of variation explained in BMI. R2 was estimated using demographics and geography-adjusted linear regression models. 2) AUC, the area under the receiver operating characteristic curve for obesity, also known as the discrimination index. The AUC corresponds to the probability that a randomly selected obese case will have a higher GRS as compared to a randomly selected non-obese control. A marker that discriminates no better than chance has an AUC of 0.50. A marker that discriminates perfectly has an AUC of 1. A related metric is the partial AUC (PAUC). The PAUC sets a specificity threshold and calculates an AUC-like statistic specific to that specificity. Analyses of PAUC for the GRS set specificity at 80% (the bottom 5th of the ROC curve). AUC and PAUC analyses were stratified by ARIC Study Center using Pepe’s method 35. To determine whether the GRS improved discrimination over and above demographic and geographic information, we calculated a second set of statistics, delta AUC and delta PAUC. Probit regression models were used to generate predicted probabilities of obesity for each ARIC participant using a baseline model that included demographic and geographic information and a test model that also included the GRS. AUCs and were calculated using
