Abstract:
Schizophrenia, which is one of the most commonly reported illnesses among all psychiatric disorders, is defined as a complex disease; this implicates that it does not show a simple Mendelian pattern of inheritance. The discovery of the role of ERBB4-NRG1 axis has led to many publications, which studied the independent as well as interactive effects of ERBB4 and NRG1, associated with schizophrenia. However, most of these findings could not be validated in other studies. Also the all 21 genome-wide association studies in schizophrenia, published so far, could not identify any of ERBB4 and NRG1 SNPs as significant. This study exploits three publicly available genome-wide association study datasets: CATIE, GAIN and nonGAIN and aims to establish an innovative analysis methodology to highlight genetic significance of genes, such as ERBB4 and NRG1 that play crucial roles in molecular pathways leading to schizophrenia. In the framework of this study, novel regions of the ERBB4 and NRG1 genes associated with schizophrenia were identified in three large GWAS datasets using two different haplotype analyses, haplotype-based logistic regression and Haploview. Using the same methodology, previously associated ERBB4 and NRG1 blocks were validated in at least two datasets. To predict the functional effects of novel blocks, transcription factors that bind to these regions were identified. This thesis specifically and for the first time showed that the binding of particular transcription factors to intronic regions of ERBB4 and NRG1 might have causative or protective effects on schizophrenia. This is the first study to perform an in silico and bioinformatics-based functional analysis of variants located within introns in schizophrenia subjects from GWAS datasets. The methodology exploited in this thesis gives promising results that facilitate our further understanding of the functional role of intronic variants.
