For diseases where non-genetic prediction is already well established, it is important to evaluate the information added by genetic loci. Clearly, if classical prediction is strong and genetic prediction is weak, little additional value is added. Furthermore, GWAS risk factors are not necessarily independent of the classical predictors. For instance, if a risk variant increases the risk of developing a disease through increasing the level of a blood biomarker, and that blood biomarker is part of the classical test, then the genetic factor will substantially increase the predictive accuracy. Even this example is more complex than it may appear, as genetic variants that influence lipid levels do grant some increase in prediction even when lipid levels are measured (25), likely due to the fact that they can predict lipid production over longer time periods than a blood lipid measurement at a single time point can. Prospective studies are required to disentangle these issues, and recent examples run the gamut from success stories, such as using common variants to increase the AUC of risk prediction from 0.76 to 0.83 in age-related macular degeneration (21), to negligible improvements for prediction of metabolic diseases (1,26).
