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

Publication ,  Journal Article
Arnold, M; Hartsperger, ML; Baurecht, H; Rodríguez, E; Wachinger, B; Franke, A; Kabesch, M; Winkelmann, J; Pfeufer, A; Romanos, M; Illig, T ...
Published in: BMC Genomics
September 18, 2012

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.

Duke Scholars

Published In

BMC Genomics

DOI

EISSN

1471-2164

Publication Date

September 18, 2012

Volume

13

Start / End Page

490

Location

England

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • Odds Ratio
  • Humans
  • Genome-Wide Association Study
  • Genome, Human
  • Genetic Loci
  • Cluster Analysis
  • Bioinformatics
  • 32 Biomedical and clinical sciences
  • 31 Biological sciences
 

Citation

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Arnold, M., Hartsperger, M. L., Baurecht, H., Rodríguez, E., Wachinger, B., Franke, A., … Weidinger, S. (2012). Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases. BMC Genomics, 13, 490. https://doi.org/10.1186/1471-2164-13-490
Arnold, Matthias, Mara L. Hartsperger, Hansjörg Baurecht, Elke Rodríguez, Benedikt Wachinger, Andre Franke, Michael Kabesch, et al. “Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases.BMC Genomics 13 (September 18, 2012): 490. https://doi.org/10.1186/1471-2164-13-490.
Arnold M, Hartsperger ML, Baurecht H, Rodríguez E, Wachinger B, Franke A, et al. Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases. BMC Genomics. 2012 Sep 18;13:490.
Arnold, Matthias, et al. “Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases.BMC Genomics, vol. 13, Sept. 2012, p. 490. Pubmed, doi:10.1186/1471-2164-13-490.
Arnold M, Hartsperger ML, Baurecht H, Rodríguez E, Wachinger B, Franke A, Kabesch M, Winkelmann J, Pfeufer A, Romanos M, Illig T, Mewes H-W, Stümpflen V, Weidinger S. Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases. BMC Genomics. 2012 Sep 18;13:490.
Journal cover image

Published In

BMC Genomics

DOI

EISSN

1471-2164

Publication Date

September 18, 2012

Volume

13

Start / End Page

490

Location

England

Related Subject Headings

  • Polymorphism, Single Nucleotide
  • Odds Ratio
  • Humans
  • Genome-Wide Association Study
  • Genome, Human
  • Genetic Loci
  • Cluster Analysis
  • Bioinformatics
  • 32 Biomedical and clinical sciences
  • 31 Biological sciences