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Network properties of complex human disease genes identified through genome-wide association studies
The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden.
The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden.
Department of Physics, Umeå University, Umeå, Sweden; Department of Energy Science, Sungkyunkwan University, Suwon, Korea.
The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden.
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2009 (Engelska)Ingår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 4, nr 11, s. e8090-Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

BACKGROUND: Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias.

PRINCIPAL FINDINGS: We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process.

CONCLUSIONS: This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

Ort, förlag, år, upplaga, sidor
San Francisco, CA San Francisco, CA, United StatesUnited States: Public Library of Science , 2009. Vol. 4, nr 11, s. e8090-
Nationell ämneskategori
Medicinsk genetik
Identifikatorer
URN: urn:nbn:se:liu:diva-81894DOI: 10.1371/journal.pone.0008090ISI: 000272828400029PubMedID: 19956617Scopus ID: 2-s2.0-77951240586OAI: oai:DiVA.org:liu-81894DiVA, id: diva2:556239
Tillgänglig från: 2012-09-24 Skapad: 2012-09-24 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
Ingår i avhandling
1. Bioinformatic identification of disease associated pathways by network based analysis
Öppna denna publikation i ny flik eller fönster >>Bioinformatic identification of disease associated pathways by network based analysis
2012 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2012. s. 48
Serie
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1326
Nationell ämneskategori
Medicin och hälsovetenskap
Identifikatorer
urn:nbn:se:liu:diva-81898 (URN)978-91-7519-802-6 (ISBN)
Disputation
2012-10-12, Linden, Hälsouniversitetet, Campus US, Linköpings universitet, Linköping, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2012-09-24 Skapad: 2012-09-24 Senast uppdaterad: 2019-12-10Bibliografiskt granskad

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