"Background: Schizophrenia and bipolar disorder are severe mental illnesses which cause high rates of disability, social and economic costs. Although there is compelling evidence of a genetic contribution to psychosis and we know deficits in neuronal connectivity are a key factor, the pathophysiology and aetiology of the disease remain poorly understood. Aims: (i) To identify biomarkers of neural dys-connectivity characterising psychosis through neurophysiological and neuropsychological techniques. (ii) To apply such biomarkers in genetic association studies in order to understand the mechanisms by which variation at certain loci leads to the onset of the disease. Methods: Neurophysiology is a non-invasive and inexpensive technique with a superb temporal resolution that allows the study of perceptual and cognitive processes in vivo. My UCL supervisor and I have collected a large sample of families with psychosis cases including 300 patients, 220 unaffected relatives and 300 controls. Participants completed a battery of experiments including resting EEG, P300 wave, P50 gating, Mismatch Negativity and cognitive assessments. I will train in advanced analytics using Dynamic Causal Modeling (DCM), which tests models of neural activity and its synchronisation deficits and is a new tool in psychiatry. Through DCM I will identify novel measures of brain dys-connectivity to use as phenotypes for genetic association. I will examine candidate genes, identified through association studies using Plink and Unphased software. I will train in gene ontology tools to perform pathway analyses using the extensive genome-wide genotyping that is already available on my sample. UCL, a centre of excellence in neuroscience, will host my research. Implications: Biomarkers of brain connectivity could be used towards the earlier detection and treatment of psychosis. Understanding the mechanisms of action of susceptibility genes is essential to develop safer and more effective new treatments."