Grant: DRA : Canada Graduate Research Scholarship - Doctoral Program (CGRS D)

Schizophrenia (SCZ) is a debilitating mental illness affecting over 24 million people worldwide. Treatment with antipsychotics (APs) is guided by trial-and-error, with only 40% of patients showing response. AP efficacy could be improved using pharmacogenetics (PGx), where key genetic markers of a patient are used to guide prescription. However, most studies are based on Europeans, and important PGx genes in non-Europeans remain understudied. Additionally, AP response is influenced by hundreds of other genes and environmental factors. Machine learning (ML) would be ideal to address this complexity, but few ML models integrate genetic and environmental factors. This integration is crucial, and can lead to enhanced predictions of response. Therefore, the objective is to study genetic contributors to AP response, and combine these with environmental factors to better understand the drivers of treatment outcome. I will use the 'All of Us' cohort (N=2,377 SCZ patients with long-term AP treatment), which is diverse in ancestry and sex. I will first assess the overall importance of PGx testing in non-Europeans by evaluating which key PGx genes are associated with AP response, and determining the proportion of individuals whose genetic profiles have prescription guidelines. Then, I will study genetic risk scores integrating hundreds of variants influencing AP response. Most scores are based on Europeans, but often perform poorly in other ancestries. Therefore, I will compare advanced scoring methods incorporating ancestry to evaluate which ones perform best in non-Europeans. Lastly, I will use ML to combine key PGx variants from the first aim, with the optimal risk score from the second aim, along with environmental factors (comorbidities, hospitalization, drug use etc.). This will help provide an overall understanding of the factors influencing AP response. The outcomes of this project will support the development of precision medicine tools to improve treatment efficacy.