Network properties of complex human disease genes identified through genome-wide association studies - PubMed (original) (raw)

Network properties of complex human disease genes identified through genome-wide association studies

Fredrik Barrenas et al. PLoS One. 2009.

Abstract

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. Networks of complex diseases and complex disease genes.

A) Complex Disease Network (CDN). Each node is a complex disease studied in GWAs with the link representing sharing of disease genes. The color of the nodes corresponds to disease class as identified using MeSH (Medical Subject Headings) terms as given on the right side. Notably, complex diseases are hard to define using single MeSH term. The node size refers to the number of associated genes identified. Diseases with most number of associated genes identified through GWAs are listed on the right side with the numbers in the parenthesis indicating the number of associated genes. B) Complex Disease Gene Network (CGN): Each node represents a gene and connections between two genes represent their association with the same disease. The node size refers to the number of diseases a gene is associated with. Genes associated with many diseases are listed on the top right side with the number of diseases they are implicated with, in the parenthesis. A node (highlighted in gray) each in lung cancer and Alzheimer's disease gene cluster are singular associations in idiopathic pulmonary fibrosis and narcolepsy respectively.

Figure 2

Figure 2. Comparative distribution of topological measures (A) Degree, (B) Closeness and (C).

Eccentricity among monogenic disease genes, complex disease genes and non-disease genes in human interactome. The co-ordinates on the Y-axis indicate the relative frequency of each of the above mentioned classes in a given bin with the error bars indicating the fraction of genes in each bin for each class of genes. For example, monogenic disease genes are over-represented in the bin of 0.30< for closeness while closeness values for most of the genes in each class lie in the interval of 0.21–0.25.

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