Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia

Peilin Jia, Lily Wang, Ayman H Fanous, Carlos N Pato, Todd L Edwards, The International Schizophrenia Consortium and Zhongming Zhao

PLOS Computational Biology, 2012, vol. 8, issue 7, 1-11

Abstract: With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had Pmeta

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1002587

DOI: 10.1371/journal.pcbi.1002587

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