Integrated Approaches to the Risk Prediction of First-episode Psychosis

Student thesis: Doctoral ThesisDoctor of Philosophy


Psychosis is a complex condition that features in many psychiatric disorders, and significantly affects the quality of life for both patients and family members. As part of the Genetics and Psychosis (GAP) study, this thesis presents one of the largest blood gene expression datasets on first-episode psychosis patients to date. This work aimed to characterise the blood-based biological perturbations in psychosis and to investigate the predictive ability of gene expression data.
Firstly changes in expression, between healthy controls and first-episode psychosis patients was explored, to identify genes associated with psychosis. I identified hundreds of differentially expressed genes and found associations to pathways involved in transcription, oxidative stress and viral replication.
Secondly, network approaches were used to construct modules of genes based on co-expression. I identified modules correlated to the severity of positive symptoms, and enrich-ment for pathways associated with the stress response and multiple brain regions.
Thirdly regularised generalised linear models with bootstrapping were used to generate predictions based on combinations of gene expression, genetic and demographic data. The highest performance was found for models incorporating gene expression data, with minimal improvement using additional data. Prediction accuracy for identifying psychosis samples was found to increase with severity of positive symptoms in schizophrenia samples, but not in other psychoses.
Finally, machine learning methods were used on public schizophrenia gene expression data to build a variety of predictive models. These models were tested on the Genetics and Psychosis (GAP) gene expression data. The results show increased predictive performance on samples with a schizophrenia diagnosis, compared to other types of psychosis.
Overall the thesis presents work analysing a novel gene expression dataset. The results suggest that blood gene expression signatures are more predictive for positive symptoms in schizophrenia than for other psychoses. This work also highlights expression differences in innate immune pathways and the stress response.
Date of Award2018
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorRichard Dobson (Supervisor) & Robin Murray (Supervisor)

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