Schizophrenia is a severe psychiatric disorder characterized by psychotic symptoms, cognitive dysfunction and poor outcome, and is a major public health problem with a high cost for society. Brain imaging studies have shown structural abnormalities and dy sfunction in specific brain regions. The heritability is high, but despite the clear genetic component, the pathophysiology is still not known and few susceptibility genes are discovered. We propose a new approach to gene discovery, integrating two previo usly separate methodologies, functional neuroimaging (fMRI) and genome-wide scans. We will develop Imaging Geno-Phenotypes; which are brain imaging correlates of disease that are influenced by normal genetic variation. By using brain activation difference s in addition to diagnosis, we will have quantitative measures which enable us to use general linear statistical models with interaction. The project is based on clinical, neuroimaging and genetic data collected from the large cohort of schizophrenia and controls (approx. n=200/group) in the ongoing TOP study, and focus on data analysis with new biostatistical tools necessary for the huge amount of data. Specifically, we will establish a database combining all variables, perform statistical analysis using general lineal models and methods to adjust for multiple comparisons, and develop new statistical methods and software. Gene findings will be replicated in a larger schizophrenia sample. The project is based on the TOP study, a multicenter transdisciplin ary research effort in the Oslo area, in collaboration with Univ. of Bergen and FUGE bioinformatics platform. It will also take advantage of an ongoing collaboration with a bioinformatics network at the Univer. of California. The project will have a strat egic impact on functional genomics research in Norway, since it brings together fMRI, molecular genetic and biostatistical expertise in a concerted effort, and use the new technique of whole genome scans.