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Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases

Matthias Arnold1*, Mara L Hartsperger1, Hansjörg Baurecht23, Elke Rodríguez2, Benedikt Wachinger1, Andre Franke4, Michael Kabesch5, Juliane Winkelmann678, Arne Pfeufer689, Marcel Romanos10, Thomas Illig11, Hans-Werner Mewes112, Volker Stümpflen1 and Stephan Weidinger2*

Author affiliations

1 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany

2 Department of Dermatology and Allergy Biederstein, Technische Universität München, 80802, Munich, Germany

3 TUM Graduate School of Information Science in Health (GSISH), Technische Universität München, 85748, Garching, Germany

4 Institute for Clinical Molecular Biology, University of Kiel, 24105, Kiel, Germany

5 Clinic for Pneumology and Neonatology, Hannover Medical School, 30625, Hannover, Germany

6 Institute for Human Genetics, Technische Universität, München, 81675, Munich, Germany

7 Department of Neurology, Technische Universität München, 81675, Munich, Germany

8 Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany

9 Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), 39100 Bolzano, Italy – Affiliated Institute of the University Lübeck, 23562, Lübeck, Germany

10 Department of Child and Adolescent Psychiatry, University Clinic of Munich, 80336, Munich, Germany

11 Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany

12 Chair of Genome Oriented Bioinformatics, Center of Life and Food Science, Freising-Weihenstephan, Technische Universität München, 80333, Munich, Germany

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Citation and License

BMC Genomics 2012, 13:490  doi:10.1186/1471-2164-13-490

Published: 18 September 2012



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.


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.


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.

Genome-wide association study; Genetic overlap; Shared variant network; Disease comorbidity