The genetics of disease progression; Are we using the right definition?

About the Project

Genetic studies of disease primarily look at risk by comparing cases (with the disease) to controls. There is now great interest in investigating the potential role of genomic data on progression and prognosis for those living with disease. A recent paper has highlighted the limited overlap between genetic signals between studies on risk and mortality1. However, this creates a question as to the best and most appropriate definition of disease progression as well as the best model to adopt when investigating an outcome that is no longer binary.

Here the PhD candidate will explore cardiovascular disease progression, focusing on two diseases with known genomic heritability, Aortic Stenosis (AS)2 and Abdominal Aortic Aneurysm (AAA)3, using data collected in Leicester and from around the world as part of international consortia. AS is the most common valvular heart disease where the aortic valve narrows, restricting the flow of oxygen-rich blood to the body. AAA is a swelling of the aorta, the largest artery in the body. AAA are usually asymptomatic and for many do not cause a problem, but if an aneurysm continues to grow, it may rupture which is often fatal. There are currently no medical interventions to slow the progression of either disease.

These diseases have markers of disease progression routinely measured over time that can be defined and modelled in multiple ways. The initial strategy will develop and assess both the validity and clinical relevance of each potential progression phenotype/outcome. This will then be used to run genome-wide analyses as well as to build and assess polygenic risk and polygenic hazard scores to determine the role that genomics plays on progression or prognosis.

If genomics does play a role, there is potential to understand which patients are at a greater risk of poor health related outcomes, as well as the potential to identify suitable drug targets to slow disease progression.

Training opportunities:

This PhD offers the opportunity for multidisciplinary training and experience, with statistical modelling and machine learning forming a large part of the analysis.

The student will work with large unique datasets that are based in Leicester as well as access to UK Biobank to explore their research question further.