Genomic Characterization of Schizophrenia Candidate Gene Regions. A. Q. Nato1, X. Kong1,2, F. Chen1,3, C. He1,4, D. Chimento1, B. Byrne5, J. Naus6, C. Chiu1, S. Buyske1,6, L. M. Brzustowicz1, T. C. Matise1 1) Dept Gen, Rutgers Univ, Piscataway, NJ; 2) GlaxoSmithKline, King of Prussia, PA; 3) Inst Gen Sci & Policy, Duke Univ, Durham, NC; 4) Lab Stat Gen, Rockefeller Univ, New York, NY; 5) Informatics Inst, UMDNJ, Piscataway, NJ; 6) Dept Stat, Rutgers Univ, Piscataway, NJ.

   Schizophrenia (SZ) has been established to have a complex genetic aetiology with a lifetime prevalence commonly given at ~1% but systematic reviews suggested lower values of 0.44% to 0.55%. In this study, we extracted and analyzed data from the 47 published independent genomewide linkage scans for SZ from 1994 to 2009. A smoothing method and a disjoint approach were utilized to determine 19 schizophrenia candidate gene regions (SCRs). We have developed the SCR Browser for identifying the genetic markers implicated in these genome scans. We annotate each SCR by identifying the genes within these regions. We categorize these genes based on whether they are linked to diseases, functions, phenotypes, pathways, or GO terms, and on whether they are linked or associated with SZ in published linkage scans, association studies, meta-analyses, and microarray studies. We also identify copy-number variants, segmental duplications, defined regulatory regions, putative miRNA binding sites, and rearrangement hotspots within each SCR. The total SCR coverage is 427 cM with SCR sizes ranging from 16 cM to 50 cM. We identify subregions within these SCRs using the results from fine-mapping studies coupled with the diverse information used to characterize each of the SCRs. Our approach provides a novel method to determine and prioritize candidate gene regions and genomic elements that may be applied to other complex diseases.