Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms
- Equal contributors
1 The Centre for Individualized Medication, Linköping University Hospital, Linköping University, Linköping, SE-58185, Sweden
2 MRC-Laboratory of Molecular Biology, University of Cambridge, Hills Road, Cambridge, CB2 0QH, UK
3 Department of Genomics of Common Disease, School of Public Health, Imperial College, London, W2 1PG, UK
4 Department of Child and Adolescent Health, National Institute of Health and Welfare, University of Oulu, Oulu, FI-90101, Finland
5 Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, SE-412 96, Sweden
6 Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996-2250, USA
7 Respiratory Epidemiology and Public Health Group, National Heart and Lung Institute, Imperial College, London, SW7 2AZ, UK
8 Unit for Paediatric Allergology, Queen Silvia Children's Hospital, Gothenburg, SE-416 85 Sweden
Genome Biology 2012, 13:R46 doi:10.1186/gb-2012-13-6-r46Published: 15 June 2012
Complex diseases are associated with altered interactions between thousands of genes. We developed a novel method to identify and prioritize disease genes, which was generally applicable to complex diseases.
We identified modules of highly interconnected genes in disease-specific networks derived from integrating gene-expression and protein interaction data. We examined if those modules were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies. First, we analyzed publicly available gene expression microarray and genome-wide association study (GWAS) data from 13, highly diverse, complex diseases. In each disease, highly interconnected genes formed modules, which were significantly enriched for genes harboring disease-associated SNPs. To test if such modules could be used to find novel genes for functional studies, we repeated the analyses using our own gene expression microarray and GWAS data from seasonal allergic rhinitis. We identified a novel gene, FGF2, whose relevance was supported by functional studies using combined small interfering RNA-mediated knock-down and gene expression microarrays. The modules in the 13 complex diseases analyzed here tended to overlap and were enriched for pathways related to oncological, metabolic and inflammatory diseases. This suggested that this union of the modules would be associated with a general increase in susceptibility for complex diseases. Indeed, we found that this union was enriched with GWAS genes for 145 other complex diseases.
Modules of highly interconnected complex disease genes were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies.