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, Zhongming Zhao

Research output: Contribution to journalArticle

68 Citations (Scopus)

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<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.

Original languageEnglish (US)
Article numbere1002587
JournalPLoS Computational Biology
Volume8
Issue number7
DOIs
StatePublished - Jul 1 2012
Externally publishedYes

Fingerprint

Genome-Wide Association Study
Schizophrenia
Genome
genome
Genes
Gene Regulatory Networks
Gene
gene
Module
meta-analysis
Single Nucleotide Polymorphism
Meta-Analysis
genes
Protein Interaction Maps
Chromosomes, Human, Pair 6
Datasets
schizophrenia
genome-wide association study
protein-protein interactions
protein

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

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.

In: PLoS Computational Biology, Vol. 8, No. 7, e1002587, 01.07.2012.

Research output: Contribution to journalArticle

Jia, Peilin ; Wang, Lily ; Fanous, Ayman H. ; Pato, Carlos N. ; Edwards, Todd L. ; Zhao, Zhongming. / Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. In: PLoS Computational Biology. 2012 ; Vol. 8, No. 7.
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