Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis

Authors

Zhongming Zhao, Virginia Commonwealth University, Vanderbilt UniversityFollow
Bradley T. Webb, Virginia Commonwealth UniversityFollow
Peilin Jia, Vanderbilt University
T. Bernard Bigdeli, Virginia Commonwealth UniversityFollow
Brion S. Maher, Johns Hopkins Bloomberg School of Public HealthFollow
Edwin van den Oord, Virginia Commonwealth UniversityFollow
Sarah Bergen, Massachusetts General Hospital, Broad Institute of MIT and Harvard
Richard L. Amdur, Washington VA Medical Center
Francis A. O'Neill, Queens University - Belfast
Dermot Walsh, The Health Research Board - Dublin
Dawn L. Thiselton, Virginia Commonwealth University
Xianging Chen, Virginia Commonwealth UniversityFollow
Carlos N. Pato, University of Southern California
The International Schizophrenia Consortium
Brien P. Riley, Virginia Commonwealth UniversityFollow
Kenneth S. Kendler, Virginia Commonwealth UniversityFollow
Ayman H. Fanous, Virginia Commonwealth University, Georgetown University, University of Southern CaliforniaFollow

Document Type

Article

Original Publication Date

2013

Journal/Book/Conference Title

PLOS ONE

Volume

8

DOI of Original Publication

10.1371/journal.pone.0067776

Comments

Originally Published at http://dx.doi.org/10.1371/journal.pone.0067776

Date of Submission

November 2014

Abstract

Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to this ranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priorihypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.

Rights

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Is Part Of

VCU Psychiatry Publications