Journal
GENOME MEDICINE
Volume 8, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13073-016-0384-y
Keywords
Software; eXploring Genomic Relations; Genomic summary data; Enhanced interpretation; Network analysis; Enrichment analysis; Similarity analysis; Annotation analysis
Categories
Funding
- European Research Council under the European Union's Seventh Framework Programme (FP7)/ERC grant [281824]
- Medical Research Council [98082]
- European Commission (Innovative Medicine Initiative ULTRA-DD)
- Wellcome Trust [090532/Z/09/Z]
- NIHR Oxford Biomedical Research Centre
- MRC [G1001708] Funding Source: UKRI
- Medical Research Council [G1001708] Funding Source: researchfish
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Background: 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. Results: 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. Conclusions: 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.
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