Computational tools for sequence-based large-scale epidemiology studies

 描述(申请人提供)：我们开发了变量关联工具(VAT)，这是一个使用下一代测序(NGS)数据进行疾病易感基因关联分析的软件应用程序。与许多其他专门研究稀有变异关联分析某些方面的应用不同，增值税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。尽管增值税已经成功地应用于包括NHLBI外显子组测序项目在内的许多大规模全外显子序列(WES)关联研究，但还需要继续开发，以有效地分析来自新兴的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，并提出了方法学和生物信息学方面的挑战，包括有效存储、检索和分析兆兆字节的基因数据，获取不同种类的注释资源，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的研究。为了应对这些挑战，我们建议用新的能力和新的分析方法来扩大增值税。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式来存储WGS样本，从而消除增值税的一个主要瓶颈。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，并提供一个编程接口，允许研究人员在增值税中实现他们自己的方法。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，以促进增值税在现实世界的研究中的应用。

 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.