In this paper, we provide insight into the genetic interpretation of AUC. We begin by considering quantitative traits for which the concepts of accuracy of risk prediction are well developed. For disease traits we differentiate between measures on the observed scale of disease versus the underlying scale of disease risk as we believe recognition of scale of measurement is often overlooked. We define AUCmax as the maximum AUC that could be achieved for a disease when the test classifier is a perfect predictor of genetic risk. We quantify the relationship between AUCmax and heritability of liability and disease prevalence (lifetime morbidity risk). We show how to interpret AUC (which is a measure on the observed disease scale) of a genomic profile as the proportion of variance explained (or accuracy of prediction squared) on the underlying liability scale. Finally, we benchmark the value of genomic profiles by comparing them to the AUC expected when family history resulting from shared genetic risk factors is used as a predictor of genetic risk.
