188宝金博页面版

SOFTWARE Open AccessXGR software for enhanced interpretationof genomic summary data, illustrated byapplication to immunological traitsHai Fang, Bogdan Knezevic, Katie L. Burnham and Julian C. Knight *AbstractBackground: Biological interpretation of genomic summary data such as those resulting from genome-wideassociation studies (GWAS) and expression quantitative trait loci (eQTL) studies is one of the major bottlenecks inmedical genomics research, calling for efficient and integrative tools to resolve this problem.Results: We introduce eXploring Genomic Relations (XGR), an open source tool designed for enhanced interpretationof 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 makeuser-input genomic summary datasets more interpretable. We show how by incorporating ontology, annotation, andsystems biology network-driven approaches, XGR generates more informative results than conventional analyses. Weapply XGR to GWAS and eQTL summary data to explore the genomic landscape of the activated innate immuneresponse and common immunological diseases. We provide genomic evidence for a disease taxonomy supporting theconcept of a disease spectrum from autoimmune to autoinflammatory disorders. We also show how XGR can defineSNP-modulated gene networks and pathways that are shared and distinct between diseases, how it achievesfunctional, phenotypic and epigenomic annotations of genes and variants, and how it enables exploringannotation-based relationships between genetic variants.Conclusions: XGR provides a single integrated solution to enhance interpretation of genomic summary datafor downstream biological discovery. XGR is released as both an R package and a web-app, freely available athttp://galahad.well.ox.ac.uk/XGR.Keywords: Software, eXploring Genomic Relations, Genomic summary data, Enhanced interpretation, Networkanalysis, Enrichment analysis, Similarity analysis, Annotation analysisBackgroundOne of the defining characteristics of medical genomicsresearch is the large volume of genomic data availablebut the comparatively limited amount of biologicalknowledge revealed. This ‘big-data-limited-knowledge’discrepancy stems from the heterogeneous forms andhandling of raw data (usually unstructured), but is alsoattributed to imprecision in downstream interpretation[1, 2]. Data ready for downstream interpretation can beconveniently expressed as ‘genomic summary data’; thatis, a list of genes or SNPs (or, more generally, genomicregions) along with summary statistics regarding thesignificance level (e.g. p values).Using genomic summary data as a starting point forknowledge discovery is appealing. Cases in point aregenome-wide association studies (GWAS) producingsummary data on disease-associated genetic variants(GWAS SNPs) and expression quantitative trait loci(eQTL) mapping producing summary data on expression-associated genetic variants (eQTL SNPs). Firstly, it simpli-fies raw data (usually complex) and captures the essentialinformation content. Secondly, GWAS and eQTL sum-mary data are publicly available and well curated inrelational databases, such as the GWAS Catalog [3],ImmunoBase [4], GTEx Portal [5], and Blood eQTLbrowser [6]. By comparison, the limited availability of* Correspondence: julian@well.ox.ac.ukWellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX37BN, UK© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Fang et al. Genome Medicine (2016) 8:129 DOI 10.1186/s13073-016-0384-y