Common variants conferring risk of schizophrenia: A pathway analysis of GWAS data

Abstract

Unlike the typical analysis of single markers in genome-wide association studies (GWAS), we incorporated Gene Set Enrichment Analysis (GSEA) and hypergeometric test and combined them using Fisher's combined method to perform pathway-based analysis in order to detect genes' combined effects on mediating schizophrenia. A few pathways were consistently found to be top ranked and likely associated with schizophrenia by these methods; they are related to metabolism of glutamate, the process of apoptosis, inflammation, and immune system (e.g., glutamate metabolism pathway, TGF-beta signaling pathway, and TNFR1 pathway). The genes involved in these pathways had not been detected by single marker analysis, suggesting this approach may complement the original analysis of GWAS dataset.

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.