TitleGenomic characterization of schizophrenia candidate gene regions
NameNato, Alejandro (author), Matise, Tara C. (chair), Brzustowicz, Linda M. (co-chair), Gordon, Derek (internal member), Buyske, Steven (outside member), Rutgers University, Graduate School - New Brunswick,
SubjectMicrobiology and Molecular Genetics,
DescriptionSchizophrenia (SZ) is a severe mental disorder with a complex genetic etiology and a lifetime prevalence of ~0.55-1%. In this study, we extracted and analyzed data from 47 independent genomewide scans for linkage to SZ. The genome was partitioned into 22 schizophrenia candidate gene regions (SCRs) by applying three methods: single significant approach, disjoint approach, and smoothing method. For the single significant hit approach, an SCR was defined by the presence of a significant hit by extending 10 cM upstream and downstream of that hit. For the disjoint approach, we identified criteria-events based on clustering of hits within small regions by using a sliding window approach. For the smoothing method, we imputed a randomized range of p-values (0.001697≤p<1) for the genome scans without a suggestive or significant result at each 0.1 cM interval. We combined these p-values with the genome scan results using three methods: geometric mean method, Fisher’s method, and Stouffer’s method. We characterized each SCR by identifying the genes and genomic elements such as structural variations, regulatory elements, and functional noncoding RNAs. We also categorized the genes within SCRs. The total coverage of SCRs is 880 cM (739 Mb) with SCR sizes ranging from 18 cM to 87 cM. SCRs with multiple peaks were divided into smaller subregions. We developed a ranking system to identify and prioritize SZ candidate genes by assigning weights to each of the following criteria: (a) relative significance of a peak within an SCR, (b) annotated in SZ association studies and microarray analyses, and meta-analyses, (c) associated with phenotypes or diseases, and (d) located within or near other genetic elements. We identified functions/diseases and pathways that were most significantly associated with genes that had at least the same score or better than the top 10% of candidate genes after prioritization by utilizing the Ingenuity® Pathways Analysis (IPA). We generated an interactome for SZ based on the SZ candidate genes. We created a website (http://compgen.rutgers.edu/schiz) to disseminate information about our SCRs. Our procedure, which provides a novel approach to identify and prioritize candidate gene regions and genomic elements, is applicable to other complex diseases.
NoteIncludes bibliographical references
Noteby Alejandro Q. Nato, Jr.
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.