Schizophrenia is a psychiatric disorder resulting from a combination of genetic and environmental factors. Previous studies have identified several risk genes for the disorder, however these studies have been unable to provide mechanistic meaning to these genes. Stratifying genetic risk by aggregated sets of genes participating in the same biological processes may enhance early diagnostic and prognostic efforts. Here, we plan to calculate an aggregated genetic risk score as well as a gene-based gene expression risk score for schizophrenia, using genes identified in previous large-scale studies. We then plan to identify the relationship between these risk scores and structural brain regions, as well as individual symptoms, in an effort to identify whether specific gene-sets are more likely to result in different symptom presentations and structural alterations. We plan to initially analyse this in a large-scale population with sub-clinical genetic risk for schizophrenia, and then apply this to a patient population. The potential clinical applications of this work include focusing preventative interventions for specific symptoms (or cognitive deficits) should individuals present with an elevated genetic risk aggregated in a specific location. Future applications could therefore include targeting pharmacological or psychological treatment aimed at specific biological or cognitive processes that may be linked to individual symptoms.