At Allelica we’re very excited about the incredible advances that analyses of large DNA datasets are bringing to medicine.
In addition to the increasingly nuanced view of the polygenic complexity of common disease, these studies confirm that your DNA doesn’t, on its own, determine disease.
It’s usually the case that your risk of disease is modulated by a combination of multiple factors including your lifestyle, diet, general health and age. And, of course, genetics also plays a role.
We also know that modulating these non-genetic factors can lead to improvements in lifetime risk of disease.
To use a well known example: once Richard Doll and his team had identified the clear link between smoking and lung cancer, a smoker’s risk of the disease could be reduced through cessation of smoking.
A multifactorial approach to risk prediction
Whilst the risk of lung cancer is drastically lower in non-smokers, quitting the smokes can lead to reductions in the risk of other diseases, most notably cardiovascular disease.
However smoking is just one of several risk factors that contribute to current risk assessments for cardiovascular disease.
Blood pressure, LDL and HDL cholesterol levels all contribute to an increased risk of disease. But each can be controlled through modifications to lifestyle, like dietary change and exercises, or therapeutics like statins if necessary.
So knowledge of these factors gives us the information needed to modify our overall risk of disease.
Incorporating genetics into risk predictions
Our white paper describes an analysis of how Polygenic Risk Score (PRS) can be used to identify those at high genetic risk of cardiovascular disease.
The majority of these individuals would be unlikely to be identified through traditional risk metrics, since PRS is orthogonal to conventional risk factors, such as LDL cholesterol or blood pressure.
Because of this, they’re invisible to current classification procedures. So being able to identify the invisible is a key gap that PRS can fill.
But, at Allelica, we also believe that this information is actionable.
This is because a healthy lifestyle can improve parameters used in risk models, mitigating polygenic risk and bringing those individuals with the highest PRS back into line with the population average.
For example, this analysis of the UK Biobank data explored the relationship between cardiovascular disease, genetic risk and lifestyle. The researchers show that poor lifestyle increased risk of heart disease regardless of genetic risk.
This is important for two reasons: the first is that genetics and lifestyle act independently when it comes to assigning an individual’s risk. So they are complementary pieces of information.
Whilst this analysis is based on the UK Biobank dataset, there’s no reason to believe that this isn’t generalisable to other European populations.
The second, more subtle, conclusion is that individuals at high polygenic risk, here identified as being in the top 20% of polygenic risk scores, are not all at high absolute risk of disease.
Those individuals in this high polygenic risk group who followed healthy lifestyles had a lifetime risk of disease that was in line with the average for their population.
Encouragingly, cardiovascular disease isn’t the only disease for which polygenic risk can be mitigated through behavioural change.
Lifetime risk of diabetes, stroke, and dementia also show the same reductions in high polygenic risk groups for those individuals who follow a healthy lifestyle.
A unified approach to managing risk
So PRS can provide actionable information that help individuals reduce their lifetime risk of disease.
We’re clear that understanding genetics is only part of the solution, but a substantial part nevertheless, that helps us to identify those at high risk and then, crucially, do something about it.