Decoding the complexity of depression: genetic drivers of heterogeneity and treatment outcomes

Depression is a major public health crisis, affecting one in five Australians over their lifetime. The heterogeneous nature of depression complicates both its diagnosis and the advancement of effective therapeutic strategies. Alarmingly, about a third of those diagnosed do not respond to conventional treatments, highlighting an urgent need to better understand the underlying biology of the disorder. While genetics is known to play a role in depression risk, less is understood about how it influences specific phenotypic characteristics (such as symptomatology, age of onset, recurrence, and sex differences) or why some individuals respond well to treatment while others do not.

This project aims to unravel these complexities by investigating the genetic basis of depression characteristics and treatment response, paving the way for more targeted and effective interventions.

This project will:

1. Identify genetic factors that contribute to depression risk and key clinical features, such as age of onset and recurrence.
2. Explore the relationship between depression and related traits
3. Determine whether treatment response traits, such as medication efficacy, tolerability, and side effects, are influenced by genetic variation.

Leveraging large-scale national and international genetic datasets (N=20,000 and N=500,000), this project will apply cutting-edge statistical genetics approaches, including genome-wide association studies (GWAS) and polygenic risk scoring (PRS), to uncover the genetic architecture of depression-related traits. The student will investigate how genetic risk factors shape treatment response, differ between males and females, and contribute to key phenotypic features such as age of onset, recurrence, and symptom presentation.

This project has the potential to significantly advance our understanding of the genetic underpinnings of depression, with a particular focus on identifying genetic factors that influence clinical features like age of onset, recurrence, and treatment response. By integrating large-scale genetic datasets with advanced statistical techniques, this research could pave the way for more personalized and effective approaches to depression treatment, particularly for individuals who do not respond to conventional therapies.