TY - JOUR

T1 - Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes

AU - Pers, Tune Hannes

AU - Hansen, Niclas Tue

AU - Hansen, Kasper Lage

AU - Koefoed, Pernille

AU - Dworzynski, Piotr

AU - Miller, Martin Lee

AU - Flint, Tracey J

AU - Mellerup, Erling

AU - Dam, Henrik

AU - Andreassen, Ole A

AU - Djurovic, Srdjan

AU - Melle, Ingrid

AU - Børglum, Anders D

AU - Werge, Thomas

AU - Purcell, Shaun

AU - Ferreira, Manuel A

AU - Kouskoumvekaki, Irene

AU - Workman, Christopher

AU - Hansen, Torben

AU - Mors, Ole

AU - Brunak, Søren

N1 - © 2011 Wiley-Liss, Inc.

PY - 2011/4/11

Y1 - 2011/4/11

N2 - 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 www.cbs.dtu.dk/services/metaranker. Genet. Epidemiol. 2011. © 2011 Wiley-Liss, Inc.

AB - 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 www.cbs.dtu.dk/services/metaranker. Genet. Epidemiol. 2011. © 2011 Wiley-Liss, Inc.

U2 - 10.1002/gepi.20580

DO - 10.1002/gepi.20580

M3 - Journal article

C2 - 21484861

VL - 35

SP - 318

EP - 332

JO - Genetic Epidemiology

JF - Genetic Epidemiology

SN - 0741-0395

IS - 5

ER -