Extending Tools for Visualization of Geographic Structure in Population Genomic Data

群体基因组数据中地理结构可视化的扩展工具

• 批准号: 9904741
• 负责人: John Novembre
• 金额: $ 35.58万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904741
• 关键词: Address; Admixture; Algorithms; Alleles; Area; Attention; Big Data to Knowledge; Communities; Computational Biology; Computer software; Computing Methodologies; Custom; DNA; Data; Data Analyses; Data Display; Data Set; Data Sources; Development; Dissection; European; Funding; Gene Expression; Gene Expression Profile; Gene Frequency; Genetic; Genetic Structures; Genetic Variation; Genomics; Genotype; Geographic Distribution; Geography; Health; Heterogeneity; Human; Human Genetics; Individual; Lead; Libraries; Linkage Disequilibrium; Maps; Methodology; Methods; Online Systems; Pattern; Play; Population; Population Genetics; Privatization; Publications; Pythons; Recording of previous events; Research; Research Design; Research Personnel; Resources; Role; Sampling; Source; Speed; Structure; Surface; Testing; Variant; Visualization; Visualization software; Work; Writing; analysis pipeline; base; computerized tools; data access; data portal; data to knowledge; data tools; data visualization; effective therapy; flexibility; follow-up; genetic variant; genome wide association study; genomic data; geographic difference; human genomics; improved; information display; insight; interest; migration; open source; portability; precision medicine; rapid growth; rare variant; real world application; study population; tool; transcriptomics; user friendly software; web-based tool

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.

项目概要 当代遗传学和基因组学研究的一个主要挑战是大量数据。维苏- 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 与人类健康密切相关。这对于精准医学尤其重要，精准医学旨在开发 对所有血统的人都有效的治疗。目前，全基因组范围内存在有据可查的偏差 针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 结果在很大程度上可以跨人群移植，另一些研究则表明存在重大错误 将 GWAS 结果应用于人群中时会出现，而其他人则通过跨人群利用人群差异 种族精细绘图。鉴于人口结构的广泛重要性，多种计算工具已经 为揭示种群结构而开发，其中一些是最常被引用的算法之一 计算生物学。 然而，现有的计算基因组方法很少能明确地解决地理问题。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 基因组数据中的图形模式。首先，我们将在“遗传变异地理”浏览器的基础上构建一个 基于网络的工具，用于访问和显示有关遗传变异地理分布的信息 人类。第二，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 人群之间。在第三个中，我们开发了一种新的以变体为中心的视图，用于显示流行模式 具有多种应用的结构。总的来说，我们期望开发出有效、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，我们将特别注重构建用户友好的软件和交互式数据 显示例如由数据驱动文档 (d3) JavaScript 可视化库生成的显示。我们的目标 使用简单而灵活的 python 后端并提供补充 R 库以方便定制 以及与现有分析管道的集成。最后，虽然群体遗传学应用激发了我们的 工作中，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。