Personalised medicine, in which genetic testing is the basis for informing future health status and determining intervention, is effectively applied for a number of monogenic disorders1. For common complex disorders, which are those that are underlain by multiple genetic and environmental factors2, predictive genetic testing that can discriminate individuals who are most at risk is currently limited, mainly because much of the genetic variation remains poorly understood3,4. The potential of genetic risk prediction to (i) inform early interventions and (ii) aid diagnosis by identifying individuals with an increased genetic risk of disease could be improved substantially by increasing the accuracy of genetic risk predictors5. While genome-wide association studies (GWASs) of increased sample size will continue to unravel the role of genetic factors for complex diseases6, improved prediction models are also required to maximise the accuracy of a risk predictor.