Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.
Citation:
Jia P, Wang L, Fanous AH, Pato CN, Edwards TL, Zhao Z, Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia., PLoS computational biology, 8, 7, 2012, e1002587Download Item:
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
6
10
2
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
Sponsor
Grant Number
National Institutes of Health (NIH)
R01LM011177
Author's Homepage:
http://people.tcd.ie/acorvinDescription:
PUBLISHED
Author: CORVIN, AIDEN PETER
Sponsor:
National Institutes of Health (NIH)Type of material:
Journal ArticleCollections
Series/Report no:
PLoS computational biology8
7
Availability:
Full text availableKeywords:
module search algorithm,Metadata
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