Association study of 167 candidate genes for schizophrenia selected by a multi-domain evidence-based prioritization algorithm and neurodevelopmental hypothesis.
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 priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Zhongming
Z
Zhao
Bradley T
BT
Webb
Peilin
P
Jia
T Bernard
TB
Bigdeli
Brion S
BS
Maher
Edwin
E
van den Oord
Sarah E
SE
Bergen
Richard L
RL
Amdur
Francis A
FA
O'Neill
Dermot
D
Walsh
Dawn L
DL
Thiselton
Xiangning
X
Chen
Carlos N
CN
Pato
Brien P
BP
Riley
Kenneth S
KS
Kendler
Ayman H
AH
Fanous
D000465:Algorithms; D030541:Databases, Genetic; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D009420:Nervous System / Q000254:growth & development* / Q000473:pathology*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011643:Publishing; D012559:Schizophrenia / Q000235:genetics*