MOTIVATION: During the past decade, we have seen an exponential growth of vast 
amounts of genetic data generated for complex disease studies. Currently, across 
a variety of complex biological problems, there is a strong trend towards the 
integration of data from multiple sources. So far, candidate gene prioritization 
approaches have been designed for specific purposes, by utilizing only some of 
the available sources of genetic studies, or by using a simple weight scheme. 
Specifically to psychiatric disorders, there has been no prioritization approach 
that fully utilizes all major sources of experimental data.
RESULTS: Here we present a multi-dimensional evidence-based candidate gene 
prioritization approach for complex diseases and demonstrate it in 
schizophrenia. In this approach, we first collect and curate genetic studies for 
schizophrenia from four major categories: association studies, linkage analyses, 
gene expression and literature search. Genes in these data sets are initially 
scored by category-specific scoring methods. Then, an optimal weight matrix is 
searched by a two-step procedure (core genes and unbiased P-values in 
independent genome-wide association studies). Finally, genes are prioritized by 
their combined scores using the optimal weight matrix. Our evaluation suggests 
this approach generates prioritized candidate genes that are promising for 
further analysis or replication. The approach can be applied to other complex 
diseases.
AVAILABILITY: The collected data, prioritized candidate genes, and gene 
prioritization tools are freely available at 
http://bioinfo.mc.vanderbilt.edu/SZGR/.