Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.
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 anal...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC3390381?pdf=render |
| _version_ | 1818511917121011712 |
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| author | Peilin Jia Lily Wang Ayman H Fanous Carlos N Pato Todd L Edwards International Schizophrenia Consortium Zhongming Zhao |
| author_facet | Peilin Jia Lily Wang Ayman H Fanous Carlos N Pato Todd L Edwards International Schizophrenia Consortium Zhongming Zhao |
| author_sort | Peilin Jia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T23:39:33Z |
| format | Article |
| id | doaj.art-bcc2a5fa4b0146918ca082a4ba1e16d2 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-10T23:39:33Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-bcc2a5fa4b0146918ca082a4ba1e16d22022-12-22T01:29:06ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582012-01-0187e100258710.1371/journal.pcbi.1002587Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.Peilin JiaLily WangAyman H FanousCarlos N PatoTodd L EdwardsInternational Schizophrenia ConsortiumZhongming ZhaoWith 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.http://europepmc.org/articles/PMC3390381?pdf=render |
| spellingShingle | Peilin Jia Lily Wang Ayman H Fanous Carlos N Pato Todd L Edwards International Schizophrenia Consortium Zhongming Zhao Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. PLoS Computational Biology |
| title | Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. |
| title_full | Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. |
| title_fullStr | Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. |
| title_full_unstemmed | Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. |
| title_short | Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. |
| title_sort | network assisted investigation of combined causal signals from genome wide association studies in schizophrenia |
| url | http://europepmc.org/articles/PMC3390381?pdf=render |
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