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: 9901082
• 负责人: SUZANNE M LEAL
• 金额: $ 41.8万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 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 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9901082
• 关键词: Address; Base Sequence; Bioinformatics; Chromosome Mapping; Cloud Computing; Code; Collection; Complex; Computer software; Copy Number Polymorphism; Custom; Data; Data Analyses; Data Quality; Data Set; Databases; Development; Disease; Disease susceptibility; Environment; Factor Analysis; Family; Future; Genes; Genetic Structures; Genetic Variation; Genome; Genotype; Goals; Health; Human; Hybrids; Individual; Methodology; Methods; Modeling; Names; National Heart, Lung, and Blood Institute; Pathway interactions; Phenotype; Population; Quality Control; Reproducibility; Research Personnel; Resources; Retrieval; Sampling; Source; Statistical Methods; Susceptibility Gene; System; Technology; Variant; analysis pipeline; analytical method; base; biological systems; computerized tools; computing resources; data access; data management; epidemiologic data; epidemiology study; exome; exome sequencing; genetic analysis; genetic architecture; genetic epidemiology; genetic information; genetic pedigree; genetic variant; genomic data; human disease; next generation sequencing; online resource; 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）：我们开发了变体关联工具（VAT），这是一种使用下一代测序（NGS）数据进行疾病易感基因关联分析的软件应用程序。与许多其他专门从事罕见变异关联分析某些方面的应用程序不同，VAT旨在为遗传流行病学研究的关联分析提供有效和全面的解决方案。尽管VAT已成功应用于包括NHLBI外显子组测序计划在内的许多大规模全外显子组序列（WES）关联研究，但仍需要继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。这些研究产生了前所未有的数据量，并提出了方法和生物信息学的挑战，包括有效的存储，检索和分析TB的基因型数据，获得异质性注释资源，选择适当的统计基因定位方法，并应用到现实世界的研究与适当的控制错误和偏见。为了应对这些挑战，我们建议通过新的能力和新的分析方法来扩展增值税。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，从而消除VAT的主要瓶颈。在目标2中，我们将实现最近开发的和新兴的基于序列的变异关联分析的统计方法，并提供编程接口，允许研究人员在VAT中实现自己的方法。在目标3中，我们将提供用户友好的管道，在线资源，教程和网络界面，以促进增值税的应用到现实世界的研究。

项目成果

Novel candidate genes and variants underlying autosomal recessive neurodevelopmental disorders with intellectual disability.

• DOI: 10.1007/s00439-018-1928-6
• 发表时间: 2018-09
• 期刊: Human genetics
• 影响因子: 5.3
• 作者: Santos-Cortez RLP;Khan V;Khan FS;Mughal ZU;Chakchouk I;Lee K;Rasheed M;Hamza R;Acharya A;Ullah E;Saqib MAN;Abbe I;Ali G;Hassan MJ;Khan S;Azeem Z;Ullah I;Bamshad MJ;Nickerson DA;Schrauwen I;Ahmad W;Ansar M;Leal SM
• 通讯作者: Leal SM