Schizophrenia is a major debilitating psychiatric disorder affecting approximately one percent of the population worldwide.1
It is commonly considered to be a complex disorder with multiple genetic and environmental factors involved; however, genetic factors impact substantially upon risk for developing the disease, with heritability estimates ~80%.2
The genetic approaches used so far to identifying risk genes or markers for schizophrenia have been largely inconclusive, as investigators often frustratingly found a low replication rate of significant markers or genes in the linkage or association studies, or found no clear connection between the risk to schizophrenia and structural changes in these susceptibility genes. It is likely that a number of genes, each of which contributes a small risk, interact with each other or with environmental risk factors to cause this psychiatric phenotype.3
Thus, collection and systematic annotations of candidate genes with genetic evidence from multiple studies is urgently needed for the examination of gene × gene (G×G) and gene × environment (G×E) interactions.
We have seen during the past two decades an exponential growth of vast amounts of biological data in schizophrenia genetics, including those generated by traditional positional cloning approach,4
individual gene/marker association studies and emerging genome-wide association studies,5–8
more than 32 genome-wide linkage scans and several meta-analyses,9, 10
and a large number of microarray experiments.11
Besides these genetic datasets, abundant biological information for the schizophrenia candidate genes can be extracted from public databases such as Gene Ontology annotations,12
protein-protein interaction (PPI) networks, and regulatory and cellular pathways.13
At present, there is a strong trend towards integrating the data from various genetic studies and their related biological information in the cellular systems so that promising candidate genes can be prioritized for follow up bioinformatics analysis and experimental verification. Some examples are National Cancer Institute (NCI) Cancer Gene Data Curation Project and a number of databases for specific categories of cancer (e.g. Tumor Suppressor Gene Database and Breast Cancer Database). For schizophrenia and the related psychiatric disorders, the VSD database focuses on variation data for publicly available schizophrenia candidate genes.14
This database seems no longer available, as its web link is not functional. Most recently, there is a SchizophreniaGene database that is specifically for the published association studies for schizophrenia.5
Another database, Sullivan Lab Evidence Project (SLEP), has been recently developed for the linkage and association evidence of genes or loci based on curation of the data.4
Each of these three databases focuses on specific genetic information for schizophrenia with only few computational tools available for the user. So far, we have been unable to find a comprehensive and integrative resource for schizophrenia.
Here, we present Schizophrenia Gene Resource (SZGR), a comprehensive database with user-friendly web interface. SZGR deposits genetic data collected from all the available sources including association studies, linkage scans, gene expression, literature, Gene Ontology (GO) annotations, gene networks, cellular and regulatory pathways, and microRNAs (miRNAs) and their target sites. Besides, SZGR provides online tools for data integration and custom gene ranking, powerful data browse and search function, and graphical presentation. It has dynamic links to many public databases such as NCBI and the SchizophreniaGene. SZGR has been applied in several projects including schizophrenia gene network analysis and a large-scale genotyping project based on the prioritized candidate genes. This system can be easily applied to other complex diseases.