title: Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases.
abstract: BACKGROUND
  Genome-wide association studies (GWAS) have provided a large set of genetic loci influencing the risk for many common diseases. Association studies typically analyze one specific trait in single populations in an isolated fashion without taking into account the potential phenotypic and genetic correlation between traits. However, GWA data can be efficiently used to identify overlapping loci with analogous or contrasting effects on different diseases.
  
  
  RESULTS
  Here, we describe a new approach to systematically prioritize and interpret available GWA data. We focus on the analysis of joint and disjoint genetic determinants across diseases. Using network analysis, we show that variant-based approaches are superior to locus-based analyses. In addition, we provide a prioritization of disease loci based on network properties and discuss the roles of hub loci across several diseases. We demonstrate that, in general, agonistic associations appear to reflect current disease classifications, and present the potential use of effect sizes in refining and revising these agonistic signals. We further identify potential branching points in disease etiologies based on antagonistic variants and describe plausible small-scale models of the underlying molecular switches.
  
  
  CONCLUSIONS
  The observation that a surprisingly high fraction (>15%) of the SNPs considered in our study are associated both agonistically and antagonistically with related as well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. Genetic overlaps between two diseases also suggest the importance of the affected entities in the specific pathogenic pathways and should be investigated further.
authors.affiliation: Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany. matthias.arnold@helmholtz-muenchen.de
authors.forename: Matthias
authors.initials: M
authors.lastname: Arnold
authors.forename: Mara L
authors.initials: ML
authors.lastname: Hartsperger
authors.forename: Hansjörg
authors.initials: H
authors.lastname: Baurecht
authors.forename: Elke
authors.initials: E
authors.lastname: Rodríguez
authors.forename: Benedikt
authors.initials: B
authors.lastname: Wachinger
authors.forename: Andre
authors.initials: A
authors.lastname: Franke
authors.forename: Michael
authors.initials: M
authors.lastname: Kabesch
authors.forename: Juliane
authors.initials: J
authors.lastname: Winkelmann
authors.forename: Arne
authors.initials: A
authors.lastname: Pfeufer
authors.forename: Marcel
authors.initials: M
authors.lastname: Romanos
authors.forename: Thomas
authors.initials: T
authors.lastname: Illig
authors.forename: Hans-Werner
authors.initials: HW
authors.lastname: Mewes
authors.forename: Volker
authors.initials: V
authors.lastname: Stümpflen
authors.forename: Stephan
authors.initials: S
authors.lastname: Weidinger
mesh_terms: D016000:Cluster Analysis; D056426:Genetic Loci; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D016017:Odds Ratio; D020641:Polymorphism, Single Nucleotide