Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.
Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.
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DOI：
10.1371/journal.pcbi.1002587
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发表时间：
2012
影响因子：
4.3
通讯作者：
Zhao Z
中科院分区：
文献类型：
--
作者：
Jia P;Wang L;Fanous AH;Pato CN;Edwards TL;International Schizophrenia Consortium;Zhao Z
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. The recent success of genome-wide association studies (GWAS) has generated a wealth of genotyping data critical to studies of genetic architectures of many complex diseases. In contrast to traditional single marker analysis, an integrative analysis of multiple genes and the assessment of their joint effects have been particularly promising, especially upon the availability of many GWAS datasets and other high-throughput datasets for numerous complex diseases. In this study, we developed an integrative analysis framework for multiple GWAS datasets and demonstrated it in schizophrenia. We first constructed a GWAS-weighted protein-protein interaction (PPI) network and then applied a dense module search algorithm to identify subnetworks with combinatory disease effects. We applied combinatorial criteria for module selection based on permutation tests to determine whether the modules are significantly different from random gene sets and whether the modules are associated with the disease in investigation. Importantly, considering there are many complex diseases with multiple GWAS datasets available, we proposed a discovery-evaluation strategy to search for modules with consistent combined effects from two or more GWAS datasets. This approach can be applied to any diseases or traits that have two or more GWAS datasets available.
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影响因子：
4.5
作者：
Sun, Jingchun;Wan, Chunling;Jia, Peilin;Fanous, Ayman H.;Kendler, Kenneth S.;Riley, Brien P.;Zhao, Zhongming
通讯作者：
Zhao, Zhongming
影响因子：
4.5
作者：
Rossin EJ;Lage K;Raychaudhuri S;Xavier RJ;Tatar D;Benita Y;International Inflammatory Bowel Disease Genetics Constortium;Cotsapas C;Daly MJ
通讯作者：
Daly MJ
影响因子：
64.8
作者：
Stefansson, Hreinn;Ophoff, Roel A.;Steinberg, Stacy;Andreassen, Ole A.;Cichon, Sven;Rujescu, Dan;Werge, Thomas;Pietilainen, Olli P. H.;Mors, Ole;Mortensen, Preben B.;Sigurdsson, Engilbert;Gustafsson, Omar;Nyegaard, Mette;Tuulio-Henriksson, Annamari;Ingason, Andres;Hansen, Thomas;Suvisaari, Jaana;Lonnqvist, Jouko;Paunio, Tiina;Borglum, Anders D.;Hartmann, Annette;Fink-Jensen, Anders;Nordentoft, Merete;Hougaard, David;Norgaard-Pedersen, Bent;Bottcher, Yvonne;Olesen, Jes;Breuer, Rene;Moeller, Hans-Jurgen;Giegling, Ina;Rasmussen, Henrik B.;Timm, Sally;Mattheisen, Manuel;Bitter, Istvan;Rethelyi, Janos M.;Magnusdottir, Brynja B.;Sigmundsson, Thordur;Olason, Pall;Mason, Gisli;Gulcher, Jeffrey R.;Haraldsson, Magnus;Fossdal, Ragnheidur;Thorgeirsson, Thorgeir E.;Thorsteinsdottir, Unnur;Ruggeri, Mirella;Tosato, Sarah;Franke, Barbara;Strengman, Eric;Kiemeney, Lambertus A.;Melle, Ingrid;Djurovic, Srdjan;Abramova, Lilia;Kaleda, Vasily;Sanjuan, Julio;de Frutos, Rosa;Bramon, Elvira;Vassos, Evangelos;Fraser, Gillian;Ettinger, Ulrich;Picchioni, Marco;Walker, Nicholas;Toulopoulou, Timi;Need, Anna C.;Ge, Dongliang;Yoon, Joeng Lim;Shianna, Kevin V.;Freimer, Nelson B.;Cantor, Rita M.;Murray, Robin;Kong, Augustine;Golimbet, Vera;Carracedo, Angel;Arango, Celso;Costas, Javier;Joensson, Erik G.;Terenius, Lars;Agartz, Ingrid;Petursson, Hannes;Nothen, Markus M.;Rietschel, Marcella;Matthews, Paul M.;Muglia, Pierandrea;Peltonen, Leena;St Clair, David;Goldstein, David B.;Stefansson, Kari;Collier, David A.
通讯作者：
Collier, David A.
影响因子：
4.4
作者：
Wang, Lily;Jia, Peilin;Wolfinger, Russell D.;Chen, Xi;Zhao, Zhongming
通讯作者：
Zhao, Zhongming
影响因子：
9.8
作者：
Wang, Kai;Li, Mingyao;Bucan, Maja
通讯作者：
Bucan, Maja