Journal article
Meta-Analysis of Heterogeneous Data Sources for Genome-Scale Identification of Risk Genes in Complex Phenotypes
Department of Systems Biology, Technical University of Denmark1
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark2
University of Copenhagen3
Technical University of Denmark4
Aarhus University5
Copenhagen University Hospital Herlev and Gentofte6
Oslo University Hospital7
Broad Institute of Harvard University and Massachusetts Institute of Technology8
Hagedorn Research Institute9
Meta‐analyses of large‐scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome‐wide association (GWA) studies, protein‐protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi‐layered evidence network which is used to prioritize the entire protein‐coding part of the genome identifying a shortlist of candidate genes.
We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e−3) with an odds ratio of 1.28 [1.12–1.48], which replicates a previous case‐control study.
In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at .
Genet. Epidemiol. 2011. © 2011 Wiley‐Liss, Inc. 35:318‐332, 2011
Language: | English |
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Publisher: | Wiley Subscription Services, Inc., A Wiley Company |
Year: | 2011 |
Pages: | 318-332 |
ISSN: | 10982272 and 07410395 |
Types: | Journal article |
DOI: | 10.1002/gepi.20580 |
ORCIDs: | 0000-0003-0207-4831 , 0000-0001-8748-3831 , 0000-0003-0316-5866 and Workman, Christopher |
Bipolar disorder Data integration Genome-wide association Meta-analysis SDG 3 - Good Health and Well-being Type 2 diabetes
Bipolar Disorder Data Interpretation, Statistical Databases, Genetic Diabetes Mellitus, Type 2 Genetic Association Studies Genome-Wide Association Study Humans Models, Genetic Models, Statistical Polymorphism, Single Nucleotide Protein Interaction Mapping bipolar disorder data integration genome‐wide association meta‐analysis type 2 diabetes