humans. In the second, we will expand the functionality of our software titled EEMS (for Estimating Effective Migration Surfaces), which provides a visualization tool that builds maps that reveal the genetic connectivity among populations. In the third, we develop a new variant-centric view for displaying patterns of popula- tion structure that has multiple applications. Overall, we expect to produce effective, important tools that will illuminate the relationships between genetic ancestry and geography. Throughout the project we will pay special attention to building user-friendly software and interactive data displays such as those generated by the Data Driven Documents (d3) JavaScript visualization libraries. We aim to use simple, yet flexible python backends and provide complementary R libraries to facilitate customization and integration with existing analysis pipelines. Finally, while population genetic applications motivate our work, the tools we are generating will be generally applicable to other forms of structured biomedical data.