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

Barrenäs, Fredrik, 1981 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper,Institute of Clinical Sciences,The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden
Chavali, Sreenivas (författare)
Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper,Institute of Clinical Sciences,The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden
Holme, Petter, 1973- (författare)
Umeå universitet,Institutionen för fysik
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Mobini, Reza, 1965 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper,Institute of Clinical Sciences,The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden
Benson, Mikael, 1954 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper,Institute of Clinical Sciences,The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden
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 (creator_code:org_t)
2009-11-30
2009
Engelska.
Ingår i: PloS one. - San Francisco, CA San Francisco, CA, United StatesUnited States : Public Library of Science (PLoS). - 1932-6203. ; 4:11
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Medicinsk genetik (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Medical Genetics (hsv//eng)
NATURVETENSKAP  -- Fysik (hsv//swe)
NATURAL SCIENCES  -- Physical Sciences (hsv//eng)

Nyckelord

topological features; interactome; microarray; expression; centrality; disorders; traits

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

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