PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Arnold, M. ; Hartsperger, M.L. ; Baurecht, H.* ; Rodriguez, E.* ; Wachinger, B. ; Franke, A.* ; Kabesch, M.* ; Winkelmann, J. ; Pfeufer, A. ; Romanos, M.* ; Illig, T. ; Mewes, H.-W. ; Stuempflen, V. ; Weidinger, S.*

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

BMC Genomics 13:490 (2012)
Verlagsversion Volltext DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
4.073
0.000
2
2
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Genome-wide association study, Genetic overlap, Shared variant network, Disease comorbidity; GENOME-WIDE ASSOCIATION; IDIOPATHIC PULMONARY-FIBROSIS; INFLAMMATORY-BOWEL-DISEASE; CORONARY-ARTERY-DISEASE; SUSCEPTIBILITY LOCI; CROHNS-DISEASE; HAPLOTYPE MAP; LARGE-SCALE; ATRIAL-FIBRILLATION; CHROMOSOME 4Q25
Sprache englisch
Veröffentlichungsjahr 2012
HGF-Berichtsjahr 2012
ISSN (print) / ISBN 1471-2164
e-ISSN 1471-2164
Zeitschrift BMC Genomics
Quellenangaben Band: 13, Heft: 1, Seiten: , Artikelnummer: 490 Supplement: ,
Verlag BioMed Central
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)
Institute of Human Genetics (IHG)
Research Unit Molecular Epidemiology (AME)
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
Forschungsfeld(er) Enabling and Novel Technologies
Genetics and Epidemiology
PSP-Element(e) G-503700-001
G-500700-001
G-504200-001
PubMed ID 22988944
DOI 10.1186/1471-2164-13-490
WOS ID WOS:000312149400001
Scopus ID 84866294462
Erfassungsdatum 2012-10-31
[➜Korrektur melden]