Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms
Artikel i vetenskaplig tidskrift, 2012

BACKGROUND: 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. RESULTS: 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 single nucleotide polymorphisms (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 siRNA-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. CONCLUSIONS: Modules of highly interconnected complex disease genes were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies.


Fredrik Barrenäs

Universitetssjukhuset i Linkoping

Sreenivas Chavali

University of Cambridge

Alexessander Couto Alves

Imperial College London

Lachlan Coin

Imperial College London

Marjo-Riitta Jarvelin

Imperial College London

Oulun Yliopisto

Rebecka Jörnsten

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

Michael A Langston

University of Tennessee, Knoxville

Adaikalavan Ramasamy

Imperial College London

National Heart and Lung Institute

G. Rogers

University of Tennessee, Knoxville

Hui Wang

Universitetssjukhuset i Linkoping

Mikael Benson

Universitetssjukhuset i Linkoping

Queen Silvia Children's Hospital

Genome Biology

1474760X (eISSN)

Vol. 13 R46- R46