Professor Nicholas Martin: firstname.lastname@example.org
Dr Miguel Renteria: Miguel.Renteria@qimrberghofer.edu.au
This project will characterise the genetic architecture that underlies variation in both symptom profiles and treatment response across patients with major depressive disorder (MDD). This will be achieved by applying a set of computational and statistical approaches to the analysis of a recently collected dataset of ~20,000 Australian MDD patients who have answered a comprehensive online questionnaire and provided saliva samples for DNA profiling.
MDD is a common complex disease that results from genetic and environmental factors. It has a lifetime prevalence of ~15%, and is accompanied by considerable morbidity, excess mortality, and substantial social and economic costs. According to the World Health Organisation, it is currently the fourth leading cause of disability worldwide, and its prevalence is projected to rise in the upcoming years.
Characterising the genetic architecture of major depression has been challenging, even compared to other psychiatric conditions such as schizophrenia and bipolar disorder. This is in part due to the highly heterogeneous and polygenic nature of MDD. A wide array of differences exists in symptom profiles, age of onset and triggers across patients. For instance, not all patients experience sleep dysfunction, fatigue or suicidal ideation in the same way. Importantly, patients respond differently to specific antidepressants. An antidepressant that is very effective for a patient, might have adverse side effects in another one and vice versa. These differences arise due to multiple molecular pathways implicated in the genetic architecture of the disease.
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