Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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, Holmans, Peter Alan, Kirov, George, O'Donovan, Michael Conlon and Williams, Nigel Melville 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

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

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: 21 May 2019 16:47
URI: http://orca.cf.ac.uk/id/eprint/79796

Citation Data

Cited 71 times in Google Scholar. View in Google Scholar

Cited 65 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics