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Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms
Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
MRC-Laboratory of Molecular Biology, University of Cambridge, Hills Road, Cambridge, CB2 0QH, UK.
Department of Genomics of Common Disease, School of Public Health, Imperial College, UK.
Department of Genomics of Common Disease, School of Public Health, Imperial College, UK.
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2012 (English)In: Genome Biology, ISSN 1465-6906, E-ISSN 1474-760X, Vol. 13, no 6, R46- p.Article in journal (Refereed) Published
Abstract [en]

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 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.

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.

Place, publisher, year, edition, pages
BioMed Central, 2012. Vol. 13, no 6, R46- p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-81896DOI: 10.1186/gb-2012-13-6-r46ISI: 000308546300006PubMedID: 22703998OAI: oai:DiVA.org:liu-81896DiVA: diva2:556246
Note

funding agencies|European Community|223367|US National Institutes of Health|R01-AA-0187763P20MD000516-07S1|Swedish Research Council||

Available from: 2012-09-24 Created: 2012-09-24 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Bioinformatic identification of disease associated pathways by network based analysis
Open this publication in new window or tab >>Bioinformatic identification of disease associated pathways by network based analysis
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many common diseases are complex, meaning that they are caused by many interacting genes. This makes them difficult to study; to determine disease mechanisms, disease-associated genes must be analyzed in combination. Disease-associated genes can be detected using high-throughput methods, such as mRNA expression microarrays, DNA methylation microarrays and genome-wide association studies (GWAS), but determining how they interact to cause disease is an intricate challenge. One approach is to organize disease-associated genes into networks using protein-protein interactions (PPIs) and dissect them to identify disease causing pathways. Studies of complex disease can also be greatly facilitated by using an appropriate model system. In this dissertation, seasonal allergic rhinitis (SAR) served as a model disease. SAR is a common disease that is relatively easy to study. Also, the key disease cell types, like the CD4+ T cell, are known and can be cultured and activated in vitro by the disease causing pollen.

The aim of this dissertation was to determine network properties of disease-associated genes, and develop methods to identify and validate networks of disease-associated genes. First, we showed that disease-associated genes have distinguishing network properties, one being that they co-localize in the human PPI network. This supported the existence of disease modules within the PPI network. We then identified network modules of genes whose mRNA expression was perturbed in human disease, and showed that the most central genes in those network modules were enriched for disease-associated polymorphisms identified by GWAS. As a case study, we identified disease modules using mRNA expression data from allergen-challenged CD4+ cells from patients with SAR. The case study identified and validated a novel disease-associated gene, FGF2 using GWAS data and RNAi mediated knockdown.

Lastly, we examined how DNA methylation caused disease-associated mRNA expression changes in SAR. DNA methylation, but not mRNA expression profiles, could accurately distinguish allergic patients from healthy controls. Also, we found that disease-associated mRNA expression changes were associated with a low DNA methylation content and absence of CpG islands. Specifically within this group, we found a correlation between disease-associated mRNA expression changes and DNA methylation changes. Using ChIP-chip analysis, we found that targets of a known disease relevant transcription factor, IRF4, were also enriched among non CpG island genes with low methylation levels.

Taken together, in this dissertation the network properties of disease-associated genes were examined, and then used to validate disease networks defined by mRNA expression data. We then examined regulatory mechanisms underlying disease-associated mRNA expression changes in a model disease. These studies support network-based analyses as a method to understand disease mechanisms and identify important disease causing genes, such as treatment targets or markers for personalized medication.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 48 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1326
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-81898 (URN)978-91-7519-802-6 (ISBN)
Public defence
2012-10-12, Linden, Hälsouniversitetet, Campus US, Linköpings universitet, Linköping, 13:00 (English)
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Available from: 2012-09-24 Created: 2012-09-24 Last updated: 2012-09-24Bibliographically approved

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Barrenäs, FredrikWang, HuiBenson, Mikael

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