How can we answer questions of causation (“what if?” questions) when we can’t perform experiments directly? In the case of COVID-19, a typical causal question would look like this: what would happen if we prescribed people anti-inflammatory medications? Would it improve outcomes? While we can answer these questions definitively by performing a randomized trial, this approach is slow. With this outbreak, time is of the essence and there are many potential treatments to evaluate. Instead of performing the randomization ourselves, our approach is to exploit a randomization that nature has provided for us in genetics.
The intrinsically random nature of genetic transmission from parent to child provides us with a natural experiment. In one recent piece of work , we consider genetic variants that mimic the effect of angiotensin-converting enzyme inhibitors (ACE-inhibitors). ACE-inhibitors are blood pressure lowering drugs that have anecdotally been linked to risk of COVID-19 infection. We compare individuals with genetic variants that predispose them to higher or lower levels of ACE in a similar way to how in a randomized trial, we would compare individuals allocated at random to ACE-inhibitors or to a placebo. In another piece of work , we consider genetic variants that mimic inhibition of interleukin-6, suggesting that interleukin-6 inhibition has opposing effects on COVID-19 and pneumonia.
Another strand of work concerns causal questions about modifiable risk factors, such as blood pressure and smoking status. Conventional epidemiological studies assess associations between risk factors and outcomes. However, a risk factor and outcome may be correlated without the risk factor being a cause of the outcome. The chief reasons are confounding (shared causes influencing both risk factor and outcome) and reverse causation. Investigating whether genetic variants predicting a risk factor are associated with the outcome is a more reliable assessment of causation, as the genetic code cannot be influenced by confounders, and it comes before the risk factor in time, mitigating against reverse causation.
Our research tests whether genetically-predicted levels of cardiometabolic risk factors associate with sepsis and COVID-19 risk . If genetically-predicted levels of a risk factor are associated with increased risk of disease, this provides evidence the risk factor causally increases disease risk.
We observed consistent results for severe COVID-19 and sepsis: genetically-predicted BMI and smoking were associated with both COVID-19 and sepsis. The results were consistent across a range of methods. This provides a reliable line of evidence indicating that BMI and smoking both increase the risk of COVID-19.
For more information contact Stephen Burgess