Extending Tools for Visualization of Geographic Structure in Population Genomic Data

项目摘要 当代遗传学和基因组学研究的一个主要挑战是大量的数据。Visu- 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。基因组数据结构的一个重要来源是地理。理解 遗传变异的地理学对于人类基因组学以及其他物种的研究至关重要， 与人类健康息息相关。这在精准医学中尤为重要， 对所有祖先的个体都有效的治疗。目前，在全基因组范围内存在有据可查的偏见， 对欧洲血统人群的关联研究（GWAS），尽管其相关性尚不清楚- 一些研究发现，GWAS的结果在很大程度上可以在人群中移植，另一些研究则表明存在重大错误 将出现在跨人群应用GWAS结果，还有一些人通过跨- 种族精细映射考虑到人口结构的广泛重要性，多种计算工具 被开发用于揭示人口结构，其中一些是最常被引用的算法之一， 计算生物学 然而，现有的计算基因组学方法很少明确地与地理相结合。这里我们 建议开发和改进多种工具，使研究人员能够直观地了解和解释地理信息， 基因组数据中的图形模式。首先，我们将建立在我们的“遗传变异的地理”浏览器， 一个基于网络的工具，用于访问和显示遗传变异的地理分布信息， 人类在第二部分中，我们将扩展名为EEMS（用于估计有效 迁移表面），它提供了一个可视化工具，可以构建揭示遗传连接性的地图 在人群中。在第三部分中，我们开发了一个新的以变量为中心的视图来显示群体的模式， 结构，具有多种应用。总的来说，我们希望产生有效的，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，我们将特别注意建立用户友好的软件和交互式数据 诸如由数据驱动文档（d3）JavaScript可视化库生成的那些显示。我们的目标 使用简单但灵活的python后端，并提供补充的R库以方便定制 并与现有分析管道集成。最后，虽然人口遗传学的应用激励我们 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.