Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia

Jia, Peilin, Wang, Lily, Fanous, Ayman H., Pato, Carlos N., Edwards, Todd L., Zhao, Zhongming, Craddock, Nicholas John ORCID: https://orcid.org/0000-0003-2171-0610, Holmans, Peter Alan ORCID: https://orcid.org/0000-0003-0870-9412, Kirov, George ORCID: https://orcid.org/0000-0002-3427-3950, O'Donovan, Michael Conlon ORCID: https://orcid.org/0000-0001-7073-2379 and Williams, Nigel Melville ORCID: https://orcid.org/0000-0003-1177-6931 2012. Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. PLoS Computational Biology 8 (7) , e1002587. 10.1371/journal.pcbi.1002587

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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 P(meta)<1 × 10⁻⁴, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Medicine Systems Immunity Research Institute (SIURI) Neuroscience and Mental Health Research Institute (NMHRI)
Subjects:	R Medicine > R Medicine (General)
Additional Information:	Nick Craddock, Peter Holmans, George Kirov, Michael O'Donovan and Nigel Williams are collaborators on this article.
Publisher:	Public Library of Science
ISSN:	1553-7358
Date of First Compliant Deposit:	30 March 2016
Last Modified:	06 May 2023 18:18
URI:	https://orca.cardiff.ac.uk/id/eprint/79796

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