XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits.
XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits.
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XGR软件可增强对基因组摘要数据的解释，并通过应用于免疫特征说明。
DOI：
10.1186/s13073-016-0384-y
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发表时间：
2016-12-13
期刊：
影响因子：
12.3
通讯作者：
Knight JC
中科院分区：
文献类型：
--
作者：
Fang H;Knezevic B;Burnham KL;Knight JC
全基因组关联研究（genome-wide association studies，GWAS）和表达数量性状基因座（expression quantitative trait loci，eQTL）等基因组数据的生物学解释是医学基因组学研究的主要瓶颈之一，需要有效的综合工具来解决这一问题。我们介绍eXploring基因组关系（XGR），一个开源工具，旨在增强基因组摘要数据的解释，使下游知识发现。针对不同计算技能的用户，XGR以高度集成但易于访问的方式利用先前的生物学知识和关系，使用户输入的基因组摘要数据集更具可解释性。我们展示了如何通过将本体论，注释和系统生物学网络驱动的方法，XGR产生更多的信息比传统的分析结果。我们将XGR应用于GWAS和eQTL总结数据，以探索激活的先天免疫应答和常见免疫性疾病的基因组景观。我们提供了一个疾病分类的基因组证据，支持从自身免疫性疾病到自身炎症性疾病的疾病谱的概念。我们还展示了XGR如何定义疾病之间共享和不同的SNP调制基因网络和途径，它如何实现基因和变体的功能，表型和表观基因组注释，以及它如何能够探索遗传变体之间基于注释的关系。XGR提供了一个单一的集成解决方案，以增强下游生物发现的基因组汇总数据的解释。XGR作为R包和Web应用程序发布，可在http://galahad.well.ox.ac.uk/XGR免费获得。本文的在线版本（doi：10.1186/s13073-016-0384-y）包含补充材料，可供授权用户使用。
Biological interpretation of genomic summary data such as those resulting from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is one of the major bottlenecks in medical genomics research, calling for efficient and integrative tools to resolve this problem. We introduce eXploring Genomic Relations (XGR), an open source tool designed for enhanced interpretation of genomic summary data enabling downstream knowledge discovery. Targeting users of varying computational skills, XGR utilises prior biological knowledge and relationships in a highly integrated but easily accessible way to make user-input genomic summary datasets more interpretable. We show how by incorporating ontology, annotation, and systems biology network-driven approaches, XGR generates more informative results than conventional analyses. We apply XGR to GWAS and eQTL summary data to explore the genomic landscape of the activated innate immune response and common immunological diseases. We provide genomic evidence for a disease taxonomy supporting the concept of a disease spectrum from autoimmune to autoinflammatory disorders. We also show how XGR can define SNP-modulated gene networks and pathways that are shared and distinct between diseases, how it achieves functional, phenotypic and epigenomic annotations of genes and variants, and how it enables exploring annotation-based relationships between genetic variants. XGR provides a single integrated solution to enhance interpretation of genomic summary data for downstream biological discovery. XGR is released as both an R package and a web-app, freely available at http://galahad.well.ox.ac.uk/XGR. The online version of this article (doi:10.1186/s13073-016-0384-y) contains supplementary material, which is available to authorized users.
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