New approaches in precision psychiatry: Exploring genomic markers and linked health records to predict symptom improvement and drug response in schizophrenia.
John Grace QC PhD Scholarship 2022
Supervisors: Professor Michael C. O’Donovan and Dr. Antonio F. Pardiñas
Lay Summary: Not everyone with schizophrenia responds in the same way to the same drug treatments. In fact, nearly a third of people do not benefit even after several treatments have been tried. We do not know why this happens, although we think genetic differences between people are part of the answer. What we do know is that poor response to treatment can have a very bad impact on quality of life, and that people with schizophrenia and their families think that finding out how to improve treatment response is a high priority. It is very difficult to do research on this topic because large amounts of complicated data are needed, which are hard to collect using the sort of methods that have been possible until very recently. In Cardiff, we can now overcome this problem. Over many years, we have recruited large numbers of people who have given us samples of DNA to study genetic influences on schizophrenia. Thanks to our research collaborations with other institutions and the NHS, we can now look at the connections between that genetic information and lots of data available on medical records. Importantly, we can do this in a way that keeps the identities of the people we are studying secret, unknown to us and anyone else. We will use this new information to see how people respond to different treatments they receive. We will also be able to see how people respond differently depending on their genes and other possibly important factors such as symptoms, age, and sex. We think that the results of this project can show us how information that is collected as a matter of routine when people use health services can help to identify the treatment for individuals that is the safest for them and also gives them the best benefits.
Aims: Our main hypothesis is that treatment response in schizophrenia is a complex construct with biological and environmental influences, related to the presence and intensity of specific symptoms. Thus, this PhD project will use unique datasets to assess how genetic, demographic, environmental and clinical information can be combined to predict the effects of antipsychotic treatment. Primarily, the PhD candidate will evaluate the association between standardised symptom scales and (i) composite metrics of psychiatric genomic risk known as polygenic scores, (ii) pharmacogenomic markers with known functional consequences for the activity of drug-metabolising enzymes and (iii) extensive non-genetic information derived from clinical interview records. The study will be based upon thoroughly characterised patient cohorts recruited by our research group and our close collaborators, most of which can be linked to NHS electronic health records (eHR). The candidate will also develop skills in the computational discipline of Natural Language Processing (NLP) to enhance these cohorts through the extraction of data from routinely collected clinical notes, enabling the assessment of environmental risk factors and construction of longitudinal symptom profiles.