Computational tools for sequence-based large-scale epidemiology studies

基于序列的大规模流行病学研究的计算工具

• 批准号: 9078292
• 负责人: SUZANNE M LEAL
• 金额: $ 39.81万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-14 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9078292
• 关键词: Address; Architecture; Arts; Base Sequence; Bioinformatics; Chromosome Mapping; Cloud Computing; Code; Collection; Complex; Computer software; Copy Number Polymorphism; Data; Data Analyses; Data Quality; Data Set; Databases; Development; Disease; Disease susceptibility; Environment; Epidemiologic Studies; Family; Future; Genes; Genetic; Genetic Structures; Genetic Variation; Genome; Genotype; Goals; Health; Human; Hybrids; Individual; Large-Scale Sequencing; Maps; Methods; Modeling; Names; National Heart, Lung, and Blood Institute; Pathway interactions; Phenotype; Population; Quality Control; Research Personnel; Resources; Retrieval; Sampling; Sequence Analysis; Source; Statistical Methods; Susceptibility Gene; System; Technology; Variant; analytical method; base; biological systems; computerized tools; computing resources; data access; data management; epidemiology study; exome sequencing; genetic epidemiology; genetic information; genetic pedigree; genetic variant; genome sequencing; genomic data; human disease; next generation sequencing; programs; public health relevance; rare variant; relational database; success; terabyte; tool; trait; user-friendly; web interface; whole genome

 DESCRIPTION (provided by applicant): We developed Variant Association Tools (VAT), which is a software application for the association analysis of disease susceptibility genes using next-generation sequencing (NGS) data. Unlike many other applications that specialize in certain aspects of rare variant association analysis, VAT aims to provide an effective and comprehensive solution to association analysis of genetic epidemiology studies. Although VAT has been successfully applied to a number of large-scale whole exome sequence (WES) association studies including the NHLBI Exome Sequencing Project, continued development is required to effectively analyze whole genome sequence (WGS) data from emerging large-scale genetic epidemiology studies. Such studies generate unprecedented amount of data and pose methodological and bioinformatics challenges including efficient storage, retrieval, and analysis of terabytes of genotype data, access to heterogeneous annotation resources, choices of appropriate statistical gene mapping methods, and applications to real-world studies with proper controls for errors and biases. To address these challenges, we propose to extend VAT with new capacity and new analytical methods. More specifically, in aim 1, we will remove a major bottleneck of VAT by implementing a highly efficient hybrid storage model to store WGS samples. In aim 2 we will implement recently developed and emerging statistical methods for sequence-based variant association analysis, with a programming interface to allow researchers to implement their own methods in VAT. In aim 3, we will provide user-friendly pipelines, online resources, tutorials, and web interfaces to facilitate the applications of VAT to real-world studie.

 描述（由申请人提供）：我们开发了变异关联工具（VAT），这是一种使用下一代测序（NGS）数据对疾病易感性基因进行关联分析的软件应用程序，与许多其他专门研究罕见病某些方面的应用程序不同。尽管VAT已成功应用于包括NHLBI外显子组在内的多项大规模全外显子组序列（WES）关联研究，但VAT旨在为遗传流行病学研究的关联分析提供有效且全面的解决方案。测序项目需要持续开发，以有效分析来自新兴大规模遗传流行病学研究的全基因组序列 (WGS) 数据，此类研究产生了前所未有的数据量，并带来了方法学和生物信息学挑战，包括有效存储、检索和分析 TB 级数据。基因型数据、获取异质注释资源、选择适当的统计基因图谱方法以及在适当控制错误和偏差的情况下应用于现实世界研究。为了应对这些挑战，我们建议以新的能力扩展增值税。更具体地说，在目标 1 中，我们将通过实施高效的混合存储模型来存储 WGS 样本，消除 VAT 的主要瓶颈。在目标 2 中，我们将实施最近开发的基于序列的变异统计方法。关联分析，通过编程接口允许研究人员在增值税中实施自己的方法。在目标 3 中，我们将提供用户友好的管道、在线资源、教程和 Web 界面，以促进增值税在实际研究中的应用。