Genetics-based Approaches to Precision Medicine for Chronic or Noncommunicable Diseases

BACKGROUND

Noncommunicable diseases (NCDs) also known as chronic diseases are the leading cause of morbidity, mortality, and health cost burden in the world. They account for 74% (41 million) of all deaths globally, including 17 million premature deaths (before age 70). Common chronic diseases include cardiovascular diseases which account for 17.9 million NCD deaths annually, cancers (9.3 million deaths), chronic respiratory diseases (4.1 million deaths), and diabetes (2 million deaths). A combination of genetic, physiological, environmental and behavioural factors are responsible for NCDs. Genetic studies have shown that we can use genetics to predict the risk of common chronic diseases. NCDs or chronic diseases tend to be of long duration requiring long term treatment or management. However, the conventional strategies to prevent, diagnose, and treat common chronic diseases are failing. First, the current model for management of NCDs is “a one-size fits all”, reactive, and focuses on treating the sick. This has resulted in great variability in treatment outcomes among patients. Second, the current prevention strategies such as large population screening (e.g. for colorectal cancer) are based on coarse risk indicators such as age, and also the interventions are initiated late when disease symptoms are present and advanced. There is an increasing need to effectively manage these NCDs at both an individual and population levels, using a precision medicine approach. Precision or personalized medicine is an approach that uses an individual’s genetic profile to guide decisions made in regard to the prevention, diagnosis, and treatment of disease.

AIMS

Assess the efficacy of using genetics-based tools to guide personalized management of common chronic diseases.  

Assess the efficacy of current treatments for common chronic diseases in patients with varying genetic risk profiles.

Assess the implications of using genetics-based tools in guiding personalised treatment for common chronic diseases.

Assess the efficacy of using genetics-based tools to guide personalized diet and physical activity to control common chronic diseases.  

METHODS

The candidate will use statistical genetics approaches (e.g. Genome-wide association study techniques), multi-omics data (DNA, gene expression, metabolomics), and clinical data to develop, validate, and apply robust genetics-based prediction models or tools for common chronic disease such as cancer. They will also use other approaches such as Mendelian randomisation to investigative putative causal factors (e.g. gut microbiome, diet, etc) for treatment response. They will then apply the prediction models in “real world” scenarios to test their efficacy in guiding decisions for prevention, diagnosis, and treatment of common chronic diseases by healthcare providers and policy makers. 

PROJECT POTENTIAL

We have large-scale genetic and clinical data sets available in the lab on common chronic diseases (risk, treatment and treatment outcomes). We also have access to other national and international biobanks, as well as deeply phenotyped data sets. The candidate will focus on common cancers (e.g. colorectal or skin cancer). The candidate will use a range of statistical genetic approaches to interrogate the available genetic, and clinical data to determine the genes and pathways underlying these cancers and use these in prediction models to guide prevention, diagnosis, and treatment of these cancers.

Scholarship Details Page: https://www.qimrberghofer.edu.au/education/for-university-students/international-phd-scholarship/