Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases
- Equal contributors
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
Citation and License
BMC Genomics 2012, 13:490 doi:10.1186/1471-2164-13-490Published: 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.