Open Access Research article

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

Published: 18 September 2012

Additional files

Additional file 1:

Figure S1. Disease-centric projection of the SVN. The SVN (see Figure 1C) is transformed in a network consisting of diseases only. Here, two traits are connected if they are associated with the same variant. The colors of the disease nodes correspond to disease classes according to the MeSH ontology, multi-colored nodes indicate an association with different disease classes. The node size reflects the number of traits a disease has shared associations with. The direction of the shared variants is indicated by the edge color reflecting the corresponding allelic information: gray indicates agonistic variant(s), red corresponds to antagonistic variant(s), and blue mark both agonistic and antagonistic signals in the two corresponding traits.

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Additional file 2:

Table S1. List of all disease-variant associations contained in the SVN. Contained is the high-quality data set which was used for the construction of the SVN, ordered by the rs-number of the tagging SNP. The first column contains this rs-number of the tagging SNP, the second column lists the disease associations and the third column gives the PubMed ID of the GWAS publication the association was reported in. In the fourth column the (gene or intergenic) locus of the tagging SNP can be found. The sixth column gives the SNP and the risk allele reported in the GWAS. If the rs-numbers of the tagging SNP (column 1) diverges from the rs-number listed here, the association was assigned via LD. For these cases, in column seven the corresponding allele of the tagging SNP is given, followed by the P-value and the odds ratio reported with the SNP (i.e. the reported SNP in column six). Blue row-coloring identifies non-HLA located antagonistic SNPs, while rows containing agonistic SNPs are not colored. Rows in green list antagonistic SNPs in the HLA region (not considered in the manuscript). Tagging SNPs which we included in our rationale are marked in bold red font.

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Additional file 3:

Table S2. CPMA P-values for autoimmune-linked SNPs and their corresponding loci in the SVN. Listed are all SNPs contained in Supplementary Table 1 for which association data could be obtained from [8]. The second column gives the LD-based loci of the SNPs as used in the SVN. The third column contains the CPMA P-Values.

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Additional file 4:

Figure S2. Network properties of the SVN. A: The log-log-plot of the degree distribution of the SVN follows a power-law (γ = 1.32; R2 = 0.69) and therefore attributes the SVN to be scale-free and, thus, non-random. B: The modular structure of the SVN was confirmed by the topological coefficient which follows a power-law distribution on a log-log-scale. When considering the two node types separately, in both cases a scale-free topology can be identified: C: disease nodes (γ = 0.97; R2 = 0.71) and D: locus nodes (γ = 2.98; R2 = 0.93).

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Additional file 5:

Graph data of the SLN in yEd graphml format. View with yEd ( http://www.yworks.com/en/products_yed_about.html webcite). Using yEd, the file can be converted to GML format which is readable by Cytoscape ( http://www.cytoscape.org/ webcite).

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Additional file 6:

Graph data of the SVN in yEd graphml format. View with yEd ( http://www.yworks.com/en/products_yed_about.html webcite). Using yEd, the file can be converted to GML format which is readable by Cytoscape ( http://www.cytoscape.org/ webcite).

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