Background
Chronic pain is a complex condition that significantly diminishes the quality of life of those affected and imposes a heavy burden on healthcare systems globally. Despite its widespread impact, the genetic and environmental contributors to chronic pain are not well understood. This project aims to elucidate these contributors by leveraging extensive and unique datasets from the Australian Genetics of Depression Study (AGDS), the Australian Parkinson’s Genetics Study (APGS), and other cohorts such as the UK Biobank. By exploring the genetic aetiology, anatomical distribution, and associated clinical, environmental, and socioeconomic factors of chronic pain, this research will provide new insights into its underlying mechanisms.
Approaches, skills and techniques that will be developed
The student will utilise a comprehensive and integrative approach, combining multiple statistical and computational techniques to achieve the project’s objectives.
- Data Integration and Management: Harmonize data from AGDS, APGS, and the UK Biobank to create a unified dataset for analysis. The student will develop skills in managing large datasets, appropriately handling missing data, and controlling for potential confounders.
- Genome-Wide Association Studies (GWAS): Perform GWAS to identify genetic variants associated with chronic pain. This will involve conducting fine-mapping and functional annotation to pinpoint causal genes and pathways, providing a deep understanding of the genetic architecture of chronic pain.
- Post-GWAS Analyses: Conduct advanced analyses, including polygenic risk scoring to assess cumulative genetic risk, Mendelian randomisation to infer causal relationships, genomic structural equation modelling (genomicSEM) to understand genetic architecture, and functional annotation to identify biological functions of associated variants.
- Genetic Overlap with Other Complex Phenotypes: Investigate genetic overlap between chronic pain and other complex phenotypes using methods such as LD-score regression, enhancing understanding of shared genetic bases and pleiotropic effects.
- Statistical and Predictive Modelling: Apply advanced statistical techniques, including mixed-effects models and machine learning, to assess the influence of clinical, environmental, and socioeconomic factors on chronic pain. Develop and validate predictive models using integrated genetic, clinical, and environmental data, employing cross-validation to ensure robustness.
Expected outcomes
- Enhanced Understanding of Chronic Pain: The project will provide a comprehensive characterisation of the prevalence and anatomical distribution of chronic pain in the Australian and UK populations.
- Identification of Genetic Biomarkers: Discovery of novel genetic variants and pathways implicated in chronic pain, offering insights into its biological mechanisms and potential therapeutic targets.
- Risk Stratification Models: Developing predictive models that combine genetic, clinical, and environmental data to identify individuals at high risk for chronic pain, guiding targeted interventions and personalised treatment strategies.
Suitable background
- Knowledge of genetic epidemiology, statistical genetics, and understanding of complex traits.
- Experience with statistical software (e.g., R, Python) and bioinformatics tools is desirable. Familiarity with GWAS, polygenic risk scoring, Mendelian randomisation, and machine learning techniques is also desirable.
- Skills in managing and analysing large datasets, including data cleaning, harmonisation, and handling missing data.
- Ability to critically analyse complex data, interpret results and integrate findings from multiple sources.
- Strong written and verbal communication skills to effectively present research findings and collaborate with a multidisciplinary team.
Scholarship Details Page: https://www.qimrberghofer.edu.au/education/for-university-students/international-phd-scholarship/