Approved Research

Profiling information provided by the genetic and non-genetic components of risk of common diseases from a sample to a population.

Lay summary

We will assess how genetic risk models have the potential to inform individuals' choice on what health interventions to seek. We will determine whether public health policy needs to consider genetic risk, both now and as tests increase in availability and accuracy. We will predict changes in population level risk based on the most recent research available. The potential for individuals to seek treatments based on their own genetic profile will be assessed.

Our first aim is to determine whether this information could be of use at policy level and if so to quantify how and when. The potential for a win-win strategy wherein individuals are incentivised to seek early intervention for diseases for which they have a predicted high risk will be explored. These could be payments for treatments or encouragement to make lifestyle changes. In such scenarios the individual avoids a worse outcome and the state health service avoids a more expensive later intervention.

We also expect that useful group level information about disease risk can be found even from genetic risks of low individual level predictive accuracy. Our second aim is therefore to identify scenarios under which risk models cannot accurately identify who will contract a disease but can provide useful forecasts of the number at risk. Such information could prove useful in increasing efficiencies in health services planning. We will research how to quantify and project such information in to the future, based on aspects of the population and availability, heritability, and accuracy of predicted risk.

To this end, we will assess whether strategies for public health policy based on summaries of the genetic risk profile of large samples of people could be developed. We will explore how public health decision makers can predict increases in average and extreme risk for common diseases and how incentives for self-identified high risk customers might lead to reduced overall payouts as well as better outcomes due to early interventions.

We expect the project to take approximately two years. The PhD student who will spend 100% of her time on the project is due to finish in January 2022.