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.

项目摘要 当代遗传学和基因组学研究的主要挑战是大量数据。 vis 化学工具和自定义数据门户有助于征服这种复杂性，并极大地帮助研究人员 从数据到知道的路径。基因组数据的重要结构来源是地理。理解 遗传变异的地理对于人类基因组学以及对其他物种的研究至关重要 与人类健康密切相关。它在精密医学中尤其重要，该医学旨在发展 对所有祖先的个人的有效治疗。目前，全基因组有据可查的偏见 协会研究（GWAS）对欧洲血统人群，尽管这尚不清楚 - 一些研究发现，GWAS结果在很大程度上是可移植的，其他人则提出了实质性错误 将在跨人群中应用GWAS结果时出现，而其他人则通过反式来利用人口变化 种族图。鉴于人口结构的重要性，多种计算工具具有 开发用于揭示人口结构，其中一些是最引用的算法之一 计算生物学。 然而，很少有现有的计算基因组方法明确地与地理纠缠。在这里，我们 提出开发和改进多种工具的建议，这些工具将使研究人员能够可视化和解释地理位置 基因组数据中的图形模式。首先，我们将建立在“遗传销售地理”浏览器的基础上 基于Web的工具，用于访问和显示有关遗传变异的地理分布的信息 人类。在第二个中，我们将扩大标题为EEM的软件的功能（以估算有效 迁移表面），它提供了一个可视化工具，该工具构建了揭示遗传连接性的地图 在人群中。在第三章中，我们开发了一种新的以变体的视图，以显示Popula-的模式 具有多个应用程序的结构。总体而言，我们期望生产有效的重要工具 阐明遗传血统与地理之间的关系。 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式数据 显示诸如数据驱动文档（D3）JavaScript可视化库产生的显示。我们的目标 使用简单而灵活的Python后端，并提供完整的R库来促进自定义 并与现有分析管道集成。最后，尽管人口遗传应用激发了我们的 工作，我们生成的工具通常适用于其他形式的结构化生物医学数据。