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.



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