Common variants conferring risk of schizophrenia: A pathway analysis of GWAS data
Introduction
Genome-wide association studies (GWAS) have become a powerful approach to searching for common genetic variants which increase susceptibility to complex diseases or traits. So far, the search for common susceptibility variants has been less successful in schizophrenia than in many other complex diseases/traits (O'Donovan et al., 2009). Among several recent schizophrenia GWA studies, essentially no marker or gene has achieved genome-wide statistical significance level in any single study (Purcell et al., 2009, Shi et al., 2009, Stefansson et al., 2009, Sullivan et al., 2008), although combining data from several studies suggested the MHC region on chromosome 6p and a few other genes (e.g., NRGN and TCF4) might be promising for future validation (Purcell et al., 2009, Shi et al., 2009, Stefansson et al., 2009). Although it is commonly accepted that schizophrenia may result from many genes or genetic variants, each of which makes a small risk contribution, and through interactions with each other or environmental factors to cause this disorder, the genetic signal has always been examined at single marker level in the schizophrenia GWA studies.
Here we examined the association signal of GWAS markers in a set of genes categorized by biological pathways, assuming a complex disease such as schizophrenia may result from a number of genes which disrupt one or more pathways. To reduce bias, we applied two statistical methods to identify overrepresented pathways in a single GWAS dataset. The first method is Gene Set Enrichment Analysis (GSEA), which was initially developed for microarray gene expression analysis (Subramanian et al., 2005) but was recently adapted to GWA studies. The second method is the hypergeometric test which identifies pathways overrepresented with significant genes. We identified 4 pathways that had P value < 0.05 by both methods. We further combined the P values using Fisher's method (Fisher, 1932) to assess the consistency of evidence. Importantly, these pathways are related to glutamate metabolism, the process of apoptosis, inflammation, and the immune system, implicating their involvement in the underlying pathology of schizophrenia.
Section snippets
GWAS data preparation
We used GAIN (Genetic Association Information Network) GWAS dataset for schizophrenia since most other schizophrenia GWAS datasets (e.g., ISC GWAS) have not been publicly available to general investigators (Manolio et al., 2007). The data access was approved by the GAIN DAC through National Human Genome Research Institute and was recently used in our candidate gene selection for schizophrenia (Sun et al., 2010, Sun et al., 2009). The data was extracted from the NCBI dbGaP (//www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=gap
Results and discussion
We found 6 pathways having significant nominal P values (P < 0.05) by the GSEA method, and 10 by the hypergeometric test. The following four pathways had nominal P values < 0.05 by both methods: CARM_ER pathway (BioCarta), glutamate metabolism (BioCarta), TNFR1 pathway (BioCarta), and TGF beta signaling pathway (KEGG). Table 1 lists these overrepresented pathways ordered by GSEA NES value. There were additional 7 pathways having nominal P value < 0.05 by either method (Supplementary Table 1). When
Role of the funding source
This work was supported by National Institutes of Health Grant nos. AA017437 and MH083094, a NARSAD Young Investigator Award to Z.Z., and a Thomas F. and Kate Miller Jeffress Memorial Trust Fund grant No. J-900. The funding agencies had no further role in the design, implementation, or generation of this research report.
Contributors
Authors PJ, LW, HYM and ZZ designed the study. PJ and LW carried out the analyses. HYM provided guidance on data analysis and phenotype assessment. PJ, LW, HYM and ZZ wrote the manuscript.
Conflict of interest
All authors have no conflicts of interest to declare.
Acknowledgements
We thank two anonymous reviewers for valuable suggestions and Drs. Douglas Levinson and Edwin van den Oord for the valuable suggestions and discussion. The genotyping of samples was provided through the Genetic Association Information Network (GAIN). The dataset(s) used for the analyses described in this manuscript were obtained from the database of Genotype and Phenotype (dbGaP) found at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number [phs000021.v2.p1]. Samples and associated
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