One method to assess diagnostic efficiency is to graph a receiver operator criteria (ROC) curve, which is a plot of the true positive rate (sensitivity) against the false positive rate (1 – specificity) and calculate the corresponding area under the curve (AUC). An AUC may range from 0.5, non-informative, to a maximum of 1.0, perfect discrimination between cases and controls. An AUC is the probability that the predictor is greater for cases than controls (Armitage and Colton 2005; Hanley and McNeil 1982). Generally, an AUC of 0.80 is suitable for screening while 0.99 is acceptable for diagnosis (Janssens et al. 2006). To test various BMI thresholds, current BMI was dichotomized to create categories of overweight and obesity class I, II and III which had corresponding ranges of BMI > 25, 30, 35 and 40 kg/m2, respectively. Binary logistic regression was used to calculate predicted probabilities of the models and was used as the predictor to generate ROC curves. Discriminative accuracy of the GRSS and covariates (molecularly derived ancestry, sex, age, ancestry by sex interactions) to predict BMI category was estimated by calculating the AUC from ROC curves using PASW Statistics version 17.0.
