BioMed Central Blog

Predicting genetic risk for disease

A lot has been written about the potential for genomic information to revolutionize medicine. Much of the excitement centers around the idea that if we know an individual’s genome we can use this information to predict risk of a particular disease and then give specific treatment. In a research article published in the latest issue of Genome Medicine, Cecile Janssens, Muin Khoury and colleagues improve current models of genetic risk, taking us one step nearer to this aim.

Genetic risk prediction is a very active area of research,  boosted by the number of genetic variants associated with common diseases that have been recently discovered through genome-wide association studies. Previous studies looked at how single mutations affect risk, but increasingly scientists and clinicians are finding that multiple mutations contribute to disease and so risk prediction needs to take into account multiple parameters.

The authors explore how several parameters of risk prediction, especially sensitivity and positive predictive value (PPV) vary under different models. They find that sensitivity and PPV are jointly maximized when the proportion of individuals identified at high risk by the test equals the population disease frequency. Otherwise, either the sensitivity is high or the PPV is high, but not both.

These results will help to advance strategies for predicting disease risk based on genetic data, as we move towards an era of personalized healthcare.

Posted by Maria Hodges at 17:38    Comments (0)