As a common and severe psychiatric disorder, large numbers of genetic studies of schizophrenia, especially the genome-wide association study (GWAS), have identified a large amount of genetic variants associated with schizophrenia. However, the biological function of these genetic variants is still elusive. As the accumulation of regulatory data, the cross analysis between regulatory data and genetic data shows that most of the genetic variants from GWAS are in the non-coding regions and function through the regulatory mechanism. Therefore, construction and analysis of the regulatory network involved by the genetic variants in schizophrenia will facilitate the understanding of the function mechanism of genetic variants. In this project, we will use two strategies to construct the regulatory network for genetic variants of schizophrenia and further perform integration analysis. Firstly, we will use the significant SNPs from GWASs and candidate genes from candidate gene association studies for schizophrenia to construct their involved regulatory network. Secondly, we will use the dense module search algorithm to identify disease related network modules from GWAS data based on the integrated human interactome, which includes both regulatory network and protein-protein interaction network. Finally, network integration, network structure analysis and functional enrichment analysis would be performed for the two networks to identify the important regulatory genes and motifs, and functional pathways enriched by the regulatory genes. The result of the project will provide new insights for the understanding of regulatory function of genetic variants in schizophrenia, and provide data support for the selection of functional candidates and drug target for further study.
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