To test the discriminative accuracy of the GRSS and covariates (molecularly derived ancestry, sex, age, ancestry by sex interactions) to predict obesity, ROC curves were plotted and the corresponding AUC were calculated. To test various BMI thresholds, current BMI was dichotomized to create categories of overweight and obesity class I, II and III. Table 3 displays statistics from ROC curve analysis by BMI category. AUC estimates indicated that the model significantly predicted overweight and obesity classification with maximum discriminating ability when predicting class III obesity (AUC = 0.697, 95% CI = [0.663, 0.731]). We note that the clinical setting may prefer to use self-identified ancestry as opposed to molecularly derived ancestry in risk prediction because of genotyping cost. In the MGS-C data, the use of self-identified ancestry did not greatly change AUC estimates. For example, when predicting BMI > 30 kg/m2, an AUC = 0.588 was reported when using molecularly derived ancestry versus an AUC = 0.586 when using self-identified ancestry in the model (full data not shown).
